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An automated essay scoring systems: a systematic literature review

Dadi ramesh.

1 School of Computer Science and Artificial Intelligence, SR University, Warangal, TS India

2 Research Scholar, JNTU, Hyderabad, India

Suresh Kumar Sanampudi

3 Department of Information Technology, JNTUH College of Engineering, Nachupally, Kondagattu, Jagtial, TS India

Associated Data

Assessment in the Education system plays a significant role in judging student performance. The present evaluation system is through human assessment. As the number of teachers' student ratio is gradually increasing, the manual evaluation process becomes complicated. The drawback of manual evaluation is that it is time-consuming, lacks reliability, and many more. This connection online examination system evolved as an alternative tool for pen and paper-based methods. Present Computer-based evaluation system works only for multiple-choice questions, but there is no proper evaluation system for grading essays and short answers. Many researchers are working on automated essay grading and short answer scoring for the last few decades, but assessing an essay by considering all parameters like the relevance of the content to the prompt, development of ideas, Cohesion, and Coherence is a big challenge till now. Few researchers focused on Content-based evaluation, while many of them addressed style-based assessment. This paper provides a systematic literature review on automated essay scoring systems. We studied the Artificial Intelligence and Machine Learning techniques used to evaluate automatic essay scoring and analyzed the limitations of the current studies and research trends. We observed that the essay evaluation is not done based on the relevance of the content and coherence.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10462-021-10068-2.

Introduction

Due to COVID 19 outbreak, an online educational system has become inevitable. In the present scenario, almost all the educational institutions ranging from schools to colleges adapt the online education system. The assessment plays a significant role in measuring the learning ability of the student. Most automated evaluation is available for multiple-choice questions, but assessing short and essay answers remain a challenge. The education system is changing its shift to online-mode, like conducting computer-based exams and automatic evaluation. It is a crucial application related to the education domain, which uses natural language processing (NLP) and Machine Learning techniques. The evaluation of essays is impossible with simple programming languages and simple techniques like pattern matching and language processing. Here the problem is for a single question, we will get more responses from students with a different explanation. So, we need to evaluate all the answers concerning the question.

Automated essay scoring (AES) is a computer-based assessment system that automatically scores or grades the student responses by considering appropriate features. The AES research started in 1966 with the Project Essay Grader (PEG) by Ajay et al. ( 1973 ). PEG evaluates the writing characteristics such as grammar, diction, construction, etc., to grade the essay. A modified version of the PEG by Shermis et al. ( 2001 ) was released, which focuses on grammar checking with a correlation between human evaluators and the system. Foltz et al. ( 1999 ) introduced an Intelligent Essay Assessor (IEA) by evaluating content using latent semantic analysis to produce an overall score. Powers et al. ( 2002 ) proposed E-rater and Intellimetric by Rudner et al. ( 2006 ) and Bayesian Essay Test Scoring System (BESTY) by Rudner and Liang ( 2002 ), these systems use natural language processing (NLP) techniques that focus on style and content to obtain the score of an essay. The vast majority of the essay scoring systems in the 1990s followed traditional approaches like pattern matching and a statistical-based approach. Since the last decade, the essay grading systems started using regression-based and natural language processing techniques. AES systems like Dong et al. ( 2017 ) and others developed from 2014 used deep learning techniques, inducing syntactic and semantic features resulting in better results than earlier systems.

Ohio, Utah, and most US states are using AES systems in school education, like Utah compose tool, Ohio standardized test (an updated version of PEG), evaluating millions of student's responses every year. These systems work for both formative, summative assessments and give feedback to students on the essay. Utah provided basic essay evaluation rubrics (six characteristics of essay writing): Development of ideas, organization, style, word choice, sentence fluency, conventions. Educational Testing Service (ETS) has been conducting significant research on AES for more than a decade and designed an algorithm to evaluate essays on different domains and providing an opportunity for test-takers to improve their writing skills. In addition, they are current research content-based evaluation.

The evaluation of essay and short answer scoring should consider the relevance of the content to the prompt, development of ideas, Cohesion, Coherence, and domain knowledge. Proper assessment of the parameters mentioned above defines the accuracy of the evaluation system. But all these parameters cannot play an equal role in essay scoring and short answer scoring. In a short answer evaluation, domain knowledge is required, like the meaning of "cell" in physics and biology is different. And while evaluating essays, the implementation of ideas with respect to prompt is required. The system should also assess the completeness of the responses and provide feedback.

Several studies examined AES systems, from the initial to the latest AES systems. In which the following studies on AES systems are Blood ( 2011 ) provided a literature review from PEG 1984–2010. Which has covered only generalized parts of AES systems like ethical aspects, the performance of the systems. Still, they have not covered the implementation part, and it’s not a comparative study and has not discussed the actual challenges of AES systems.

Burrows et al. ( 2015 ) Reviewed AES systems on six dimensions like dataset, NLP techniques, model building, grading models, evaluation, and effectiveness of the model. They have not covered feature extraction techniques and challenges in features extractions. Covered only Machine Learning models but not in detail. This system not covered the comparative analysis of AES systems like feature extraction, model building, and level of relevance, cohesion, and coherence not covered in this review.

Ke et al. ( 2019 ) provided a state of the art of AES system but covered very few papers and not listed all challenges, and no comparative study of the AES model. On the other hand, Hussein et al. in ( 2019 ) studied two categories of AES systems, four papers from handcrafted features for AES systems, and four papers from the neural networks approach, discussed few challenges, and did not cover feature extraction techniques, the performance of AES models in detail.

Klebanov et al. ( 2020 ). Reviewed 50 years of AES systems, listed and categorized all essential features that need to be extracted from essays. But not provided a comparative analysis of all work and not discussed the challenges.

This paper aims to provide a systematic literature review (SLR) on automated essay grading systems. An SLR is an Evidence-based systematic review to summarize the existing research. It critically evaluates and integrates all relevant studies' findings and addresses the research domain's specific research questions. Our research methodology uses guidelines given by Kitchenham et al. ( 2009 ) for conducting the review process; provide a well-defined approach to identify gaps in current research and to suggest further investigation.

We addressed our research method, research questions, and the selection process in Sect.  2 , and the results of the research questions have discussed in Sect.  3 . And the synthesis of all the research questions addressed in Sect.  4 . Conclusion and possible future work discussed in Sect.  5 .

Research method

We framed the research questions with PICOC criteria.

Population (P) Student essays and answers evaluation systems.

Intervention (I) evaluation techniques, data sets, features extraction methods.

Comparison (C) Comparison of various approaches and results.

Outcomes (O) Estimate the accuracy of AES systems,

Context (C) NA.

Research questions

To collect and provide research evidence from the available studies in the domain of automated essay grading, we framed the following research questions (RQ):

RQ1 what are the datasets available for research on automated essay grading?

The answer to the question can provide a list of the available datasets, their domain, and access to the datasets. It also provides a number of essays and corresponding prompts.

RQ2 what are the features extracted for the assessment of essays?

The answer to the question can provide an insight into various features so far extracted, and the libraries used to extract those features.

RQ3, which are the evaluation metrics available for measuring the accuracy of algorithms?

The answer will provide different evaluation metrics for accurate measurement of each Machine Learning approach and commonly used measurement technique.

RQ4 What are the Machine Learning techniques used for automatic essay grading, and how are they implemented?

It can provide insights into various Machine Learning techniques like regression models, classification models, and neural networks for implementing essay grading systems. The response to the question can give us different assessment approaches for automated essay grading systems.

RQ5 What are the challenges/limitations in the current research?

The answer to the question provides limitations of existing research approaches like cohesion, coherence, completeness, and feedback.

Search process

We conducted an automated search on well-known computer science repositories like ACL, ACM, IEEE Explore, Springer, and Science Direct for an SLR. We referred to papers published from 2010 to 2020 as much of the work during these years focused on advanced technologies like deep learning and natural language processing for automated essay grading systems. Also, the availability of free data sets like Kaggle (2012), Cambridge Learner Corpus-First Certificate in English exam (CLC-FCE) by Yannakoudakis et al. ( 2011 ) led to research this domain.

Search Strings : We used search strings like “Automated essay grading” OR “Automated essay scoring” OR “short answer scoring systems” OR “essay scoring systems” OR “automatic essay evaluation” and searched on metadata.

Selection criteria

After collecting all relevant documents from the repositories, we prepared selection criteria for inclusion and exclusion of documents. With the inclusion and exclusion criteria, it becomes more feasible for the research to be accurate and specific.

Inclusion criteria 1 Our approach is to work with datasets comprise of essays written in English. We excluded the essays written in other languages.

Inclusion criteria 2  We included the papers implemented on the AI approach and excluded the traditional methods for the review.

Inclusion criteria 3 The study is on essay scoring systems, so we exclusively included the research carried out on only text data sets rather than other datasets like image or speech.

Exclusion criteria  We removed the papers in the form of review papers, survey papers, and state of the art papers.

Quality assessment

In addition to the inclusion and exclusion criteria, we assessed each paper by quality assessment questions to ensure the article's quality. We included the documents that have clearly explained the approach they used, the result analysis and validation.

The quality checklist questions are framed based on the guidelines from Kitchenham et al. ( 2009 ). Each quality assessment question was graded as either 1 or 0. The final score of the study range from 0 to 3. A cut off score for excluding a study from the review is 2 points. Since the papers scored 2 or 3 points are included in the final evaluation. We framed the following quality assessment questions for the final study.

Quality Assessment 1: Internal validity.

Quality Assessment 2: External validity.

Quality Assessment 3: Bias.

The two reviewers review each paper to select the final list of documents. We used the Quadratic Weighted Kappa score to measure the final agreement between the two reviewers. The average resulted from the kappa score is 0.6942, a substantial agreement between the reviewers. The result of evolution criteria shown in Table ​ Table1. 1 . After Quality Assessment, the final list of papers for review is shown in Table ​ Table2. 2 . The complete selection process is shown in Fig. ​ Fig.1. 1 . The total number of selected papers in year wise as shown in Fig. ​ Fig.2. 2 .

Quality assessment analysis

Final list of papers

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Selection process

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Year wise publications

What are the datasets available for research on automated essay grading?

To work with problem statement especially in Machine Learning and deep learning domain, we require considerable amount of data to train the models. To answer this question, we listed all the data sets used for training and testing for automated essay grading systems. The Cambridge Learner Corpus-First Certificate in English exam (CLC-FCE) Yannakoudakis et al. ( 2011 ) developed corpora that contain 1244 essays and ten prompts. This corpus evaluates whether a student can write the relevant English sentences without any grammatical and spelling mistakes. This type of corpus helps to test the models built for GRE and TOFEL type of exams. It gives scores between 1 and 40.

Bailey and Meurers ( 2008 ), Created a dataset (CREE reading comprehension) for language learners and automated short answer scoring systems. The corpus consists of 566 responses from intermediate students. Mohler and Mihalcea ( 2009 ). Created a dataset for the computer science domain consists of 630 responses for data structure assignment questions. The scores are range from 0 to 5 given by two human raters.

Dzikovska et al. ( 2012 ) created a Student Response Analysis (SRA) corpus. It consists of two sub-groups: the BEETLE corpus consists of 56 questions and approximately 3000 responses from students in the electrical and electronics domain. The second one is the SCIENTSBANK(SemEval-2013) (Dzikovska et al. 2013a ; b ) corpus consists of 10,000 responses on 197 prompts on various science domains. The student responses ladled with "correct, partially correct incomplete, Contradictory, Irrelevant, Non-domain."

In the Kaggle (2012) competition, released total 3 types of corpuses on an Automated Student Assessment Prize (ASAP1) (“ https://www.kaggle.com/c/asap-sas/ ” ) essays and short answers. It has nearly 17,450 essays, out of which it provides up to 3000 essays for each prompt. It has eight prompts that test 7th to 10th grade US students. It gives scores between the [0–3] and [0–60] range. The limitations of these corpora are: (1) it has a different score range for other prompts. (2) It uses statistical features such as named entities extraction and lexical features of words to evaluate essays. ASAP +  + is one more dataset from Kaggle. It is with six prompts, and each prompt has more than 1000 responses total of 10,696 from 8th-grade students. Another corpus contains ten prompts from science, English domains and a total of 17,207 responses. Two human graders evaluated all these responses.

Correnti et al. ( 2013 ) created a Response-to-Text Assessment (RTA) dataset used to check student writing skills in all directions like style, mechanism, and organization. 4–8 grade students give the responses to RTA. Basu et al. ( 2013 ) created a power grading dataset with 700 responses for ten different prompts from US immigration exams. It contains all short answers for assessment.

The TOEFL11 corpus Blanchard et al. ( 2013 ) contains 1100 essays evenly distributed over eight prompts. It is used to test the English language skills of a candidate attending the TOFEL exam. It scores the language proficiency of a candidate as low, medium, and high.

International Corpus of Learner English (ICLE) Granger et al. ( 2009 ) built a corpus of 3663 essays covering different dimensions. It has 12 prompts with 1003 essays that test the organizational skill of essay writing, and13 prompts, each with 830 essays that examine the thesis clarity and prompt adherence.

Argument Annotated Essays (AAE) Stab and Gurevych ( 2014 ) developed a corpus that contains 102 essays with 101 prompts taken from the essayforum2 site. It tests the persuasive nature of the student essay. The SCIENTSBANK corpus used by Sakaguchi et al. ( 2015 ) available in git-hub, containing 9804 answers to 197 questions in 15 science domains. Table ​ Table3 3 illustrates all datasets related to AES systems.

ALL types Datasets used in Automatic scoring systems

Features play a major role in the neural network and other supervised Machine Learning approaches. The automatic essay grading systems scores student essays based on different types of features, which play a prominent role in training the models. Based on their syntax and semantics and they are categorized into three groups. 1. statistical-based features Contreras et al. ( 2018 ); Kumar et al. ( 2019 ); Mathias and Bhattacharyya ( 2018a ; b ) 2. Style-based (Syntax) features Cummins et al. ( 2016 ); Darwish and Mohamed ( 2020 ); Ke et al. ( 2019 ). 3. Content-based features Dong et al. ( 2017 ). A good set of features appropriate models evolved better AES systems. The vast majority of the researchers are using regression models if features are statistical-based. For Neural Networks models, researches are using both style-based and content-based features. The following table shows the list of various features used in existing AES Systems. Table ​ Table4 4 represents all set of features used for essay grading.

Types of features

We studied all the feature extracting NLP libraries as shown in Fig. ​ Fig.3. that 3 . that are used in the papers. The NLTK is an NLP tool used to retrieve statistical features like POS, word count, sentence count, etc. With NLTK, we can miss the essay's semantic features. To find semantic features Word2Vec Mikolov et al. ( 2013 ), GloVe Jeffrey Pennington et al. ( 2014 ) is the most used libraries to retrieve the semantic text from the essays. And in some systems, they directly trained the model with word embeddings to find the score. From Fig. ​ Fig.4 4 as observed that non-content-based feature extraction is higher than content-based.

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Usages of tools

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Number of papers on content based features

RQ3 which are the evaluation metrics available for measuring the accuracy of algorithms?

The majority of the AES systems are using three evaluation metrics. They are (1) quadrated weighted kappa (QWK) (2) Mean Absolute Error (MAE) (3) Pearson Correlation Coefficient (PCC) Shehab et al. ( 2016 ). The quadratic weighted kappa will find agreement between human evaluation score and system evaluation score and produces value ranging from 0 to 1. And the Mean Absolute Error is the actual difference between human-rated score to system-generated score. The mean square error (MSE) measures the average squares of the errors, i.e., the average squared difference between the human-rated and the system-generated scores. MSE will always give positive numbers only. Pearson's Correlation Coefficient (PCC) finds the correlation coefficient between two variables. It will provide three values (0, 1, − 1). "0" represents human-rated and system scores that are not related. "1" represents an increase in the two scores. "− 1" illustrates a negative relationship between the two scores.

RQ4 what are the Machine Learning techniques being used for automatic essay grading, and how are they implemented?

After scrutinizing all documents, we categorize the techniques used in automated essay grading systems into four baskets. 1. Regression techniques. 2. Classification model. 3. Neural networks. 4. Ontology-based approach.

All the existing AES systems developed in the last ten years employ supervised learning techniques. Researchers using supervised methods viewed the AES system as either regression or classification task. The goal of the regression task is to predict the score of an essay. The classification task is to classify the essays belonging to (low, medium, or highly) relevant to the question's topic. Since the last three years, most AES systems developed made use of the concept of the neural network.

Regression based models

Mohler and Mihalcea ( 2009 ). proposed text-to-text semantic similarity to assign a score to the student essays. There are two text similarity measures like Knowledge-based measures, corpus-based measures. There eight knowledge-based tests with all eight models. They found the similarity. The shortest path similarity determines based on the length, which shortest path between two contexts. Leacock & Chodorow find the similarity based on the shortest path's length between two concepts using node-counting. The Lesk similarity finds the overlap between the corresponding definitions, and Wu & Palmer algorithm finds similarities based on the depth of two given concepts in the wordnet taxonomy. Resnik, Lin, Jiang&Conrath, Hirst& St-Onge find the similarity based on different parameters like the concept, probability, normalization factor, lexical chains. In corpus-based likeness, there LSA BNC, LSA Wikipedia, and ESA Wikipedia, latent semantic analysis is trained on Wikipedia and has excellent domain knowledge. Among all similarity scores, correlation scores LSA Wikipedia scoring accuracy is more. But these similarity measure algorithms are not using NLP concepts. These models are before 2010 and basic concept models to continue the research automated essay grading with updated algorithms on neural networks with content-based features.

Adamson et al. ( 2014 ) proposed an automatic essay grading system which is a statistical-based approach in this they retrieved features like POS, Character count, Word count, Sentence count, Miss spelled words, n-gram representation of words to prepare essay vector. They formed a matrix with these all vectors in that they applied LSA to give a score to each essay. It is a statistical approach that doesn’t consider the semantics of the essay. The accuracy they got when compared to the human rater score with the system is 0.532.

Cummins et al. ( 2016 ). Proposed Timed Aggregate Perceptron vector model to give ranking to all the essays, and later they converted the rank algorithm to predict the score of the essay. The model trained with features like Word unigrams, bigrams, POS, Essay length, grammatical relation, Max word length, sentence length. It is multi-task learning, gives ranking to the essays, and predicts the score for the essay. The performance evaluated through QWK is 0.69, a substantial agreement between the human rater and the system.

Sultan et al. ( 2016 ). Proposed a Ridge regression model to find short answer scoring with Question Demoting. Question Demoting is the new concept included in the essay's final assessment to eliminate duplicate words from the essay. The extracted features are Text Similarity, which is the similarity between the student response and reference answer. Question Demoting is the number of repeats in a student response. With inverse document frequency, they assigned term weight. The sentence length Ratio is the number of words in the student response, is another feature. With these features, the Ridge regression model was used, and the accuracy they got 0.887.

Contreras et al. ( 2018 ). Proposed Ontology based on text mining in this model has given a score for essays in phases. In phase-I, they generated ontologies with ontoGen and SVM to find the concept and similarity in the essay. In phase II from ontologies, they retrieved features like essay length, word counts, correctness, vocabulary, and types of word used, domain information. After retrieving statistical data, they used a linear regression model to find the score of the essay. The accuracy score is the average of 0.5.

Darwish and Mohamed ( 2020 ) proposed the fusion of fuzzy Ontology with LSA. They retrieve two types of features, like syntax features and semantic features. In syntax features, they found Lexical Analysis with tokens, and they construct a parse tree. If the parse tree is broken, the essay is inconsistent—a separate grade assigned to the essay concerning syntax features. The semantic features are like similarity analysis, Spatial Data Analysis. Similarity analysis is to find duplicate sentences—Spatial Data Analysis for finding Euclid distance between the center and part. Later they combine syntax features and morphological features score for the final score. The accuracy they achieved with the multiple linear regression model is 0.77, mostly on statistical features.

Süzen Neslihan et al. ( 2020 ) proposed a text mining approach for short answer grading. First, their comparing model answers with student response by calculating the distance between two sentences. By comparing the model answer with student response, they find the essay's completeness and provide feedback. In this approach, model vocabulary plays a vital role in grading, and with this model vocabulary, the grade will be assigned to the student's response and provides feedback. The correlation between the student answer to model answer is 0.81.

Classification based Models

Persing and Ng ( 2013 ) used a support vector machine to score the essay. The features extracted are OS, N-gram, and semantic text to train the model and identified the keywords from the essay to give the final score.

Sakaguchi et al. ( 2015 ) proposed two methods: response-based and reference-based. In response-based scoring, the extracted features are response length, n-gram model, and syntactic elements to train the support vector regression model. In reference-based scoring, features such as sentence similarity using word2vec is used to find the cosine similarity of the sentences that is the final score of the response. First, the scores were discovered individually and later combined two features to find a final score. This system gave a remarkable increase in performance by combining the scores.

Mathias and Bhattacharyya ( 2018a ; b ) Proposed Automated Essay Grading Dataset with Essay Attribute Scores. The first concept features selection depends on the essay type. So the common attributes are Content, Organization, Word Choice, Sentence Fluency, Conventions. In this system, each attribute is scored individually, with the strength of each attribute identified. The model they used is a random forest classifier to assign scores to individual attributes. The accuracy they got with QWK is 0.74 for prompt 1 of the ASAS dataset ( https://www.kaggle.com/c/asap-sas/ ).

Ke et al. ( 2019 ) used a support vector machine to find the response score. In this method, features like Agreeability, Specificity, Clarity, Relevance to prompt, Conciseness, Eloquence, Confidence, Direction of development, Justification of opinion, and Justification of importance. First, the individual parameter score obtained was later combined with all scores to give a final response score. The features are used in the neural network to find whether the sentence is relevant to the topic or not.

Salim et al. ( 2019 ) proposed an XGBoost Machine Learning classifier to assess the essays. The algorithm trained on features like word count, POS, parse tree depth, and coherence in the articles with sentence similarity percentage; cohesion and coherence are considered for training. And they implemented K-fold cross-validation for a result the average accuracy after specific validations is 68.12.

Neural network models

Shehab et al. ( 2016 ) proposed a neural network method that used learning vector quantization to train human scored essays. After training, the network can provide a score to the ungraded essays. First, we should process the essay to remove Spell checking and then perform preprocessing steps like Document Tokenization, stop word removal, Stemming, and submit it to the neural network. Finally, the model will provide feedback on the essay, whether it is relevant to the topic. And the correlation coefficient between human rater and system score is 0.7665.

Kopparapu and De ( 2016 ) proposed the Automatic Ranking of Essays using Structural and Semantic Features. This approach constructed a super essay with all the responses. Next, ranking for a student essay is done based on the super-essay. The structural and semantic features derived helps to obtain the scores. In a paragraph, 15 Structural features like an average number of sentences, the average length of sentences, and the count of words, nouns, verbs, adjectives, etc., are used to obtain a syntactic score. A similarity score is used as semantic features to calculate the overall score.

Dong and Zhang ( 2016 ) proposed a hierarchical CNN model. The model builds two layers with word embedding to represents the words as the first layer. The second layer is a word convolution layer with max-pooling to find word vectors. The next layer is a sentence-level convolution layer with max-pooling to find the sentence's content and synonyms. A fully connected dense layer produces an output score for an essay. The accuracy with the hierarchical CNN model resulted in an average QWK of 0.754.

Taghipour and Ng ( 2016 ) proposed a first neural approach for essay scoring build in which convolution and recurrent neural network concepts help in scoring an essay. The network uses a lookup table with the one-hot representation of the word vector of an essay. The final efficiency of the network model with LSTM resulted in an average QWK of 0.708.

Dong et al. ( 2017 ). Proposed an Attention-based scoring system with CNN + LSTM to score an essay. For CNN, the input parameters were character embedding and word embedding, and it has attention pooling layers and used NLTK to obtain word and character embedding. The output gives a sentence vector, which provides sentence weight. After CNN, it will have an LSTM layer with an attention pooling layer, and this final layer results in the final score of the responses. The average QWK score is 0.764.

Riordan et al. ( 2017 ) proposed a neural network with CNN and LSTM layers. Word embedding, given as input to a neural network. An LSTM network layer will retrieve the window features and delivers them to the aggregation layer. The aggregation layer is a superficial layer that takes a correct window of words and gives successive layers to predict the answer's sore. The accuracy of the neural network resulted in a QWK of 0.90.

Zhao et al. ( 2017 ) proposed a new concept called Memory-Augmented Neural network with four layers, input representation layer, memory addressing layer, memory reading layer, and output layer. An input layer represents all essays in a vector form based on essay length. After converting the word vector, the memory addressing layer takes a sample of the essay and weighs all the terms. The memory reading layer takes the input from memory addressing segment and finds the content to finalize the score. Finally, the output layer will provide the final score of the essay. The accuracy of essay scores is 0.78, which is far better than the LSTM neural network.

Mathias and Bhattacharyya ( 2018a ; b ) proposed deep learning networks using LSTM with the CNN layer and GloVe pre-trained word embeddings. For this, they retrieved features like Sentence count essays, word count per sentence, Number of OOVs in the sentence, Language model score, and the text's perplexity. The network predicted the goodness scores of each essay. The higher the goodness scores, means higher the rank and vice versa.

Nguyen and Dery ( 2016 ). Proposed Neural Networks for Automated Essay Grading. In this method, a single layer bi-directional LSTM accepting word vector as input. Glove vectors used in this method resulted in an accuracy of 90%.

Ruseti et al. ( 2018 ) proposed a recurrent neural network that is capable of memorizing the text and generate a summary of an essay. The Bi-GRU network with the max-pooling layer molded on the word embedding of each document. It will provide scoring to the essay by comparing it with a summary of the essay from another Bi-GRU network. The result obtained an accuracy of 0.55.

Wang et al. ( 2018a ; b ) proposed an automatic scoring system with the bi-LSTM recurrent neural network model and retrieved the features using the word2vec technique. This method generated word embeddings from the essay words using the skip-gram model. And later, word embedding is used to train the neural network to find the final score. The softmax layer in LSTM obtains the importance of each word. This method used a QWK score of 0.83%.

Dasgupta et al. ( 2018 ) proposed a technique for essay scoring with augmenting textual qualitative Features. It extracted three types of linguistic, cognitive, and psychological features associated with a text document. The linguistic features are Part of Speech (POS), Universal Dependency relations, Structural Well-formedness, Lexical Diversity, Sentence Cohesion, Causality, and Informativeness of the text. The psychological features derived from the Linguistic Information and Word Count (LIWC) tool. They implemented a convolution recurrent neural network that takes input as word embedding and sentence vector, retrieved from the GloVe word vector. And the second layer is the Convolution Layer to find local features. The next layer is the recurrent neural network (LSTM) to find corresponding of the text. The accuracy of this method resulted in an average QWK of 0.764.

Liang et al. ( 2018 ) proposed a symmetrical neural network AES model with Bi-LSTM. They are extracting features from sample essays and student essays and preparing an embedding layer as input. The embedding layer output is transfer to the convolution layer from that LSTM will be trained. Hear the LSRM model has self-features extraction layer, which will find the essay's coherence. The average QWK score of SBLSTMA is 0.801.

Liu et al. ( 2019 ) proposed two-stage learning. In the first stage, they are assigning a score based on semantic data from the essay. The second stage scoring is based on some handcrafted features like grammar correction, essay length, number of sentences, etc. The average score of the two stages is 0.709.

Pedro Uria Rodriguez et al. ( 2019 ) proposed a sequence-to-sequence learning model for automatic essay scoring. They used BERT (Bidirectional Encoder Representations from Transformers), which extracts the semantics from a sentence from both directions. And XLnet sequence to sequence learning model to extract features like the next sentence in an essay. With this pre-trained model, they attained coherence from the essay to give the final score. The average QWK score of the model is 75.5.

Xia et al. ( 2019 ) proposed a two-layer Bi-directional LSTM neural network for the scoring of essays. The features extracted with word2vec to train the LSTM and accuracy of the model in an average of QWK is 0.870.

Kumar et al. ( 2019 ) Proposed an AutoSAS for short answer scoring. It used pre-trained Word2Vec and Doc2Vec models trained on Google News corpus and Wikipedia dump, respectively, to retrieve the features. First, they tagged every word POS and they found weighted words from the response. It also found prompt overlap to observe how the answer is relevant to the topic, and they defined lexical overlaps like noun overlap, argument overlap, and content overlap. This method used some statistical features like word frequency, difficulty, diversity, number of unique words in each response, type-token ratio, statistics of the sentence, word length, and logical operator-based features. This method uses a random forest model to train the dataset. The data set has sample responses with their associated score. The model will retrieve the features from both responses like graded and ungraded short answers with questions. The accuracy of AutoSAS with QWK is 0.78. It will work on any topics like Science, Arts, Biology, and English.

Jiaqi Lun et al. ( 2020 ) proposed an automatic short answer scoring with BERT. In this with a reference answer comparing student responses and assigning scores. The data augmentation is done with a neural network and with one correct answer from the dataset classifying reaming responses as correct or incorrect.

Zhu and Sun ( 2020 ) proposed a multimodal Machine Learning approach for automated essay scoring. First, they count the grammar score with the spaCy library and numerical count as the number of words and sentences with the same library. With this input, they trained a single and Bi LSTM neural network for finding the final score. For the LSTM model, they prepared sentence vectors with GloVe and word embedding with NLTK. Bi-LSTM will check each sentence in both directions to find semantic from the essay. The average QWK score with multiple models is 0.70.

Ontology based approach

Mohler et al. ( 2011 ) proposed a graph-based method to find semantic similarity in short answer scoring. For the ranking of answers, they used the support vector regression model. The bag of words is the main feature extracted in the system.

Ramachandran et al. ( 2015 ) also proposed a graph-based approach to find lexical based semantics. Identified phrase patterns and text patterns are the features to train a random forest regression model to score the essays. The accuracy of the model in a QWK is 0.78.

Zupanc et al. ( 2017 ) proposed sentence similarity networks to find the essay's score. Ajetunmobi and Daramola ( 2017 ) recommended an ontology-based information extraction approach and domain-based ontology to find the score.

Speech response scoring

Automatic scoring is in two ways one is text-based scoring, other is speech-based scoring. This paper discussed text-based scoring and its challenges, and now we cover speech scoring and common points between text and speech-based scoring. Evanini and Wang ( 2013 ), Worked on speech scoring of non-native school students, extracted features with speech ratter, and trained a linear regression model, concluding that accuracy varies based on voice pitching. Loukina et al. ( 2015 ) worked on feature selection from speech data and trained SVM. Malinin et al. ( 2016 ) used neural network models to train the data. Loukina et al. ( 2017 ). Proposed speech and text-based automatic scoring. Extracted text-based features, speech-based features and trained a deep neural network for speech-based scoring. They extracted 33 types of features based on acoustic signals. Malinin et al. ( 2017 ). Wu Xixin et al. ( 2020 ) Worked on deep neural networks for spoken language assessment. Incorporated different types of models and tested them. Ramanarayanan et al. ( 2017 ) worked on feature extraction methods and extracted punctuation, fluency, and stress and trained different Machine Learning models for scoring. Knill et al. ( 2018 ). Worked on Automatic speech recognizer and its errors how its impacts the speech assessment.

The state of the art

This section provides an overview of the existing AES systems with a comparative study w. r. t models, features applied, datasets, and evaluation metrics used for building the automated essay grading systems. We divided all 62 papers into two sets of the first set of review papers in Table ​ Table5 5 with a comparative study of the AES systems.

State of the art

Comparison of all approaches

In our study, we divided major AES approaches into three categories. Regression models, classification models, and neural network models. The regression models failed to find cohesion and coherence from the essay because it trained on BoW(Bag of Words) features. In processing data from input to output, the regression models are less complicated than neural networks. There are unable to find many intricate patterns from the essay and unable to find sentence connectivity. If we train the model with BoW features in the neural network approach, the model never considers the essay's coherence and coherence.

First, to train a Machine Learning algorithm with essays, all the essays are converted to vector form. We can form a vector with BoW and Word2vec, TF-IDF. The BoW and Word2vec vector representation of essays represented in Table ​ Table6. 6 . The vector representation of BoW with TF-IDF is not incorporating the essays semantic, and it’s just statistical learning from a given vector. Word2vec vector comprises semantic of essay in a unidirectional way.

Vector representation of essays

In BoW, the vector contains the frequency of word occurrences in the essay. The vector represents 1 and more based on the happenings of words in the essay and 0 for not present. So, in BoW, the vector does not maintain the relationship with adjacent words; it’s just for single words. In word2vec, the vector represents the relationship between words with other words and sentences prompt in multiple dimensional ways. But word2vec prepares vectors in a unidirectional way, not in a bidirectional way; word2vec fails to find semantic vectors when a word has two meanings, and the meaning depends on adjacent words. Table ​ Table7 7 represents a comparison of Machine Learning models and features extracting methods.

Comparison of models

In AES, cohesion and coherence will check the content of the essay concerning the essay prompt these can be extracted from essay in the vector from. Two more parameters are there to access an essay is completeness and feedback. Completeness will check whether student’s response is sufficient or not though the student wrote correctly. Table ​ Table8 8 represents all four parameters comparison for essay grading. Table ​ Table9 9 illustrates comparison of all approaches based on various features like grammar, spelling, organization of essay, relevance.

Comparison of all models with respect to cohesion, coherence, completeness, feedback

comparison of all approaches on various features

What are the challenges/limitations in the current research?

From our study and results discussed in the previous sections, many researchers worked on automated essay scoring systems with numerous techniques. We have statistical methods, classification methods, and neural network approaches to evaluate the essay automatically. The main goal of the automated essay grading system is to reduce human effort and improve consistency.

The vast majority of essay scoring systems are dealing with the efficiency of the algorithm. But there are many challenges in automated essay grading systems. One should assess the essay by following parameters like the relevance of the content to the prompt, development of ideas, Cohesion, Coherence, and domain knowledge.

No model works on the relevance of content, which means whether student response or explanation is relevant to the given prompt or not if it is relevant to how much it is appropriate, and there is no discussion about the cohesion and coherence of the essays. All researches concentrated on extracting the features using some NLP libraries, trained their models, and testing the results. But there is no explanation in the essay evaluation system about consistency and completeness, But Palma and Atkinson ( 2018 ) explained coherence-based essay evaluation. And Zupanc and Bosnic ( 2014 ) also used the word coherence to evaluate essays. And they found consistency with latent semantic analysis (LSA) for finding coherence from essays, but the dictionary meaning of coherence is "The quality of being logical and consistent."

Another limitation is there is no domain knowledge-based evaluation of essays using Machine Learning models. For example, the meaning of a cell is different from biology to physics. Many Machine Learning models extract features with WordVec and GloVec; these NLP libraries cannot convert the words into vectors when they have two or more meanings.

Other challenges that influence the Automated Essay Scoring Systems.

All these approaches worked to improve the QWK score of their models. But QWK will not assess the model in terms of features extraction and constructed irrelevant answers. The QWK is not evaluating models whether the model is correctly assessing the answer or not. There are many challenges concerning students' responses to the Automatic scoring system. Like in evaluating approach, no model has examined how to evaluate the constructed irrelevant and adversarial answers. Especially the black box type of approaches like deep learning models provides more options to the students to bluff the automated scoring systems.

The Machine Learning models that work on statistical features are very vulnerable. Based on Powers et al. ( 2001 ) and Bejar Isaac et al. ( 2014 ), the E-rater was failed on Constructed Irrelevant Responses Strategy (CIRS). From the study of Bejar et al. ( 2013 ), Higgins and Heilman ( 2014 ), observed that when student response contain irrelevant content or shell language concurring to prompt will influence the final score of essays in an automated scoring system.

In deep learning approaches, most of the models automatically read the essay's features, and some methods work on word-based embedding and other character-based embedding features. From the study of Riordan Brain et al. ( 2019 ), The character-based embedding systems do not prioritize spelling correction. However, it is influencing the final score of the essay. From the study of Horbach and Zesch ( 2019 ), Various factors are influencing AES systems. For example, there are data set size, prompt type, answer length, training set, and human scorers for content-based scoring.

Ding et al. ( 2020 ) reviewed that the automated scoring system is vulnerable when a student response contains more words from prompt, like prompt vocabulary repeated in the response. Parekh et al. ( 2020 ) and Kumar et al. ( 2020 ) tested various neural network models of AES by iteratively adding important words, deleting unimportant words, shuffle the words, and repeating sentences in an essay and found that no change in the final score of essays. These neural network models failed to recognize common sense in adversaries' essays and give more options for the students to bluff the automated systems.

Other than NLP and ML techniques for AES. From Wresch ( 1993 ) to Madnani and Cahill ( 2018 ). discussed the complexity of AES systems, standards need to be followed. Like assessment rubrics to test subject knowledge, irrelevant responses, and ethical aspects of an algorithm like measuring the fairness of student response.

Fairness is an essential factor for automated systems. For example, in AES, fairness can be measure in an agreement between human score to machine score. Besides this, From Loukina et al. ( 2019 ), the fairness standards include overall score accuracy, overall score differences, and condition score differences between human and system scores. In addition, scoring different responses in the prospect of constructive relevant and irrelevant will improve fairness.

Madnani et al. ( 2017a ; b ). Discussed the fairness of AES systems for constructed responses and presented RMS open-source tool for detecting biases in the models. With this, one can change fairness standards according to their analysis of fairness.

From Berzak et al.'s ( 2018 ) approach, behavior factors are a significant challenge in automated scoring systems. That helps to find language proficiency, word characteristics (essential words from the text), predict the critical patterns from the text, find related sentences in an essay, and give a more accurate score.

Rupp ( 2018 ), has discussed the designing, evaluating, and deployment methodologies for AES systems. They provided notable characteristics of AES systems for deployment. They are like model performance, evaluation metrics for a model, threshold values, dynamically updated models, and framework.

First, we should check the model performance on different datasets and parameters for operational deployment. Selecting Evaluation metrics for AES models are like QWK, correlation coefficient, or sometimes both. Kelley and Preacher ( 2012 ) have discussed three categories of threshold values: marginal, borderline, and acceptable. The values can be varied based on data size, model performance, type of model (single scoring, multiple scoring models). Once a model is deployed and evaluates millions of responses every time for optimal responses, we need a dynamically updated model based on prompt and data. Finally, framework designing of AES model, hear a framework contains prompts where test-takers can write the responses. One can design two frameworks: a single scoring model for a single methodology and multiple scoring models for multiple concepts. When we deploy multiple scoring models, each prompt could be trained separately, or we can provide generalized models for all prompts with this accuracy may vary, and it is challenging.

Our Systematic literature review on the automated essay grading system first collected 542 papers with selected keywords from various databases. After inclusion and exclusion criteria, we left with 139 articles; on these selected papers, we applied Quality assessment criteria with two reviewers, and finally, we selected 62 writings for final review.

Our observations on automated essay grading systems from 2010 to 2020 are as followed:

  • The implementation techniques of automated essay grading systems are classified into four buckets; there are 1. regression models 2. Classification models 3. Neural networks 4. Ontology-based methodology, but using neural networks, the researchers are more accurate than other techniques, and all the methods state of the art provided in Table ​ Table3 3 .
  • The majority of the regression and classification models on essay scoring used statistical features to find the final score. It means the systems or models trained on such parameters as word count, sentence count, etc. though the parameters extracted from the essay, the algorithm are not directly training on essays. The algorithms trained on some numbers obtained from the essay and hear if numbers matched the composition will get a good score; otherwise, the rating is less. In these models, the evaluation process is entirely on numbers, irrespective of the essay. So, there is a lot of chance to miss the coherence, relevance of the essay if we train our algorithm on statistical parameters.
  • In the neural network approach, the models trained on Bag of Words (BoW) features. The BoW feature is missing the relationship between a word to word and the semantic meaning of the sentence. E.g., Sentence 1: John killed bob. Sentence 2: bob killed John. In these two sentences, the BoW is "John," "killed," "bob."
  • In the Word2Vec library, if we are prepared a word vector from an essay in a unidirectional way, the vector will have a dependency with other words and finds the semantic relationship with other words. But if a word has two or more meanings like "Bank loan" and "River Bank," hear bank has two implications, and its adjacent words decide the sentence meaning; in this case, Word2Vec is not finding the real meaning of the word from the sentence.
  • The features extracted from essays in the essay scoring system are classified into 3 type's features like statistical features, style-based features, and content-based features, which are explained in RQ2 and Table ​ Table3. 3 . But statistical features, are playing a significant role in some systems and negligible in some systems. In Shehab et al. ( 2016 ); Cummins et al. ( 2016 ). Dong et al. ( 2017 ). Dong and Zhang ( 2016 ). Mathias and Bhattacharyya ( 2018a ; b ) Systems the assessment is entirely on statistical and style-based features they have not retrieved any content-based features. And in other systems that extract content from the essays, the role of statistical features is for only preprocessing essays but not included in the final grading.
  • In AES systems, coherence is the main feature to be considered while evaluating essays. The actual meaning of coherence is to stick together. That is the logical connection of sentences (local level coherence) and paragraphs (global level coherence) in a story. Without coherence, all sentences in a paragraph are independent and meaningless. In an Essay, coherence is a significant feature that is explaining everything in a flow and its meaning. It is a powerful feature in AES system to find the semantics of essay. With coherence, one can assess whether all sentences are connected in a flow and all paragraphs are related to justify the prompt. Retrieving the coherence level from an essay is a critical task for all researchers in AES systems.
  • In automatic essay grading systems, the assessment of essays concerning content is critical. That will give the actual score for the student. Most of the researches used statistical features like sentence length, word count, number of sentences, etc. But according to collected results, 32% of the systems used content-based features for the essay scoring. Example papers which are on content-based assessment are Taghipour and Ng ( 2016 ); Persing and Ng ( 2013 ); Wang et al. ( 2018a , 2018b ); Zhao et al. ( 2017 ); Kopparapu and De ( 2016 ), Kumar et al. ( 2019 ); Mathias and Bhattacharyya ( 2018a ; b ); Mohler and Mihalcea ( 2009 ) are used content and statistical-based features. The results are shown in Fig. ​ Fig.3. 3 . And mainly the content-based features extracted with word2vec NLP library, but word2vec is capable of capturing the context of a word in a document, semantic and syntactic similarity, relation with other terms, but word2vec is capable of capturing the context word in a uni-direction either left or right. If a word has multiple meanings, there is a chance of missing the context in the essay. After analyzing all the papers, we found that content-based assessment is a qualitative assessment of essays.
  • On the other hand, Horbach and Zesch ( 2019 ); Riordan Brain et al. ( 2019 ); Ding et al. ( 2020 ); Kumar et al. ( 2020 ) proved that neural network models are vulnerable when a student response contains constructed irrelevant, adversarial answers. And a student can easily bluff an automated scoring system by submitting different responses like repeating sentences and repeating prompt words in an essay. From Loukina et al. ( 2019 ), and Madnani et al. ( 2017b ). The fairness of an algorithm is an essential factor to be considered in AES systems.
  • While talking about speech assessment, the data set contains audios of duration up to one minute. Feature extraction techniques are entirely different from text assessment, and accuracy varies based on speaking fluency, pitching, male to female voice and boy to adult voice. But the training algorithms are the same for text and speech assessment.
  • Once an AES system evaluates essays and short answers accurately in all directions, there is a massive demand for automated systems in the educational and related world. Now AES systems are deployed in GRE, TOEFL exams; other than these, we can deploy AES systems in massive open online courses like Coursera(“ https://coursera.org/learn//machine-learning//exam ”), NPTEL ( https://swayam.gov.in/explorer ), etc. still they are assessing student performance with multiple-choice questions. In another perspective, AES systems can be deployed in information retrieval systems like Quora, stack overflow, etc., to check whether the retrieved response is appropriate to the question or not and can give ranking to the retrieved answers.

Conclusion and future work

As per our Systematic literature review, we studied 62 papers. There exist significant challenges for researchers in implementing automated essay grading systems. Several researchers are working rigorously on building a robust AES system despite its difficulty in solving this problem. All evaluating methods are not evaluated based on coherence, relevance, completeness, feedback, and knowledge-based. And 90% of essay grading systems are used Kaggle ASAP (2012) dataset, which has general essays from students and not required any domain knowledge, so there is a need for domain-specific essay datasets to train and test. Feature extraction is with NLTK, WordVec, and GloVec NLP libraries; these libraries have many limitations while converting a sentence into vector form. Apart from feature extraction and training Machine Learning models, no system is accessing the essay's completeness. No system provides feedback to the student response and not retrieving coherence vectors from the essay—another perspective the constructive irrelevant and adversarial student responses still questioning AES systems.

Our proposed research work will go on the content-based assessment of essays with domain knowledge and find a score for the essays with internal and external consistency. And we will create a new dataset concerning one domain. And another area in which we can improve is the feature extraction techniques.

This study includes only four digital databases for study selection may miss some functional studies on the topic. However, we hope that we covered most of the significant studies as we manually collected some papers published in useful journals.

Below is the link to the electronic supplementary material.

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Publisher's Note

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Contributor Information

Dadi Ramesh, Email: moc.liamg@44hsemaridad .

Suresh Kumar Sanampudi, Email: ni.ca.hutnj@idupmanashserus .

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The Importance of Employee Recognition: Statistics and Research

the importance of employee recognition

Table of Contents

What is employee recognition, what is the importance of employee recognition, types of employee recognition , 8 employee recognition statistics, employee recognition examples, creating a successful employee recognition program, leveraging a successful employee recognition platform.

The Importance of Employee Recognition: Statistics and Research

Think about the last time you put your heart and soul into a project or presentation, molded it into something you were proud of, and absolutely nailed the execution. That feeling of accomplishment is uplifting—but it is multiplied exponentially when others take notice.

The simple act of acknowledging achievement is a major boost for employee morale and performance. And that’s why employee recognition is so critical.

When you reward employees for their contributions, they feel ownership and pride—and are willing to work just as hard on their next project. Recognition connects them to the organization, elevates performance, and increases the likelihood they’ll stay.

This article provides research-backed advice on how to give the kind of employee recognition that will truly make a difference in employee engagement, performance, and retention.

Check out the 2024 Employee Engagement Trends Report  to see how you can drive employee success in your organization.

Employee recognition is the open acknowledgment and praise of employee behavior or achievement. It’s used by organizations to express appreciation, motivate employees, and reinforce desired behavior.

If you regularly give out authentic, deserved recognition to employees, you’ll be that much closer to unlocking their full potential. Authentic recognition serves three key purposes:

1. Showcase goal achievement

A simple “thank you” is often all it takes to show appreciation to employees. People want to know that their hard work and achievements aren't going unnoticed. When a person achieves a goal—personal or work-related—they feel a rush of achievement, and that good feeling is only amplified when others recognize and acknowledge the achievement as well.

2. Motivate effort

Recognition can be tied to more than just performance. Celebrate strong effort when employees go above and beyond. This helps them develop emotional connections to the workplace that fuel future performance.

3. Reinforce values

Behaviors and actions that are recognized more frequently show employees what’s valued by managers, leaders, and the organization as a whole. When employees receive recognition for adopting a behavior aligned with company values, they’re likely to continue that behavior and set a positive example for others. 

Recognition is a powerful feedback tool . When employees feel valued, they’re more engaged, motivated, and likely to go the extra mile for their company. Organizations with formal employee recognition programs have 31% less voluntary turnover than organizations that don't have any program at all. And they're 12x more likely to have strong business outcomes.

If leaders want to drive employee, team, and business success, they need to prioritize employee recognition. 

The connection between employee recognition and engagement

Aspects such as performance, goals, recognition, development, and manager effectiveness are all inextricably linked to employee engagement. And recognition is one of the top drivers of employee engagement .

In fact, our research found that when employees believe they will be recognized, they are 2.7x more likely to be highly engaged .

recognition_top_driver

Other items related to recognition that drive employee engagement, include:

  • The senior leaders of this organization value people as their most important resource.
  • If I contribute to the organization’s success, I know I will be recognized.
  • I understand how my job helps the organization achieve success.

Benefits of employee recognition

Employee appreciation is a fundamental human need. When employees feel appreciated and recognized for their individual contributions, they will be more connected to their work, their team, and your organization as a whole.

Here are a few other benefits of employee recognition:

  • Increased productivity and engagement
  • Decreased employee turnover
  • Greater employee satisfaction and enjoyment of work
  • Improved team culture
  • Higher loyalty and satisfaction scores from customers
  • Increased retention of quality employees
  • Decreased stress and absenteeism

Research shows a gap in employee recognition

Quantum Workplace research shows a gap between what employees want and what they're actually getting when it comes to employee recognition. Only 35% of employees receive recognition monthly or weekly. And 1 in 2 employees would like more recognition for their work. Employees who receive less frequent recognition want more—especially those who get it less than monthly. All employees crave more recognition, even managers! Everyone wants to be recognized for their contributions and feel like a valuable member of the team and organization.

employee recognition research

The “how” of recognition is very important. Every employee embraces recognition differently. Some get a boost from public praise, while introverted workers prefer a subtle or private gesture. After identifying the employee’s personality type, openly encourage them through the types of recognition that mean the most to them.

Peer vs. superior

Attributed vs. anonymous, social vs. private, behavior vs. achievement, the types of recognition employees crave most.

Different employees care about different things. But our research shows the most and least preferred reasons for recognition—with performance or role accomplishments, value to the organization, and teamwork or collaboration coming out on top. 

research - employees prefer these types of recognition

Employees want to feel valued at work. They want recognition for their contributions to team and organizational success. 

While extremely important, recognition isn't only about making employees more engaged and feeling good about their job. It can be a differentiator for organizations in their employee value proposition and can affect an employee’s intent to stay at an organization.

Here, we look at 8 statistics that demonstrate the importance of employee recognition on employee, team, and business success.

1. The #3 reason most people leave their jobs is a lack of recognition.

Source: Quantum Workplace Research

Don’t miss an opportunity to recognize your employees. Celebrate employee accomplishments and progress throughout the employee cycle to demonstrate your investment in their career growth and success. Regular, frequent recognition shows you want to help keep them motivated to hit future milestones.

Pro Tip : Authentically recognize employees in real-time.

2. Organizations with recognition programs have 31% lower voluntary turnover than those without.

Source: Bersin by Deloitte

If your organization doesn't have a formal recognition program, there are a few ways to start. Use your one-on-one meetings or employee surveys to find out if your employees feel valued and how you can improve your strategy. If you do, make sure employees know it exists and how they can take part.

Pro Tip : Continuously communicate to keep the program alive.

3. Organizations with recognition programs in place experience 28.6% lower frustration levels than those without.

Source: SHRM Globoforce Employee Recognition Survey

Recognition shouldn't only be about success and goal achievement. Having a strategic recognition program in place can help you easily celebrate the micro-moments along the way—such as quality work, taking on new tasks, or going above and beyond for their team.

Pro Tip : Recognize the employee behaviors you want to encourage.

4. 52.5% of employees want more recognition from their immediate manager.

Source: Recognition in the Workplace, Quantum Workplace and BambooHR

Recognition should be public and available to all employees—especially managers. Employees want to know what they are doing well, how they can improve, and what support they have available to them. Public recognition gives managers visibility into how often their employees are giving and receiving recognition to impact their engagement individually.

Pro Tip : Make recognition public and easy to give.

5. Organizations with sophisticated recognition programs are 12x more likely to have strong business outcomes.

Recognition should be incorporated within one-on-ones, feedback, and talent reviews. Including recognition in frequent performance conversations helps solidify the importance of employee recognition in your culture and organization. When employees understand how their contributions impact the business (and are recognized for it) they'll be more likely to contribute again.

Pro Tip : Give recognition in the context of a larger goal or business outcome.

6. 4 in 10 respondents (41%) want more recognition from their immediate coworkers.

Recognition from immediate managers is key—but so is recognition from peers and coworkers. Give your team members plenty of opportunities to encourage each other and show appreciation. Peers often have more insight into employee effort and morale than senior leaders.

Pro Tip : Treat employees as valued team members, not as numbers.

7. Recognition for work is one of the top drivers of candidate attraction.

Source: Willis Towers Watson

If your company is hiring, recognition might just be the key to attracting top talent. If you don't have a recognition program, make sure you're at least getting creative about showing appreciation. Generic and inauthentic recognition strategies can have a negative effect on the employee experience.

Pro Tip : Match effort and results, or else recognition loses meaning.

8. When companies spend 1% or more of payroll on recognition, 85% notice a positive impact on engagement.

Awards, compensation, and incentives are good appreciation ideas, but make sure they aren't your only outlet for recognizing employees. Consider an investment in user-friendly employee recognition software to make every aspect of your employee recognition more efficient, more accessible, and more personable.

Pro Tip : Choose the right employee recognition platform .

Recognition and appreciation are essential in a healthy work environment. But finding the right words to recognize employees may seem unnatural or uncomfortable without the right experience. While you should tweak your messages to resonate with each employee, we’ve compiled a list of recognition ideas to guide your efforts. 

Recognizing excellent performance

  • “Thank you for always going above and beyond in your work. You are a great asset to this team!”
  • “Your ability to drive consistent business outcomes is inspiring. Thank you for everything you contribute to this company!”
  • “I want to take a moment to acknowledge you for all of your hard work. You’re growing and continuing to set a higher standard everyday.” 

Recognizing good attitudes

  • “Your constant positivity makes the work environment better everyday. I’m always excited when I get to work alongside you!”
  • “Thank you for helping out with this project. You’re a huge asset to this team and it doesn’t go unnoticed!” 
  • “Thank you for living out our company values day-in and day-out. You set a good example for everyone on our team.”

Recognizing goal accomplishment  

  • “Your ability to persevere and meet this goal is inspiring! Despite any roadblocks you face, you always pull through and drive amazing outcomes.”
  • “Great job exceeding your goals the past quarter! Your talent and drive is key to our business.”
  • “Your adaptability and ability to take on any challenge is impressive. You completely exceeded my expectations with this project!” 

A successful employee recognition program can look different based on your employees’ needs. Regardless, there are some things you should do —and shouldn’t do—when recognizing employees. Here are some best practices to keep in mind to shape an effective retention program.

1. Be detailed and specific

Recognition resonates better when it’s tied to a particular accomplishment. When you’re detailed and specific, employees understand exactly what they did well and are likely to continue those behaviors in the future. 

2. Be prompt

Recognition should happen at an appropriate time—not months after the fact. If you recognize an employee for a contribution made months ago, they may believe you’re simply going through the motions. Your words will be more meaningful when they come right after an achievement. 

3. Tie to company values

When employees demonstrate behavior that aligns with your company values, don’t let it go unnoticed. When you recognize these behaviors, employees are likely to continue them and inspire others to do the same. This actively fosters your ideal company culture. 

4. Elevate across the organization

When you spread recognition across the company, employees often feel a greater sense of pride knowing that others are aware of their achievements. Plus, employees company-wide can see how each individual and team contribution fits into the big picture. 

5. Recognize both big and small things

While it’s great to recognize the big accomplishments, employees need to feel appreciated for smaller contributions too. Daily thank-yous go a long way and reduce the risk of employee burnout. 

It’s important that recognition is practiced across the organization. Recognition should be a part of your overarching company culture. But getting started with an employee recognition program—and getting everyone on board—can be difficult without the right tools. Here’s what you should look for in employee recognition software to maximize your program’s success. 

Employee recognition is critical to your organization’s bottom line. Without it, employee morale decreases, motivation plummets, and turnover skyrockets. A robust recognition tool will empower employees, teams, and leaders to celebrate each other, creating an environment focused on achievement, appreciation, and business success. 

Ready to uncover the latest research on employee recognition? Download our Recognition in the Workplace ebook and fuel your employee appreciation strategy.

Recognition in the Workplace

Published July 6, 2023 | Written By Natalie Wickham

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Why Employees Need Both Recognition and Appreciation

  • Mike Robbins

comprehensive essay on recognition

One is about what people do; the other is about who they are.

We often use the words “recognition” and “appreciation” interchangeably, but there’s a big difference between them. The former is about giving positive feedback based on results or performance. The latter, on the other hand, is about acknowledging a person’s inherent value. This distinction matters because recognition and appreciation are given for different reasons. Even when people succeed, inevitably there will be failures and challenges along the way; depending on the project, there may not even be tangible results to point to. If you focus solely on praising positive outcomes, on recognition , you miss out on lots of opportunities to connect with and support your team members — to appreciate them. Managers should make sure they’re doing both.

Recognition and appreciation. We often use these words interchangeably, and think of them as the same thing. But while they’re both important, there’s a big difference between them. For leaders who want their teams to thrive and organizations that want to create cultures of engagement, loyalty, and high performance, it’s important to understand the distinction.

comprehensive essay on recognition

  • MR Mike Robbins is the author of four books , including his latest, Bring Your Whole Self to Work . He’s a sought-after speaker and consultant who has worked with leaders, teams, and Fortune 500 companies for the past two decades.

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How to Write a Comprehensive Essay| Steps and Examples

  • Comprehensive Essay
  • August 22, 2022
  • Ellie Cross

How to Write a Comprehensive Essay Steps and the Examples

How to Write a Comprehensive Essay| Steps and Examples? If you are planning to write an essay, you will have to follow a set of steps to make the entire process easy. These steps include: Writing a thesis statement, locating sources, drafting an outline, and creating a logical flow of ideas. If you don’t have any idea on how to write an essay, you can always refer to some examples to get some ideas.

Writing a thesis statement

In a comprehensive essay, the thesis statement provides a clear focus and points toward the conclusion of the paper. A weak thesis statement will undermine the entire essay. For example, I would argue that the death penalty is wrong for violent crimes, but not for nonviolent crimes. Furthermore, I would argue that more women should run for political office and become active members of society. These are just some examples of powerful thesis statements.

In addition to a strong thesis statement for an essay , you can also include supporting points in the body of the essay. One example is a book that embodies a social issue. Trust Me by John Updike provides valuable themes that can easily be related to other works. In addition, it is an important addition to the college syllabus and offers a number of easy-to-connect themes. You can also write several paragraphs on different ideas that support the thesis statement.

Finding sources

While it is essential to gather the right information and sources to write a high-quality essay, you should keep in mind that you must also adhere to a deadline. The amount of research required for an essay can take weeks, months, or even years. To help you save time, you can use a two-pronged approach to evaluate your sources: first, read the abstracts and introductions of the articles. Second, read the citations and references. Finally, you should learn how to use the information you have gathered.

Creating an outline

First, create an outline for your paper. Outlines help you organize your ideas and write more easily. Use sequential numbers to represent the major points in your paper. Organize your paragraphs and ideas in the same order and number each one consecutively. You can re-arrange your outline as necessary, but remember that an outline is not a final draft. If you need help creating an outline, visit the Writing Center.

After writing the outline, proofread and edit it thoroughly. Then, go over it one more time to check for errors. Check grammar and style and make sure all references are cited correctly. If you use proper grammar and citation styles, your essay will be a success. Remember to check all sources and check all spellings and grammatical errors! Following proper formatting and referencing style will make your essay look better and will boost your grades.

When writing a comprehensive essay follow a logical flow of ideas

When writing a comprehensive essay, it is imperative that you follow a logical flow of ideas. For example, if your topic is a family problem, your essay structure should move from a student problem to a family problem. The same holds true for an essay that deals with a complex issue. As such, your outline should include topic sentences and a brief summary of each paragraph.

For science articles, logical flow is crucial to the presentation of important results and the whole story. A well-structured, logical flow allows readers to grasp the main message and the argument more easily. In addition, a well-organized piece of writing will foster a valuable exchange of ideas between the author and the reader. The best scientific articles follow a logical flow of ideas.

Creating a conclusion

A comprehensive essay should be rounded out with a powerful conclusion. A conclusion should summarize the main points from the body paragraphs and not repeat them. It should be a clear statement that carries a persuasive message to the audience. It should also be free of quotations and information from outside sources. The conclusion should sound convincing and confident. It should also address the reader’s questions and concerns. Here are a few tips for writing an effective conclusion.

 The most important step in composing a conclusion is to make sure the reader knows the purpose of your paper. You want to capture their attention and make them feel as though they are reading your essay. Using an appropriate ending statement can make your audience feel connected to your argument and will leave a lasting impression. Try to make it as memorable as possible by addressing the most important points of the essay. It also gives the reader a sense of closure.

Ellie Cross is a research-based content writer, who works for Cognizantt, a globally recognised  wordpress development agency uk  and Research Prospect, a  Tjenester til at skrive afhandlinger og essays . Ellie Cross holds a PhD degree in mass communication. He loves to express his views on a range of issues including education, technology, and more.

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4. Language Comprehension Ability: One of Two Essential Components of Reading Comprehension

Maria S. Murray

After a brief commentary on the overall importance of knowledge to language comprehension ability, learning, and memory, this chapter then goes on to describe in more detail the elements that contribute to language comprehension. Language comprehension is one of the two essential components for learning to read in the Simple View of Reading. The other is word recognition, which was covered in Chapter 3 . Similar to the previous chapter that emphasized word recognition, this chapter presents the skills, elements, and components of language comprehension using the framework of the Simple View of Reading. The Simple View is a representative model explaining that during reading both word recognition and language comprehension coordinate to produce skillful reading comprehension, and it also portrays the many elements that combine to build each component. Each element that ultimately contributes to strategic language comprehension is described, and an explanation of its importance along with suggested instructional activities is provided.

Learning Objectives

After reading this chapter, readers will be able to

  • discuss the importance of knowledge for language comprehension, learning, and memory;
  • explain the underlying elements of language comprehension;
  • identify instructional activities to provide and activate background knowledge, teach vocabulary, and teach language structures;
  • discuss how the underlying elements of language comprehension contribute to successful reading comprehension.

Introduction

As noted in the previous chapter on word recognition’s contribution to reading comprehension, the Simple View of Reading (Gough & Tunmer, 1986) is a research-supported model of the reading process. It portrays skillful reading comprehension as a combination of two separate but equally important components—word recognition skills and language comprehension ability. In other words, to unlock comprehension of printed text (as opposed to other modes such as visual or audio that would not require a person to aim for reading comprehension), two keys are required: the ability to read the words on the page and the ability to understand the meaning of the words (Davis, 2006). The previous chapter ( Chapter 3 ) discussed the importance of improving word recognition and methods for doing so. This chapter will cover the other essential component of successful reading comprehension—language comprehension. As you will see, the elements required for language comprehension are all related to gaining meaning from what is being read.

Figure 1. Strands of early literacy development. Reprinted from Connecting early language and literacy to later reading (dis)abilities: Evidence, theory, and practice, by H. S. Scarborough, in S. B. Newman & D. K. Dickinson (Eds.), 2002, Handbook of early literacy research, p. 98, Copyright 2002, New York, NY: Guilford Press. Reprinted with permission.

The two essential components of the Simple View of Reading are represented by an illustration created by Scarborough (2002). In her illustration, seen in Figure 1, the two necessary braids that contribute to reading comprehension are themselves comprised of underlying skills and strands. Because the Simple View of Reading represents the progression toward proficient reading comprehension as requiring two components, it is termed “simple.” In actuality, each of the components is complex due to its underlying elements. In the case of language comprehension discussed in this chapter, students need to steadily accumulate a fundamental base of background knowledge, vocabulary, verbal reasoning, and literacy knowledge (see below for definitions and explanations of each), and the ability to strategically apply these elements during reading to comprehend texts. To apply strategically means that during the reading of text, readers must continually monitor how well they comprehend its meaning, and bring forth any knowledge they have about the topic, words, sayings, and more. This process is called “metacognition,” or thinking about thinking. After a brief commentary about language comprehension below, the importance of overall knowledge for three elements that lead to the strategic, metacognitive application of the skills and elements in the service of language comprehension will be presented, and instructional methods for each will be provided.

Language Comprehension and Its Connections to Knowledge

Davis (2006) wrote that “even the best phonics-based skills program will not transform a child into a strong reader if the child has limited knowledge of the language, impoverished vocabulary, and little knowledge of key subjects” (p. 15). Language comprehension consists of three elements that must be taught so that students apply them strategically (as opposed to automatically) during reading. As students interpret the meaning of texts, they must strategically apply their background knowledge, their knowledge of the vocabulary, and their understanding of the language structures that exist between words and within sentences.

First consider how reading comprehension is typically developed. Remember that in this textbook (see Chapter 1 ), reading comprehension includes “the process of simultaneously extracting and constructing meaning through interaction and involvement with written language” (Snow, 2002, xiii), as well as the “capacities, abilities, knowledge, and experiences” one brings to the reading situation (p. 11). In line with the first part of this definition, it is expected that once children have been taught sounds and letters, how to blend them together to decode so that they read text fluently, along with lessons in vocabulary, they will be on the way to successful reading comprehension. Reading instruction in schools focuses so heavily on developing reading comprehension because this ability is the ultimate goal of reading.

A surface skim through the teachers manuals from published reading programs will reveal that a multitude of comprehension skills and their corresponding strategies are often taught at each grade level (e.g., finding main idea, summarizing, using graphic organizers), but ultimately these skills and strategies do not necessarily transition students to successfully comprehending texts. Reading comprehension ability is complex and multifaceted; it is comprised of understanding a text’s vocabulary, knowledge of the particular topic, and comprehension of its language structures (see Cain & Oakhill, 2007). Recall from Chapter 1 that language comprehension includes the interaction among someone’s background knowledge, vocabulary, language structures like grammar, verbal reasoning abilities, and literary knowledge (e.g., genres). Language comprehension is a more general term than listening comprehension, which is the ability to understand and make sense of spoken language.

One of the many aspects of reading comprehension that is often overlooked during instruction is students’ language comprehension. For example, a student who has general difficulty with reading comprehension, may, in actuality, comprehend a text about sharks or reefs quite well if his/her parents are marine biologists because he or she has accumulated experiences with ocean-related “language”—its words, phrases, and facts. This same student may not comprehend the next text about ham radio operation or the Appalachian Trail. Successful reading comprehension, then, often depends on the language of a text because the more familiarity and knowledge students have with its language, the stronger comprehension will be. Students from disadvantaged backgrounds often struggle with reading comprehension, despite being able to decode accurately and read fluently. They are often believed to have poor reading comprehension ability when in actuality the snag is a lack of language comprehension stemming from less overall knowledge which in turn stems from fewer experiences aligning with the language encountered in school and school texts. Reading comprehension strategy instruction, which involves teaching children how to comprehend or remember written text using deliberate mental actions, entails instruction in questioning, visualization, and summarizing, for example. However, teaching children how to apply such strategies during reading simply cannot replace a lack of knowledge.

Not surprisingly, in the earliest grades, an important facilitator of reading comprehension is automatic word recognition (see Chapter 3 ), since comprehension of a text cannot take place if its words cannot be read or recognized. However, once students become more competent at word recognition, the dominant factor driving reading comprehension transforms to become language comprehension (Foorman, Francis, Shaywitz, Shaywitz, & Fletcher, 1997). The reason for this boils down to one word—knowledge. Once students can read the words, they extract meaning from texts using their overall knowledge and experiences (background knowledge), their knowledge of words (vocabulary), and their knowledge of how words go together to create meaning (language comprehension). This accumulation of knowledge can last a lifetime and really never be considered “finished.” In fact, knowledge is so important to consider, that a brief commentary on its contribution to reading comprehension is next, before going on to discuss the three elements in Scarborough’s (2002) braid that lead to language comprehension, and ultimately reading comprehension.

Subtle differences exist between the terms “knowledge” and “background knowledge.” In this chapter, “knowledge” is broadly defined as the total accumulation of facts and information a person has gained from previous experiences (it is also called general knowledge). Knowledge is composed of concepts, ideas and factual information, which eventually come together to contribute to understanding in various situations. One does need facts and concepts and ideas to perform a procedure (e.g., putting historical events on a timeline, editing a paper for mechanical errors, reading a map), but they are even more vital when partaking in situations or conditions that require synthesizing a lot of information (e.g., write a comprehensive essay on a topic, comprehend an author’s message while reading a book) (Marzano & Kendall, 2007). “Background knowledge,” on the other hand, is a term used in education for a specific subset of knowledge needed to comprehend a particular situation, lesson, or text (it is also called “prior knowledge”). When reading a text about dog training, readers are going to use their background (prior) knowledge of dog behavior, vocabulary related to dogs, aspects of training, and so on, to comprehend text. They will not need to apply any of their knowledge of outer space, photosynthesis, or baking (any of their general, overall knowledge) in this particular instance. It is not possible for educators to teach the required background knowledge for every text that students will encounter as they progress through their school years. They can, however, provide the next best thing—a wide base of general knowledge that can be drawn upon and applied as background knowledge to problem solve and create meaning.

General knowledge comes from years of exposure to books, newspapers, knowledge-rich school curricula, television programs, experiences, and conversations. Its value cannot be understated. Willingham (2006) summarizes the findings in cognitive science regarding the significance of knowledge in education this way:

Those with a rich base of factual knowledge find it easier to learn more—the rich get richer. In addition, factual knowledge enhances cognitive processes like problem solving and reasoning. The richer the knowledge base, the more smoothly and effectively these cognitive processes—the very ones that teachers target—operate. So, the more knowledge students accumulate, the smarter they become. (p. 30)

Both the Council of Chief State School Officers (CCSSO, 2013) and the National Research Council’s Committee on Defining Deeper Learning and 21st Century Skills (NRC; 2012) call for an increase in rigorous content knowledge in order for today’s students to achieve college, career, and citizenship readiness. According to the CCSSO (2013), students must also be able to demonstrate “their ability to apply that knowledge through higher-order skills including but not limited to critical thinking and complex problem solving, working collaboratively, communicating effectively, and learning how to learn” (p. 6).

Difficulties comprehending complex texts encountered in college and careers have been attributed to a lack of general knowledge. To illustrate this difficulty, Schweizer (2009), a professor who taught freshman composition classes at Duke University, wrote about an eye-opening incident he experienced during his classes. After assigning both his remedial and advanced classes a four-page article on climate change from a popular college-level anthology of essays (see McKibben, 2006), he realized his students’ comprehension of the essay was “flat, anemic, and literal rather than deep, rich, and associative” (p. 53). Upon questioning his students on the general knowledge items within the text—general facts, figures, locations, words, and common expressions—he reached a sobering conclusion. In the remedial class, just one student could identify Gandhi, none knew Ernest Hemingway, and two knew that Job was a character in the Bible. In the more advanced class, four out of 15 students recognized Gandhi or Hemingway, none knew the word “quixotic,” and few could comprehend certain expressions within the text (e.g., “something is in the offing”) or its allusions (e.g., “the snows of Kilimanjaro are set to become the rocks of Kilimanjaro”). Reflecting on the literacy-related consequences of this lack of word and world knowledge, Schweizer noted that his students were “not only hampered by a lack of factual knowledge, but that this shortcoming translates into problems with diction and literacy as well” (p. 52). Interestingly, to have comprehended this paragraph alone, you need to be familiar with and comprehend the importance and meaning of these words and phrases: Duke University , attributed , “ eye-opening incident ,” remedial , anthology , “ sobering conclusion ,” and allusions . A lack of language comprehension related to these words will hamper your reading comprehension indeed!

Background Knowledge

One of the three elements necessary for language comprehension is background knowledge. As mentioned above, background knowledge is a particular subset of knowledge (e.g., facts about the world, events, people, sayings and phrases) that is needed to comprehend and learn from a particular situation, lesson, or text. Young readers learn to strategically apply their background knowledge in order to interpret a text’s meaning. As a small example, consider the following sentence: “Initially Richard was upset when police told him they found bugs in his office, but to avoid prosecution he agreed to let them remain until the investigation was completed.” To comprehend this sentence either in isolation or within the context of an entire text, one will need to have learned that “bugs” are spying devices, to understand that people might get upset when they discover they are being spied on, and to infer that Richard has created an arrangement of cooperation with the police. Without background knowledge, the author’s intended meaning may be misconstrued as having to do with insects.

Why background knowledge is important

Knowledge leads to more knowledge, making learning easier (Willingham, 2006). Consider another example in which students read a story about a boy who is angry that he was not selected to play on the football team. The boy insists, “I really didn’t want to play football anyway!” His mother responds, “Sounds like a case of sour grapes to me!” Students familiar with the Aesop’s fable “The Fox and the Grapes” will understand the reference to “sour grapes” in this particular story and in all subsequent texts, and they will be able to interpret the subtle nuances of resentment that comes about after rejection. A student with no exposure to the fable may believe that the boy really did not want to play football and will not understand why the mother is talking about grapes. Meaning will be incomplete. Background knowledge allows readers to strategically infer the author’s meaning with a lot less effort. Drawing inferences from a text is so much easier when a reader is already familiar with what the author is talking about.

Willingham (2006) summarized some of the findings in cognitive science regarding how background knowledge helps students comprehend what they read and remember what they have learned. Most obvious, and as seen in the sour grapes example, background knowledge of a text makes it so that fewer instances are necessary of having to stop or reread for clarification. The author’s point is comprehended right away. Less obvious, background knowledge allows readers to arrange sequences of events in texts into connected, meaningful units or sequences that can be more easily analyzed, understood, and remembered. Without background knowledge, words and sentences in a text easily become disjointed, unrelated, random sequences. For instance, imagine a passenger in a small plane who has no background knowledge of mechanics or technical things. This passenger is asked by the pilot to read off the items from preflight checklists. Due to lack of background in technical things, the items seem arbitrary and unrelated. Dozens of unfamiliar words and terms are essentially meaningless (e.g., throttle 2000 RPM, magnetos max drop 175 RPM, press-to-test annunciator panel, electric fuel pump off, fuel pressure check), and if asked after the flight, it is unlikely that the passenger would be able to remember them. Conversely, if the next traveler possesses background knowledge related to how mechanical things work and is asked to read the same checklists, his or her comprehension and recall would be greater because the items on the list would be familiar and meaningful. It would be understood that some of the items were related to engine speed, while others had to do with the fuel system and they would be retained in memory because this passenger would assign them to meaningful categories and sequences. The background knowledge of the second passenger would not only create better comprehension of the experience, it would also enable greater storage and recall of most of the events. The second passenger would have learned more and would have remembered more.

A similar phenomenon related to how meaningful categories (or “chunks”) are related to memory and learning is the frequently cited experiments of DeGroot (1946/1978) and Chase and Simon (1973). Differences in background knowledge (via the experiences) between master and novice chess players were examined in both studies, as well as how this knowledge influenced their memories. Chess masters who had experienced thousands of chess matches, and thus, had more background knowledge were pitted against novices in a simple experiment. For just a few seconds, chess masters and novices were shown pictures of chessboards in which the pieces were configured in positions from advanced level matches. The pictured pieces were not arranged on the boards randomly; their positions were realistic. After momentarily viewing the pictures, players reconstructed the positions of each piece using a real board. Masters recreated the positions almost perfectly, while the novices placed about half of the pieces successfully. The accuracy of recall was attributed to the masters’ ability to categorize and chunk information, or, in the case of chess, to chunk together multiple, meaningful groups of pieces. The novices could only memorize positions of single pieces, whereas the masters memorized positions of sets of pieces that made sense to them in terms of familiar play-structures. They had background knowledge of similar set-ups.

A video recreating this experiment with chess grandmaster Patrick Wolff (Simons, 2012) reveals his strategy in recreating the board placements. Wolff states that he noticed where the pieces clustered and that he noted the logical connections between the pieces. He recognized the meaningful chunks. In a book about how practice and effort contribute to talent, Colvin (2008) comments on chess player experiments, noting that, “instead of seeing twenty-five pieces, they may see just five or six groups of pieces” (p. 100). In any realm, meaningful chunks can only be formed by those having the knowledge and background experiences to understand what belongs with what. In the case of chess players, certain pieces defend others in strategically particular positions. For skilled readers, certain letters chunk together within long words, enabling them to be read rapidly and accurately, and certain words and ideas chunk together meaningfully, enabling comprehension of an author’s message. An example of how words and ideas chunk together meaningfully to aid reading comprehension is provided by Meurer (1991), who wrote about reading schemata. Reading schemata are patterns that organize knowledge in our minds while we read. Meurer explained that readers have schema for various concepts, such as when something “breaks.” Along with this understanding, they may possess subcomponents and ideas having to do with “breaks”: items that can be broken, ways that things can cause things to be broken, and what it means for something to be broken, just to name a few. He then provided an example of a sentence: “The karate champion broke the cinder block.” The author of that sentence does not explicitly tell the reader what the champion used to break the cinder block. It is the reader’s schema for “break” and “karate champion” that allows him or her to successfully infer that what broke the cinder block was not a hammer or a chisel, but the karate champion’s hand. Without the ability to automatically chunk together and activate various words and ideas, reading comprehension will suffer.

In any field, setting, or circumstance, new material that has familiarity is more readily learned because it is easier to understand and because it is supported by and connected meaningfully to what is already known. The beauty and value of background knowledge is that it provides the familiarity that is crucial for connections that both create new learning and allow for the new learning to be remembered.

Background knowledge instruction

As educators, we cannot teach the “big umbrella” of background knowledge since it evolves from a multitude of life experiences. However, we can provide it or activate it, and suggestions for both are described below.

Providing background knowledge

Meaningful contexts from a content-rich curriculum spanning a wide variety of content areas are ideal for providing the background knowledge that will scaffold future learning. Many curricula are deliberately designed to provide an integrated sequence of rich, engaging, multicultural content spanning history, science, music, visual arts, mathematics, language arts, and more. Without such a curriculum, knowledge from each of these areas that is likely to appear in texts in subsequent grades can still be provided. In the earliest grades, before students can read books independently, the content and concepts that build background knowledge are usually developed through teacher read-alouds of a wide variety of texts, such as nursery rhymes, rhyming poems, fairy tales and fables from a variety of cultures, and engaging nonfiction texts, to name a few.

Children’s books and other written sources of information are an authentic and abundant source of knowledge about every imaginable subject (see Chapter 7 for further discussion about children’s literature), suitable for building knowledge at all grade levels (Stanovich & Cunningham, 1993). Children’s books feature rich concepts and a high percentage of unique and sophisticated words ( Cunningham & Stanovich , 1998; Hayes & Ahrens, 1988). Reading a number books or stories to students featuring similar themes or domains (e.g., farms, seasons, culturally diverse folklore, Egypt, music, currency, weather) provides a beneficial repetition of words and concepts that build valuable background knowledge. As students hear multiple versions of a similar theme or receive repeated instruction in a particular domain, newly developed background knowledge will lead to better comprehension of the material (Cervetti, Jaynes, & Hiebert, 2009). Davis (2006) recommends twenty to thirty read-alouds per domain (e.g., from a variety of children’s books, chapters, short pieces, poems) for developing background knowledge; just two short read-alouds a day can cover 10 to 15 domains in a school year (see also Hirsch , 2006). Although read-alouds are typically done in the elementary grades, there is likely to be benefit in building background knowledge at the older grades as well.

Activating background knowledge

In addition to providing background knowledge, we can also activate existing background knowledge. Activation of background knowledge that students already possess is frequently a focus of comprehension instruction. Teachers understand the value of activating background knowledge and as a result many tend to apply a series of strategies at the expense of providing knowledge. There is not a lot of research on teaching a multitude of comprehension strategies prior to third grade, primarily because beginning readers in the early grades are learning how to decode fluently. Also, too much of an emphasis on teaching strategies for reading comprehension may not be effective ( Stahl , 2004), particularly if the text is easy to understand. For young students, particularly when using complex text, comprehension strategies should still be worked on (see the Institute of Education Sciences’  practice guide (Shanahan et al., 2010) for a summary of recommendations on improving reading comprehension for children in grades K-3), but the decoding constraint may still stand in the way. In later grades, simply applying comprehension strategies such as visualizing or predicting will not automatically enable students to understand science. If we want students to comprehend science texts, they must know something about science. Students do better if they read and write about things they know about. While isolated facts are certainly important and necessary, they will not suffice to enable meaningful comprehension unless background knowledge is developed within meaningful contexts.

Activating background knowledge is under scrutiny since the introduction of the Common Core State Standards for English Language Arts & Literacy in History/Social Studies, Science, and Technical Subjects ( CCSS ; National Governors Association & Council of Chief State School Officers [NGA & CCSSO], 2010) because students are now expected to extract information from texts by focusing on what the author intended for them to understand, rather than relying too heavily on their prior knowledge, experiences, or opinions to construct meaning. Teachers are encouraged to downplay any lengthy, explicit focus on their students’ existing knowledge before reading, and in discussions about the CCSS, some propose that this may serve to equalize the outcomes for children who have varying degrees of knowledge about various topics. However, as Shanahan (2014) explains, avoiding discussion altogether of background knowledge will not serve to allow children to interpret and comprehend texts more equally, because it would be next to impossible for children who do possess background knowledge about a topic to avoid using it to construct meaning while they read. Those without the background knowledge will not have this advantage, and will be wrongly viewed as having poor comprehension, when in fact it is their lack of knowledge that is to blame. Shanahan (2014) provides some practical instructional suggestions for activating background knowledge before and during reading. An abridged and modified list appears below:

  • When introducing texts, avoid lengthy introductions or potentially ineffective pre-reading strategies such as a “picture walks” and tedious contributions of students’ prior knowledge that could potentially impair comprehension. A simple statement such as “We’re going to read a story about how animals camouflage themselves” may suffice. The goal is to be brief and strategic (e.g., what is the purpose of the text, what will students bring to it, and what information absolutely needs to be provided; note all the other suggestions below for more clarification). Otherwise time spent during pre-reading activities may take time away from the actual reading, become boring or repetitive, and possibly steer children to the wrong focus, ruining the entire experience. See an additional blog post in which Shanahan (2012) speaks specifically about this topic http://www.shanahanonliteracy.com/2012/02/pre-reading-or-not-on-premature-demise.html
  • When introducing a topic or genre that students will be reading, avoid revealing information that you will want them to extract from the text(s) on their own.
  • Preteach necessary information students will need if it is not in the text (e.g., a text on climate change may not have been written for young students, so vital references to geography or technology may need explanation).
  • Do not focus on activating background knowledge about topics in the text that are not needed for its comprehension (e.g., a text focusing on how an octopus camouflages itself does not require discussion or instruction about oceans).
  • When using multiple texts to develop background knowledge, introduce them in an order that will support and reinforce those that may come before or after. Initial texts may cover a particular topic in a general manner, followed up by texts that cover the material in the initial texts and delve deeper into the topic.
  • Attend to the differing background knowledge needs of students from diverse cultures by considering information you may need to pre-teach in order for them to comprehend particular texts.

Having just read about background knowledge, it is probably easy for you to imagine how vocabulary—the knowledge of the meaning of words in a text—adds significantly to the construction of the meaning of texts. Vocabulary knowledge is a prominent predictor of reading comprehension and is depicted as a central thread in the language comprehension component of the Simple View of Reading because of its connections to background knowledge and language structures (Scarborough, 2002).

The development of a child’s vocabulary begins at infancy, when a baby starts hearing speech and babbling. Oral language experiences, such as in-person conversations, dialogue heard on TV, or language heard during the reading of children’s books are primary means for accumulating vocabulary. By the age of two, children usually speak about 200 to 300 words and understand many more, and once in school, they learn approximately 3,000 words per year, and can comprehend many more than they can read (Nagy, 2009). To accomplish this rate of word learning, it is critical to ensure that students are learning new words each day. This is especially true for many students from less advantaged backgrounds, who are exposed to millions fewer words in their first three years of life than students who come from more privileged backgrounds (Hart & Risley, 1995). This disparity results in students from more affluent households knowing thousands more words upon entering school, which benefits their ability to understand, participate in, and profit from the language of instruction that is predominant in U.S. school settings.

Why vocabulary is important

As stated previously, the level of a child’s vocabulary knowledge is a strong predictor of reading comprehension (Duncan et al., 2007). This seems obvious since not knowing the meaning of words in a text makes it quite difficult to comprehend it. As Adams (2010) eloquently points out, “What makes vocabulary valuable and important is not the words themselves so much as the understandings they afford. The reason we need to know the meanings of words is that they point to the knowledge from which we are to construct, interpret, and reflect on the meaning of text” (p. 8).

Vocabulary instruction

Instruction in vocabulary should begin with thinking about the different levels of “knowing” a word. Upon hearing a word, we can say (a) we have never heard of it, (b) that we have heard of it but we do not know it, (c) that we know it, or (d) that we both know it and can use it (Nagy, 2009). The more deeply we know a word, the more likely we will be to understand it when we hear it or read it, and the more likely we will be to use it when we speak or write. Ideally, instruction makes it so that students reach the level of knowing and using words when they converse, write, or read. Vocabulary learning occurs either incidentally (words are learned through exposure and experiences) or intentionally (words are deliberately and directly taught). The majority of words in our vocabularies are learned incidentally, through conversations or independent reading (Adams, 2010). This means that most vocabulary learning will not occur through explicit instructional means but through opportunities available in the child’s environment to encounter and resolve meanings of new words. Children who have learned to read independently are at an advantage in terms of learning words incidentally because they are able to independently encounter new words and infer their meaning while reading.

Incidental vocabulary instruction is enhanced through rich and varied oral language dialogue and discourse experiences, and independent reading. Even though “incidental” learning occurs as a result of some activities that do not involve any deliberate teaching, incidental learning still often involves a level of intentionality on the part of teachers. Teachers should consciously fill their everyday classroom language with rich, unique words so that they can be learned incidentally. A classroom that is rich with words promotes awareness of new vocabulary and a curiosity for learning new words. Rather than simplifying language for students, conversations should be embedded with sophisticated words: “Jordan, why don’t you amble over here and let me glance at that,” “Please shut the door; those third graders are causing quite a commotion ! What a ruckus !” and “Oh my, Jake, the lion on your t-shirt has such sinister eyes! It terrifies me!” A resource for building language rich classrooms to promote oral language, vocabulary, and comprehension is Dodson’s (2011) 50 Nifty Speaking and Listening Activities. While it is not a scientifically based intervention, it provides a multitude of listening, speaking, reading, and writing activities that adhere to a sequence of language development for students ranging from kindergarten to fifth grade.

Many words, phrases, and sayings require intentional instruction. Vocabulary words that should be intentionally taught are those essential for understanding texts, those that are likely to be encountered across multiple texts, or those that are particularly difficult to understand (Beck, McKeown, & Kucan, 2002). Activities for directly teaching vocabulary include using graphic organizers (for a collection of free graphic organizers visit https://www.teachervision.com/graphic-organizers/printable/6293.html ), or analyzing words’ semantic features (i.e., listing their attributes—hard/soft, tall/short, exciting/dull).

Text Talk (Beck & McKeown, 2001) is an evidence-based vocabulary (and comprehension) building intervention that can be easily built into daily read-alouds. Teachers pre-read the selected text, choosing three to five vocabulary words that are “Tier 2” words. Tier 2 words are sophisticated, occur frequently in conversation and print, and are used across multiple domains and contexts. Examples of Tier 2 words are unique, convenient, remarkable, and misery (See Beck et al., 2002). Tier 1 words are those that are basic and, for speakers of English, do not require instruction in school (e.g., wall, water, fun) , and Tier 3 words are low-frequency words that are specific to domains or content areas (e.g., photosynthesis, Constantinople ). During a read-aloud that is done in Text Talk fashion, open-ended comprehension questions are asked. Open-ended questions require a meaningful interactive response rather than a one-word reply. Examples of an open-ended question are “How do you think that made the boy feel?” and “Why did the fox decide to share his food?” To answer each of these questions requires an extended, multiple-word response. Examples of close-ended questions requiring only a single word response include “Is the boy mad?” and “Which food did the fox share?” Interactive extended responses and dialogue promote oral language development and allow the teacher to monitor students’ vocabulary use and comprehension. After the read-aloud or during a second reading of the story, the preselected Tier 2 vocabulary words are defined by the teacher using simple, child-friendly definitions (e.g., “To coax someone means to use your words to get them to do something”). The meanings of the words are discussed within the context of the story (e.g., “The mother coaxed her daughter to take a bath, meaning she used words to convince her to get into the bathtub”), and the teacher provides examples of the words within other contexts (“When my mother got older, I had to coax her to join us on vacation”). Finally, the students are asked to apply their knowledge and use the words in a personal context to ensure that they have the correct understanding of their meanings (“Jared, can you share an example of a time when someone coaxed you to do something?”). Additionally, during the read-aloud, it is beneficial to read the text before showing the pictures so that the illustrations do not interfere with attention or comprehension. This procedure is effective in getting students to pay attention to the words being read, and thus, is helpful toward their comprehending the language of the story (Beck & McKeown, 2001). It fosters their ability to comprehend decontextualized language—language that is “outside the here and now” (p. 10)—and leads to comprehending the vocabulary and text without relying on pictures. Teachers typically read children’s books aloud on a daily basis. Modifying read-alouds a bit to include the suggestions here fosters rich Tier 2 vocabulary and language comprehension through open-ended questions and by drawing attention to the vocabulary and meaning in texts.

Language Structures

The final element contributing to language comprehension is language structure—the relationships between the words and sentences in a text. Looking back at the model of skilled reading in Figure 1, it is evident there are many facets to language structures, including knowledge of grammar, being able to make inferences, and having knowledge of literacy concepts, such as what reading strategies to use for different types of texts (e.g., poems versus informational texts). To simplify and streamline these for the purpose of this chapter, they will be categorized as having to do with the major components of language that are interconnected: form, content, and use (see Bloom & Lahey, 1978).

Why language form is important

Language form comprises the rules for how words are structured (see ‘morphology’ described below) as well as the rules for the arrangement of words within sentences and phrases (see ‘syntax’ described below). The act of constructing meaning while reading is complex, so it is not surprising that morphology and syntax also contribute to reading comprehension.

Morphology is the study of morphemes in a language. Not to be confused with phonemes, which are the smallest units of sound in spoken words, morphemes are the smallest units of meaning in words (to remember this, consider that “morphemes” and “meaning” both begin with the letter “m”). Words contain one or more morphemes, or units of meaning. For instance, “locate” is a word that is a freestanding morpheme because it has just one unit of meaning and can stand on its own. By attaching another morpheme, the suffix “-tion,” to create “location,” there are now two units of meaning: “locate” and the action or condition of locating, “tion.” “Tion” is a bound morpheme because its meaning depends on its connection to other words; it cannot stand on its own. A third morpheme, the prefix “dis,” changes the meaning of the word yet again— “ dislocation. ” In sum, the word “dislocation” is made up of three morphemes, each of which contributes its own meaning. Similarly, “cat” is a freestanding morpheme (a singular feline animal), but adding the bound morpheme—s—signals a change in meaning and the reader now pictures more than one cat.

Another aspect of language form, syntax, is commonly referred to as grammar. It is the combining and ordering of words in sentences and phrases that enables comprehension of a text. For example, in English, when the article “a” or “an” appears in a sentence, it is expected that a noun will follow. Syntax includes sentence construction elements like statements, commands, and combined sentences as well as particular sentence components such as nouns, adjectives, and prepositional phrases. These are important for future teachers to know, because effective use of these will allow students to comprehend text more successfully, and they will also allow students to demonstrate command of the conventions of the language in their writing pieces.

Language form instruction

Typically, rules of morphology and syntax are taught directly. For example, morphology instruction includes root words, prefixes, and suffixes along with derivations of Greek and Latin roots (e.g., “chron” is the Greek root for “time” in chronicle, synchronize, and “cred” is the Latin root for “believe” in creed, incredible, credulous). Morphology charts of root words, prefixes, and suffixes can be compiled over time and displayed on a wall so that students can refer to them while reading or writing. Charts could feature a list of suffixes that indicate people nouns (e.g., -er, -or, -cian, -ist), suffixes that create verbs (e.g., -ize, ify), or base words that change spelling and pronunciation (e.g., sign/signature/design, deep/depth). Incidental exposure to such morphology elements enhances word awareness (the act of noticing and attending to features of words), vocabulary, and, of course, language comprehension.

Why language content is important

Language content that is comprised of the meaning of the relationships that exist between words, phrases, and sentences is known as semantics. Semantics is different from vocabulary because it extends beyond the individual meaning of words. Note that once again, there is an “m” in this “semantics,” but it is in the middle of the word, which may help you to remember it has to do with the meaning that ties words (and sentences) together. Understanding the semantics of language enables comprehension because it clarifies the content—the network of events and relationships that exists in texts. For example, reading a sentence about a jug breaking and glass being scattered all over the floor might cause confusion, since jugs are typically not thought of as being made of glass.

Language content instruction

Semantics requires knowledge of vocabulary (a word’s meaning, and perhaps its synonyms and antonyms), as well as syntax. Just as important is background knowledge in order to form correct judgments about the context being read. Part of this knowledge includes the meaning of humor, slang, idioms (i.e., combinations of words having a figurative meaning as in “it’s raining cats and dogs” or “he was feeling blue”), metaphors (a comparison of two things as in “she is my sunshine”) and similes (comparisons of two things using “like” or “as” as in “her laughter is like sunshine”). Languages have thousands of common and often subtle semantic attributes that involve analogy, exaggeration, sarcasm, puns, and parables to convey world knowledge. Teachers can explicitly teach these attributes so that they are recognized more readily, explicitly define particular sayings and expressions, and demonstrate examples and nonexamples. For example, a teacher could demonstrate examples and nonexamples of exaggeration (“I have a million papers to grade!” vs. “I have three papers to grade”). As soon as schooling begins, semantic conventions should be taught, such as in the way that “once upon a time” signals the beginning of a fairy tale. Like vocabulary, the majority of semantic knowledge is derived from previous experiences and background knowledge. Teaching students phrases through exposure to discussions, reading, and other venues like television, movies, and online videos does a lot to promote this language comprehension element.

Why language use is important

Language use is termed pragmatics. Pragmatics are the rules of language that lead to appropriate use in assorted settings and contexts. Each setting (e.g., school, home, restaurant, job interview, playground) or context (e.g., greeting, inquiry, negotiation, explanation) has a particular purpose. To communicate appropriately, students must learn patterns of conversation and dialogue that occur in assorted settings. For example, use of language can vary according to a person’s status, so whether talking at home to a parent (a more casual use of language) or talking to a teacher at school, (a more formal use of language), the setting and the status differ, and language use must adapt accordingly. Understanding the nuances of pragmatics contributes to language comprehension, which in turn enables a reader to recognize its uses in written text, leading to more successful reading comprehension.

Language use instruction

The pragmatics of language use in school requires students to comprehend academic language. Students, especially English language learners and students with social difficulties, must comprehend the differences between conversation and academic language. Students’ language use in assorted settings (e.g., playground conversations, discussions with teachers) often requires teachers to provide clarification and elaboration. Students can perform enjoyable skits demonstrating the differences in language use in various situations and teachers can monitor and model language use as students tell stories, describe events, or recount personal experiences.

To help students develop language comprehension, the underlying meaning-based elements of reading—background knowledge, vocabulary, and language structures—must be taught and monitored. Unlike teaching students to recognize words accurately and automatically so that they become fluent readers, teaching the elements of language comprehension must be done so that students become increasingly strategic about extracting the meaning from texts they read. This is an incremental, ongoing, developmental process that lasts a lifetime. With each new bit of background knowledge, each new vocabulary word, and each new understanding of language use, students can integrate this knowledge strategically to comprehend text.

The two essential components of the Simple View of Reading, automatic word recognition and strategic language comprehension, contribute to the ultimate goal of teaching reading: skilled reading comprehension. Once students become proficient decoders and can automatically identify words, the role of language comprehension becomes increasingly important as students shift from paying attention to the words to paying attention to meaning.

Teachers must be ever mindful of the presence or absence of background knowledge that students bring to the task. As important as it is for students to monitor their comprehension, it is equally important for teachers to continually monitor each student’s background knowledge and comprehension so that they can step in to build and supply what is missing in their understanding. The value of the knowledge that students bring to their reading should never be sacrificed for the sake of comprehension strategy instruction. They must go hand in hand.

Questions and Activities

  • What are the three underlying elements of language comprehension? How does each contribute to successful reading comprehension?
  • Which instructional activities are helpful for providing and activating background knowledge, teaching vocabulary, and promoting language use?
  • Consider a student that you have worked with who has difficulty with reading comprehension. Which of the underlying element(s) of language comprehension (i.e., background knowledge, vocabulary, language use) do you believe may be at the root of this student’s difficulties? How might you develop a new instructional plan to address these difficulties?
  • Select an informational text that you might use with students. Identify the facts, phrases, vocabulary or other knowledge items that readers would need in order to comprehend the text. Next, consider discussing which facts, phrases, vocabulary, or other knowledge items would a reader NOT necessarily need in order for comprehension to still occur.

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Steps to Success: Crossing the Bridge Between Literacy Research and Practice Copyright © 2016 by Maria S. Murray is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Comprehensive Exam, Essay Example

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Literacy teaching

The literacy concept of a person includes the ability to read, obtain knowledge, write, think, and gain a comprehensive meaning about some written word. A literacy teaching practice includes an approach that enhances the learning and obtaining of the mentioned skills in a consistence, explicit and systematic manner in a strategic approach (Ajayi, 2001). It constitutes of an effective teaching practice employed in the classroom that majorly focuses on the literacy of the student with respect to the emerging technologies and the absolute needs of the new generation (Alan & Greg 2004). The requirements of the 21st century in terms of literacy teaching practices significantly differ from previous centuries. A literacy teaching practice as the professional development of a child right from the early stages of life is so much relate to the Vygotsky theory. Vygotsky argues that the literacy teaching focused towards the full integration of the individual’s notion. Learning and education to him kept away from any form of authoritarian concept and great co-operation between the teacher and student for greater results. Vygotsky has it that education focused towards a comprehensive aim of success; with increased recognition of any cognitive or constructive suggestions more especially from the student (I-Hua, 2012). This explanations and theories of Vygotsky regarding education so much connect to literacy teaching.

Reading vs. language learning

Reading is the ability to make up a comprehensive meaning out of some written word. Learning to read is highly dependent on the teacher and somehow on the ability of the student. On the other hand, language acquisition is different from learning how to read. Language acquisition is something that could come up naturally in the process of interacting with native speakers of the language. It often occurs that some students are bilingual or multilingual. This depends on the family cultural background, or the surrounding environment. Learning a language is remarkably independent on the teacher. It is more of interest, age, and the ability to grasp and understand the new language (Martini & Sénéchal, 2012). A child for example, can easily learn a new language as compared to an adult. For an adult, learning a language would entirely depend on the interest level. The similarity between learning to read and learning a language is the level of intelligence or rather grasping level. A highly intelligent person with interest would learn the two easily as compared to a less intelligent one.

Components of reading

The first and most critical component of reading is phonemic awareness. This includes the knowledge, awareness, and manipulation of sounds in spoken word (Moats, 2000). The ability to hear and manipulate speech orally is the basic step to knowing how to read in a child. A child learns oral rhymes and identifies syllables, and gradually learns to manipulate difficult things to look simpler. The second most noteworthy is the learning of phonics, where a child easily gets the relationship between the spoken sounds and written letters. The child learns how to relate sounds and letters, and how to connect letters of form a word. With practice, students apply their knowledge of sound, symbols and the exact writing. Efficient reading of an adult so much relate to the first teacher. An excellent introduction to phonemic awareness and later the phonics leads one into being an efficient reader; a shoddy introduction results to a poor reader (Martini & Sénéchal, 2012). The other components that follow include vocabulary, fluency, and formulation of a comprehension.

Modern teaching practices

Teachers need be extremely dynamic, become more thoughtful and reflective about teaching and learning in the 21st century (Gordon & Gordon, 2003). They ought to raise their awareness information of the change facilities. They ought to formulate a collaborative approach with the students. This in one way or another would encourage the collaborated stimuli that stimulate innovation and invention that stirs imagination. Teachers ought to have a vision, mission, goals, and values that drive them to developing a commitment to the success of the student. Unlike teachers of the olden days whose wish was the command of the student, in the present generation students have developed a need to be listened. Therefore, the teachers have no choice but learn to be accomplished listeners as well as speakers. A brilliant student would feel demoralized if his views and suggestions regarding a subject not considered, or rather if the teacher is too proud to spare time to listen to the student. In general, today’s learning is a two way process that actively involves the teacher and the students especially with the raised ICT’S and Technological literacy (Alan & Greg, 2004).

Practical ways to infuse professional development efforts

Teaching is a calling, and not all persons can fit into the teaching profession. Teachers encounter several challenges in their profession compared to other professionals. A teacher ought to develop an enhanced learning approach that comprises of values, believes, commitments, missions, goals, visions, and a purpose (Gordon & Gordon, 2003). A teacher should never believe any student meant to be a failure. He /she should develop literacy skills that revolve around the ability to listen and speak. The teacher ought to have the feeling of having empathy, developing attention, identifying and learning the weaknesses of each student. If for instance a student has some weakness in numeracy, the best thing a teacher ought to do is incorporate the student into handling numerical, spatial, graphical, and statistical concepts among others in a suitable manner to raise the dying morale of the student (Minott, 2001). Teachers should give guidance to the student with a negative attitude toward technical subjects, where the teacher encourages the student[s] never to stop questioning. Every situation and problems should have an approach to solve them. Teachers should be able to make follow-ups to ensure they meet their goal of ensuring excellence. Last, but not least a teacher ought to be dynamic, to change with the changing world. He /she should learn the new methods to handle the modern generation of students (Alan & Greg, 2004).

Ajayi, Lasisi. (2001) “Teaching Alternative Licensed Literacy Teachers to Learn from Practice: A Critical Reflection Model”. Teacher Education Quarterly, vol 38(3) p169-189.

Alan K. Bowman & Greg Woolf (2004) Literacy and Power in the Ancient World , Cambridge, UK: Cambridge University Press.

Gordon, Elaine H. & Gordon, Edward E. (2003). Literacy in America: historic journey and contemporary solutions . New York: Praeger.

I-Hua Chang. (2012)”The Effect of Principals’ Technological Leadership on Teachers’ Technological Literacy and Teaching Effectiveness in Taiwanese Elementary”. Journal of Educational Technology & Society. Vol. 15 Issue 2, p328-340. 13p.

Martini, Felicity & Sénéchal, Monique. (2012) “Learning literacy skills at home: Parent teaching, expectations, and child interest” Canadian Journal of Behavioral Science Vol 44(3), pp. 210-221.

McKenna, Michael C.; Richards, Janet C. (2003). Integrating multiple literacies in K-8 classrooms: cases, commentaries, and practical applications . Hillsdale, N.J: L. Erlbaum Associates.

Minott, Mark A. (2001) “Reflective teaching, critical literacy and the teacher’s tasks in the critical literacy classroom”. Reflective Practice. Feb2011, Vol. 12(1) p73-85. 13p.

Moats, Louisa (2000). Speech to print: language essentials for teachers . Baltimore: Paul H. Brookes Pub.

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A review on face recognition systems: recent approaches and challenges

  • Published: 30 July 2020
  • Volume 79 , pages 27891–27922, ( 2020 )

Cite this article

  • Muhtahir O. Oloyede 1 , 2 ,
  • Gerhard P. Hancke 2 &
  • Hermanus C. Myburgh 2  

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Face recognition is an efficient technique and one of the most preferred biometric modalities for the identification and verification of individuals as compared to voice, fingerprint, iris, retina eye scan, gait, ear and hand geometry. This has over the years necessitated researchers in both the academia and industry to come up with several face recognition techniques making it one of the most studied research area in computer vision. A major reason why it remains a fast-growing research lies in its application in unconstrained environments, where most existing techniques do not perform optimally. Such conditions include pose, illumination, ageing, occlusion, expression, plastic surgery and low resolution. In this paper, a critical review on the different issues of face recognition systems are presented, and different approaches to solving these issues are analyzed by presenting existing techniques that have been proposed in the literature. Furthermore, the major and challenging face datasets that consist of the different facial constraints which depict real-life scenarios are also discussed stating the shortcomings associated with them. Also, recognition performance on the different datasets by researchers are also reported. The paper is concluded, and directions for future works are highlighted.

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Oloyede, M.O., Hancke, G.P. & Myburgh, H.C. A review on face recognition systems: recent approaches and challenges. Multimed Tools Appl 79 , 27891–27922 (2020). https://doi.org/10.1007/s11042-020-09261-2

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Title: videomamba: state space model for efficient video understanding.

Abstract: Addressing the dual challenges of local redundancy and global dependencies in video understanding, this work innovatively adapts the Mamba to the video domain. The proposed VideoMamba overcomes the limitations of existing 3D convolution neural networks and video transformers. Its linear-complexity operator enables efficient long-term modeling, which is crucial for high-resolution long video understanding. Extensive evaluations reveal VideoMamba's four core abilities: (1) Scalability in the visual domain without extensive dataset pretraining, thanks to a novel self-distillation technique; (2) Sensitivity for recognizing short-term actions even with fine-grained motion differences; (3) Superiority in long-term video understanding, showcasing significant advancements over traditional feature-based models; and (4) Compatibility with other modalities, demonstrating robustness in multi-modal contexts. Through these distinct advantages, VideoMamba sets a new benchmark for video understanding, offering a scalable and efficient solution for comprehensive video understanding. All the code and models are available at this https URL .

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    Dr. Xiaoning Jiang Dept. of Mechanical & Aerospace Engineering North Carolina State University Raleigh, NC 27695-7910 USA Phone: 919-515-5240 Email: [email protected]