literature review on causes of reading difficulties

Psychology: Research and Review

• Open access
• Published: 20 March 2021

Identification of struggling readers or at risk of reading difficulties with one-minute fluency measures

• Maíra Anelli Martins   ORCID: orcid.org/0000-0001-6946-6755 1 , 2 , 3 &
• Simone Aparecida Capellini 2 , 3 , 4  

Psicologia: Reflexão e Crítica volume  34 , Article number:  10 ( 2021 ) Cite this article

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To identify readers who are struggling or at risk of reading difficulties, reference standards in oral reading fluency (ORF) are used to conduct an assessment that is based on a widely reported method known as curriculum-based measurement (CBM), which itself is based on 1-min fluency measures. The purpose of this study was to evaluate students’ ORF (with a 1-min fluency measure) to characterize their fluency and to determine references of appropriate development in reading at the 50th percentile.

For this study, a database of readings made available by the Learning Studies Research Laboratory was used. This database consisted of 365 readings by elementary-school students from the third to fifth grades in two cities in the interior of the state of São Paulo from two different public school systems that use the same teaching methodology. The data consisted of digital audio recordings of the passage “The Umbrella” (text suitable for schooling levels) of the Protocol for Assessment of Reading Comprehension procedure. For this procedure, three steps were performed: step 1—listening to the 365 readings and assessing the scores for the number of words read correctly per minute; step 2—the calculation of the mean and percentiles for each grade; and step 3—the adaptation of the reference table to indicate students eligible to receive reading fluency intervention.

Third-year students who correctly read 86 or more words per minute, fourth-year students who correctly read 104 or more words per minute, and fifth-year students who correctly read 117 or more words per minute were considered students who had made adequate progress in reading.

It was possible to classify students based on the 1-min fluency measures, with reference intervals of words read correctly per minute per school year (for the third, fourth, and fifth years) for those who were making adequate progress in reading and reference intervals for those who were considered readers who were struggling or at risk of reading difficulties.

Little research has been conducted in Brazil on measures to assess reading fluency (Gentilini et al, 2020 ; Andrade, Celeste, & Alves, 2019 ; Moutinho, 2016 ; Pacheco & Santos, 2017 ; Peres & Mousinho, 2017 ), and a search for research on reading fluency in official documents of the Brazilian Ministry of Education (Martins, 2018 ) also reveals that such measures are not a type of assessment that is widely known or applied by teachers within the classroom. Nonetheless, research has continually indicated the importance of developing oral reading fluency (ORF; reading with appropriate rate, accuracy, and prosody) as a vital and necessary skill for the overall development of proficient reading (Machado, Santos, & Cruz, 2019 ; Rasinski & Young, 2017 ).

In addition to the lack of Brazilian research widely exploring this theme, the low performance data of Brazilian students in reading indicates that these students also face difficulties in learning this highly complex activity, including the many who do not become proficient, effective readers. It is noted that this is a recurring problem that affects students and, consequently, concerns educators. As is clear from the evaluations conducted throughout the national territory (large-scale evaluations), the problem has continued throughout the years and affects even the regions with the best educational indexes or socioeconomic status.

Measures assessment of reading oral fluency

The method widely publicized as curriculum-based measurement (CBM) is a curriculum-based progress-monitoring method for measuring growth in specific areas of basic knowledge and skills and assessing the effects of instructional programs (response to intervention). Curriculum-based assessment, as a longstanding assessment practice asserting that learning assessments should be based on what has been taught, has become popular in the field of special education. Thus, the CBM method is described as curriculum-based, as it is used within the context of the school curriculum (Deno, 1985 ).

The CBM method proposes simple measures for the assessment of academic competence that can be applied quickly by teachers. These measures help provide an overview of each student’s academic development; furthermore, when these simple measures are applied systematically over time, they can be used to track a student’s potential difficulties (Fuchs, 2017 ).

For example, to identify struggling readers, reference standards for ORF are used, which, based on the CBM assessment method initially proposed by Deno ( 1985 ), enable reading analysis in just 1 min (e.g., the number of words read correctly per minute–WCPM). The most widely used assessment of ORF, which focuses on two of the three components of fluency (rate and accuracy), simply requires the student to read a grade-appropriate passage, which they have not seen previously, for 1 min. At the end of 1 min, errors are subtracted from the total words read, and then the WCPM score is calculated (Hasbrouck & Tindal, 2006 ).

Thus, the method was developed to create procedures for measuring progressive development in a simple, reliable, and valid way. These procedures enable teachers to frequently and repeatedly measure students’ progress in basic reading, spelling, writing, and expression skills (Rasinski, 2004 ).

Regarding reading fluency assessment, it is recommended that the scoring of the number of words read correctly per minute (WCPM) and the number of words read incorrectly per minute (WIPM) be performed with three passages of the same difficulty level to then calculate the mean score. Thus, the WCPM measure can serve to screen for academically at-risk students, assign placement in remedial and special education programs, monitor student progress, improve teaching programs, and predict performance in high-risk assessments (Hasbrouck & Tindal, 2006 ; Rasinski, 2004 ).

A series of discussions began in the last decade in Brazil on the question of the “wait to fail to act” model, which highlighted the importance of the early identification of learning difficulties. There are also discussions about the broadening of knowledge about the advantages of early identification and scientific evidence-based assessment and screening methods (Almeida, Piza, Toledo, Cardoso, & Miranda, 2016 ; Batista & Pestun, 2019 ; Brito, Seabra, & Macedo, 2018 ; Justi & Cunha, 2016 ; Mayeda, Navatta, & Miotto, 2018 ; Nicolau & Navas, 2015 ; Palles da Silva & Guaresi, 2019 ; Rodrigues & Ciasca, 2016 ; Silva & Capellini, 2017 ; Silva & Capellini, 2019a ; Silva & Crenitte, 2016 ).

According to Elliott, Huai and Roach ( 2007 ), several factors contribute to the prevalence of the “wait to fail to act” model, such as the fact that educators understand that there is a certain heterogeneity of development and learning among students and seek to allow appropriate time for this development. By doing so, they are also allowing students a fair chance of progressing without early determination of the problem. Another factor for the prevalence of this action model is the fact that few large-scale screening instruments are time efficient and technically simple for teachers to apply.

In the Brazilian literature, early screening instruments are recent and focus primarily on metalinguistic skills, such as the “Early Identification and Reading Problems Protocol” (Capellini, César, & Germano, 2017 ), the “Evaluation of Cognitive-Language Skills Protocol: Professional and Teacher’s Book” (Capellini, Smythe, Silva, 2017 ) and the “Protocol for Cognitive-Language Skills Assessment of Students in Early Literacy” (Silva & Capellini, 2019b ). These instruments assess skills considered predictive of literacy, such as reading and writing skills; arithmetic; auditory and visual processing; metalinguistic skills; and processing speed with the rapid automatic naming test. Some tests evaluate mathematical logical reasoning, for example, the “Cognitive-Language Skills Assessment Protocol.”

Likewise, there has been a movement in Brazilian research in recent years to describe the importance of reading fluency measures, especially those related to using a chronometer for timing as measures for screening difficulties, in addition to the development of instruments to assist in this assessment. Alves et al. ( 2019 ) described such issues in the most recent publication of the LEPIC® software, which proposes a semiautomatic and instantaneous reading fluency analysis to assess and assist in diagnostics or to monitor reading skills. This analysis focuses on the importance of evaluating parameter fluency, which may include indicators of reading problems such as dyslexia. Another instrument recently developed by Brazilian researchers is a collection of passages in sequential order according to difficulty level and suitable for elementary-school students from the first through fourth grades, called the “Reading Fluency Performance Assessment” (Martins & Capellini, 2018 ).

Additionally, on 22 February 2018, the More Literacy Program (PMAlfa) was created via MEC Ordinance No. 142, a strategy by the Ministry of Education that aims to strengthen and support school units in the process of increasing the literacy of elementary-school students enrolled in the first and second grades; the program fulfills the criteria established in the Common National Curriculum Base (CNCB). The objective of the program is to perform reading, writing, and math evaluations. For the first time, a formal program of the Brazilian government will evaluate the fluency and accuracy in the reading ability of students in the second grade of elementary school. The assessment is performed individually and uses a proprietary application suitable for smartphones or tablets.

However, despite efforts to create adequate assessment procedures for ORF, research into the characterization of ORF in this population is still incipient. Pacheco and Santos ( 2017 ), for example, evaluated three groups of readers in relation to reading fluency who were classified into three groups: group I–second-grade readers with little reading experience and expectation of low reading fluency; group II–second-year high school readers with the expectation of having slightly more reading experience and moderate fluency; and group III–readers with a higher education level. However, the relatively small sample consisted of 12 participants (four participants in each group), and the reading rate was evaluated by using the number of words read compared to the total reading time measured in seconds, considering a total reading time of 180 s (3 min).

In another study (Moutinho, 2016 ), 46 sixth-grade students from public and private schools were evaluated by measuring the WCPM in 1 min from three different passages. However, the article focused on describing the accuracy errors, i.e., the number and type of WIPM, while data for the WCPM are not presented. Other researchers evaluated 55 students from the third to the seventh grades with the number of words per minute, reading four different types of passages, and analyzing student performance in each (Dellisa & Navas, 2013 ).

Some researchers have also conducted reading fluency assessment with elementary students, as in a study that evaluated 32 students in ninth grade and calculated the speed of words read per minute (using the formula of total number of words from the passage, divided by the time in seconds spent to complete the reading, and multiplied by 60) (Komeno, Ávila, Cintra, & Schoen, 2015 ). Furthermore, in another recent study, researchers characterized the ORF by 232 middle-grade students from the sixth to the ninth grades from public and private education. The study provided an estimate of the expected values for each grade surveyed by reading an easy passage based on the 1-min oral fluency assessment, with scores for words read per minute and WCPM (Andrade et al., 2019 ).

While only a small number of studies for elementary and middle students exist, even fewer studies evaluate reading fluency in high school students or adults. One research study evaluated 88 students in the second grade of high school. The CBM method was followed by selecting a passage compatible with students’ age and grade and comprising subjects corresponding to the basic curriculum studied in the classroom. Students read three different passages, lasting 1 min each, for the subsequent calculation of the number of WCPM (Oliveira, Amaral, & Picanço, 2013 ). Only one study evaluating reading fluency in adults was found, in which the sample consisted of 30 adolescents and adults who were evaluated by measuring the number of words per minute (Peres & Mousinho, 2017 ).

The assessment of ORF conducted through WCPM scores presents 30 years of validation research indicating that this is a valid and reliable measure that reflects a student's overall performance in reading development during the first years after literacy (Morris et al., 2017a , b ; Tindal, 2017 ; Valencia et al., 2010 ). Reading fluency benchmarks have been used both for screening and for monitoring reading development, and research in these fields seeks to answer questions such as “How is student performance compared to their peers?” and “Who are the students struggling with reading?” This practice of frequent assessment enables early intervention and the planning of activities that focus on the skills already acquired and those that still require further attention.

Benchmarks in ORF have been established by American researchers and collected from a range of students, from those identified as talented or otherwise exceptionally skilled to those diagnosed with reading disabilities, such as dyslexia. The largest sample of the ORF benchmark was collected from schools and districts in 23 states in the USA for over 4 years. Based on their vast experience in interpreting ORF data, it was established that a score of 10 words above or below the 50th percentile should be interpreted as an expected score, meaning that students are making satisfactory reading progress (Hasbrouck & Tindal, 2006 ).

Given the implications that ORF benchmarks would have for Brazilian education, a study to determine a fluency reference through appropriate assessment material would be of great relevance. This benchmarking considers the indication of a median score (50th percentile), with scores of 10 words above or below this median indicating students who have made appropriate reading progress, to assist in assessment and to create parameters for selecting students for interventional programs who are struggling readers or at risk for developing difficulties in reading proficiency later.

The purpose of this study was to evaluate the ORF of students from the third to the fifth grades (with a 1-min fluency measure) to characterize their fluency and determine references of appropriate development in reading at the 50th percentile and those below this reference.

This is a quantitative, descriptive-explanatory study. The dependent variable is a 1-min fluency measure. The independent variable is student grade.

General procedures and database

This study was approved by the Ethics Committee of the Faculdade de Filosofia e Ciências of Sao Paulo State University–UNESP-Campus de Marília-SP under protocol 2.550.190–CAAE 50201915.9.0000.5406.

For this study, a database of readings made available by the Investigation Learning Disabilities Laboratory (in Portuguese: Laboratório de Investigação dos Desvios da Aprendizagem–LIDA), registered by a research group of the National Counsel of Technological and Scientific Development (CNPq), called “Language, Learning, Education,” was used. All information related to the sample of students comprising our database was made available by the members of this group.

The readings database made available consists of 365 readings from elementary-school students from the third to the fifth grades in two cities in the interior of the state of São Paulo (in a medium- and a small-sized Brazilian city, Southeast Region of Brazil) from two different public school systems with the same teaching methodology. In the city of Marília-SP, there are 51 schools with regular elementary education in urban locations, in basic education, with 2221 students enrolled in the third year, 2119 students enrolled in the fourth year and 2033 students enrolled in the fifth year according to the School Census/(Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira – INEP, 2018 ).

In the city of Garça-SP, there are 14 schools with regular elementary education in urban locations, in basic education, with 478 students enrolled in the third year, 436 students enrolled in the fourth year and 401 students enrolled in the fifth year according to the School Census/(Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira – INEP, 2018 ). The schools were selected through convenience sampling (simple convenience sample). The students participating in the studies did not have a history of repeating grades; they were monolinguals and native speakers of Brazilian Portuguese. The data were digital recordings of participants reading the passage “The Umbrella” (text suitable for schooling levels) from the procedure “Protocol for Assessment of Reading Comprehension” (Cunha & Capellini, 2014 ).

Of the 365 readings, 98 were third-grade students (48.9% female), 130 were fourth-grade students (49.2% female), and 137 were fifth-grade students (51.8% female) (participants were elementary-school students ranging from 7 to 11 years old).

According to the latest results published (Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira, 2015-2017 ) by the Socioeconomic Level Indicator (Inse) of basic education schools in Brazil, developed by the National Institute of Educational Studies and Research Anísio Teixeira (Inep), in the Basic Education Assessment Directorate (Daeb), the schools from which the analyzed data were obtained have an average Inse (absolute value 58.46 and 57.47), with an average rating (group 5).

The inclusion and exclusion criteria used by the laboratory researchers in the data collection of the reading audio bank are described. The inclusion criteria for the sample selection were as follows: informed consent form signed by the parents or guardians for the students; students with no history of neurological or psychiatric illnesses, uncorrected auditory and visual impairments, and cognitive performance within normal, according to the description at the school records and teachers’ reports. The exclusion criteria for the sample selection were the presence of genetic or neurological syndromes in the students, students who did not present a satisfactory reading domain level for the observation of the variable proposed in the study, and students who presented recording errors in their respective audio files.

Specific instruments and procedures

The passage used was “The Umbrella” (history appropriate for the educational level) from the procedure “Reading Comprehension Assessment Protocol” (Cunha & Capellini, 2014 ). The choice for using this protocol occurred due to its careful assessment and development, since its issues were built from the rules for the psychometric tool development described by The Federal Council of Psychology. The Council is an official body that studies and establishes criteria and rules in Brazil for the construction of evaluation tools that ensures their accuracy and validity, and defines, as reliable procedures, those whose accuracy is understood as their level of consistency and their ability to reach the objectives for which they were built as their validity.

The protocol consists of four passages, two narratives, and two expository narratives. A medium-length (297 words) narrative passage was chosen. The choice of a passage with a narrative gender protocol occurred because the students had been more commonly exposed to such passages since childhood and throughout the education process, which would simplify the fluency evaluation and avoid the interference of any cultural issues of the passage in the reading results of the students of different schooling levels.

The choice of protocol also occurred because it presents passages that were selected to reach students from the third, fourth and fifth grades at representatively similar levels of difficulty for all school years, making it possible to apply a single passage in all school years.

Although the procedure is an instrument for assessing reading comprehension, due to the objectives of this study, only the reading recordings were used to assess fluency, while the multiple-choice questions were not applied.

The equipment used in the recordings was a Karsect microphone headset, which was unidirectional since the microphone picks up sounds with greater intensity and orients towards where it is directed, reducing the intensity of the external noise. The microphone was connected to an HP notebook with an Intel Pentium processor, 3 GB memory, and a 32-bit operating system. Recordings were made with an original HP software application and were saved as .wav files.

The collections were carried out by the researchers of the mentioned research group, following the guidelines for individual application. Each reading of the entire passage was recorded, taking an average of 5 min total for each individual recording session in spaces reserved for the researchers in the schools during class hours.

To analyze the readings on digital media, the following steps were planned and performed:

Step 1 : The rate was scored by listening to 365 digital recordings and assessing the WCPM scores, which was performed according to the reading error classification used by Begeny, Capellini, and Martins ( 2018 ) and by other researchers (Valencia et al., 2010 ). In this approach, the types of errors that are marked as WIPM are mispronounced words, words substituted with others, words omitted, words read out of order, addition or omission of word endings, and hesitation (words on which the student paused more than 3 s, after which he or she is told the word, and it is marked as incorrect. If necessary, the student is told to continue with the next word).

The following items indicate all situations that are marked as WCPM: words pronounced correctly, self-corrections, words decoded slowly but ultimately read correctly, repeated words, words mispronounced due to dialect or regional differences, and words inserted. To quantify errors, scoring rules are also proposed for certain situations: lines or multiple words omitted; when one or more lines are not read (four or more omitted words in sequence), they are not considered errors, although those words are excluded from the WCPM (such that this rule is applied whenever a student skips four or more words within a sentence). If the student skips one, two, or three consecutive words, each word should be counted as an error (WIPM). Regarding hyphenated words that can exist independently, each morpheme separated by a hyphen counts as an individual word if the two parts exist independently when the hyphen is removed, such as “Guarda-chuva ” [Umbrella in Portuguese] (counts as two words but is only marked incorrect when the student misreads), as opposed to the word “ anglo-China ” (considered as one word, regardless of which or both are misread).

Step 2 : The data thus obtained were tabulated and processed with Microsoft Excel® 2010. Data were analyzed through descriptive statistics (mean, standard deviation, and percentiles). Percentiles 5, 10, 25, 50, 75, 90, and 95 were calculated for each grade. Stratifying these percentiles helps to understand the different levels of difficulty that students may present.

Step 3 : The reference table was adjusted for the selection of students eligible to receive reading fluency interventions or programs. For this, the minimum reference threshold was the 25th percentile, and the maximum reference limit was the 50th percentile. The reference to the 25th percentile represents an approximate limit on the minimum level of ORF that a student should present to benefit from a fluency program. This reference was developed through years of research and related interventions (Begeny et al., 2018 ; Field, Begeny, & Kim, 2019 ).

Thus, it was determined that in the present research, WCPM intervals (maximum and minimum limits) would be established to select students who were not making adequate reading progress based on the ORF standard published by Hasbrouck and Tindal ( 2006 ).

The results regarding the reading fluency assessment measure as a procedure for selecting struggling readers or at risk of developing reading difficulties (grades 3 to 5) are summarized in Tables 1 and 2 .

From the data presented in Table 1 , students in the third year who read 86 or more WCPM, in the fourth year who read 104 or more WCPM, and in the fifth year who read 117 or more WCPM are considered students who are making adequate progress in reading. As shown in Table 1 , the lower the student scored beneath the 25th percentile, the more difficulties with reading the student will present, and the higher the student scored above the 50th percentile, the better the student’s performance.

Considering the standards proposed by Hasbrouck and Tindal ( 2006 , p. 639), in which students who read more than 10 WCPM above the 50th percentile present appropriate reading progress (unless there are other indicators for concern), the WCPM was established for Brazilian students (Table 2 ).

The reference intervals were calculated from the readings by the 365 students, considering that those who presented a WCPM score between the 25th and 50th percentiles did not make satisfactory progress in their reading fluency and taking the 25th percentile as the minimum reference limit and the 50th percentile as the maximum reference limit (Table 2 ). Students with WCPM scores at the 25th percentile or below are unlikely to benefit from a fluency-based intervention because they likely need assistance with decoding, phonics, and/or phonemic awareness.

Measures such as the number of WCPM offer numerous advantages for use in the context of ORF assessment. This measure has already been proven to be valid and is a quick and simple measure; it can be easily implemented in educators’ routines, either within the school routine or with professionals in their clinics. The reliability coefficient of this study could not be used if the test used because a single item test was used (number of words read correctly). If used as a screening measure for students at risk of reading difficulties, it should be performed by teachers from the third grade, since it is from this series that all students are expected to have passed the literacy phase and to move from the phase of learning to read to the phase of reading to learn. Consequently, within just a few hours, a teacher can evaluate their entire class because the assessment is performed quickly, which would also enable frequent assessments, which would, in turn, enable the monitoring of students’ progress in their fluency (Hasbrouck & Tindal, 2006 ; Rasinski, 2004 ; Rasinski & Young, 2017 ).

For reference values, the data obtained in this study served to identify students who were making adequate reading progress and those who could benefit from a fluency program. Among the academic skills considered central to reading success, fluency reveals not only its importance in assessing and screening key components but also in intervention response strategies and models for absorbing the demand encountered after the screening and early identification of reading difficulties (Kostewicz et al., 2016 ).

Considering the Brazilian studies on the characterization of ORF, we note that despite their small number (Andrade et al., 2019 ; Dellisa & Navas, 2013 ; Komeno et al., 2015 ; Moutinho, 2016 ; Oliveira et al., 2013 ; Pacheco & Santos, 2017 ; Peres & Mousinho, 2017 ), the results help to predict and compare student performance. It is necessary to advance the description of the results to create fluency references so that they can be used to screen for students with general reading difficulties, according to each region of the country. It is emphasized that due to the continental dimensions of the Brazilian national territory, there are considerable cultural and educational differences among regions.

Therefore, the method of assessing a measure of ORF in given passages can be used to assess student progress in reading fluency competence; to predict and compare students’ performance with peers or benchmarks (since their performance is compared over time) as well as conduct individual assessments; set annual goals; assess the effectiveness of intervention programs; develop standards for the class, school, and/or region; identify students at risk of dyslexia or in need of further intervention; and serve as the initial source of data collection in the response-to-intervention model (Mendonça & Martins, 2014 ).

Implications

There are public policy problems that involve this issue of early identification in Brazil, as there are no projects or actions directed at absorbing the demand of learning disabilities within the school itself. This difficulty makes the implementation of a screening process for early identification more difficult, since once these students with difficulties have been identified, there is a corresponding need for interventions, such as intervention response models together with the need for a complete structural and practical change within the classroom to modify the deeply rooted tradition of “waiting to fail to take action” (Elliott et al., 2007 ). However, as observed in a recent program created by the Ministry of Education (More Literacy Program–PMAlfa), new ways of implementing the screening of reading difficulties and continuing teacher education to ensure that they master the methodologies for progress monitoring and evaluation of student performance are beginning to appear.

It is also important to underscore that recent research has focused on the development of instruments and materials suitable for this type of evaluation and progress-monitoring, such as passages that are appropriate for the grade level and classified according to their difficulty, that not only allow the modification of the “waiting to fail to act” tradition but also allow suitable fluency assessment applications with materials that not only accelerate but also facilitate evaluation (such as software and applications) (Alves et al., 2019 ). This approach also means that three passages of the same level of difficulty can be offered (as a collection of sequential passages) to the students for assessment (Martins & Capellini, 2018 ), with sets of three passages to be applied throughout the school year to facilitate the monitoring of student progress.

Despite its limitations, this study extended the literature (Andrade et al., 2019 ; Dellisa & Navas, 2013 ; Komeno et al., 2015 ; Moutinho, 2016 ; Oliveira et al., 2013 ; Pacheco & Santos, 2017 ; Peres & Mousinho, 2017 ) as part of the research movement to obtain ORF subsidiary reference data for professionals in the health-education interface. However, it is necessary to note that one limitation of this study is the number of samples used. To complement this study and other Brazilian research in this context, new research is needed that increases the number and the representativeness of the sample of Brazilian readers who struggle.

From this study, it was possible to evaluate and characterize the reading fluency of Brazilian students. It was also possible to establish reference intervals for the assessment of ORF, which can be used to screen struggling readers or students at risk who present or may develop reading difficulties.

Therefore, similar research should be carried out and expanded to create measurement parameters related to ORF, which will help teachers make decisions about which paths need to be constructed or improved to assist those students who are presenting difficulty in this learning process.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Curriculum-based measurement

• Oral reading fluency

Words read correctly per minute

Words read incorrectly per minute

More Literacy Program

Common National Curriculum Base

Almeida, R. P., Piza, C. J., De Toledo, M. A., Cardoso, T. S. G., & Miranda, M. C. (2016). Prevenção e remediação das dificuldades de aprendizagem: Adaptação do modelo de resposta à intervenção em uma amostra brasileira. Revista Brasileira de Educação , 21 (66), 611–630. https://doi.org/10.1590/S1413-24782016216632 .

Article   Google Scholar  

Alves, L. M., Cunha, L. O., Santos, L. F., Melo, F. S. M. C., Reis, V. O. M., & Celeste, L. C. (2019). Análise tecnológica da fluência leitora: Validação do software Lepic® nos anos iniciais do Ensino Fundamental. Neurociências (Rio de Janeiro) , 15 , 33–41.

Google Scholar  

Andrade, A. J. L., Celeste, L. C., & Alves, L. M. (2019). Caracterização da fluência de leitura em escolares do Ensino Fundamental II. Audiology - Communication Research , 24 , e1983. Epub March 28. https://doi.org/10.1590/2317-6431-2018-1983 .

Batista, M., & Pestun, M. S. V. (2019). O Modelo RTI como estratégia de prevenção aos transtornos de aprendizagem. Psicologia Escolar e Educacional , 23 , e205929. Epub December 02, 2019. https://doi.org/10.1590/2175-35392019015929 .

Begeny, J. C., Capellini, S. A., & Martins, M. A. (2018). HELPS-PB: Programa de fluência de leitura para escolares: Manual do instrutor. (English translation: HELPS in Brazilian Portuguese: A Reading fluency program for children; Teacher’s Manual) . Helps Education Fund http://www.helpsprogram.org/ .

Brito, G. R., Seabra, A. G., & Macedo, E. C. (2018). Implementação do modelo de resposta à intervenção em uma classe de 5° ano do ensino fundamental da rede pública de ensino: relato de experiência. Revista Psicopedagogia , 35 (106), 82–93 Recuperado em 13 de junho de 2020, de http://pepsic.bvsalud.org/scielo.php?script=sci_arttext&pid=S0103-84862018000100010&lng=pt&tlng=pt .

Capellini, S. A., César, A. B. P. C., & Germano, G. D. (2017). Protocolo de identificação precoce e dos problemas de leitura - IPPL. Book Toy.

Capellini, S. A., Smythe, I., & Silva, C. (2017). Protocolo de avaliação de habilidades cognitivo-linguísticas: Livro do profissional e do professor. Book Toy.

Cunha, V. L. O., & Capellini, S. A. (2014). PROCOMLE: Protocolo de Avaliação da Compreensão de Leitura . Book Toy.

Dellisa, P. R. R., & Navas, A. L. G. P. (2013). Avaliação do desempenho de leitura em estudantes do 3° ao 7° anos, com diferentes tipos de texto. CoDAS , 25 (4), 342–350. https://doi.org/10.1590/S2317-17822013000400008 .

Article   PubMed   Google Scholar  

Deno, S. L. (1985). Curriculum-based measurement: The emerging alternative. Exceptional Children , 52 (3), 219–232. https://doi.org/10.1177/001440298505200303 .

Elliott, S. N., Huai, N., & Roach, A. T. (2007). Universal and early screening for educational difficulties: Current and future approaches. Journal of School Psychology , 45 (2), 137–161. https://doi.org/10.1016/j.jsp.2006.11.002 .

Field, S. A., Begeny, J. C., & Kim, E. K. (2019). Exploring the relationship between cognitive characteristics and responsiveness to a tier 3 reading fluency intervention. Reading & Writing Quarterly , 35 (4), 374–391. https://doi.org/10.1080/10573569.2018.1553082 .

Fuchs, L. S. (2017). Curriculum-Based Measurement as the emerging alternative: three decades later. Learning Disabilities Research & Practice , 32 (1), 5–7. https://doi.org/10.1111/ldrp.12127 .

Gentilini, L. K. S.,  Andrade, M. E. P.,  Basso, F. P., Salles, J. F., Martins-Reis,V. O., & Alves, L. M. (2020). Desenvolvimento de instrumento para avaliação coletiva da fluência e compreensão de leitura textual em escolares do ensino fundamental II. CoDAS, 32 (2), e20190015. Epub March 02, 2020. https://doi.org/10.1590/2317-1782/20192019015

Hasbrouck, J., & Tindal, G. A. (2006). Oral reading fluency norms: a valuable assessment tool for reading teachers. The Reading Teacher , 59 (7), 636–644 www.jstor.org/stable/20204400 .

Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira – INEP (2018). Censo Escolar. Recuperado de https://academia.qedu.org.br/censo-escolar/

Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira – INEP. Indicadores educacionais . 2015-2017. Recuperado de http://portal.inep.gov.br/indicadores-educacionais

Justi, C. N. G., & Cunha, N. (2016). Tarefas de nomeação seriada rápida: rastreando a dificuldade de leitura. Psicologia: Teoria e Pesquisa , 32 (4), e32425. Epub June 22, 2017. https://doi.org/10.1590/0102.3772e32425 .

Komeno, E. M., Ávila, C. R. B., Cintra, I. P., & Schoen, T. H. (2015). Velocidade de leitura e desempenho escolar na última série do ensino fundamental. Estudos de Psicologia (Campinas) , 32 (3), 437–447. https://doi.org/10.1590/0103-166X2015000300009 .

Kostewicz, D. E., Kubina, R., Selfridge, K. A., & Gallagher, D. L. (2016). A Review of Fixed Fluency Criteria in Repeated Reading Studies. Reading Improvement, 53 (1), 23–41.

Machado, A. P. G., Santos, I. M., & Cruz, D. S. (2019). Diagnóstico de leitura de estudantes: interfaces entre automaticidade e compreensão leitora. Revista Ponto de Vista , 8 (1), 47–61 https://periodicos.ufv.br/RPV/article/view/9203 .

Martins, M. A. (2018). Programa de fluência de leitura para escolares do 3° ao 5° ano: tradução, adaptação e aplicação (Doctoral dissertation). Retrieved from https://repositorio.unesp.br/handle/11449/152976

Martins, M. A., & Capellini, S. A. (2018). Avaliação do desempenho em fluência de leitura - ADFLU. Book Toy.

Mayeda, G. B. G., Navatta, A. C. R., & Miotto, E. C. (2018). Intervenção fonológica em escolares de risco para dislexia: revisão de literatura. Revista Psicopedagogia , 35 (107), 231–241 Recuperado em 13 de junho de 2020, de http://pepsic.bvsalud.org/scielo.php?script=sci_arttext&pid=S0103-84862018000200010&lng=pt&tlng=pt .

Mendonça, R. F. F., & Martins, A. P. L. (2014). Identificação de alunos em risco de apresentarem dislexia: Um estudo sobre a utilização da monitorização da fluência de leitura num contexto escolar. Revista Brasileira de Educação Especial , 20 (1), 09–20. https://doi.org/10.1590/S1413-65382014000100002 .

Morris, D., Trathen, W., Gill, T., Schlagal, R., Ward, D., & Frye, E. M. (2017). Assessing reading rate in the primary grades (1–3). Reading Psychology , 38 (7), 653–672. https://doi.org/10.1080/02702711.2017.1323057 .

Morris, D., Trathen, W., Perney, J., Gill, T., Schlagal, R., Ward, D., & Frye, E. M. (2017). Three DIBELS tasks vs. three informal reading/spelling tasks: A comparison of predictive validity. Reading Psychology , 38 (3), 289–320. https://doi.org/10.1080/02702711.2016.1263700 .

Moutinho, M. G. (2016). Erros de precisão na fluência em leitura oral de alunos do sexto ano do ensino fundamental de 4 escolas de Belém-Pará. Revista Escrita (PUCRJ. Online) , 21 , puc-rio.br/-17. https://doi.org/10.17771/PUCRio.escrita.25994 .

Nicolau, C. C., & Navas, A. L. G. P. (2015). Assessment of skills that predict reading success in 1st and 2nd grade children of elementary school. Revista CEFAC: Atualizacão Cientifica em Fonoaudiologia e Educacão , 17 (3), 917 Retrieved from https://link-gale.ez87.periodicos.capes.gov.br/apps/doc/A497053671/AONE?u=capes&sid=AONE&xid=d720db83 .

Oliveira, E. R., Amaral, S. B. G., & Picanço, G. (2013). Velocidade e precisão na leitura oral: Identificando alunos fluentes. Nonada: Letras em Revista , 2 (21), 1–14.  http://www.redalyc.org/articulo.oa?id=512451671025 .

Pacheco, V., & Santos, A. J. (2017). A fluência e compreensão leitora em diferentes níveis de escolaridade. Confluência , 1 (52), 232–256 http://llp.bibliopolis.info/confluencia/rc/index.php/rc/article/view/172 .

Palles da Silva, L., & Guaresi, R. (2019). Proposta de instrumento para rastreio de dificuldades de aprendizagem em alunos das séries iniciais. Revista Virtual Lingu@ Nostr@ , 6 (1), 68–76 Recuperado de http://linguanostra.net/index.php/Linguanostra/article/view/127 .

Peres, S., & Mousinho, R. (2017). Avaliação de adultos com dificuldades de leitura. Revista da Associação Brasileira de Psicopedagogia , 34 (103), 20–32 http://pepsic.bvsalud.org/scielo.php?script=sci_arttext&pid=S0103-84862017000100003&lng=pt&tlng=pt .

Rasinski, T. V. (2004). Assessing reading fluency. In Pacific Resources for Education and Learning .

Rasinski, T. V., & Young, C. (2017). Effective instruction for primary grade students who struggle with reading fluency. Inclusive Principles and Practices in Literacy Education (International Perspectives on Inclusive Education) , 11 , 143–157. Emerald Publishing Limited. https://doi.org/10.1108/S1479-363620170000011010 .

Rodrigues, S. D., & Ciasca, S. M. (2016). Dislexia na escola: identificação e possibilidades de intervenção. Revista Psicopedagogia , 33 (100), 86–97 http://pepsic.bvsalud.org/scielo.php?script=sci_arttext&pid=S0103-84862016000100010&lng=pt&tlng=pt .

Silva, C., & Capellini, S. (2019a). Indicadores cognitivo-linguístico em escolares com transtorno fonológico de risco para a dislexia. Distúrbios da Comunicação , 31 (3), 428–436. https://doi.org/10.23925/2176-2724.2019v31i3p428-436 .

Silva, C., & Capellini, S. A. (2017). Comparison of performance in metalinguistic tasks among students with and without risk of dyslexia. Journal of Human Growth and Development , 27 (2), 198–205. https://doi.org/10.7322/jhgd.118823 .

Silva, C., & Capellini, S. A. (2019b). Protocolo de avaliação das habilidades cognitivo-linguísticas para escolares em fase inicial de alfabetização . Book Toy.

Silva, N. S. M., & Crenitte, P. A. P. (2016). Performance of children at risk for reading difficulties submitted to an intervention program. CoDAS , 28 (5), 517–525. Epub September 26, 2016. https://doi.org/10.1590/2317-1782/20162015274 .

Tindal, G. (2017). Oral reading fluency: Outcomes from 30 years of research. (Technical Report 1701). University of Oregon Center Behavioral Research and Teaching. U.S. Department of Education, Institute of Education Sciences. https://www.brtprojects.org/wp-content/uploads/2017/10/TechRpt_1701ORF.pdf

Valencia, S., Smith, A., Reece, A., Li, M., Wixson, K., & Newman, H. (2010). Oral reading fluency assessment: Issues of construct, criterion, and consequential validity. Reading Research Quarterly , 45 (3), 270–291 http://www.jstor.org/stable/27822888 .

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The authors would like to thank the members of the Investigation Learning Disabilities Laboratory (LIDA) of Sao Paulo State University-UNESP for making available reading data in digital audios.

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Martins, M.A., Capellini, S.A. Identification of struggling readers or at risk of reading difficulties with one-minute fluency measures. Psicol. Refl. Crít. 34 , 10 (2021). https://doi.org/10.1186/s41155-021-00174-z

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Helping children with reading difficulties: some things we have learned so far

• Genevieve McArthur 1 &
• Anne Castles 1  

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• Human behaviour

A substantial proportion of children struggle to learn to read. This not only impairs their academic achievement, but increases their risk of social, emotional, and mental health problems. In order to help these children, reading scientists have worked hard for over a century to better understand the nature of reading difficulties and the people who have them. The aim of this perspective is to outline some of the things that we have learned so far, and to provide a framework for considering the causes of reading difficulties and the most effective ways to treat them.

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Introduction

Over 20 years ago, The Dyslexia Institute asked a 9-year-old boy called Alexander to describe his struggle with learning to read and spell. He bravely wrote: “I have blond her, Blue eys and an infeckshos smill. Pealpie tell mum haw gorgus I am and is ent she looky to have me. But under the surface I live in a tumoyl. Words look like swigles and riting storys is a disaster area because of spellings. There were no ply times at my old school untill work was fineshed wich ment no plytims at all. Thechers sead I was clevor but just didn’t try. Shouting was the only way the techors comuniccatid with me. Uther boys made fun of me and so I beckame lonly and mishroboll”. 1

Alexander’s experience is not unique. Sixteen per cent of children struggle to learn to read to some extent, and 5% of children have significant, severe, and persistent problems. 2 The impact of these children’s reading difficulties goes well beyond problems with reading Harry Potter or Snapchat. Poor reading is associated with increased risk for school dropout, attempted suicide, incarceration, anxiety, depression, and low self-concept. 3 , 4 , 5 , 6 It is therefore important to identify and treat poor readers as early as we possibly can.

Scientists have been investigating poor reading—also known as reading difficulty, reading impairment, reading disability, reading disorder, and developmental dyslexia (to name but a few)—for over a century. While it may take another century of research to reach a complete understanding of reading impairment, there are number of things that we have learned about reading difficulties, as well as the children who experience reading them, that provide key clues about how poor reading can be identified and treated effectively.

Poor readers display different reading behaviours

One thing that we have learned about poor readers is that they are highly heterogeneous; that is, they do not all display the same type of reading impairment (i.e., “reading behaviour”; 7 , 8 , 9 , 10 , 11 , 12 ). Some poor readers have a specific problem with learning to read new words accurately by applying the regular mappings between letters and sounds. 7 , 8 , 13 , 14 This problem, which is often called poor phonological recoding or decoding, can be detected by asking children to read novel “nonwords” such as YIT. Other poor readers have a particular difficulty with learning to read new words accurately that do not follow the regular mappings between letters and sounds, and hence must be read via memory representations of written words. 7 , 13 , 15 , 16 This problem, which is sometimes called poor sight word reading or poor visual word recognition, can be detected by asking children to read “exception” words such as YACHT. In contrast, some poor readers have accurate phonological recoding and visual word recognition but struggle to read words fluently. 17 , 18 , 19 Poor reading fluency can be detected by asking children to read word lists or sentences as quickly as they can. In contrast yet again, some poor readers have intact phonological recoding and visual word recognition and reading fluency, but struggle to understand the meaning of what they read. These “poor comprehenders” 20 can be identified by asking them to read paragraphs aloud (to ascertain that they can read accurately and fluently), and then ask them questions about the meaning of what they have read (to ascertain that they do not understand what they are reading). It is important to note that most poor readers have various combinations of these problems. 21 For example, Alexander’s spelling suggests that he would have poor phonological decoding (since he misspells words like playtimes as “plytims”) and poor sight word knowledge (since he misspells exception words like said as “sead”). Thus, poor readers vary considerably in the profiles of their reading behaviour.

Reading behaviours have different “proximal” causes

Another thing we have learned about poor readers is that the same reading behaviour (e.g., inaccurate reading of novel words) does not necessarily have the same “proximal cause”. A proximal cause of a reading behaviour can be defined as a component of the cognitive system that directly and immediately produces that reading behaviour. 22 , 23 , 24 Most reading behaviours will have more than one proximal cause. Reflecting this, several theoretical and computational models of reading comprise multiple cognitive components that function together to produce successful reading behaviour (e.g., refs 25 , 26 , 27 , 28 ). While these models vary in some respects, all include cognitive components that represent (1) the ability to recognise letters (e.g., S), letter-clusters (e.g., SH), and written words (e.g., SHIP), (2) the ability to recognise and produce speech sounds (e.g., “sh”, “i”, “p”) and spoken words (e.g., “ship”), (3) the ability to access stored knowledge about the meanings of words (e.g., “a floating vessel”), and (4) links between these various components. Impairment in any one of these components or links will directly and immediately impair aspects of reading behaviour. Thus, guided by theoretical and computational models, we have learned that a poor reading behaviour can have multiple proximal causes, and we have some idea about what those proximal causes might be. 10 , 11 , 12

Reading behaviours have different “distal” causes

We have also learned that even if two poor readers have exactly the same reading behaviour with exactly the same proximal cause, this reading behaviour will not necessarily have the same “distal cause”. A distal cause has a distant (i.e., an indirect or delayed) impact on a reading behaviour. 22 , 23 , 24 Distal causes reflect the fact that reading is a taught skill that unfolds over time and across development. It depends upon a range of more cognitive abilities, such as memory, attention, and language skills, to name but a few. Depending on children’s strengths and weaknesses in these underlying abilities, and how these abilities affect learning over time, children will have different profiles of developmental, or distal, causes of their reading impairment. Stated differently, there can be different causal pathways to the same impairment of the reading system.

To provide an example, as mentioned earlier, a common reading behaviour observed in poor readers is inaccurate reading of new or novel words, which can be assessed using nonwords such as YIT. Indeed, some researchers have described this as the defining symptom of reading difficulties. 29 According to theoretical and computational models of reading, one proximal cause of impaired reading of nonwords is impaired knowledge of letter-sound mappings. But what is responsible for this proximal cause of poor nonword reading? There are multiple hypotheses. The prominent “phonological deficit hypothesis” proposes a pervasive language-based difficulty in processing speech sounds that affects the ability to learn to associate written stimuli (e.g., letters) with speech sounds. 30 The “paired-associate learning deficit hypothesis” proposes a memory-based difficulty in forming cross-modal mappings across the visual (e.g, letters) and verbal domains (e.g., speech sounds) that affects letter-sound learning (e.g., ref. 31 ). And the “visual attentional deficit hypothesis” proposes an attention-based impairment in the size of the attentional window, affecting the formation of the sub-word orthographic units (e.g., letters) used in the letter-sound mapping process. 32 These three hypotheses illustrate why a single reading behaviour (e.g., poor nonword reading) with a common proximal cause (impaired knowledge of letter-sound mappings) might not have the same distal cause (e.g., a phonological deficit, a paired-associate learning deficit, or a visual attention deficit). These hypotheses also raise the possibility that the distal causes of poor readers’ reading behaviours may vary as much (if not more) than the proximal causes and the reading behaviours themselves.

Poor readers have concurrent problems with their cognition and emotional health

Another thing we have learned about poor readers is that many (but not all) have comorbidities in other aspects of their cognition and emotional health. Regarding cognition, studies have found that a significant proportion of poor readers have impairments in their spoken language. 33 , 34 , 35 , 36 , 37 , 38 , 39 Studies have also found that poor readers have atypically high rates of attention deficit disorder—a neurological problem that causes inattention, poor concentration, and distractibility (e.g., refs 40 , 41 , 42 ). Regarding emotional health, there is evidence that poor readers, as a group, have higher levels of anxiety than typical readers (e.g., refs 43 , 44 ). The same is true for low self-concept, which can be defined as a negative perception of oneself in a particular domain (e.g., academic self-concept; e.g., refs 45 , 46 ).

The fact that poor readers vary in their comorbid cognitive and emotional health problems—as well as in their reading behaviours, and the proximal and distal impairments of these behaviours—creates an impression of almost overwhelming complexity. However, it is possible to simplify this complexity somewhat using a proximal and distal schema. Specifically, comorbidities of poor reading might be categorised according to whether they represent potential proximal or distal impairment of poor reading—or possibly both. For example, a child’s current problem with spoken vocabulary might be considered a proximal cause of their poor word reading behaviour since, according to theoretical and computational models of reading, vocabulary knowledge may directly underpin word reading accuracy or reading comprehension. However, a child’s previous problem with spoken vocabulary, which may or may not still be present, might be considered a distal cause of their poor word reading: A history of poor understanding of word meanings might reduce a child’s motivation to engage in reading (distal cause), which would impair their development of phonological recoding and visual word recognition (proximal cause), and hence their word reading accuracy and fluency (reading behaviour). Thus, the proximal and distal schema can prove useful in clarifying the causal chain of events linking a reading behaviour to a potential cause.

The proximal and distal schema can also be useful in clarifying reciprocal or circular relationships between comorbidities of poor reading and reading behaviours. For example, if a poor reader has low academic self-concept (distal cause), this may stymie their motivation to pay attention in reading lessons (distal cause), which will impair their learning of letter-sound mappings (proximal cause), and hence their poor word reading (reading behaviour). At the same time, a reverse causal effect may be in play: A child’s poor word reading in the classroom (distal cause) may create a poor perception of their own academic ability (proximal cause) that lowers their academic self-concept (behaviour). Thus, the proximal and distal schema can be used to help develop hypotheses as to whether comorbidities of poor reading are proximal and/or distal causes or consequences of poor reading. Ultimately, of course, all of these hypotheses must be tested through experimental training studies.

Proximal intervention is more effective than distal intervention

Poor readers have inspired, and have been subjected to, an extraordinary array of interventions such as behavioural optometry, chiropractics, classical music, coloured glasses, computer games, fish oil, phonics, sensorimotor exercises, sound training, spatial frequency gratings, memory training, medication for the inner ear, phonemic awareness, rapid reading, visual word recognition, and vocabulary training, to name just a selection. It is noteworthy that while many of these interventions claim to be “scientifically proven”, few have been tested with a randomised controlled trial (RCT)—an experiment that randomly allocates participants to intervention and control groups in order to reduce bias in outcomes. RCTs are the gold standard method for assessing a treatment of any kind, and the method that must be used to prove the effectiveness of a pharmaceutical treatment.

In order to make sense of the chaotic variety of interventions that claim to help poor readers, it may again be helpful to use the proximal and distal schema outlined above to subdivide interventions into two types: “proximal interventions” that focus training on proximal causes of a reading behaviour that are proposed to be part of the cognitive system for reading (e.g., phonics training, vocabulary training) and “distal interventions” that focus on distal causes of a reading behaviour (e.g., coloured lenses, inner-ear medication). The idea of making a distinction between proximal and distal interventions is supported by the outcomes of a systematic review of all studies that have used an RCT to assess an intervention in poor readers. 47 These studies assessed the effect of coloured lenses or overlays, medication, motor training, phonemic awareness, phonics, reading comprehension, reading fluency, sound processing, and sunflower therapy on poor readers. One key finding of this review is that it only identified 22 RCTs, which is a small number of gold-standard intervention studies given the huge number of interventions that claim to help poor readers. A second key finding is that the majority of RCTs of interventions for poor readers have assessed the efficacy of phonics training, which trains the ability to use letter-sound mappings to learn to read new or novel words. A third key finding is that only one type of intervention produced a statistically reliable effect. This was phonics training, which focuses on improving a proximal cause of poor word reading (i.e., letter-sound mappings). In contrast, interventions that focused on distal causes of poor reading did not show a statistically reliable effect in poor readers. The outcomes of this systematic review suggest that interventions that focus on phonics—a proximal cause of reading behaviour—are more likely to be effective than interventions that focus on a distal cause. In other words, the “closer” the intervention is to an impaired reading behaviour, the more likely it is to be effective.

Translating what we know (thus far) into evidence-based practice

At first glance, what we have learned (so far) about poor readers and reading difficulties paints a picture of such complex heterogeneity that it is tempting to throw one’s hands up in despair. And yet, somewhat paradoxically, it is this very heterogeneity that provides some important clues about how to maximise the efficacy of intervention for poor readers. First, the fact that poor readers vary in the nature of their reading behaviours suggests that the first step in identifying an effective intervention for a poor reader is to assess different aspects of reading (e.g., word reading accuracy, reading fluency, and reading comprehension). There are numerous standardized tests provided commercially (e.g., the York Assessment for Reading Comprehension available from GL Assessment) 48 or for free (e.g., the Castles and Coltheart Word Reading Test—Second Edition (CC2) available at www.motif.org.au ) 49 that can be used to determine if a child falls below the average range for their age or grade for reading accuracy, fluency, or comprehension. In our experience, a teacher who has appropriate training in administrating such tests can carry out this first step effectively.

Second, the fact that poor readers’ reading behaviours can have different proximal causes suggests that the next step is to test them for the potential proximal causes of their poor reading behaviours. This is where cognitive models of reading are a useful roadmap, providing an explicit account of the key processes directly underpinning successful reading behaviour. Again, this can be done using standardized tests that are available commercially (e.g., the Peabody Picture Vocabulary Test Fourth Edition available from Pearson) 50 or for free (e.g., the Letter-Sound Test available at www.motif.org.au ). 51 And well-trained teachers can administer these tests.

Third, the fact that poor readers vary in the degree to which they experience comorbid cognitive and emotional impairments suggests that it would be useful to assess poor readers for their spoken language abilities, attention, anxiety, depression, and self-concept, at the very least. This knowledge will reveal if they need support in other areas of their development, or if their reading-related intervention needs to be adjusted to accommodate their concomitant impairment in order to maximise efficacy. Trained speech and language therapists typically carry out the assessment of children’s spoken language; neuropsychologists are experts in assessing children’s attention; and clinical psychologists have the expertise to assess children’s emotional health.

Once a poor reader’s reading behaviours, proximal impairments, comorbid cognitive, and emotional health problems have been identified, it should be possible to design an intervention that is a good match to their needs. According to the systematic review conducted by Galuschka et al. 47 , current evidence suggests that this intervention should focus on the proximal impairment of a child’s reading behaviour, rather than a possible distal impairment. Two more recent controlled trials 52 , 53 and a systematic review 54 further suggest that it is possible to selectively train different proximal impairments of poor reading behaviours in order to improve those behaviours. The outcomes of these studies and reviews tentatively suggest that proximal interventions can be executed by a reading specialist or a highly-sophisticated online reading training programme.

In sum, over the last century or so, we have learned important things about reading difficulties and the people who have them. We have learned that poor readers display different reading behaviours, that any one reading behaviour has multiple proximal and distal causes, that some poor readers have concomitant problems in other areas of their cognition and emotional health, and that interventions that focus on proximal causes of poor reading behaviours may be more effective than those that focus on distal causes. This knowledge provides some clues to how we might best assist children with reading difficulties. Specifically, we need to assess poor readers for (1) a range of reading behaviours, (2) proximal causes for each poor reading behaviour, and (3) comorbidities in their cognition and emotional health. It should be possible to design an individualised intervention programme that accommodates for a poor reader’s comorbid cognitive or emotional problems whilst targeting the proximal causes of their poor reading behaviour or behaviours. This approach, which requires the co-ordinated efforts of teachers and specialists and parents, is no mean feat. However, according to the scientific evidence thus far, this is the most effective approach we have for helping children with reading difficulties.

The Dyslexia Institute. As I See It (Walker Books, 1990).

Shaywitz, S. E., Escobar, M. D., Shaywitz, B. A., Fletcher, J. M. & Makuch, R. Evidence that dyslexia may represent the lower tail of a normal distribution of reading ability. N. Engl. J. Med. 326 , 145–150 (1992).

Article   CAS   PubMed   Google Scholar  

Daniel, S. S. et al. Suicidality, school dropout, and reading problems among adolescents. J. Learn. Disabil. 39 , 507–514 (2006).

Article   PubMed   Google Scholar  

McArthur, G., Castles, A., Kohnen, S. & Banales, E. Low self-concept in poor readers: prevalence, heterogeneity, and risk. Peer. J. 4 , e2669 (2016).

Article   PubMed   PubMed Central   Google Scholar  

Carroll, J. M., Maughan, B., Goodman, R. & Meltzer, H. Literacy difficulties and psychiatric disorders: evidence for comorbidity. J. Child Psychol. Psychiatry 46 , 524–532 (2005).

Christie, C. A. & Yell, M. L. Preventing youth incarceration through reading remediation: issues and solutions. Read. Writ. Quart. 24 , 148–176 (2008).

Article   Google Scholar  

Castles, A. & Coltheart, M. Varieties of developmental dyslexia. Cognition 47 , 149–180 (1993).

Goulandris, N. K. & Snowling, M. Visual memory deficits: a plausible cause of developmental dyslexia? Evidence from a single case study. Cogn. Neuropsychol. 8 , 127–154 (1991).

Jones, K., Castles, A. & Kohnen, S. Subtypes of developmental dyslexia: recent developments and directions for treatment. ACQuiring Knowledge Speech Lang. Hear. 13 , 79–83 (2011).

Google Scholar  

McArthur, G. et al. Getting to grips with the heterogeneity of developmental dyslexia. Cogn. Neuropsychol. 30 , 1–24 (2013).

Peterson, R., Pennington, B. & Olson, R. Subtypes of developmental dyslexia: testing predictions of the dual-route and connectionist frameworks. Cognition 126 , 20–38 (2013).

Ziegler, J. C. et al. Developmental dyslexia and the dual route model of reading: Simulating individual differences and subtypes. Cognition 107 , 151–178 (2008).

Manis, F. R., Seidenberg, M. S., Doi, L. M., McBride-Chang, C. & Petersen, A. On the bases of two subtypes of development dyslexia. Cognition 58 , 157–195 (1996).

Temple, C. M. & Marshall, J. C. A case study of developmental phonological dyslexia. Br. J. Soc. Psychol. 74 , 517–533 (1983).

Friedmann, N. & Lukov, L. Developmental surface dyslexias. Cortex 44 , 1146–1160 (2008).

Broom, Y. M. & Doctor, E. A. Developmental phonological dyslexia: a case study of the efficacy of a remediation programme. Cogn. Neuropsychol 12 , 725–766 (1995).

de Jong, P. F. & van der Leij, A. Developmental changes in the manifestation of a phonological deficit in dyslexic children learning to read a regular orthography. Educ. Psychol 95 , 22–40 (2003).

Landerl, K., Wimmer, H. & Frith, U. The impact of orthographic consistency on dyslexia: a German-English comparison. Cognition 63 , 315–334 (1997).

Torgesen, J. K. Recent discoveries on remedial interventions for children with dyslexia. in The Science of Reading: A Handbook , (eds Snowling, M. J. & Hulme, C.), pp.521–537 (Blackwell, 2005).

Nation, K. & Snowling, M. Semantic processing and the development of word-recognition skills: evidence from children with reading comprehension difficulties. J. Mem. Lang. 39 , 85–101 (1998).

Stuart, M., & Stainthorp, R. Reading Development and Teaching (Sage, 2016).

Hulme, C., & Snowling, M. Developmental Disorders of Language, Learning and Cognition (Wiley-Blackwell, 2009).

Jackson, N. E., & Coltheart, M. Routes to Reading Success and Failure: Toward an Integrated Cognitive Psychology of Atypical Reading (Psychology Press, 2001).

Castles, A., Kohnen, S., Nickels, L. & Brock, J. Developmental disorders: what can be learned from cognitive neuropsychology? Phil. Trans. R. Soc. B 369 , 20130407 (2014).

Coltheart, M., Rastle, K., Perry, C., Langdon, R. & Ziegler, J. DRC: a dual route cascaded model of visual word recognition and reading aloud. Psychol. Rev. 108 , 204 (2001).

Hoover, W. A. & Gough, P. B. The simple view of reading. Read. Writ. 2 , 127–160 (1990).

Perry, C., Ziegler, J. C. & Zorzi, M. Nested incremental modeling in the development of computational theories: the CDP+ model of reading aloud. Psychol. Rev. 114 , 273–315 (2007).

Plaut, D. C., McClelland, J. L., Seidenberg, M. S. & Patterson, K. Understanding normal and impaired word reading: computational principles in quasi-regular domains. Psychol. Rev. 103 , 56–115 (1996).

Rack, J. P., Snowling, M. J. & Olson, R. K. The nonword reading deficit in developmental dyslexia: a review. Read. Res. Q. 1 , 29–53 (1992).

Snowling, M. Dyslexia as a phonological deficit: evidence and implications. Child Psychol. Psychiatry.Rev. 3 , 4–11 (1998).

Warmington, M. & Hulme, C. Phoneme awareness, visual-verbal paired-associate learning, and rapid automatized naming as predictors of individual differences in reading ability. Sci. Studying Read. 16 , 45–62 (2012).

Bosse, M.-L., Tainturier, M. & Valdois, S. Developmental dyslexia: the visual attention span deficit hypothesis. Cognitio 104 , 198–230 (2007).

Bishop, D. V. M. & Snowling, M. J. Developmental dyslexia and specific language impairment: same or different? Psychol. Bull. 130 , 858–886 (2004).

Eisenmajer, N., Ross, N. & Pratt, C. Specificity and characteristics of learning disabilities. J. Child Psychol. Psychiatry. 46 , 1108–1115 (2005).

Fraser, J., Goswami, U. & Conti-Ramsden, G. Dyslexia and specific language impairment: the role of phonology and auditory processing. Sci. Studies Read. 14 , 8–29 (2010).

McArthur, G. & Castles, A. Phonological processing deficits in specific reading disability and specific language impairment: same or different? J. Res. Read. 36 , 280–302 (2013).

McArthur, G. M., Hogben, J. H., Edwards, V. T., Heath, S. M. & Mengler, E. D. On the “specifics” of specific reading disability and specific language impairment. J Child Psychol. Psychiatry Allied Disciplines 41 , 869–874 (2000).

Article   CAS   Google Scholar  

Rispens, J. & Been, P. Subject–verb agreement and phonological processing in developmental dyslexia and specific language impairment (SLI): a closer look. Int.J. Lang. Commun.Dis. 42 , 293–305 (2007).

Catts, H. W., Adolf, S. M., Hogan, T. P. & Weismer, S. E. Are specific language impairment and dyslexia distinct disorders? J. Speech. Lang. Hear. Res. 48 , 1378–1396 (2005).

Gilger, J. W., Pennington, B. F. & DeFries, J. C. A twin study of the etiology of comorbidity: attention-deficit hyperactivity disorder and dyslexia. J. Am. Acad. Child Adolesc. Psychiatry 31 , 343–348 (1992).

Shaywitz, B. A., Fletcher, J. M. & Shaywitz, S. E. Defining and classifying learning disabilities and attention-deficit/hyperactivity disorder. J. Child. Neurol. 10 , S50–S57 (1995).

Willcutt, E. G. & Pennington, B. F. Comorbidity of reading disability and attention-deficit/hyperactivity disorder differences by gender and subtype. J. Learn. Disabil. 33 , 179–191 (2000).

Maughan, B. & Carroll, J. Literacy and mental disorders. Curr. Opin. Psychiatry 19 , 350–354 (2006).

Mugnaini, D., Lassi, S., La Malfa, G. & Albertini, G. Internalizing correlates of dyslexia. World J. Clin. Pediatr. 5 , 255–264 (2009).

Snowling, M. J., Muter, V. & Carroll, J. Children at family risk of dyslexia: a follow-up in early adolescence. J. Child Psychol. Psychiatry 48 , 609–618 (2007).

Taylor, L. M., Hume, I. R. & Welsh, N. Labelling and self‐esteem: the impact of using specific vs. generic labels. Educ. Psychol. 30 , 191–202 (2010).

Galuschka, K., Ise, E., Krick, K. & Schulte-Körne, G. Effectiveness of treatment approaches for children and adolescents with reading disabilities: a meta-analysis of randomized controlled trials. PLoS ONE 9 , e89900 (2014).

Snowling, M. J. et al. YARC York Assessment of Reading for Comprehension Passage Reading (GL Assessment, 2009).

Castles, A. et al. Assessing the basic components of reading: a revision of the Castles and Coltheart test with new norms. Aust. J. Learn. Diffic. 14 , 67–88 (2009).

Dunn, L. M., & Dunn, D. M. Peabody Picture Vocabulary Test: PPVT 4 (Pearson, 2015).

Larsen, L., Kohnen, S., Nickels, L. & McArthur, G. The letter-sound test (LeST): a reliable and valid comprehensive measure of grapheme-phoneme knowledge. Aust. J. Learn. Diffic. 20 , 129–142 (2015).

McArthur, G. et al. Sight word and phonics training in children with dyslexia. J. Learn. Disabil. 48 (4), 391–407 (2015a).

McArthur, G. et al. Replicability of sight word training and phonics training in poor readers: a randomised controlled trial. Peer. J. 3 , e922 (2015b).

Article   CAS   PubMed   PubMed Central   Google Scholar  

McArthur, G. et al. Phonics training for English‐speaking poor readers. The Cochrane Library . 12 , 1–102 (2012).

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Understanding the nature and severity of reading difficulties among students with language and reading comprehension difficulties

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• Sandra L. Gillam   ORCID: orcid.org/0000-0003-4401-4669 2 ,
• Anna-Maria Fall   ORCID: orcid.org/0000-0002-6257-6684 1 ,
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• Jordan T. Dille   ORCID: orcid.org/0000-0002-5110-8973 3 &
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This study investigated the presence of word reading difficulties in a sample of students in Grades 1–4 ( n  = 357) identified with language and reading comprehension difficulties. This study also examined whether distinct word reading and listening comprehension profiles emerged within this sample and the extent to which these groups varied in performance on cognitive and demographic variables. Findings showed that the majority of students (51%) with language and reading comprehension difficulties demonstrated significant risk in word reading (more than 1 SD below the mean), even though the participant screening procedures did not examine word reading directly. Three latent profiles emerged when students were classified into subgroups based on their performance in listening comprehension (LC) and word reading (WR): (1) severe difficulties in LC and moderate difficulties in WR (11%), (2) mild difficulties in both LC and WR (50%), and (3) moderate difficulties in LC and mild difficulties in WR (39%). Of note, even though students were identified for participation on the basis of poor oral language and reading comprehension abilities, all profiles demonstrated some degree of word reading difficulties. Findings revealed there were differences in age and performance on measures of working memory, nonverbal reasoning, and reading comprehension performance between profiles. Implications for educators providing instruction to students with or at risk for dyslexia and developmental language disorders were discussed.

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Reading Disorders/Dyslexia

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Timothy N. Odegard, Emily A. Farris & Julie A. Washington

Adlof, S. M. (2020). Promoting reading achievement in children with developmental language disorders: What can we learn from research on specific language impairment and dyslexia? Journal of Speech, Language, and Hearing Research, 63 (10), 3277–3292.

Google Scholar  

Akaike, H. (1987). Factor analysis and AIC. In Selected Papers of Hirotugu Akaike (pp. 371–386). Springer.

Anthony, J. L., Aghara, R. G., Dunkelberger, M. J., Anthony, T. I., Williams, J. M., & Zhang, Z. (2011). What factors place children with speech sound disorders at risk for reading problems? American Journal of Speech-Language Pathology., 20 (2), 146–160.

Asparouhov, T., & Muthén, B. (2021). Expanding the Bayesian structural equation, multilevel and mixture models to logit, negative-binomial, and nominal variables. Structural Equation Modeling: A Multidisciplinary Journal , 28 (4), 622–637.

Bakk, Z., & Vermunt, J. K. (2016). Robustness of stepwise latent class modeling with continuous distal outcomes. Structural Equation Modeling: A Multidisciplinary Journal, 23 (1), 20–31.

Bauer, D. J., & Curran, P. J. (2003). Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes. Psychological Methods, 8 (3), 338.

Bedore, L. M., Peña, E. D., Gillam, R. B., & Ho, T.-H. (2010). Language sample measures and language ability in Spanish English bilingual kindergarteners. Journal of Communication Disorders, 43 , 498–510. https://doi.org/10.1016/j.jcomdis.2010.05.002

Article   Google Scholar  

Biancarosa, G., & Snow, C. E. (2004). Reading next: A vision for action and research in middle and high school literacy: A report from Carnegie Corporation of New York . Alliance for Excellent Education.

Bishop, D. V. (2017). Why is it so hard to reach agreement on terminology? The case of developmental language disorder (DLD). International Journal of Language & Communication Disorders, 52 (6), 671–680.

Bishop, D. V., McDonald, D., Bird, S., & Hayiou-Thomas, M. E. (2009). Children who read words accurately despite language impairment: Who are they and how do they do it? Child Development, 80 (2), 593–605.

Bolck, A., Croon, M., & Hagenaars, J. (2004). Estimating latent structure models with categorical variables: One-step versus three-step estimators. Political Analysis, 12 , 3–27.

Bradley, R., Danielson, L., & Hallahan, D. P. (2002). Identification of learning disabilities: Research to practice . Routledge.

Brasseur-Hock, I. F., Hock, M. F., Kieffer, M. J., Biancarosa, G., & Deshler, D. D. (2011). Adolescent struggling readers in urban schools: Results of a latent class analysis. Learning and Individual Differences, 21 (4), 438–452.

Buly, M. R., & Valencia, S. W. (2002). Below the bar: Profiles of students who fail state reading assessments. Educational Evaluation and Policy Analysis, 24 (3), 219–239.

Capin, P., Cho, E., Miciak, J., Roberts, G., & Vaughn, S. (2021). Examining the reading and cognitive profiles of students with significant reading comprehension difficulties.  Learning Disabilities Quarterly, 44 (3), 183–196.

Castles, A., Rastle, K., & Nation, K. (2018). Ending the reading wars: Reading acquisition from novice to expert. Psychological Science in the Public Interest, 19 , 5–51. https://doi.org/10.1177/1529100618772271

Catts, H. W., Fey, M. E., Zhang, X., & Tomblin, J. B. (1999). Language basis of reading and reading disabilities: Evidence from a longitudinal investigation. Scientific Studies of Reading, 3 (4), 331–361.

Catts, H. W., Fey, M. E., Tomblin, J. B., & Zhang, X. (2002). A longitudinal investigation of reading outcomes in children with language impairments. Journal of Speech, Language, and Hearing Research, 45 (6), 1142–1157.

Catts, H. W., Adlof, S. M., Hogan, T. P., & Weismer, S. E. (2005). Are specific language impairment and dyslexia distinct disorders? Journal of Speech, Language, and Hearing Research, 48 , 1378–1396.

Catts, H. W., Adlof, S. M., & Weismer, S. E. (2006). Language deficits in poor comprehenders: A case for the simple view of reading. Journal of Speech, Language, and Hearing Research, 49 , 278–293.

Catts, H. W., Herrera, S., Nielsen, D. C., & Bridges, M. S. (2015). Early prediction of reading comprehension within the simple view framework. Reading and Writing, 28 (9), 1407–1425.

Catts, H. W., McIlraith, A., Bridges, M. S., & Nielsen, D. C. (2017). Viewing a phonological deficit within a multifactorial model of dyslexia. Reading and Writing, 30 (3), 613–629.

Catts, H. W., & Petscher, Y. (2020). A cumulative risk and protection model of dyslexia . PsyArXiv Preprints.

Catts, H. W. (2021). The critical role of oral language deficits in reading disorders: reflections on Snowling and Hulme (2021).  Journal of Child Psychology and Psychiatry . Advanced online publication.

Cho, E., Capin, P., Roberts, G., & Vaughn, S. (2018). Examining predictive validity of oral reading fluency slope in upper elementary grades using quantile regression. Journal of Learning Disabilities, 51 (6), 565–577.

Cho, E., Capin, P., Roberts, G., Roberts, G. J., & Vaughn, S. (2019). Examining sources and mechanisms of reading comprehension difficulties: Comparing English learners and non-English learners within the simple view of reading. Journal of Educational Psychology, 111 (6), 982.

Cleave, P., Becker, S., Curran, M., Owen Van Horne, A., & Fey, M. (2015). The efficacy of recasts in language intervention: A systematic review and meta-analysis. American Journal of Speech Language Pathology, 24 , 237–255.

Clemens, N. H., Simmons, D., Simmons, L. E., Wang, H., & Kwok, O. M. (2017). The prevalence of reading fluency and vocabulary difficulties among adolescents struggling with reading comprehension. Journal of Psychoeducational Assessment, 35 (8), 785–798.

Compton, D. L. (2021). Focusing our view of dyslexia through a multifactorial lens: A commentary. Learning Disability Quarterly, 44 (3), 225–230.

Connor, C. M., Morrison, F. J., Fishman, B. J., Schatschneider, C., & Underwood, P. (2007). The early years: Algorithm-guided individualized reading instruction. Science, 315 , 464–465.

Dawes, E., Leitao, S., Claessen, M., & Kane, R. (2019). A randomized controlled trial of an oral inferential comprehension intervention for young children with developmental language disorder. Child Language Teaching and Therapy, 35 , 39–54.

Denton, C. A., Barth, A. E., Fletcher, J. M., Wexler, J., Vaughn, S., Cirino, P. T., Romain, M., & Francis, D. J. (2011). The relations among oral and silent reading fluency and comprehension in middle school: Implications for identification and instruction of students with reading difficulties. Scientific Studies of Reading, 15 (2), 109–135.

Donegan, R. E., & Wanzek, J. (2021). Effects of reading interventions implemented for upper elementary struggling readers: A look at recent research. Reading and Writing, 34 (8), 1943–1977.

Duff, F. J., & Clarke, P. J. (2011). Practitioner Review: Reading disorders: What are the effective interventions and how should they be implemented and evaluated? Journal of Child Psychology and Psychiatry, 52 (1), 3–12.

Duff, D., Hendricks, A. E., Fitton, L., & Adlof, S. (2021). Reading and math achievement in children with dyslexia, developmental language disorder, or typical development: Achievement gaps persist from second through fourth grades. EdArXiv preprints.

Ebbels, S. (2014). Effectiveness of intervention for grammar in school-aged children with primary language impairments: A review of the evidence. Child Language Teaching and Therapy, 30 , 7–40.

Ferguson, S. L., Moore, G., & E. W., & Hull, D. M. (2020). Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers. International Journal of Behavioral Development, 44 (5), 458–468.

Fletcher, J. M., Lyon, G. R., Fuchs, L. S., & Barnes, M. A. (2018). Learning disabilities: From identification to intervention . Guilford Publications.

Florit, E., & Cain, K. (2011). The simple view of reading: Is it valid for different types of alphabetic orthographies? Educational Psychology Review, 23 (4), 553–576.

Foorman, B. R., Petscher, Y., Stanley, C., & Truckenmiller, A. (2017). Latent profiles of reading and language and their association with standardized reading outcomes in kindergarten through tenth grade. Journal of Research on Educational Effectiveness, 10 (3), 619–645.

Ford, K. L., Cabell, S. Q., Konold, T. R., Invernizzi, M., & Gartland, L. B. (2013). Diversity among Spanish-speaking English language learners: Profiles of early literacy skills in kindergarten. Reading and Writing, 26 (6), 889–912.

Francot, R., Blom, E., Broekhuizen, M., & Leseman, P. (2021). Profiles of bilingualism in early childhood: A person-centred Latent Profile Transition Approach. Bilingualism: Language and Cognition, 24 (3), 569–582. 

Fuchs, D., Hendricks, E., Walsh, M. E., Fuchs, L. S., Gilbert, J. K., Zhang Tracy, W., & Peng, P. (2018). Evaluating a multidimensional reading comprehension program and reconsidering the lowly reputation of tests of near-transfer. Learning Disabilities Research & Practice, 33 (1), 11–23.

Gallinat, E., & Spaulding, T. J. (2014). Differences in the performance of children with specific language impairment and their typically developing peers on nonverbal cognitive tests: A meta-analysis. Journal of Speech, Language, and Hearing Research, 57 (4), 1363–1382.

Geiser, C. (2013). Methodology in the social sciences. Data analysis with Mplus.  Guilford Press.

Gillam, R. B., & Pearson, N. A. (2017). Test of narrative language. Pro-Ed.

Gillam, R. B., Loeb, D. F., Hoffman, L. M., Bohman, T., Champlin, C. A., Thibodeau, L., Widen, J., Brandl, L., & Friel-Patti, S. (2008). The efficacy of Fast ForWord-language intervention in school-age children with language impairment: A randomized clinical trial. Journal of Speech, Language, and Hearing Research, 51 , 97–119.

Gillam, S., Holbrook, S., Mecham, J., & Weller, D. (2018). Pulling the Andon rope on working memory capacity interventions: A return to slow fixes. Language, Speech and Hearing Services in Schools, 49 , 434–448.

Gillam, R.B., Gillam, S.L., Holbrook, S., & Orellana, C. (2017). Language disorder in children. In S. Goldstein & M. DeVries (Eds.), Handbook of DSM-5 disorders in children and adolescents (pp. 57–76). Springer. https://doi.org/10.1007/978-3-319-57196-6

Gillam, S.L., Vaughn, S., Roberts, G., Capin, P., Fall, A.M., Holbrook, S., Wada, R., Dille, J, & Gillam, R.B. (2021). Improving oral and written narration and reading comprehension of children at-risk for language and literacy difficulties: Results of a randomized clinical trial . [Manuscript submitted preparation]. Department of Special Education, The University of Texas at Austin.

Gosse, C., Hoffman, L., & Invernizzi, M. (2012). Overlap in speech-language and reading services for kindergartners and first graders. Language, Speech and Hearing Services in Schools, 43 , 66–80.

Gough, P. B., & Tunmer, W. E. (1986). Decoding, reading, and reading disability. Remedial and Special Education, 7 (1), 6–10.

Gough, P. B., Hoover, W. A., Peterson, C. L., Cornoldi, C., & Oakhill, J. (1996). Some observations on a simple view of reading. In C. Cornoldi & J. Oakhill (Eds.), Reading comprehension difficulties: Processes and intervention (pp. 1–13). Lawrence Erlbaum.

Harlaar, N., Cutting, L., Deater-Deckard, K., DeThorne, L. S., Justice, L. M., Schatschneider, C., & Petrill, S. A. (2010). Predicting individual differences in reading comprehension: A twin study. Annals of Dyslexia, 60 (2), 265–288.

Hendricks, A. E., Adlof, S. M., Alonzo, C. N., Fox, A. B., & Hogan, T. P. (2019). Identifying children at risk for developmental language disorder using a brief, whole-classroom screen. Journal of Speech, Language, and Hearing Research, 62 (4), 896–908.

Hoover, W. A., & Gough, P. B. (1990). The simple view of reading. Reading and Writing, 2 (2), 127–160.

Kapantzoglou, M., Restrepo, M. A., Gray, S., & Thompson, M. S. (2015). Language ability groups in bilingual children: A latent profile analysis. Journal of Speech, Language, and Hearing Research, 58 (5), 1549–1562.

Kaufman, A. S., & Kaufman, N. L. (2004). Kaufman Brief Intelligence Test (2nd ed.). Pearson.

Kim, Y. S. G., Petscher, Y., Uccelli, P., & Kelcey, B. (2020). Academic language and listening comprehension—Two sides of the same coin? An empirical examination of their dimensionality, relations to reading comprehension, and assessment modality. Journal of Educational Psychology, 112 (7), 1367–1387.

Kim, Y. S. G., & Petscher, Y. (2020). Influences of individual, text, and assessment factors on text/discourse comprehension in oral language (listening comprehension). Annals of Dyslexia , 71 (2), 218–237.

Lervåg, A., Hulme, C., & Melby-Lervåg, M. (2018). Unpicking the developmental relationship between oral language skills and reading comprehension: It’s simple, but complex. Child Development, 89 (5), 1821–1838.

Lesaux, N. K., & Kieffer, M. J. (2010). Exploring sources of reading comprehension difficulties among language minority learners and their classmates in early adolescence. American Educational Research Journal, 47 (3), 596–632.

Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88 (3), 767–778.

Logan, J. A. R., & Pentimonti, J. M. (2016). Introduction to latent class analysis for reading fluency research. In K. Cummings & Y. Petscher (Eds.), The Fluency Construct (pp. 309–332). Springer.

Lonigan, C. J., Goodrich, J. M., & Farver, J. M. (2018a). Identifying differences in early literacy skills across subgroups of language-minority children: A latent profile analysis. Developmental Psychology, 54 (4), 631.

Lonigan, C. J., Burgess, S. R., & Schatschneider, C. (2018b). Examining the simple view of reading with elementary school children: Still simple after all these years. Remedial and Special Education, 39 (5), 260–273.

Lyon, G. R., Shaywitz, S. E., & Shaywitz, B. A. (2003). A definition of dyslexia. Annals of Dyslexia, 53 (1), 1–14.

MacGinitie, W. H., MacGinitie, R. K., Maria, K., Dryer, L. G., & Hughes, K. E. (2000). Gates-MacGinitie Reading Tests (GMRT). Riverside Publishing.

Mather, N., & Woodcock, R. W. (2001). Woodcock-Johnson III Tests of Cognitive Abilities Examiners Manual . Riverside Publishing Company.

McCutcheon, A. L. (2002). Basic concepts and procedures in single-and multiple-group latent class analysis. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied latent class analysis (pp. 57–88). Cambridge University Press.

Melby-Lervåg, M., Redick, T. S., & Hulme, C. (2016). Working memory training does not improve performance on measures of intelligence or other measures of “far transfer” evidence from a meta-analytic review. Perspectives on Psychological Science, 11 (4), 512–534.

Miciak, J., & Fletcher, J. M. (2020). The critical role of instructional response for identifying dyslexia and other learning disabilities. Journal of Learning Disabilities, 53 (5), 343–353.

Miciak, J., Fletcher, J. M., Stuebing, K. K., Vaughn, S., & Tolar, T. D. (2014). Patterns of cognitive strengths and weaknesses: Identification rates, agreement, and validity for learning disabilities identification. School Psychology Quarterly, 29 (1), 21.

Miciak, J., Williams, J. L., Taylor, W. P., Cirino, P. T., Fletcher, J. M., & Vaughn, S. (2016). Do processing patterns of strengths and weaknesses predict differential treatment response? Journal of Educational Psychology, 108 (6), 898.

Miciak, J., Cirino, P. T., Ahmed, Y., Reid, E., & Vaughn, S. (2019). Executive functions and response to intervention: Identification of students struggling with reading comprehension. Learning Disability Quarterly, 42 (1), 17–31.

Morin, A. J. S., & Wang, J. C. K. (2016). A gentle introduction to mixture modeling using physical fitness data. In N. Ntoumanis & N. Myers (Eds.), an introduction to intermediate and advanced statistical analyses for sport and exercise scientists (pp. 183–210). Wiley.

Morris, R. D., Stuebing, K. K., Fletcher, J. M., Shaywitz, S. E., Lyon, G. R., Shankweiler, D. P., & Shaywitz, B. A. (1998). Subtypes of reading disability: Variability around a phonological core. Journal of Educational Psychology, 90 (3), 347.

Muthén, B. (2003). Statistical and substantive checking in growth mixture modeling: Comment on Bauer and Curran (2003). Psychological Methods., 8 (3), 369–377.

Muthén, B. (2004). Latent variable analysis. The Sage Handbook of Quantitative Methodology for the Social Sciences, 345 (368), 106–109.

Nation, K., Clarke, P., Marshall, C. M., & Durand, M. (2004). Hidden language impairments in children: Parallels between poor reading comprehension and specific language impairment? Journal of Speech, Language, and Hearing Research, 47 , 199–211.

National Center for Education Statistics. (2019). National Assessment of Educational Progress: An overview of NAEP . National Center for Education Statistics, Institute of Education Sciences, U.S. Dept. of Education.  https://nces.ed.gov/nationsreportcard/

Norwalk, K. E., DiPerna, J. C., Lei, P. W., & Wu, Q. (2012). Examining early literacy skill differences among children in head start via latent profile analysis. School Psychology Quarterly, 27 (3), 170.

Park, J., Ritter, M., Lombardino, L. J., Wiseheart, R., & Sherman, S. (2014). Phonological awareness intervention for verbal working memory skills in school-age children with specific language impairment and concomitant word reading difficulties. International Journal of Research Studies in Language Learning, 3 (4), 3–22.

Pastor, D. A., Barron, K. E., Miller, B. J., & Davis, S. L. (2007). A latent profile analysis of college students’ achievement goal orientation. Contemporary Educational Psychology, 32 (1), 8–47.

Peng, P., Barnes, M., Wang, C., Wang, W., Li, S., Swanson, H. L., & Tao, S. (2018). A meta-analysis on the relation between reading and working memory. Psychological Bulletin, 144 (1), 48.

Perfetti, C., & Stafura, J. (2014). Word knowledge in a theory of reading comprehension. Scientific Studies of Reading, 18 (1), 22–37.

Plante, E., Mettler, H., Tucci, A., & Vance, R. (2019). Maximizing treatment efficiency in developmental language disorder: Positive effects in half the time. American Journal of Speech Language Pathology, 28 , 1233–1247.

Puranik, C., & Lombardino, L. (2006). An assessment paradigm for speech-language pathologists working with children with reading disabilities. Contemporary Issues in Communication Science and Disorders, 33 , 101–112.

Ramus, F., Marshall, C. R., Rosen, S., & van der Lely, H. K. (2013). Phonological deficits in specific language impairment and developmental dyslexia Towards a multidimensional model. Brain, 136 , 630–645. https://doi.org/10.1093/brain/aws356

Schrank, F. A. (2011). Woodcock-Johnson III tests of cognitive abilities. In A.S. Davis (Ed.), Handbook of pediatric neuropsychology (pp. 415–434). Springer.

Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6 (2), 461–464.

Sclove, S. L. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52 (3), 333–343.

Silverman, R. D., Speece, D. L., Harring, J. R., & Ritchey, K. D. (2013). Fluency has a role in the simple view of reading. Scientific Studies of Reading, 17 (2), 108–133.

Snowling, M., Bishop, D. V. M., & Stothard, S. E. (2000). Is preschool language impairment a risk factor for dyslexia in adolescence? The Journal of Child Psychology and Psychiatry and Allied Disciplines, 41 (5), 587–600.

Snowling, M. J., Nash, H. M., Gooch, D. C., Hayiou-Thomas, M. E., & Hulme, C. (2019). Wellcome Language and Reading Project Team. Developmental outcomes for children at high risk of dyslexia and children with developmental language disorder. Child Development, 90 (5), e548–e564.

Snowling, M. J., Hayiou-Thomas, M. E., Nash, H. M., & Hulme, C. (2020). Dyslexia and Developmental Language Disorder: Comorbid disorders with distinct effects on reading comprehension. Journal of Child Psychology and Psychiatry, 61 (6), 672–680.

Snowling, M. J., & Hulme, C. (2021). Annual Research Review: Reading disorders revisited–the critical importance of oral language. Journal of Child Psychology and Psychiatry, 62 (5), 635–653.

Spurk, D., Hirschi, A., Wang, M., Valero, D., & Kauffeld, S. (2020). Latent profile analysis: a review and “how to” guide of its application within vocational behavior research. Journal of Vocational Behavior, 120 , 103445.

Stevens, E. A., Austin, C., Moore, C., Scammacca, N., Boucher, A. N., & Vaughn, S. (2021). Current state of the evidence: Examining the effects of Orton-Gillingham reading interventions for students with or at risk for word-level reading disabilities . Advanced online publication.

Swanson, H. L., Arizmendi, G. D., & Li, J. T. (2020). The stability of learning disabilities among emergent bilingual children: A latent transition analysis. Journal of Educational Psychology, 113 (6), 1244–1268.

Tambyraja, S. R., Farquharson, K., & Justice, L. (2020). Reading risk in children with speech sound disorder: Prevalence, persistence, and predictors. Journal of Speech, Language, and Hearing Research, 63 (11), 3714–3726.

Thissen, D., Steinberg, L., & Kuang, D. (2002). Quick and easy implementation of the Benjamini-Hochberg procedure for controlling the false positive rate in multiple comparisons. Journal of Educational and Behavioral Statistics, 27 (1), 77–83.

Tolar, T. D., Barth, A. E., Fletcher, J. M., Francis, D. J., & Vaughn, S. (2014). Predicting reading outcomes with progress monitoring slopes among middle grade students. Learning and Individual Differences, 30 , 46–57.

Torgesen, J., Alexander, A., Wagner, R., Rashotte, C., Voeller, K., & Conway, T. (2001). Intensive remedial instruction for children with severe reading disabilities: Immediate and long-term outcomes from two instructional approaches. Journal of Learning Disabilities, 34 (1), 33–58.

Torgesen, J. K., Wagner, R. K., & Rashotte, C. A. (2012). Test of Word Reading Efficiency–2 . Austin, TX: Pro-Ed.

Van Horne, A. J., Fey, M., & Curran, M. (2017). Do the hard things first: A randomized controlled trial testing the effects of exemplar selection on generalization following therapy for grammatical morphology. Journal of Speech Language and Hearing Research, 60 , 2569–2588.

van Kleeck, A., Gillam, R. B., & Hoffman, L. M. (2006). Training in phonological awareness generalizes to phonological working memory: A preliminary investigation. The Journal of Speech and Language Pathology-Applied Behavior Analysis, 1 (3), 228.

Vaughn, S., & Fletcher, J. M. (2012). Response to intervention with secondary school students with reading difficulties. Journal of Learning Disabilities, 45 (3), 244–256.

Vermunt, J. K. (2010). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis, 18 (4), 450–469.

Wagner, R. K., Torgesen, J. K., Rashotte, C. A., & Pearson, N. A. (2010). TOSREC: Test of Sentence Reading Efficiency and Comprehension . Pro-Ed.

Wang, M. C., Deng, Q., Bi, X., Ye, H., & Yang, W. (2017). Performance of the entropy as an index of classification accuracy in latent profile analysis: A monte carlo simulation study. Acta Psychologica Sinica., 49 (11), 1473–1482.

What Works Clearinghouse. (2020). What Works Clearinghouse Standards Handbook, Version 4.1 . U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance.

Whitehurst, G. J., & Lonigan, C. J. (2001). Emergent literacy: Development from prereaders to readers. Handbook of Early Literacy Research, 1 , 11–29.

Wilson, L., McNeill, B., & Gillon, G. T. (2015). The knowledge and perceptions of prospective teachers and speech language therapists in collaborative language and literacy instruction. Child Language Teaching and Therapy, 31 (3), 347–362.

Woodcock, R., McGraw, K., & Mather, N. (2001). Woodcock-Johnson Third Edition, Tests of Cognitive Abilities . Riverside Publishing Company.

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This research was supported by the Institute of Education Sciences, US Department of Education, through Grant R305A170111. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Institute of Education Sciences or the US Department of Education.

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Capin, P., Gillam, S.L., Fall, AM. et al. Understanding the nature and severity of reading difficulties among students with language and reading comprehension difficulties. Ann. of Dyslexia 72 , 249–275 (2022). https://doi.org/10.1007/s11881-022-00255-3

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Primary School Students with Reading Comprehension Difficulties and Students with Learning Disabilities: Exploring Their Goal Orientations, Classroom Goal Structures, and Self-Regulated Learning Strategies

Christina kampylafka.

1 Department of Philosophy, Pedagogy and Psychology, School of Philosophy, National and Kapodistrian University of Athens, 15703 Athens, Greece

2 Department of Education, School of Education, University of Nicosia, 1700 Nicosia, Cyprus

Fotini Polychroni

3 Department of Psychology, School of Philosophy, National and Kapodistrian University of Athens, 15703 Athens, Greece

Alexandros-Stamatios Antoniou

4 Department of Primary Education, National and Kapodistrian University of Athens, 10680 Athens, Greece

Associated Data

Not applicable.

The aim of the present study was to investigate goal orientations and classroom goal structures and their relationship with strategies of self-regulated learning (SRL) in students with and without learning disabilities (LD) and reading comprehension difficulties (RCD). The sample consisted of 537 students attending the two last grades of primary school, fifth and sixth grade (Mage = 11.28 years, SD = ±0.59). Of these, 58 students were diagnosed with LD, and 70 students, after individually administered assessments in reading accuracy and reading comprehension, were assigned to the RCD group. Self-reported questionnaires were administered, assessing students’ personal goal orientations, classroom goal structures, and strategies of SRL. The results showed that students with LD and students with RCD scored lower in mastery orientation and higher in performance avoidance compared to their peers without difficulties (ND). LD students reported lower scores of adaptive strategies than their peers. In addition, the results confirmed the adaptive character of mastery-approach goals and mastery goal structures and the negative effects of performance-avoidance goals and performance goal structures on the adaptive strategies of SRL. Performance-approach goals predicted adaptive behaviors for all students, confirming the argument of an adaptive type of motivation. The findings of the current study highlight the importance of goal orientations and classroom goal structures for students’ SRL. Implications of the findings for enhancing motivation for students with LD and students with RCD are discussed.

1. Introduction

Self-regulated learning (SRL) constitutes an essential skill for learning and psychosocial adjustment of students of all ages [ 1 , 2 ]. According to the literature [ 3 , 4 , 5 ], learners are self-regulated to the degree that they are cognitively, metacognitively, motivationally, and strategically active participants in their own learning. Improving students’ self-regulation is critical for the educational process, especially for at-risk students. Achievement goal theory [ 6 , 7 ], one of the most prominent theories in the field of motivation and school psychology [ 8 ], also extended in the classroom [ 9 , 10 ], provides the conceptual framework for this study. Different goal orientations are associated with different, more or less adaptive behaviors in the school context [ 11 , 12 ].

Especially for students with learning disabilities (LD), for whom motivation is of primary importance for learning involvement and achievement, research on achievement goals is relatively limited and the findings conflicting [ 13 ]. Moreover, taking into account the shallow orthographic system of the Greek language, Greek students with LD face prominent difficulties in reading fluency and comprehension rather than solely in reading accuracy. The identification of learning disabilities usually takes place after the third year of primary school and usually follows the discrepancy criterion, including administering an intelligence test and reading, spelling, and writing standardized, and informal tests. (see Method for assessment process in students with LD). Apart from students with LD, there are also students with reading comprehension difficulties (RCD) almost in every classroom. Because of the nature of reading comprehension, these deficits are often not as easily detected as reading difficulties, and there is no universally agreed identification criterion, so, consequently, students are not assessed and supported on time [ 14 , 15 ]. Children with RCD are usually identified as having low scores in reading comprehension tasks, with typical reading accuracy and intelligence scores. There is evidence that children who struggle with reading employ fewer and less effective learning and self-regulating strategies and report decreasing motivation [ 16 ]. Nevertheless, there is limited evidence on the motivational profiles and self-regulation of children who struggle with reading comprehension with no apparent reading difficulties and who remain undetected for a long time. Drawing from the achievement goal theoretical model, the present study explores personal and classroom goals and their link to the self-regulating strategies of students with RCD compared to students with LD and a group with no difficulties.

1.1. Goal Orientations, Classroom Goal Structures, and Self-Regulation

Achievement goals are conceptualized as the reason or the purpose for which someone is engaged in an achievement situation [ 17 ]. According to the theoretical framework in achievement tasks, students can pursue two distinct types of personal goals [ 18 ]: mastery goals, which correspond to the opportunity to learn, acquire knowledge, and develop competency [ 10 , 11 ] and performance goals , which correspond to competence demonstration and social/peer comparison [ 7 , 19 ]. According to the 2 × 2 framework [ 20 ], by adding the dimensions of approach (desire to engage in a task) and avoidance (effort of non-involvement in a task), four distinct goal orientations were formed. Students with mastery-approach goals are usually engaged in school tasks motivated by personal interest, aiming at the improvement of their own skills, while students with mastery-avoidance goals focus on avoiding task-based or intrapersonal incompetence [ 21 ]. Nevertheless, research findings for mastery-avoidance goals are relatively limited compared to other types of goals [ 22 , 23 ]. On the contrary, attaining superior competence or outperforming others is the basic aim for students with performance-approach goals [ 18 , 24 , 25 , 26 ]. Finally, performance-avoidance goals focus on avoiding failure and avoiding demonstrating lack of ability relative to others [ 27 , 28 ].

Given that personal attitudes and students’ motivation toward learning tasks are gradually shaped by the influence received from familiar surroundings, including the school classroom setting and the opposite [ 29 , 30 ], goal achievement theory has been extended from the individual-level construct to the characteristics of the educational setting, forming classroom goal structures [ 9 , 24 ]. Classroom goals reflect the perceptions of students about the messages received by the educational practices promoted during the learning process, such as the lesson’s organization, structure, and evaluation process [ 27 , 31 ]. Although the majority of goal orientation studies have widely adopted the trichotomy framework, research on classroom goal structures followed the initial dichotomy framework of mastery versus performance goals [ 32 ], including mastery goal structures, which focus on knowledge acquisition and individual progress, and performance goal structures, which focus on school performance and social comparison [ 33 ].

Empirical research data have shown that adopting a goal orientation and being in a classroom that promotes a specific goal is associated with either positive or negative outcomes, creating a divided model of adaptive and maladaptive goals [ 34 , 35 ]. Μastery-approach goals are positively linked to internal motivation and adaptive patterns of behavior [ 36 , 37 , 38 ], such as attention, effort, on-task persistence, deep processing of information [ 39 , 40 ], effective help-seeking [ 41 , 42 ], self-effectiveness, and use of cognitive and metacognitive strategies [ 43 , 44 , 45 ]. Strategies aiming at deep processing and a better understanding of the cognitive material fit a more adaptive pattern of knowledge acquisition than those aiming to surface processing, temporary memorization, and retrieval, which are considered to be more maladaptive [ 46 , 47 ].

On the other hand, findings regarding performance-approach goals are contradictory [ 48 , 49 ]. Τhere is evidence of an absence of a link between this orientation and deep processing strategies, particularly for primary school pupils [ 11 , 50 ], or that performance-approach goals are associated with undesirable outcomes, including anxiety, negative affect [ 51 ] and surface learning strategies [ 52 ]. However, data exist showing that in the case of demanding learning tasks [ 53 ], performance-approach goals have, at times, predicted more strongly than mastery-approach goals grades and academic achievement [ 54 , 55 ], self-regulation [ 56 ], engagement, effort [ 57 ], and persistence [ 33 , 58 ] among people high in need for achievement [ 35 ]. Based on these patterns, researchers accepted the benefits stemming from adopting this goal and promoted the revision of achievement goal theory through multiple goals [ 59 ]. Through multiple goals, students take advantage of the positive effects of both mastery and performance goals, as they are not mutually exclusive, and each of them could be beneficial and useful in a different way [ 35 ]. It is possible that controversies over the effects of performance-approach goals are due to differences in definition [ 60 ]. More specifically, as both elements are reported in the definition, the performance-approach goals’ emphasis mostly demonstrates competence and earning favorable judgments or outperforming peers [ 20 , 61 ]. This distinction is maybe crucial for these goals’ outcomes, as normative comparison seems to evoke more engagement, interest, and effort [ 62 , 63 ].

Finally, performance-avoidance goals have been described mainly as maladaptive, as they are positively linked to negative outcomes in both cognitive and emotional domains [ 30 , 64 ], including self-handicapping strategies [ 65 , 66 ], instances of acquired helplessness, withdrawal [ 33 , 67 ], procrastination behavior [ 68 ], high anxiety [ 69 ], strategies of surface processing [ 70 , 71 ], and low achievement [ 28 ]. In addition, performance-avoidance goals were found to be negatively correlated with effective strategies of self-regulation [ 72 ] and school performance [ 73 ].

As with personal achievement goals, students’ perceptions of mastery classroom goals or goal structures have been associated with positive behavior patterns, such as mastery goals, deep strategies, effort, internal motives, help-seeking, support autonomy, and positive affect [ 74 , 75 , 76 ]. Mastery goal structures were found to be a positive predictor of deep-level learning strategies, critical thinking, metacognitive skills, effort, and school engagement [ 77 , 78 , 79 ]. In contrast, performance-oriented classrooms are linked to maladaptive educational behaviors, such as self-handicapping strategies, surface strategies, anxiety, and shame [ 67 , 71 , 80 ]. They are also negatively associated with internal motives, deep strategies, effective management of demanding tasks [ 24 , 30 , 81 ] and persistence [ 76 ].

1.2. Personal and Classroom Goals and Self-Regulated Learning of Children with LD and RCD

At-risk students or low achievers often present difficult motivational and behavioral profiles [ 82 , 83 , 84 ]. Nevertheless, relatively few studies have been conducted on personal goals, classroom goal structures, and their outcomes for students with LD or RCD [ 13 , 85 , 86 , 87 , 88 , 89 ]. Particularly, students with LD may report low levels of motivation and engagement in learning tasks [ 90 ], task avoidance [ 91 ], high levels of learned helplessness, academic procrastination, negative affect, self-handicapping, low levels of academic self-efficacy, and low help-seeking [ 13 , 92 , 93 , 94 , 95 ]. Empirical data also show that students with LD adopt high levels of performance-avoidance goals, low mastery-approach goals [ 13 , 96 ], perceive their class as more performance-oriented [ 85 ], use reduced self-regulating strategies, adopt surface approach to learning, present diminished persistence toward the learning goal, high levels of shallow cognitive processing strategies, low use of deep strategies, deficits in metacognitive skills, low levels of monitoring, and high anxiety [ 97 , 98 , 99 , 100 ]. Moreover, for students with LD, mastery-approach goals have been positively linked with adaptive motivational and behavioral outcomes, whereas performance-avoidance goals are associated with mostly maladaptive patterns [ 101 ], indicating similar outcomes with students without difficulties. For example, Sideridis [ 102 ] found that mastery-approach goals negatively predicted helplessness and positively predicted performance, while performance-avoidance goals positively predicted helplessness.

Struggling readers present a similar motivational profile to LD students reporting low motivation, negative attitudes toward reading, surface approach to learning, poor self-regulation skills, and low use of deep approach as compared to skilled readers [ 103 , 104 , 105 ]. Especially for poor comprehenders, empirical findings indicate deficits in motivation and working memory, low levels of monitoring, lower use of evaluation/integration and self-regulation strategies, lower school enjoyment, and higher levels of burnout than typical students [ 106 , 107 , 108 , 109 ]. Moreover, students with reading disabilities are significantly more performance-avoidant compared to typical students [ 110 ].

Although the effects of performance-approach goals are not sufficiently clear [ 111 ], it is argued that performance-approach goals are more adaptive for high-risk students, since they are positively associated with effort and school performance [ 102 , 112 ]. In addition, performance-approach goals are more adaptive for students with low self-perceived competence [ 35 , 113 ]. Finally, there are a few results highlighting the importance of mastery goal structures in reading comprehension [ 32 ], indicating that performance goal structures are associated with less positive affect and less engagement, adopting the opposite pattern of mastery goal structures [ 114 ].

1.3. The Present Study

In the present study, we examined goal orientations, classroom goal structures, and strategies of SRL in three separate groups: students with LD, students with RCD, and students with no difficulties, with the aim of describing their motivational profiles. A second aim was to explore the different patterns of predictors for adaptive and surface strategies for the three groups, aiming to investigate the outcomes of personal goals and classroom goal structures. In this context, we explored the adaptive or non-adaptive role of performance-approach goals on self-regulation. The literature regarding the effects of this personal goal has been inconclusive, on the one hand supporting the adaptive character of the goal and, on the other, promoting the negative outcomes [ 56 , 115 ].

In this study, the emphasis is given to students with LD and students with RCD, given the few and inconsistent findings of previous studies that have not led to a clear pattern for students facing difficulties [ 49 , 116 ]. Most of the existing literature for students with RCD focuses on the fundamental skills of the reading process [ 117 , 118 , 119 ], disregarding the psychosocial ramifications of this specific difficulty [ 84 ]. Furthermore, motivation and SRL in relation to reading comprehension skills have been rather neglected in empirical studies [ 120 , 121 ]. Finally, the sample was primary school students since they have received relatively limited research attention regarding these variables [ 122 ] compared to high school and University students [ 123 , 124 , 125 ].

Taking into account the existing literature, the research hypotheses and research question were formed as below:

• It is more likely that students with LD and students with RCD would report lower scores of mastery-approach goals and mastery goal structures and higher performance-avoidance goals and performance goal structures than students without difficulties;
• It is more likely that students with LD and students with RCD would report lower levels of adaptive self-regulating strategies (i.e., deep, motivational, persistence, and monitoring) and higher levels of surface strategies as compared to students with no difficulties;
• In terms of differences between the LD group and the RCD group, it is less likely that LD students would present an adaptive profile, reporting fewer mastery goals, more performance-avoidance goals, more performance goal structures, and fewer adaptive strategies;
• Are personal goals and classroom goal structures predictors of SRL strategies, and which of them is a predictor of adaptive and surface strategies separately for the three groups?

2. Materials and Methods

2.1. participants and procedure.

Five hundred and thirty-seven students attending the 5th and 6th grades of 28 public primary schools in Athens, Greece, participated in the study, selected from a larger pool of 568 students. The mean age of the participating students was 11.28 years (SD = ±0.59, Min = 10 years, Max = 12.75 years). All students were native speakers of Greek. Of them, 58 students had a formal statement of learning disabilities (LD group; 31 fifth graders and 27 sixth graders; 41 boys and 17 girls) by the state assessment Centers for Differential Diagnosis, Diagnosis, and Support (KEDASY). To get a formal statement of learning disabilities, the following criteria were met: average intelligence using the WISC-III test [ 126 ] (standardized in Greek), low reading ability (decoding and fluency) as measured by standardized and informal tests and no other coexisting neurodevelopmental difficulties. The LD group included children with average and above-average comprehension abilities. For the reading comprehension group (RCD group), 70 students from the total sample, 42 fifth graders and 28 sixth graders, 36 boys and 34 girls (excluding the students identified with LD), were selected after individualized assessments. For inclusion in the RCD group, the following criteria were met: students (a) scored at or above the 25th percentile on reading accuracy, (b) scored lower than the 25th percentile on reading comprehension [ 119 , 127 ] (see the measures in the next section) following the criteria used in other relevant empirical studies [ 128 , 129 ], and (c) had no known coexisting difficulties. Students included in the RCD group were not included in the LD group, and vice versa. The third group of the study consisted of 409 students (189 fifth graders and 220 sixth graders; 191 boys and 218 girls) with no formal statement and no other known difficulties according to their teachers and comprised the non-difficulties (ND) group. Finally, it should be stated that students who according to their classroom teachers’ perceptions had reading difficulties or were in the process of assessment for any other developmental disorder were not included nor in the ND group or in the RCD group.

2.2. Measures

2.2.1. personal goal orientations.

The Questionnaire of Achievement Goal Orientations [ 96 , 102 ] was used to assess students’ personal achievement goals. The questionnaire is domain specific (language) and consists of 18 items with three subscales, i.e., Mastery-Approach Goals-MAP goals- (6 items; e.g., “How important is to you to understand the language course?”), Performance-Approach Goals-PAP goals- (6 items; e.g., “How important is to you to get the best grade in the language course?”), and Performance-Avoidance Goals-PAV goals- (6 items; e.g., “Are you worried that you might not get a high grade in the language course?”). Τhe mastery-avoidance subscale (3 items) was not included in the present study since it is argued that this construct is more present in academic and competitive settings and less so in young students and different settings, especially in elementary school students [ 22 , 130 , 131 ]. A four-point Likert-type scale (from 1 = not at all to 4 = very much) was used. Cronbach’s alpha for MAP goals was α = 0.88, α = 0.92, and α = 0.91; for PAP goals α = 0.86, α = 0.92, and α = 0.83; and, finally, for PAV goals was α = 0.74, α = 0.90, and α = 0.74 (for ND, LD, and RCD groups, respectively).

2.2.2. Classroom Goal Structures

Classroom goal structures were assessed with the Questionnaire of Classroom Goal Structures [ 114 ]. Its items derived from a synthesis of scales (e.g., the Patterns of Adaptive Learning Scales-PALS) [ 31 , 67 ]. The questionnaire includes 16 items, and the students select a response from a four-point Likert-type scale, ranging from 1 (Not at all) to 4 (Very much). The questionnaire contains 2 subscales, Mastery Goal Structures (M-STR) (e.g., “The teacher tells us that mistakes don’t matter”) and Performance Goal Structures (P-STR) (e.g., “During the lesson, there is a lot of competition between students”) (M-STR α = 0.89, α = 0.85, α = 0.91; P-STR α = 0.87, α = 0.91, α = 0.89 for ND, LD, RCD groups, respectively).

2.2.3. Strategies of Self-Regulated Learning

Children’s Perceived Use of Self-Regulated Learning Inventory [ 132 ] is a self-report tool that assesses students’ self-regulation in school tasks. The questionnaire includes 75 items in a 5-point Likert-type scale from 1 (Never) to 5 (Always) that examine 9 basic components of SRL, i.e., task orientation, planning, motivation, self-efficacy, monitoring, learning strategies, motivational strategies, persistence, and self-evaluation. For the purpose of the present study, four subscales were used, learning strategies, motivational strategies, persistence, and monitoring. The learning strategies contain 14 items grouped into two factors, the surface strategy with four items (e.g., “When studying, I read or recall everything again and again until I know it by heart”) (α = 0.71, α = 0.74, α = 0.75 for ND, LD group, and RCD, respectively) and the deep strategies with 10 items (e.g., “When studying, I make a scheme or a mind map”) (α = 0.87, α = 0.90, α = 0.91 for the three groups, respectively). Motivational strategies contain 4 items (e.g., “During my schoolwork, I say to myself: You can do it, just keep on working!”) (α = 0.82, α = 0.79, and α = 0.87 for ND, LD, and RCD group, respectively), monitoring contains 7 items, (e.g., “If I notice something isn’t working out, I try a different approach”) (α = 0.83, α = 0.79, and α = 0.89 for ND, LD, and RCD group, respectively), and persistence includes 6 items such as “Even if I would rather do other things, I finish my schoolwork (α = 0.80 for ND group, α = 0.91 for LD group, and α = 0.92 for RCD group). In accordance with the theoretical models, deep strategies, motivational strategies, monitoring, and persistence were categorized as adaptive self-regulating strategies.

2.2.4. Reading Accuracy

A word reading task and a non-word reading task of the Greek standardized Reading Test TEST-A [ 133 ] were administered individually as indicators of reading accuracy. The word reading task consists of 53 isolated words, and the non-word reading task consists of 24 non-words printed in two columns. Both words and non-words are presented in order of difficulty. The test is discontinued when children score zero on five consecutive items (α = 0.86, α = 0.74, and α = 0.76 for ND, LD, and RCD group, respectively).

2.2.5. Reading Comprehension

Two short informative texts, from the reading comprehension task of the Reading Test TEST-A [ 133 ] were administered to the students. Students were asked to read the texts aloud or silently and then to respond to six multiple choice literal, vocabulary-dependent, and inferential questions. The number of correct responses in both texts was employed as measure of comprehension (α = 0.72, α = 0.78, and α = 0.73 for ND, LD, and RCD group, respectively).

2.3. Procedure and Ethics

The questionnaires and individual assessments were administered to students during school hours by the researcher. Students completed the self-report questionnaires in the classroom, and then they were assessed individually in reading accuracy and comprehension tasks. Students were assisted with their reading by the researcher, if necessary, especially in the case of students with learning disabilities. The study was approved by the Hellenic Institute of Educational Policy of the Ministry of Education, a body that granted consent for access to schools, and parental consent was a prerequisite for students’ participation in the study. Students having parental consent were also asked if they were willing to participate in the study, and they were informed that they had the opportunity to withdraw from the study at any stage.

3.1. Descriptives and Group Comparisons

Kruskal–Wallis tests were performed to test for differences in median scores on goal orientations and classroom goal structures between the three groups of students (ND, LD, RCD) as the normality hypothesis was rejected for all variables (Shapiro–Wilk normality test). There were statistically significant differences between the medians of the groups for all the goal orientations and classroom goal structures ( p < 0.05).

Post hoc analyses based on Mann–Whitney U test pairwise comparisons showed that students with LD and students with RCD reported significantly lower MAP goals, M-STR, and higher PAV goals and P-STR as compared with the ND group. Moreover, LD students reported significantly lower PAP goals as compared with the ND group. Finally, LD students presented lower MAP goals, higher PAV goals, and P-STR as compared to RCD group ( Table 1 ).

Mean scores, median scores, and standard deviations for the study variables.

Notes: Median scores that share the same index (a,b,c) are not statistically different according to the post hoc test Mann–Whitney U test for α = 0.05. MAP = mastery-approach goals; PAP = performance-approach goals; PAV = performance-avoidance goals; M-STR = mastery goal structures; P-STR = performance goal structures. * p < 0.05 ** p < 0.001.

3.2. Predictors of Surface and Adaptive Strategies

Pearson’s r coefficients for the study variables are presented in Table 2 for the three groups of the study. The correlations between the predictor variables of the study were examined, and they were found to be low to moderate, indicating that collinearity was unlikely to be a problem [ 134 ].

Correlations of personal goals, classroom goal structures, and strategies of self-regulated learning.

Note: MAP = mastery-approach goals; PAP = performance-approach goals; PAV = performance-avoidance goals; M-STR = mastery goal structures; P-STR = performance goal structures. ** p < 0.001.

The results for all groups showed that MAP goals and M-STR were positively correlated to adaptive strategies and negative correlated to surface strategies of SRL. PAP goals were also positively linked to adaptive strategies. Finally, PAV goals and P-STR were negatively linked to adaptive strategies and positively linked to surface strategies.

For each one of the two dependent variables (Surface and Adaptive Strategies), a series of linear regressions analyses was run to estimate the effect of each one of the predictors (MAP, PAP, PAV, M-STR, P-STR, Group, Gender, Class) and their possible interactions on the dependent variables. The assumptions of the linear regression analysis were not violated (normality assumption, assumption of equal variance, multicollinearity as GVIF values <10). Starting from “Surface Strategies”, a linear regression model that included all the main effects was conducted. Moving forward, we eliminated all the predictors that did not have a statistically significant effect-each time, the predictor with the highest non-significant p-value was excluded-and then tried to add only significant interaction terms.

The procedure was repeated until the Model 5 ( F (5, 531) = 140.579, p = 0.000, R 2 = 0.570) included only statistically significant main effects ( Table 3 ). To evaluate if the latest reduced model (Model 5) was similar to the initial full one (Model 1), an Anova test was utilized. No statistically significant difference was found in the fit between Model 1 and Model 5 since p = 0.834 > α = 0.05, so Model 5 consisted the base to identify possibly significant interactions (MAP and Group, PAP and Group, P-STR and Group).

Model 5: Linear regression analysis for predicting surface strategies.

Note: MAP = mastery-approach goals; PAP = performance-approach goals; P-STR = performance goal structures. * p < 0.05 ** p < 0.001.

Table 4 shows that only in Model 8 ( F (7, 529) = 102.809, p = 0.000 R 2 = 0.576) the group RCD by P-STR term had a statistically significant effect on surface strategies ( p = 0.005 < α = 0.05). It was observed that, for the models 6 and 7 that contained the other two possible interaction terms, the interaction terms did not have a statistically significant effect on surface strategies (both p > 0.05).

Model 8: Linear regression analysis for predicting surface strategies.

The final model was Model 8, as the ANOVA tests comparison showed that Model 8 had a statistically significantly better fit than Model 5 ( p = 0.017 < α = 0.05). Looking into Model 8, it was observed that there was a positive effect of MAP, PAP, and P-STR on surface strategies, and more specifically, if MAP was increased by one unit, then the surface strategies was increased by 0.097 on average. Similarly, if PAP was increased by one unit, the surface strategies was increased by 0.490 ceteris paribus. In the case of the ND group (level of reference of group), when P-STR was increased by one unit, the surface strategies was increased by 0.363 on average, ceteris paribus. Furthermore, we observed that also for the LD group, P-STR had a similar effect on surface strategies with the ND group since the interaction term group LD by P-STR was not statistically significant ( p = 0.854 > α = 0.05). On the other hand, for the RCD group, the interaction term group RCD by P-STR was statistically significant ( p = 0.005 < α = 0.05) with a positive coefficient of 0.257, which means that the increase rate in the surface strategies was higher than the other two groups (0.36 + 0.26 = 0.62) when the P-STR was increased by one unit. It should be noted that on average, the group RCD had a lower coefficient (−0.411) as an initial value compared to ND (level of reference) and LD (0.518). Bringing together the main group effect and the interaction term, regarding the RCD group, for low values of P-STR, the surface strategies value was lower than the other two groups but as P-STR increased, the surface strategies increase was more aggressive than for the other two groups. To better visualize the interaction effect of P-STR and groups on surface strategies, we utilized the sjPlot R package [ 135 ] to construct the plot of surface strategies with respect to P-STR for each group separately.

As expected, the RCD green line had lower surface strategies values than the other two groups for low P-STR values, but it had a higher slope, and it crossed the two other lines, resulting in higher values of surface strategies than the two other groups for high values of P-STR ( Figure 1 ).

Interaction effect of P-STR and Groups on Surface Strategies.

Passing now to adaptive strategies, a linear regression model that included all the main effects of the adaptive strategies was conducted. As in the case of surface strategies, we excluded all the predictors that did not have a statistically significant effect and then added only significant interaction terms.

As Model 2 ( Table 5 ) consisted of only the statistically significant terms ( F (8, 528) = 199.570, p = 0.000, R 2 = 0.751), an ANOVA test was utilized to evaluate if the latest model (Model 2) showed a similar fit to the initial one (Model 1). No statistically significant difference was found in the fit between Model 1 and Model 2 ( p = 0.8054 > 0.05), so Model 2 was used as a base to identify significant interactions (PAP and Group, MAP and Group, PAV and Group, M-STR and Group, P-STR and Group).

Model 2: Linear regression analysis for predicting adaptive strategies.

Note: MAP= mastery-approach goals; PAP= performance-approach goals; PAV= performance-avoidance goals; M-STR= mastery goal structures. * p < 0.05 ** p ≤ 0.001.

Running linear regressions models with interactions, we observed that only the Group LD by MAP interaction term in Model 4 ( Table 6 ) ( F (10, 526) = 162.208, p = 0.000, R 2 = 0.755) had a statistically significant effect on adaptive strategies ( p = 0.011 < α = 0.05). All the other models did not have any extra significant terms.

Model 4: Linear regression analysis for predicting adaptive strategies.

Note: MAP = mastery-approach goals; PAP = performance-approach goals; PAV = performance-avoidance goals; M-STR = mastery goal structures. * p < 0.05 ** p ≤ 0.001.

An Anova test was utilized to examine if the fit of Model 4 is statistically significantly better than Model 2, which contains all the main effects. Since p = 0.020 < α = 0.05, we concluded that Model 4 with the interaction term of Group LD by MAP had a statistically significantly better fit to the data.

As far as Model 4 is concerned, there was a main positive effect of MAP on adaptive strategies for the level of reference ND of Group, where the adaptive strategies score was increased by 0.493 when the MAP was increased by one unit. For the RCD group, a similar pattern was observed as the interaction term was small and non-significant ( p = 0.477 > α = 0.05). However, for the LD group, there was a negative interaction term MAP by group (−0.266) that decreased the overall positive effect of MAP on the adaptive strategies score (0.493 − 0.266 = 0.227). So, for the LD group, a unit increase in MAP brought a 0.227 increase in adaptive strategies. The above differences in the slopes of MAP for each group level are also presented in the estimated marginal means interaction plot below, where the blue line (LD group) has a smaller slope than the slopes of the other two groups ( Figure 2 ).

Interaction effect of MAP goals and Groups on Adaptive Strategies.

3.3. Descriptives and Group Comparisons Based on Final Models

For an overall comparison of the mean Surface and Adaptive Strategies between the three groups (ND, LD, RCD), the estimated marginal means together with their 95% confidence intervals were calculated ( Table 7 ) and pairwise Tukey comparisons between the three groups were conducted using the “emmeans” R package [ 136 ].

Descriptives of Surface and Adaptive Strategies based on final models.

From the Tukey tests, we concluded that the mean ND surface strategies were statistically significantly lower than the ones of LD and RCD ( p < 0.0001 and p = 0.0021 < 0.05), and marginally, there is no statistically significant difference between LD and RCD surface strategies ( p = 0.0641 < α = 0.05). Moreover, based on the estimated marginal means comparison t-test with Tukey adjustment, it was observed that the LD group had a statistically significantly lower adaptive strategies score than the two other groups ( p < 0.001 and p = 0.003), while the ND and RCD groups showed similar performance ( p = 0.124).

4. Discussion

The aim of the present study was to explore self-reported goal orientations, classroom goal structures, and strategies of self-regulated learning of students with learning disabilities, students with reading comprehension difficulties, and students with no difficulties. Moreover, we examined the predictors of adaptive and surface strategies for the three groups.

The results of the present study showed that the students with learning disabilities and the students with comprehension difficulties were less mastery and more performance-avoidant-oriented and reported higher levels of performance goal structures as compared to students without difficulties. This result confirms the first hypothesis and is in line with extant research showing that students with learning disabilities and poor comprehenders present low mastery goals and avoid learning tasks because of their fear of poor performance and deficits exposure, and they usually perceive the classroom context as performance-oriented [ 85 , 137 ].

In terms of self-regulated strategies, students with learning disabilities and reading comprehension difficulties reported more surface strategies than their typical peers, partly confirming the second hypothesis and the existing literature [ 90 , 138 ]. Moreover, only the group with learning disabilities reported lower scores on the adaptive self-regulating strategies (deep, motivational, monitoring, and persistence) as compared to students with no difficulties. This finding partly confirms the second hypothesis and agrees with previous studies showing that students with learning disabilities report lower levels of self-regulation strategies than typical peers [ 97 , 139 ]. Nevertheless, poor comprehenders showed similar use of adaptive strategies to students without difficulties, contrary to the bulk of evidence showing the use of fewer self-regulating strategies [ 106 ]. This may imply that these students may try to use adaptive strategies, but since these are particularly demanding, they may not be effective and lead to low comprehension performance. These findings underline the motivational deficits of students with learning disabilities and comprehension difficulties, highlight the importance of enhancing their self-regulation for learning and turn the focus on students with comprehension difficulties, a group whose difficulties remain undetected and are largely underestimated [ 14 ]. In the case of students with comprehension difficulties, suitable decoding skills might mask comprehension deficits, and low performance in comprehension tasks is often attributed to external causes, such as low attention or lack of effort [ 128 ]. For poor comprehenders, being intrinsically motivated and using effective strategies of self-regulated learning during the reading process can lead to the successful completion of a comprehension task. Therefore, further research is needed using methods such as behavioral observation tasks for students with comprehension difficulties, as these groups frequently do not have a formal special educational needs statement and whose self-regulation profile is not straightforward.

Regarding the differences between the students with learning disabilities and the students with comprehension difficulties, the former group reported lower levels of mastery-approach goals, higher levels of performance-avoidance goals and performance goal structures, and fewer adaptive strategies than their peers with poor comprehension. These findings confirm our third hypothesis and are in agreement with other studies showing that students with learning disabilities are a highly heterogeneous group, facing a wide range of deficits in motivational, behavioral, cognitive, and psychosocial characteristics [ 140 , 141 ]. As far as their goal orientation is concerned, given that they have difficulties in more than one learning area [ 90 , 142 ], it is expected that they adopt fewer mastery-approach goals and more performance-avoidance goals, probably in order to avoid exposure of their weak areas [ 143 ]. On the other hand, students with comprehension difficulties present difficulties mainly in one specific domain [ 118 ].

The present study also explored the different patterns of predictors for strategies of self-regulated learning for the three groups, looking into the prediction of more and fewer adaptive strategies. Regarding the research question, the results highlighted the importance of motivation (personal and contextual) by adopting a personal goal or being in an environment that promotes specific goals for self-regulated learning of all students, regardless of the type of difficulty [ 144 ]. Mastery-approach goals and mastery classroom goals were positive predictors of adaptive strategies of self-regulated learning for all three groups (typical students, students with learning disabilities, and students with reading comprehension difficulties). This finding confirmed that mastery orientation personal goals and classroom mastery orientation goals are associated with positive behavior models at a cognitive level, not only for the group without any known difficulties but also for students with learning disabilities and reading comprehension difficulties. In essence, students who estimate that the educational practices in their class are focused on learning and who, on their own, aim at self-improvement might be led to the use of more strategies that aim at in-depth processing of the learning material and demonstrate higher levels of motivational strategies, monitoring, and persistence. This result concurs with other findings and confirms the adaptive character of mastery goals and the corresponding classroom goal structures for all students [ 8 ]. Given that mastery goal structures are associated with adaptive self-regulatory strategies, such as monitoring, deep, and motivational strategies, these should be reinforced at school [ 55 , 114 ]. Reinforcing mastery goal structures will probably increase the adoption of personal mastery goals since the school setting, and the classroom’s practices seem to affect the students’ personal attitudes and behaviors [ 10 ]. In the case of LD students, mastery goals had a lower impact as a predictor of adaptive strategies. This finding is probably due to the fact that students with learning disabilities are less oriented to learning, adopt lower mastery goals than other students, and their mastery goals are not stable during learning tasks. As a result, they have very low engagement in a learning task and avoid using deep and complex strategies for it [ 90 ].

However, the reverse pattern was observed in performance-avoidance goals. Avoiding learning engagement for fear of low performance and adopting performance-avoidance goals were found to be positively linked to surface strategies and were negatively linked to adaptive strategies. More specifically, performance-avoidance goals negatively predicted adaptive strategies of self-regulated learning in all groups. This result confirms the extant literature that supports the less adaptive character of one’s performance-avoidance goals toward learning behaviors and school performance [ 28 , 70 , 96 ]. Performance goal structures positively predicted surface strategies, and they were also negatively linked to adaptive strategies of self-regulating learning for all groups. This finding concurs with previous research [ 24 , 71 , 76 ] for typical students and both at-risk groups. On the other hand, for students with comprehension difficulties, performance classroom goals were a stronger predictor of surface strategies. Taking into account that poor comprehenders’ difficulties are detected in a specific domain, these students may better adapt and respond according to the classroom’s orientation. In other words, in a performance-oriented classroom, they might use more surface strategies since they are simpler and easier strategies. However, further research is also warranted. On the whole, the results regarding performance classroom goals seem to justify the researchers who have considerable reservations about the introduction of such structures into the educational setting [ 81 ].

According to the present study, performance-approach goals significantly predicted surface strategies for all groups, in agreement with the literature [ 51 ]. Moreover, performance-approach goals, whose role is questionable [ 61 ], were a positive predictor of adaptive strategies for all groups. This finding is consistent with studies showing that performance-approach goals could be effective for all students, leading to the adoption of more adaptive behavioral models [ 61 , 115 ]. Additionally, researchers claim that students with learning disabilities and students with reading comprehension difficulties or low-performance students, by adopting performance-approach goals, are motivated toward the achievement of a specific goal and refrain from task resignation or avoidance, so even this presence of motivation becomes beneficial as compared to the absence of any motives [ 88 ]. Moreover, awareness of shortcomings and elevated stress levels due to fear of exposure to weaknesses and desire for higher performance, apart from negatively affecting psychosocial adjustment and well-being [ 51 ], could possibly intensify effort and lead to the use of more effective strategies [ 145 ]. It might facilitate our understanding of the results if we take into consideration that the performance-approach goals can include both normative and appearance standards and that the normative standards activate the use of self-regulating strategies [ 146 ]. Consequently, this finding highlights that avoidance orientation is the one negatively linked to adaptive behaviors for all students, while the effect of performance-approach goals warrants further research.

Taking the above results into consideration, emphasis is put on the value of classroom goals and personal mastery goals for learning, but also on the negative effect of personal performance-avoidance goals for all students, regardless of their presented difficulties. Promoting mastery of classroom goals and therefore encouraging students to adopt personal goals of acquiring knowledge and improving themselves by individual standards is of the essence in the classroom [ 80 ]. At the same time, the importance of performance-approach goals for more adaptive learning behaviors, in the case of poor-performance students, is not to be understated. The above findings are in favor of promoting educational orientation toward learning in the classroom, while further investigation is required concerning the role played by performance-approach goals to the performance of students with learning disabilities and comprehension difficulties. Furthermore, these results are in agreement with studies that favor the revision of the goal orientation theory since they demonstrate that performance-approach goals do not necessarily lead to negative behavior models [ 59 ]. Consequently, multiple goals, which aim both at learning and performance, are more likely to lead to a more adaptive behavioral model [ 26 , 147 ].

Limitations and Conclusion

The use of self-report may lead students to report strategies that they have not used, offering socially desirable responses, or that they might be unaware of strategies they used automatically. It is equally probable that the individual characteristics of students are incorporated into the perceived classroom goals, thus highlighting the subjective nature of the interpretation of educational practices within the classroom. For example, the relationship between teachers and students or between peers could be an important factor affecting students’ attitudes toward the school environment; however, this was not taken into account in the present study [ 122 ]. Future studies may employ rating scales by teachers and parents and behavioral observation as additional methodological tools. In addition, the learning disabilities group included students with a formal statement of learning disabilities, without further assessment by the authors. Students might present difficulties in other learning areas, and the majority of these might have already participated in intervention programs, and this may have an effect on the reported strategies. It should also be noted that students identified with poor reading comprehension were only selected by the pool of students with no known difficulties following reading comprehension tasks. This therefore, did not explore students’ other possible learning needs.

These findings of the present study expand our knowledge of achievement goals and self-regulated learning strategies in students with comprehension difficulties and students with learning disabilities. Given the importance of motivation in the learning process, the results of the present study may have implications for the identification of the motivational profile of at-risk students and the implementation of primary and secondary prevention programs with the aim of forming self-regulated learners.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, C.K. and F.P.; methodology, C.K., F.P. and A.-S.A.; formal analysis, C.K. and F.P.; investigation, C.K.; writing—original draft preparation, C.K.; writing—review and editing, C.K., F.P. and A.-S.A.; supervision, F.P. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved as a Ph.D. by the Hellenic Institute of Educational Policy of Ministry of Education, Protocol Code: Φ15/734/162551/Γ1, Approval Date: October 2014.

Informed Consent Statement

Informed consent was obtained from all students’ parents involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

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