research paper on multiple intelligences

Multiple Intelligences Theory—Howard Gardner

• First Online: 09 September 2020

Cite this chapter

• Bulent Cavas 3 &
• Pinar Cavas 4  

Part of the book series: Springer Texts in Education ((SPTE))

21k Accesses

8 Citations

Multiple intelligences theory (MI) developed by Howard Gardner, an American psychologist, in late 1970s and early 1980s, asserts that each individual has different learning areas. In his book, Frames of Mind: The Theory of Multiple Intelligences published in 1983, Gardner argued that individuals have eight different intelligence areas and added one more intelligence area in the later years. Howard Gardner named these nine intelligence areas as “musical–rhythmic”, “visual–spatial”, “verbal–linguistic”, “logical–mathematical”, “bodily–kinesthetic”, “interpersonal”, “intrapersonal”, “naturalistic”, and “existential intelligence. Gardner indicates that these intelligences are constructed through the participation of individuals in culturally valued activities, and these activities help individuals to develop unique patterns in their mind. Multiple intelligences theory states that there are many ways to be intelligent not only just two ways measured by IQ tests. Appearance of multiple intelligences theory has provided significant practices and studies particularly in the field of education to be carried out and has changed educators’ views toward the concepts of learning and intelligence. This chapter discusses the historical and theoretical dimensions of multiple intelligences as well as the research conducted on the theory. We have also provided the advantages and disadvantages of MI implementation in science education.

An intelligence is the ability to solve problems, or to create products, that are valued within one or more cultural settings. Howard Gardner — Frames of Mind ( 1983 ).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Interrelation of Multiple Intelligences—An Approach to Enhance Learning

Multiple Intelligences Theory

Students’ multiple intelligences in video-assisted learning environments

Abdi, A., Laei, S., & Ahmadyan, H. (2013). The effect of teaching strategy based on multiple intelligences on students’ academic achievement in science course. Universal Journal of Educational Research, 1 (4), 281–284.

Google Scholar  

Blumenfield-Jones, D. (2009). Bodily-kinesthetic intelligence and dance education: Critique, revision, and potentials for the democratic idea. Journal of Aesthetic Education, 43 (1), 59–76.

Article   Google Scholar  

Campbell, L., & Campbell, B. (1999). Multiple intelligences and student achievement: Success stories from six schools . Alexandria, VA: Association for Supervision and Curriculum Development. Retrieved from https://goo.gl/Y7Cz3w .

Crowther, G. J., Williamson, J. L., Buckland, H. T., & Cunningham, S. L. (2013). Making material more memorable with music. American Biology Teacher, 75, 713–714. https://doi.org/10.1525/abt.2013.75.9.16 .

Davis, K., Christodoulou, J. A., Seider, S., & Gardner, H. (2011). The theory of multiple intelligences. In R. J. Sternberg & S. B. Kaufman (Eds.), Cambridge handbook of intelligence (pp. 485–503). New York: Cambridge University Press.

Chapter   Google Scholar  

Gardner, H. (1983). Frames of mind: The theory of multiple intelligences . New York: Basic Books.

Gardner, H. (1991). The unschooled mind: How children think and how schools should teach. New York, NY: Basic Books.

Gardner, H. (1993). Multiple intelligences: The theory in practice . New York, NY: Basic Books.

Gardner, H. (1999a). Intelligence reframed: Multiple intelligences for the 21st century . New York, NY: Basic Books.

Gardner, H. (1999b). The disciplined mind: What all students should understand . New York: Simon & Schuster.

Gardner, H. (2006). Multiple intelligences: New horizon . New York, NY: Basic Books.

Gardner, H. (2011). The unschooled mind: How children think and how schools should teach . United Kingdom: Hachette.

Gardner, H., & Walters, J. (1993). Questions and answers about multiple intelligences theory. Multiple intelligences: The theory in practice (pp. 35–48). New York: Basic Books.

Goodnough, K. (2001). Multiple intelligences theory: A framework for personalizing science curricula. School Science and Mathematics., 101 (4), 180–193.

Hoerr, R. T. (2000). Becoming a multiple intelligences school. Retrieved from http://www.ascd.org/publications/books/100006.aspx .

Hom, E. (2014). What is STEM education? Retrieved from https://www.livescience.com/43296-what-is-stem-education.html .

Junchun, W. (2015). To explore the relationship of temporal spatial reasoning between music and mathematics by an inventory based on the multiple intelligence theory. Education and Psychological Research, 38 (3). https://doi.org/10.3966/102498852015093803002 .

Klein, P. (1997). Multiplying the problems of intelligence by eight: A critique of Gardner’s theory. Canadian Journal of Education, 22, 377–394.

Lai, H. Y., & Yap, S. L. (2016). Application of multiple intelligence theory in the assessment for learning. In S. Tang & L. Logonnathan (Eds.), Assessment for learning within and beyond the classroom (pp. 427–436). Singapore: Springer.

Martin, M. R., Bishop, J., Ciotto, C., & Gagnon, A. (2014). Teaching the whole child: Using the multiple intelligence theory and interdisciplinary teaching in physical education. In The chronicle of kinesiology in higher education ( Special Edition , pp. 25–29).

Martín, J. S., Gragera, G. J. A., Dávila-Acedo, M., & Mellado, V. (2017). What do K-12 students feel when dealing with technology and engineering issues? Gardner’s multiple intelligence theory implications in technology lessons for motivating engineering vocations at Spanish secondary school. European Journal of Engineering Education, 42 (6), 1330–1343.

Martin, M., & McKenzie, M. (2013). Sport education and multiple intelligences: A path to student success. Strategies, 26 (4), 31–34.

Morgan, H. (1996). An analysis of Gardner’s theory of multiple intelligence. Roeper Review, 18, 263–269.

Ozdermir, P., Güneysu, S., & Tekkaya, C. (2006). Enhancing learning through multiple intelligences. Journal of Biological Education, 40 (2), 74–78.

Richards, D. (2016). The integration of the multiple intelligence theory into the early childhood curriculum. American Journal of Educational Research, 4 (15), 1096–1099. Retrieved from http://pubs.sciepub.com/education/4/15/7 .

Schulkind, M. D. (2009). Is memory for music special? Annals of the New York Academy of Sciences, 1169, 216–224. https://doi.org/10.1111/j.1749-6632.2009.04546.x .

Schwert, A. (2004). Using the theory of multiple intelligences to enhance science education . Unpublished Master’s Thesis. The University of Toledo, USA. Retrieved from http://utdr.utoledo.edu/cgi/viewcontent.cgi?article=1487&context=graduate-projects .

Suprapto, N., Liu, W. Y., & Ku, C. K. (2017). The implementation of multiple intelligence in (science) classroom: From empirical into critical. Pedagogika, 126 (2), 214–227.

Viadero, D. (2003). Staying power. Education Week, 22 (39), 24.

Download references

Author information

Authors and affiliations.

Buca Faculty of Education, Dokuz Eylül University, Izmir, Turkey

Bulent Cavas

Faculty of Education, Ege University, Izmir, Turkey

Pinar Cavas

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Bulent Cavas .

Editor information

Editors and affiliations.

Science Teachers Association of Nigeria, Abuja, Nigeria

University of Texas at Tyler, Tyler, TX, USA

Teresa J. Kennedy

Recommended Resources

Gardner, H. (1999a). Intelligence reframed: Multiple intelligences for the 21st century . New York: Basic Books.

Gardner, H. (2004). Frequently asked questions—Multiple intelligences and related educational topics. Retrieved March 9, 2018, from http://multipleintelligencesoasis.org/wp-content/uploads/2013/06/faq.pdf .

Gardner, H. (2006). Multiple intelligences: New Horizon . New York: Basic Books.

Gardner, H. (2011). The unschooled mind: How children think and how schools should teach . UK: Hachette.

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Cavas, B., Cavas, P. (2020). Multiple Intelligences Theory—Howard Gardner. In: Akpan, B., Kennedy, T.J. (eds) Science Education in Theory and Practice. Springer Texts in Education. Springer, Cham. https://doi.org/10.1007/978-3-030-43620-9_27

Download citation

DOI : https://doi.org/10.1007/978-3-030-43620-9_27

Published : 09 September 2020

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-43619-3

Online ISBN : 978-3-030-43620-9

eBook Packages : Education Education (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Gardner’s multiple intelligences in science learning: A literature review

[email protected]

• Article contents
• Figures & tables
• Supplementary Data
• Peer Review
• Reprints and Permissions
• Cite Icon Cite
• Search Site

Fibriyana Safitri , Dadi Rusdiana , Wawan Setiawan; Gardner’s multiple intelligences in science learning: A literature review. AIP Conf. Proc. 28 April 2023; 2619 (1): 100014. https://doi.org/10.1063/5.0122560

Download citation file:

• Ris (Zotero)
• Reference Manager

The purpose of this article is to review articles related to multiple intelligences in learning and figure out the multiple intelligences approach in learning activities and several bits of intelligence that play an important role in science learning. This paper is the result of a review of 40 articles related to learning activities that use multiple intelligence-based approaches, media, and learning models, published from 2011 to 2021. The multiple intelligences theory was put forward by Howard Gardner, an expert in education and psychology. There are nine types of intelligence based on Gardner’s theory, namely: verbal-linguistic intelligence, visual-spatial intelligence, musical intelligence, logical-mathematics intelligence, interpersonal intelligence, intrapersonal intelligence, bodily-kinesthetic intelligence, naturalist intelligence, and existential intelligence, which have different characteristics. The method used in this study is a systematic literature review with the following stages: determining research questions; determining criteria; generating a framework for articles; searching, filtering, and selecting; analyzing and interpreting the content of each reviewed article; article writing, and publishing. This study discusses Gardner’s multiple intelligence theory, the multiple intelligences approach in learning activities, and the most influential intelligence in science learning. The results show that several bits of intelligence play an important role in science learning.

Sign in via your Institution

Citing articles via, publish with us - request a quote.

Sign up for alerts

• Online ISSN 1551-7616
• Print ISSN 0094-243X
• For Researchers
• For Librarians
• For Advertisers
• Our Publishing Partners  
• Physics Today
• Conference Proceedings
• Special Topics

pubs.aip.org

• Privacy Policy
• Terms of Use

Connect with AIP Publishing

This feature is available to subscribers only.

Sign In or Create an Account

PERSPECTIVE article

Building a functional multiple intelligences theory to advance educational neuroscience.

• Implicit Social Cognition Lab, Department of Psychology, Harvard University, Cambridge, MA, USA

A key goal of educational neuroscience is to conduct constrained experimental research that is theory-driven and yet also clearly related to educators’ complex set of questions and concerns. However, the fields of education, cognitive psychology, and neuroscience use different levels of description to characterize human ability. An important advance in research in educational neuroscience would be the identification of a cognitive and neurocognitive framework at a level of description relatively intuitive to educators. I argue that the theory of multiple intelligences (MI; Gardner, 1983 ), a conception of the mind that motivated a past generation of teachers, may provide such an opportunity. I criticize MI for doing little to clarify for teachers a core misunderstanding, specifically that MI was only an anatomical map of the mind but not a functional theory that detailed how the mind actually processes information. In an attempt to build a “functional MI” theory, I integrate into MI basic principles of cognitive and neural functioning, namely interregional neural facilitation and inhibition. In so doing I hope to forge a path toward constrained experimental research that bears upon teachers’ concerns about teaching and learning.

The nascent field of educational neuroscience challenges scientists to conduct well defined research with relevance to learning processes. However, the fields of education, cognitive psychology, and neuroscience use different levels of description to characterize human ability. In this context it has been relatively difficult to conduct constrained research that remains theory-driven and also maintains its relevance to educators’ complex set of concerns.

To this point, researchers and theorists have set forth broad suggestions about how to build the educational neuroscience community. For example, Fischer et al. (2007) have called for “reciprocal interactions” among neuroscience and education. Researchers have been cautioned to pay more than “lip service” to the different levels of description that characterize the different disciplines comprising educational neuroscience ( Anderson and Reid, 2009 ). Many researchers hope for “bilingual” ( Byrnes and Fox, 1998 ; Mason, 2009 ) or “multilingual” scholars ( Ansari and Coch, 2006 ) engaged in “bidirectional” work ( Ansari, 2005 ; Ansari and Coch, 2006 ). Szúcs and Goswami (2007) state that merely “sending information across bridges is not the answer” and instead the field needs “a new colony of interdisciplinary researchers.”

For the neurocognitive research community, an important step beyond these broad suggestions would be the identification of a cognitive framework at a level of description relatively intuitive to educators. If such a cognitive framework exists, then it may be used to shape educators’ questions and concerns into theory-driven, testable neurocognitive research that may advance the education neuroscience field.

In this paper I will suggest that the theory of multiple intelligences (MI; Gardner, 1983 ), a conception of the mind that motivated a past generation of teachers, may provide cognitive neuroscientists with a framework in which to conduct rigorous educational neuroscience research. However, I will argue that MI prodded teachers to misconstrue the nature of a scientific theory of cognition; teachers took strongly to MI’s value-based claims of the “plurality of intellect,” yet largely failed to recognize that MI did not offer a description of how cognitive processes actually operate nor how an individual child’s mind learns. Finally, I will attempt to integrate into MI basic principles of cognitive and neural functioning, and in so doing I hope to forge a path toward constrained experimental research that bears upon teachers’ concerns about teaching and learning.

Anatomical Model vs. Functional Theory

Gardner (1993) never intended MI to be applied to education. Though it may come as a surprise to many progressive educators, the MI model was created “not as a program for developing a certain kind of mind or nurturing a certain kind of human being,” Gardner (1993) has written, but rather to explain “the evolution and topography of the human mind.” That is, MI was a map of sorts, seeking to explain what the mind consists of, but not how it works. As such, MI did not address issues critical to the practical and applied needs of educators; beyond recognizing that an intelligence merely exists, MI did not characterize how any one intelligence actually operates, how these intelligences functionally interact with one another, nor how best to teach any one intelligence.

If MI does not, in fact, make any claims about how minds operate nor how to nurture them, what then can explain the affinity educators had for MI immediately upon its introduction in 1983? For space considerations, this question is ultimately beyond the scope of the current analysis. Yet an important clue may come in Gardner’s1987 suggestion that the “real point here,” as he wrote a quarter century ago, “is to make the case for the plurality of intellect.” Gardner was motivated by what he saw as a cultural definition of intelligence that was restricted to verbal and logical–mathematical thought alone. If MI did indeed ride a changing socio-cultural wave in a particular era of history, perhaps this explains teachers’ strong attraction to the pluralistic values MI put forth. MI’s crucial contribution, I believe, was to argue convincingly for the value of kinds of intelligence beyond verbal and logical–mathematical.

Though Gardner considers an intelligence to be an information processing capacity ( Gardner and Moran, 2006 ) MI makes no explicit claims about how information is processed. Critically, MI’s value-based claims did not necessarily impact how teachers actually taught nor how they understood the neurocognitive processes of learning. By broadening the definition of intelligence, MI may have been a “catalyzing idea” that “let a hundred flowers bloom” ( Gardner, 1995 ). And yet Gardner (1993 , 1999) himself has noted that in many ways MI resembles a Rorschach test, and he credits a colleague with the observation that “MI is popular because it does not come with directions. Educators can say they have adopted it without doing anything differently” ( Gardner, 2004 ). By Gardner’s ( 1993 , 1999 , 2006 ) own reckoning, MI did not at its inception, and never has, made any claim about the actual workings of intelligences.

Why is this a problem of great importance to teachers? In starkest terms, MI simply does not explain how children’s minds learn. Put differently, positing that eight intelligences exist does not characterize in any way how they process information. For example, MI cannot inform a teacher about whether a child’s mathematical–logical intelligence may be nurtured by employing verbal or spatial or kinesthetic intelligence. A teacher with an affinity for MI may indeed view her many students as each having different – and equally valuable – profiles of intelligences. But, critically, this does not provide the teacher insight about whether a child’s mind may benefit from engaging one “relatively independent” ( Gardner and Moran, 2006 ) intelligence to facilitate learning in another intelligence; if the intelligences are indeed largely independent, it may be extremely difficult and inefficient to use kinesthetic intelligence, for example, in an instructional activity that aims to improve verbal intelligence. The main point is that MI simply was not built to explain how the mind works – or how it learns. Yet such knowledge is at the heart of instructional decisions teachers must make.

To scientists, the most pressing problem with Gardner’s model is equally stark: using classical scientific definitions, the theory of MI is not, in truth, a theory. Scientific theories must make falsifiable predictions about thought and behavior ( Schacter et al., 2011 ). Yet MI “makes no claims” (Gardner, personal communication) about how the mind operates or functions, about whether spatial intelligence supports verbal intelligence, for example. With no specific claims, hypotheses or predictions about cognitive processes to make, constrained experimental research is, simply, impossible. With no experimental research that may prove or disprove it, MI may remain only a “catalyzing idea” ( Gardner, 1995 ), though one that I believe had a profound effect on our culture’s views of intelligence and children. Regardless, from a scientific perspective, MI is not a theory. While this claim may appear abstract and of little practical consequence, it is central to my analysis about how to develop MI such that it becomes a proper scientific theory, one that both is generative for the educational neuroscience community and also one that informs teachers’ understanding of learning processes and drives principled, theory-driven instructional decisions.

In sum, two qualitatively distinct propositions have been tremendously conflated in understandings of MI, I argue, a condition that has plagued applications of MI since its inception. On the one hand is a values-based claim, which advocates for making greater efforts to reach the variety of students with different profiles of intelligences that inevitably comprise any teacher’s classroom. And, yet, on the other hand is the need for scientifically and empirically derived claims about how the child’s mind learns. The following distinction is crucial: assigning value to the different intelligences different students exhibit is fundamentally and qualitatively distinct from the scientific enterprise of characterizing how those intelligences work. Teachers may value all kinds of intelligences; knowing how to teach to and develop them is an entirely different, and critical, endeavor.

In this analysis I define MI as a limited “anatomical” map, reflecting Gardner’s ( 1993 ) sense that MI was intended to describe the “topography” of the mind. I have belabored the point that MI describes the existence – but not the function – of MI within the mind. In making this distinction I hope to clarify misunderstandings about MI and identify the limits of its scientific reach. Yet in doing so, I hope to advance an argument for how MI may be a suitable framework in which to integrate teachers’ questions and concerns with the experimental research methods of cognitive neuroscience.

Cognitive Psychology: Building a Functional Theory of Multiple Intelligences

The functional MI(fMI) theory I propose focuses on neurocognitive connectivity. A functional theory will build upon an anatomical or structural map by characterizing the patterns of connectivity among relatively autonomous intelligences.

The fMI theory makes two conceptual moves, one qualitative and one quantitative. First, I ask about the quality, or nature, of the interactions between intelligences: Are the interactions between any two intelligences facilitatory, inhibitory, or neutral? Second, I ask about the quantity, or strength, of interactions across intelligences: Is one intelligence relatively more strongly or weakly connected to other intelligences? These questions are of practical consequence for effective classroom instruction that is focused on how minds learn.

If each intelligence is “relatively independent yet interacting” ( Gardner and Moran, 2006 ) and subserved by specific neurological structures ( Gardner, 1998 ), then a functional theory would predict that any two intelligences can interact in one of three basic ways. In lay terms we would say they may work together, compete, or be indifferent to each other. I will use the terms facilitation and inhibition to describe the former two, indicating that one intelligence can improve the functioning of another, or that one intelligence can impair another. In neurological terms we know that, on the very short timescale at which neurons operate, any two brain regions may be connected such that when one region activates it sends an electrical projection to another region that can excite those downstream neurons, or instead can inhibit, or reduce, the electrical firing of those neurons.

What might be the utility of a functional MI theory centered on a facilitation–inhibition connectivity paradigm? In short, it will help us predict whether an instructional activity largely employing one intelligence is likely to improve, or instead impair, ability in another intelligence. For example, research on the phenomenon known as verbal overshadowing ( Schooler and Engstler-Schooler, 1990 ) has shown that people asked to speak about non-verbal experiences (e.g., face recognition or emotions) often perform more poorly on subsequent tests of memory or analysis [for a review, see Cerruti and Wilkey (2011) ]. In one study, young children asked to speak about emotions after watching an emotionally disturbing video performed more poorly on a subsequent learning task compared to a control group ( Rice et al., 2007 ). Given especially how much classroom instruction is verbal, teachers will benefit from understanding when employing verbal cognitive processes helps, and when it hinders, the operations of other cognitive processes. In my experience as a middle school teacher over a decade, I observed that teachers very largely assumed that intelligences facilitate one another, but they did not recognize the real possibility that activity in one part of the mind can in fact inhibit another.

A functional MI theory can also help frame functional and structural neurocognitive experiments. Functional magnetic resonance imaging (fMRI) studies can compare activity in occipital regions of the brain dedicated to visualization when a child verbalizes about a visual geometry problem to a no-verbalization condition. Studies of brain structures may avail themselves of a technology such as diffusion tensor imaging (DTI), which measures fractional anisotropy (FA), thought to be a correlate of the extent of myelination in a region and thus an indicator of speed and efficiency of neural connectivity between two brain regions. Higher FA between two regions known to instantiate the core operations of different intelligences would indicate that those intelligences interact relatively strongly. Then, fMRI studies would need to determine whether these interactions are more facilitatory or more inhibitory.

An fMI theory is very well suited to instructional intervention and longitudinal studies. fMRI and DTI may assess changes in response to an intervention in regional activity, functional connectivity, and FA. For example, these technologies can assess whether intense musical training affects activity in core areas that instantiate numerical cognition, as well as myelination between these areas and core musical brain regions.

Moreover, newer technologies may be of potentially great value for examinations of facilitatory and inhibitory connectivity. Transcranial direct current stimulation (tDCS) sends a very mild electrical current between two electrodes placed on the scalp. Depending on where the electrodes are located, different underlying brain regions will be affected, and in this way specific aspects of cognition can be targeted. In my own work I have found intriguing effects, both facilitatory and inhibitory. For example, anodal stimulation, which increases the propensity for neural firing in the affected region, of left prefrontal cortex improved performance on a verbal task with a high working memory load ( Cerruti and Schlaug, 2008 ). In another study, cathodal stimulation, which blocks or inhibits regional activity, of Broca’s right-hemisphere homolog in fact improved performance on a task of verbal semantic categorization ( Cerruti, 2010 ). Because verbal ability presumably depends relatively strongly on the left hemisphere, this was interpreted as a disinhibition effect: decreased activity in Broca’s right-hemishphere homolog also decreased interhemispheric inhibitory projections, thus permitting increased activity in Broca’s. Studies such as this one reveal the complex functional interconnectivity among the multiple regions of the brain that are invariably involved in complex cognition.

The purpose of a functional theory of MI is to describe how the mind works. The MI framework was not created with the intention of applying it to education ( Gardner, 2006 ), yet educators took strongly to it. In turn, Gardner (1987 , 1991) soon took to advocating for MI-inspired environments in schools. In such environments, MI encourages teachers to value and encourage intelligences other than verbal and mathematical. However, MI is incapable of informing teachers about how the individual child’s mind processes information or learns new information.

My analysis has not questioned the anatomical basis of the MI framework. In fact I take as my starting point MI’s assumption that the brain is home to relatively autonomous information processing modules. My approach aims only to detail the interactions of cognitive information processing mechanisms. Such an approach owes much to experimental psychology and neurology, fields that have often been critical of MI ( Kornhaber and Gardner, 2006 ).

My core intention is plain: to advance the utility of MI to both teachers and researchers by building a functional theory of MI. I have argued that as the field of educational neuroscience grows MI may be a particularly useful foundation upon which to build a proper scientific theory of neurocognitive learning processes – one that is at a level of description teachers find to be fairly intuitive. For researchers, a functional theory will help organize experimental research in mind, brain, and education, three disciplines that examine cognition and behavior at different levels of description. For teachers, specification of the functional properties of intelligences will help guide instructional decisions about how a child’s mind learns.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Anderson, M., and Reid, C. (2009). Don’t forget about levels of explanation. Cortex 45, 560–561. doi: 10.1016/j.cortex.2008.06.005

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

Ansari, D. (2005). Paving the way towards meaningful interactions between neuroscience and education. Dev. Sci. 8, 466–467. doi: 10.1111/j.1467-7687.2005.00435.x

Ansari, D., and Coch, D. (2006). Bridges over troubled waters: education and cognitive neuroscience. Trends Cogn. Sci. 10, 146–151. doi: 10.1016/j.tics.2006.02.007

Byrnes, J. P., and Fox, N. A. (1998). The educational relevance of research in cognitive neuroscience. Educ. Psychol. Rev. 10, 297–342. doi: 10.1023/A:1022145812276

CrossRef Full Text

Cerruti, C. (2010). Non-invasive Modulation of Brain Activity: A New Bridge for Mind-Brain-Education Research . Harvard University, Cambridge.

Cerruti, C., and Schlaug, G. (2008). Anodal transcranial direct current stimulation of the prefrontal cortex enhances complex verbal associative thought. J. Cogn. Neurosci. 21, 1980–1987. doi: 10.1162/jocn.2008.21143

Cerruti, C., and Wilkey, E. (2011). “Verbal overshadowing and verbal facilitation in creative cognition,” in Perspectives on Creativity, Vol. 2, ed. L. Della Pietra (Newcastle upon Tyne: Cambridge Scholars).

Fischer, K. W., Daniel, D. B., Immordino-Yang, M. H., Stern, E., Battro, A., Koizumi, H., et al. (2007). Why mind, brain, and education? Why now? Mind Brain Educ. 1, 1–2. doi: 10.1111/j.1751-228X.2007.00006.x

Gardner, H. (1983). Frames of Mind : The Theory of Multiple Intelligences. New York: Basic Books.

Gardner, H. (1987). Beyond the IQ: education and human development. Harv. Educ. Rev. 57, 187–193.

Gardner, H. (1991). The Unschooled Mind . New York: BasicBooks.

Gardner, H. (1993). Multiple Intelligences: The Theory in Practice . New York: Basic Books.

Gardner, H. (1995). Reflections on multiple intelligences: myths and messages. Phi Delta Kappan 77, 200–209.

Gardner, H. (1998). A reply to Perry D. Klein’s “Multiplying the Problems of Intelligence by Eight”. Can. J. Educ. 23, 96–102. doi: 10.2307/1585968

Gardner, H. (1999). Intelligence Reframed: Multiple Intelligences for the 21st Century . New York: Basic Books.

Gardner, H. (2004). “Foreword,” in Multiple Intelligences: Best Ideas from Research and Practice , eds M. L. Kornhaber, E. Fierros, and S. Veenema (Boston: Pearson).

Gardner, H. (2006). Multiple Intelligences: New Horizons . New York: BasicBooks.

Gardner, H., and Moran, S. (2006). The science of multiple intelligences theory: a response to Lynn Waterhouse. Educ. Psychol . 41, 227–232.

Kornhaber, M. L., and Gardner, H. (2006). “Multiple intelligences: developments in implementation and theory,” in Translating Theory and Research into Educational Practice , eds M. A. Constas and R. J. Sternberg, (Mahwah, NJ: Lawrence Erlbaum), 255–276.

Mason, L. (2009). Bridging neuroscience and education: a two-way path is possible. Cortex 45, 548–549. doi: 10.1016/j.cortex.2008.06.003

Rice, J. A., Levine, L. J., and Pizarro, D. A. (2007). “Just stop thinking about it”: effects of emotional disengagement on children’s memory for educational material. Emotion 7, 812–823. doi: 10.1037/1528-3542.7.4.812

Schacter, D. L., Gilbert, D. T., and Wegner, D. M. (2011). Psychology . 2nd Edn, New York: Worth Publishers.

Schooler, J. W., and Engstler-Schooler, T. Y. (1990). Verbal overshadowing of visual memories: some things are better left unsaid. Cognit. Psychol. 22, 36–71. doi: 10.1016/0010-0285(90)90003-M

Szúcs, D., and Goswami, U. (2007). Educational neuroscience: defining a new discipline for the study of mental representations. Mind Brain Educ. 1, 114–127. doi: 10.1111/j.1751-228X.2007.00012.x

Keywords : functional multiple intelligences, fMI, multiple intelligences, learning, education, cognitive inhibition, educational neuroscience

Citation: Cerruti C (2013) Building a functional multiple intelligences theory to advance educational neuroscience. Front. Psychol. 4 :950. doi: 10.3389/fpsyg.2013.00950

Received: 05 February 2013; Accepted: 02 December 2013; Published online: 19 December 2013.

Reviewed by:

Copyright © 2013 Cerruti. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Carlo Cerruti, Implicit Social Cognition Lab, Department of Psychology, Harvard University, William James Hall, Cambridge, MA 02138, USA e-mail: Carlo_Cerruti@ mail.harvard.edu

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Our Mission

Multiple Intelligences: What Does the Research Say?

Proposed by Howard Gardner in 1983, the theory of multiple intelligences has revolutionized how we understand intelligence. Learn more about the research behind his theory.

Many educators have had the experience of not being able to reach some students until presenting the information in a completely different way or providing new options for student expression. Perhaps it was a student who struggled with writing until the teacher provided the option to create a graphic story, which blossomed into a beautiful and complex narrative. Or maybe it was a student who just couldn't seem to grasp fractions, until he created them by separating oranges into slices.

Because of these kinds of experiences, the theory of multiple intelligences resonates with many educators. It supports what we all know to be true: A one-size-fits-all approach to education will invariably leave some students behind. However, the theory is also often misunderstood, which can lead to it being used interchangeably with learning styles  or applying it in ways that can limit student potential. While the theory of multiple intelligences is a powerful way to think about learning, it’s also important to understand the research that supports it.

Howard Gardner's Eight Intelligences

The theory of multiple intelligences challenges the idea of a single IQ, where human beings have one central "computer" where intelligence is housed. Howard Gardner, the Harvard professor who originally proposed the theory, says that there are multiple types of human intelligence, each representing different ways of processing information:

• Verbal-linguistic intelligence refers to an individual's ability to analyze information and produce work that involves oral and written language, such as speeches, books, and emails.
• Logical-mathematical intelligence describes the ability to develop equations and proofs, make calculations, and solve abstract problems.
• Visual-spatial intelligence allows people to comprehend maps and other types of graphical information.
• Musical intelligence enables individuals to produce and make meaning of different types of sound.
• Naturalistic intelligence refers to the ability to identify and distinguish among different types of plants, animals, and weather formations found in the natural world.
• Bodily-kinesthetic intelligence entails using one's own body to create products or solve problems.
• Interpersonal intelligence reflects an ability to recognize and understand other people's moods, desires, motivations, and intentions.
• Intrapersonal intelligence refers to people's ability to recognize and assess those same characteristics within themselves.

The Difference Between Multiple Intelligences and Learning Styles

One common misconception about multiple intelligences is that it means the same thing as learning styles. Instead, multiple intelligences represents different intellectual abilities. Learning styles, according to Howard Gardner, are the ways in which an individual approaches a range of tasks. They have been categorized in a number of different ways -- visual, auditory, and kinesthetic, impulsive and reflective, right brain and left brain, etc. Gardner argues that the idea of learning styles does not contain clear criteria for how one would define a learning style, where the style comes, and how it can be recognized and assessed. He phrases the idea of learning styles as "a hypothesis of how an individual approaches a range of materials."

Everyone has all eight types of the intelligences listed above at varying levels of aptitude -- perhaps even more that are still undiscovered -- and all learning experiences do not have to relate to a person's strongest area of intelligence. For example, if someone is skilled at learning new languages, it doesn’t necessarily mean that they prefer to learn through lectures. Someone with high visual-spatial intelligence, such as a skilled painter, may still benefit from using rhymes to remember information. Learning is fluid and complex, and it’s important to avoid labeling students as one type of learner. As Gardner states, "When one has a thorough understanding of a topic, one can typically think of it in several ways."

What Multiple Intelligences Theory Can Teach Us

While additional research is still needed to determine the best measures for assessing and supporting a range of intelligences in schools, the theory has provided opportunities to broaden definitions of intelligence. As an educator, it is useful to think about the different ways that information can be presented. However, it is critical to not classify students as being specific types of learners nor as having an innate or fixed type of intelligence.

Practices Supported by Research

Having an understanding of different teaching approaches from which we all can learn, as well as a toolbox with a variety of ways to present content to students, is valuable for increasing the accessibility of learning experiences for all students. To develop this toolbox, it is especially important to gather ongoing information about student strengths and challenges as well as their developing interests and activities they dislike. Providing different contexts for students and engaging a variety of their senses -- for example, learning about fractions through musical notes, flower petals, and poetic meter -- is supported by research. Specifically:

• Providing students with multiple ways to access content improves learning (Hattie, 2011).
• Providing students with multiple ways to demonstrate knowledge and skills increases engagement and learning, and provides teachers with more accurate understanding of students' knowledge and skills (Darling-Hammond, 2010).
• Instruction should be informed as much as possible by detailed knowledge about students' specific strengths, needs, and areas for growth (Tomlinson, 2014).

As our insatiable curiosity about the learning process persists and studies continue to evolve, scientific research may emerge that further elaborates on multiple intelligences, learning styles, or perhaps another theory. To learn more about the scientific research on student learning, visit our Brain-Based Learning topic page .

Darling-Hammond, L. (2010). Performance Counts: Assessment Systems that Support High-Quality Learning . Washington, DC: Council of Chief State School Officers.

Hattie, J. (2011). Visible Learning for Teachers: Maximizing Impact on Learning . New York, NY: Routledge.

Tomlinson, C. A. (2014). The Differentiated Classroom: Responding to the Needs of All Learners . Alexandria, VA: ASCD.

Resources From Edutopia

• Are Learning Styles Real - and Useful? , by Todd Finley (2015)
• Assistive Technology: Resource Roundup , by Edutopia Staff (2014)
• How Learning Profiles Can Strengthen Your Teaching , by John McCarthy (2014)
• An Interview with the Father of Multiple Intelligences , by Owen Edwards (2009)

Additional Resources on the Web

• Howard Gardner’s website
• Howard Gardner: ‘Multiple intelligences’ are not ‘learning styles’ (The Washington Post, 2013)
• Books published by Howard Gardner
• Multiple Intelligences Resources (ASCD)
• Project Zero (Harvard Graduate School of Education)
• Multiple Intelligences Research Study (MIRS)
• Multiple Intelligences Lesson Plan (Discovery Education)
• Multiple Intelligences Resources (New Horizons for Learning [NHFL], John Hopkins University)

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

Types of Intelligence and Academic Performance: A Systematic Review and Meta-Analysis

Raquel lozano-blasco.

1 Department of Psychology and Sociology, Faculty of Humanities and Science Education, University of Zaragoza, 50001 Zaragoza, Spain

Alberto Quílez-Robres

2 Department of Educational Sciences, Faculty of Human Sciences and Education, University of Zaragoza, 22003 Huesca, Spain

Pablo Usán

3 Department of Psychology and Sociology, Faculty of Education, University of Zaragoza, 50009 Zaragoza, Spain

Carlos Salavera

4 Department of Educational Sciences, Faculty of Humanities and Science Education, University of Zaragoza, 50001 Zaragoza, Spain

Raquel Casanovas-López

5 Instituto de Estudios Interdisciplinarios de la Niñez y la Adolescencia (INEINA), Universidad Nacional, Heredia 40101, Costa Rica

The concept of intelligence has been extensively studied, undergoing an evolution from a unitary concept to a more elaborate and complex multidimensional one. In addition, several research studies have focused their efforts for decades on the study of intelligence as a predictor of academic performance of students at different educational stages, being a stable and highly relevant predictor along with other variables such as executive functions, social context, culture or parental guardianship. Thus, the present study, based on a systematic review and meta-analysis, includes 27 studies with a total sample of 42,061 individuals. The main objective was to analyse the relationship between intelligence and academic performance using different predictive models that include moderating variables such as country of origin, type of intelligence, gender and age. The findings of this research highlight the significant, positive and moderate relationship between intelligence and academic performance (r = 0.367; p < 0.001), highlighting the predictive capacity on school performance when the type of intelligence (general and implicit; 35%) or the country of origin (45%) is taken as a moderating variable, with the explanatory models on age or sex not being significant. Therefore, it can be concluded that intelligence, in addition to being a good predictor of academic performance, is influenced depending on the type of intelligence or theoretical model taken as a reference, and also depending on the country or culture of origin.

1. Introduction

The educational community has traditionally and extensively studied academic performance. This concept is closely related to the teaching–learning process focused on a specific goal: achievement in school ( Von Stumm and Ackerman 2013 ). Therefore, issues such as school success or failure, discouragement and dropout have produced a great deal of research ( Balkis 2018 ). Proof of this would be the study by Nieto Martín ( 2008 ), who reviewed 654 studies conducted between 1970 and 1990. The author stresses that the variables under study and related to academic success have changed over time; for example, intelligence was traditionally studied from a single-factor point of view, but later this approach was expanded and, at present, other variables such as executive functions, motivation or self-esteem and self-efficacy are at the forefront of the study. In addition, the new century has seen the emergence of new methodological variables such as group collaboration, collaborative work, project-based learning and the length of the school day. The literature has traditionally categorised these variables as contextual or personal: socio-environmental variables (family, friends, colleagues), institutional variables (school, school organisation, teachers) and instructional variables (content, methods, tasks). In addition, another group included are cognitive (intelligence, learning styles) and motivational (self-image, goals, values) variables ( Quílez-Robres et al. 2021b ). Therefore, academic performance can be understood as a construct that includes quantitative and qualitative values (quantitative if we talk about numerical measurement, test results and qualitative if we talk about the development of skills, values and competences), related to knowledge, attitudes and values developed by the student in the teaching–learning process” ( Navarro 2003, pp. 15–16 ).

However, this study focuses on the relationship and explanation of academic performance through intelligence, understood as different types of intelligence. There is extensive literature on the relationship, prediction and explanation of intelligence with academic performance. However, studies concerning intelligence have expanded conventional psychometric notions by introducing modalities such as crystallised intelligence, fluid intelligence, emotional intelligence, multiple intelligences, etc., in an attempt to provide greater predictive validity in reference to academic achievement ( Sternberg 2019 ).

The conceptual definitions of intelligence are diverse. For ( Quílez-Robres et al. 2021a ) it consists of the ability to understand and adapt to solve everyday problems. On the other hand, for Plomin and Deary ( 2015 ), intelligence has a biological substrate that varies according to individuals and cultures, being the potential that facilitates learning, planning, reasoning, adaptation and decision-making.

Catell ( 1963 ) differentiated between two kinds of intelligence: fluid intelligence and crystallised intelligence and suggested that intelligence is composed of different capacities that form general intelligence and that are complementary. Crystallised intelligence is the result of education and culture and therefore depends directly on the individual’s prior knowledge and ability to learn ( Nisbett et al. 2012 ) and fluid intelligence with a genetic component is the ability to solve problems through non-verbal abstract reasoning and adaptation to different contexts ( Nisbett et al. 2012 ). In addition, it is linked to individual learning and memory ( Amin et al. 2015 ). The latter is considered as one of the main predictors of individual academic achievement according to several studies in different settings ( Deary et al. 2007 ; Geary 2011 ; Laidra et al. 2007 ; Monir et al. 2016 ; Verbitskaya et al. 2020 ). Rabbitt and Lowe ( 2000 ) suggest that fluid intelligence is altered in the ageing process, while crystallised intelligence remains stable.

The study of intelligence expanded, and Gardner ( 1985 ) proposed an alternative and a critique of the general intelligence approach by elaborating the theory of multiple intelligences. He proposed the existence of several independent intelligences that interact and mutually enhance each other, such as linguistic, logical–mathematical, spatial, kinaesthetic bodily and others. Thus, most students possess more than one. However, Singh et al. ( 2017 ) report that only logical–mathematical, spatial and musical intelligence are related to IQ. These results are consistent with the research of Castejon et al. ( 2010 ) and Visser et al. ( 2006 ), who reported a strong relationship between cognitive component intelligences and general intelligence.

Sternberg ( 1985 ) elaborated the Triarchic Theory of intelligence, establishing three categories within it: competency, experiential and contextual. Thus, the acquisition and storage of information, the ability to encode, combine and compare that information and finally the adaptation of information to context were involved. He expanded on this theory and called it successful intelligence, which combined ability, exploitation, adaptation, creativity, etc. It is about being able to solve problems, and depending on the way it is done, analytical intelligence (both familiar and abstract problems), creative intelligence (formulating ideas, problems of a novel nature) and practical intelligence (applying ideas and analysis effectively) will appear ( Sternberg et al. 2010 ).

The theories of Gardner ( 1985 ) and Sternberg ( 1985 ) were fundamental for the emergence of the theory of emotional intelligence since these ideas underlay the new concept that would germinate in the theories of Salovey and Mayer ( 1990 ), but it would be Goleman ( 1996 ) who popularised it by stating that emotional intelligence consists of a series of skills such as discovering, recognising and managing emotions and feelings ( Goleman 1999 ). The relevant role of emotional aspects in academic results is evident in previous studies such as the meta-analysis conducted by Molero-Puertas et al. ( 2020 ), which concludes with a significant effect size between emotional intelligence and academic performance. Other research suggests that this variable is a good predictor of academic achievement at different educational stages and even indicates that it is second only to general intelligence ( MacCann et al. 2020 ; Perera and DiGiacomo 2013 ; Sanchez-Ruiz et al. 2013 ).

Finally, implicit intelligence, regarded as the self-perception of intelligence grounded in everyday experience, is a key variable for understanding academic performance ( Enea-Drapeau et al. 2017 ). Since this includes a component of expectation as cognitive self-representation, some authors point out that the relationship is especially direct in the early years and concerning specific performance areas rather than global performance ( Dinger et al. 2013 ; Geary 2011 ; Priess-Groben and Hyde 2017 ; Wigfield et al. 2016 ); other authors argue that implicit intelligence is a good predictor for academic performance in maths ( Kriegbaum et al. 2015 ; Steinmayr and Spinath 2009 ); for Steinmayr et al. ( 2019a ) and Lotz et al. ( 2018 ), this predictive value extends over all areas, as confidence in one’s own abilities can be a more important variable than cognitive abilities in the analysis of academic performance. In this sense, implicit theories are presented as definitions, or theories that scientists have about some phenomena ( Sternberg 1985 ). Precisely in these beliefs lies the importance of understanding people’s implicit theories. This is important because these beliefs guide people’s attitudes and behaviours and, as discussed in various theories of the development of talent and intelligence, intelligence is not composed of a single factor but is multidimensional, with contextual, creative and motivational aspects related to people’s behaviours intervening in its conception. The theory of social cognition indicates that beliefs determine attitudes and willingness to engage in certain behaviours ( Pintrich 2002 ). Undoubtedly, these aspects mean that implicit intelligence must be taken into account in relation to academic performance and learning.

With regard to academic performance, its prediction has been a relevant topic for a long time and different variables have been analysed to help explain the academic results of schoolchildren. Different research has related it to individual characteristics of basic cognitive processes such as processing speed, working memory, fluid intelligence, etc. ( El Jaziz et al. 2020 ; Kiuru et al. 2012 ; Kuncel et al. 2004 ; Richardson et al. 2012 ; Sternberg et al. 2001 ). However, academic performance as a product of learning serves as an indicator of the level of learning ( Alquichire R and Arrieta R 2018 ). For Ariza et al. ( 2018 ), it is nothing more than a measure of what students have learned as a result of an educational process. He defines it as the ability to respond to a series of educational stimuli, which in turn is interpreted on the basis of the established objectives.

In view of previous research, it is not new that measures of general intelligence predict academic performance ( Deary et al. 2007 ; Quílez-Robres et al. 2021b ; Sternberg 2019 ). Systematic study has resulted in the predictive value of intelligence in the educational world and has pointed to significant correlations with different variables, but there is also some variation depending on the educational stage analysed ( Sternberg et al. 2001 ). While agreeing that intelligence is one of the most important variables in academic performance as it has a direct impact on learning ( González et al. 2008 ), it should be noted that it does not behave uniformly, as the correlation between intelligence and academic performance decreases when the student reaches the university stage ( Ren et al. 2015 ).

If the aim is to increase the predictive value of the different measures of intelligence, one possibility is to broaden the concept of intelligence itself. A review of the scientific literature shows that there are no studies that integrate the different types of intelligence theorised in reference to academic achievement. This meta-analysis aims to analyse the relationship between different types of intelligence and academic performance from a meta-analytical perspective by reviewing the scientific literature with a broad conception of the concept of intelligence, taking into account the studies that indicate that there is no single way of understanding and defining this construct.

A research registry protocol ( Figure 1 ) was established following the Cochrane systematic review manual in Higgins and Green ( 2011 ) and PRISMA ( 2015 ). Inclusion criteria were determined using the specifications set out by Ausina and Sánchez-Meca ( 2015 ) and Moreau and Gamble ( 2020 ): (a) Research methodology: quantitative, correlational, longitudinal, cross-sectional and clinical. (b) Time frame: 2000–2020. (c) Methodological rigour: studies indexed in prestigious rankings (Scimago Journal and Country Rank). (d) Measuring instruments: psychometric tests rated in academic publications and in accordance with the culture of the sample. (e) Language: English.

Flowchart of search methodology.

The exclusion criteria were established according to the manuals of Ausina and Sánchez-Meca ( 2015 ) and Moreau and Gamble ( 2020 ): (a) Adult population with previous disorders or pathologies, including, however, research in which there were control groups without pathologies. (b) The appearance of imprecise, poorly defined data, unclear methodology or with indications of non-compliance with ethical principles, as well as statistical or psychometric errors in the measurement of the tests, following the indications of Hunter and Schmidt ( 2004 ) and Friese and Frankenbach ( 2020 ).

The search strategy was carried out using the criteria of Botella and Gambara ( 2002 ), Ausina and Sánchez-Meca ( 2015 ) and PRISMA ( 2015 ). Three databases were used: Psycoinfo, Pubmed and Science Direct, and research was performed in February 2021. The Boleean action was “academic achievement” and “intelligence” in the range 2000–2020.

Eligibility criteria for sample selection were defined according to the Cochrane systematic review manual in Higgins and Green ( 2011 ) and PRISMA ( 2015 ). It should be noted that manual coding was carried out by reviewing each article returned by Boolean actions according to the inclusion and exclusion criteria. Firstly, the abstract was screened so that only those that dealt with the subject of the study were selected. On the other hand, the criteria of methodological rigour and measurement instruments led to the exclusion of a significant percentage of the research. This was due to the absence of standardised instruments or the incorrect measurement of the study parameters according to the pre-established psychometric test.

The transformation of all means to Fisher Z ( Martín-Andrés and Luna del Castillo 2004 ), the execution of the relevant analyses (model comparison and meta-regression), the study of heterogeneity, the performance of the Eggers test for publication bias and the obtaining of figures were carried out using the CMA statistical software.

3.1. Demographic Description

In recent years (2000–2020), the relationship between types of intelligence and academic performance at different educational stages has been studied in depth. In total, the meta-analysis ( Table 1 ) consists of 27 studies with k = 47 samples from Europe, Asia, Africa, America and Oceania. According to Bonett ’s ( 2006 ) criteria, the sample k = 47 exceeds the minimum required to avoid distortion of the upper confidence limit. On the other hand, heterogeneity is evident in the sample sizes, with the smallest sample size being 81 participants and the largest 4036 participants.

Sociodemographic data.

The total sample is made up of 42,061 participants, 47.16% of whom are male and 48.99% female. In this sense, it is necessary to clarify that the two studies do not provide data on the sex of their participants. The average age of the participants is 16.45 years, although some studies did not report a specific average age, but rather a range of years or school years, making it necessary to take the arithmetic mean to be able to manage the data quantitatively.

In terms of culture, social anthropology points to the need to attend to cultural diversity ( Molano 2007 ). In this study, 30.13% are Asian (China, Indonesia and Malaysia), 4.73% are Central European (Germany, UK), 37.37% are Eastern European (Russia), 6.01% are Northern European (Norway and Finland), 2.32% are North African (Morocco and Egypt), 18.83% are American (USA and Barbados) and 0.57% are from Oceania (Australia).

3.2. Statistical Analysis

The aim of this meta-analysis is to study the relationship between type of intelligence and student achievement, but encompassing different educational stages and different contexts. To this end, 108 effect sizes were coded, taking as a reference the data based on Pearson’s r and their subsequent treatment using the CMA statistical programme.

Figure 2 (forest plot) shows the effect size with a 99% confidence interval (0.302–0.428, p = 0.001) for the different studies, the effect size being r = 0.367, p = 0.001. In other words, a moderate level of correlation is obtained according to Cohen between the intelligence presented by the students and academic performance. The ethical criteria set out by Moreau and Gamble ( 2020 ) are followed when exposing all the conversions, opting for a policy of “open materials”.

Forest Plot.

On the other hand, it is crucial to study the heterogeneity of the sample according to Cochrane in Higgins and Green ( 2011 ). The Q statistic of Dersimonian and Laird ( 1986 ) (Q = 2478.71, df = 46, p < 0.0001) describes a high variability, i.e., the homogeneity hypothesis is rejected. The statistic I2 = 98.144% explains the percentage of variability resulting from heterogeneity and not from chance. In other words, the sample is highly heterogeneous in its statistical nature ( Higgins et al. 2013 ). Consistently, the Random model or random effects model is applied ( Bonett 2006 ; Martín-Andrés and Luna del Castillo 2004 ). Although the inclusion and exclusion criteria contemplate the reliability and methodological and psychometric quality of the research, the Egg’s test with 99% reliability was carried out to study the effect of bias ( Botella and Gambara 2002 ; Ausina and Sánchez-Meca 2015 ). The results of the test show the inexistence of publication bias with a 99% confidence interval ( p -value 1 tailed = 0.07; p -value 2 tailed = 0.15) ( Egger et al. 1997 ). The standard error value (SE = 2.04) reaffirms the absence of bias, as it is very close to the regression line ( Martín-Andrés and Luna del Castillo 2004 ).

The diversity shown in the Q and I2 statistics could be a sign of extreme data; however, the tight confidence interval (0.302–0.428, p = 0.001) limits this heterogeneity. These results are consistent with the Funnel Plot graph ( Figure 3 ) where the variability and heterogeneity of the sample are reaffirmed. This situation reiterates the diversity of studies, as concluded by the Egger test, without any bias effect. However, it should be noted that the apparent variability could be affected by the transformation to Fisher Z -values since x-values >0.5 tend to be more distorted on the T-Student curve than, in comparison, on the normal curve, although this transformation is accepted by the scientific community for meta-analysis methodology ( Martín-Andrés and Luna del Castillo 2004 ).

Funnel plot and standard error.

3.3. Moderating Variables and Meta-Regression Analysis

The state-of-the-art research shows the existence of moderating factors, which is why it is considered necessary to establish the study of seven moderating variables: type of intelligence, type of performance, age, country, male sex, female sex and geographical distribution. The objective pursued through the use of both techniques is to statistically determine the reason for such heterogeneity ( Ausina and Sánchez-Meca 2015 ; Jak and Cheung 2020 ). In this way, a comparison of models is established (see Table 2 ) by generating seven models: (1) type of intelligence; (2) type of performance; (3) age; (4) country; (5) male gender; (6) female gender; (7) geographical distribution.

Comparison of models: Random effects (MM), Z distribution, Fisher’s Z.

The first model, which specifies the type of intelligence, explains 35% of academic performance, with an efficiency level of over 99%, although, as model 2 shows, the type of performance has no effect. In other words, it is intelligence that determines student academic performance and success, but doing well in these subjects does not seem to affect intelligence overall. On the other hand, the models of age, gender and geographical distribution do not explain the relationship between the two factors to any extent. However, there are important differences between countries, which may be explained by diversity in the education system. Association with a given nation accounts for 45% of the variability in the sample ( Table 2 ).

It is therefore necessary to study in greater depth the type of intelligence that seems to determine academic performance. For this reason, a meta-regression ( Table 3 ) is carried out in which it is evident that general intelligence (Z = 2.00, p = 0.04) and implicit intelligence (Z = 3.69, p = 0.00) are the ones that stand out, showing a clear difference.

Meta-regression of model 1: Intelligence.

As far as the different countries are concerned, significant differences are found in the comparison models. Therefore, it is necessary to perform a meta-regression (see Table 4 ) that points out the differences between education systems. In this case, the countries that differ from the sample are China, Indonesia and the UK (United Kingdom).

Meta-regression of model 2: countries.

4. Discussion

Given that the review of the scientific literature indicates that there is no single way of understanding, defining and analysing the construct of intelligence, this meta-analysis analyses the relationship between intelligence and academic performance in terms of the different types of intelligence studied in previous research, as well as the existence of models of moderating variables that clarify their predictive nature. Therefore, effect size, type of intelligence (general, crystallised, fluid, implicit, emotional, etc.), age, gender, country of residence or geographical area are of interest for this study. Of all these variables, effect size, general intelligence, implicit intelligence (R 2 = 0.35; p < 0.001) and country of residence (R 2 = 0.45; p < 0.001) are those that appear to be relevant and significant.

From the results obtained, a number of factors stand out, such as the relationship between academic performance and intelligence with a moderate effect size (0.367; significance < 0.001). Previous research addressing the interrelations between intelligence and academic performance indicates that it is the most stable and powerful predictor of school performance (r = 0.5) ( Geary 2011 ; Laidra et al. 2007 ; Luo et al. 2006 ; Rodic et al. 2015 ). These results are corroborated in the meta-analysis of Cortés Pascual et al. ( 2019 ) who equate it with that obtained for executive functions. They point out that intelligence is decisive for new learning and, on the contrary, executive functions are primordial for repetitive and competence-focused learning and also show their relationship in different educational disciplines.

Another noteworthy element of the research is that when analysing moderating variables and comparing models, it is found that intelligence determines that the relationship with academic performance is unidirectional. That is, intelligence is a good predictor of academic achievement, but not the other way around, so the predictive model of intelligence type explains 35% of the variance. Consistent with this result, Buckle et al. ( 2005 ) assigned it a predictive power of 26%. This is in line with previous research findings that intelligence is the best predictor of academic success ( Blankson et al. 2019 ; Erath et al. 2015 ; Li et al. 2017 ; Quílez-Robres et al. 2021a ; Ren et al. 2015 ; Rhodes et al. 2017 ; Tikhomirova et al. 2020 ). However, most studies have related it to the cognitive dimension ( Castejon et al. 2010 ; Visser et al. 2006 ), marginalising the behavioural and emotional aspects ( Gioia et al. 2017 ). Therefore, it is necessary to consider other facets of intelligence, as they are nothing more than different capacities that complement each other ( Catell 1963 ).

From the meta-regression of the intelligence model, general and implicit intelligence emerge with significance ( p < 0.05 and p < 0.01). Implicit intelligence is decisive in school outcomes, as the beliefs that are elaborated about one’s own intelligence and the nature of intelligence guide student behaviours towards achieving success or failure at school ( Chen and Tutwiler 2017 ; Lotz et al. 2018 ; Steinmayr et al. 2019b ). Thus, it is considered relevant for its efficacy in considering that cognitive ability is not a fixed trait but has an adaptive quality that gives it incremental strength. This malleability performs a protective function against school failure, as there is confidence in one’s own abilities. However, it seems that this incremental capacity decreases with age ( Chen and Tutwiler 2017 ). In the same line of research, Kornilova et al. ( 2009 ) found that implicit intelligence predicts general intelligence, as by adopting learning goals and increasing their competence, students overcome setbacks and seek new challenges. It should also be noted that both general and implicit intelligence show an indirect effect with academic success through other variables such as motivation or executive functions ( Aditomo 2015 ). Furthermore, it should be noted that general intelligence has traditionally been broken down into fluid and crystallised intelligence. “Crystallised” intelligence has been considered one of the most significant predictors of individual achievement in different contexts, age ranges and educational conditions ( Deary et al. 2007 ; Nisbett et al. 2012 ; Verbitskaya et al. 2020 ), and “fluid” intelligence has been found to be a better predictor of processing speed tests ( Luo et al. 2006 ) and mathematics performance ( Blankson et al. 2019 ; Sarver et al. 2012 ).

Neither emotional intelligence nor the different types of multiple intelligences show remarkable values in this research. In this regard, the scientific literature is not conclusive. Some studies find that emotional intelligence occupies a pre-eminent position behind general or global intelligence ( MacCann et al. 2020 ; Perera and DiGiacomo 2013 ) and explain that this type of intelligence is related to academic performance due to its importance in promoting adaptive behaviours ( Chew et al. 2013 ; Fayombo 2012 ; Usán Supervía and Quílez Robres 2021 ). The perception of positive interpersonal and intrapersonal emotional intelligence substantially explains academic success, as it comprises learner ability to control, regulate and manage the demands of the academic context ( Cheshire et al. 2015 ; Chew et al. 2013 ; Kornilova et al. 2018 ; Okwuduba et al. 2021 ; Romero et al. 2014 ). However, the research that studies this claim presents mixed results since some authors such as Engin ( 2017 ) or Zhoc et al. ( 2018 ) did not observe associations between academic performance and emotional intelligence. On the other hand, and within the multiple intelligences, musical intelligence has been related to academic performance, especially through cross-sectional data, from which it is difficult to infer a generalisation of cause and effect with respect to school achievement ( Müllensiefen et al. 2015 ; Schellenberg 2011 ). This is despite the fact that Castejon et al. ( 2010 ) and Visser et al. ( 2006 ) point to the existence of a relationship between some of the multiple intelligences and general intelligence due to their cognitive component.

Concerning the type of “perfomance”, comparison analysis model showed its non-significance. However, it is necessary to point out that the performance types are not homogeneous in the meta-sample. There is a large amount of general performance, but there are hardly any cases of music or mathematics. Despite its non-significance, investigation of this aspect in further research is considered necessary, and nevertheless, there are studies that advocate the importance of this variable.

As for gender and age, they are not moderating variables, perhaps influenced by the type of assessment of these variables and the different theoretical concepts of greater or lesser importance assigned to the relationship between them. On the other hand, there are difficulties in predicting the role of gender and age in implicit intelligence ( Diseth et al. 2014 ), but Robins and Trzesniewski ( 2005 ) point out that there is a strong relationship in favour of girls and at an older age. These results can be related to emotional intelligence and higher perceived self-efficacy ( King et al. 2012 ).

As noted above, the country of residence model is the moderating variable that explains 45% of the variance, increasing the predictive power of the intelligence type model. These results are consistent with previous research pointing to the importance of adaptation to different contexts ( Deary et al. 2007 ; Verbitskaya et al. 2020 ) or those indicating that the relationship between intelligence and academic performance was the result of education and the culture in which one was immersed ( Nisbett et al. 2012 ; Plomin and Deary 2015 ; Rodic et al. 2015 ). In his theories, Sternberg, for example, pointed out the importance of adaptation to the context of different skills and abilities, as well as of the differences originating in the beliefs of one’s own abilities in their contribution to academic achievement as a function of the cultural environment that generate individual profiles with different strengths and weaknesses ( Sternberg 2019 ; Sternberg et al. 2001 ). Ultimately, intelligence is related to social competence ( Sternberg 1985 ).

When analysing the meta-regression across countries, three countries are significant: Indonesia, the United Kingdom (UK) and China. Indonesia is considered a very deterministic culture (if you are not very smart, you do not pass) ( Aditomo 2015 ). On the other hand, the United Kingdom (UK) as a model of the Anglo-Saxon education system associates intelligence with linguistic ability and problem-solving skills ( Sternberg 1985 ). Finally, in China, authoritarian filial piety beliefs are associated with an entity view of intelligence, which impairs the students’ academic performance ( Chen and Wong 2014 ). Cultural views of motivational processes can shed light on the ways in which motivational beliefs develop as a product of cultural or socialisation processes, which, in turn, contribute to or determine the students’ academic success ( Chen and Wong 2014 ; Li et al. 2017 ). These differences by country of origin are likely to point to the meanings attributed to intelligence by different cultural groups. There are indications that individuals from Western countries attach a much broader meaning to the concept of intelligence (skills, context, etc.) and, therefore, when studying subjects from non-Western countries, consideration should be given to using specific domains that provide greater certainty to the results, always bearing in mind that the mindset about intelligence and academic ability is very different ( Aditomo 2015 ). As Carroll ( 1992 ) points out, intelligence is a concept within the mind of a society and personal references are those of each culture where individuals are immersed. Some cultures such as the Asian ones continue to use teaching–learning methods based on cognitive aspects such as memory and one’s own intelligence, while the European and Anglo-Saxon models are based on the development of competence through social interaction ( Quílez-Robres et al. 2021a ).

Other reason may be due to different factors such as, for example, the statistical weight of the samples, or others related to cultural elements such as different understandings of academic performance and different assessments of different types of intelligence.

Furthermore, following Serpell ( 2000 ), culture can be approached from three perspectives: culture as a language, culture as a womb, and culture as a forum. According to the language perspective, culture would constitute a distinct system of meanings in the mind within which the concept of intelligence would be embedded. According to the womb perspective, human cultures create environments that nurture personal growth and stimulate the development of human intelligence. Finally, the forum view, which is based on the interaction of members of a community organising aspects of education and constructing new meanings about intelligence, proposes research on cognitive development as a function of culture.

On the other hand, Sternberg and Grigorenko ( 2004 ) indicate that intelligence cannot be understood completely outside of cultural control or influence. There are behaviours that are considered intelligent in some cultures, and those same behaviours are considered unintelligent in other cultures. Furthermore, each culture has implicit (folk) theories of intelligence, and therefore the aspects that fall under this concept vary from culture to culture. In this sense, the three influential cultures in this study belong to two different cultural approaches: individualistic (UK) versus collectivistic (China and Indonesia). Moreover, these countries have different ways of understanding academic performance and attach different degrees of importance to intelligence in academic, social and occupational performance ( Quílez-Robres et al. 2021b ).

5. Conclusions

This research was conducted to identify the ways in which different aspects of student intelligence contribute to differences in academic performance. Of the seven models studied, the country of residence model was found to be the most important predictor of academic performance, explaining 45% of the variance, followed by the type of intelligence model, which explains 35% of the variance. The latter model highlights the importance of general intelligence and implicit intelligence for student grades in academic subjects. The results therefore extend knowledge about the role of intelligence for academic achievement. Implicit intelligence scores better in relation to academic achievement than global intelligence, highlighting the importance of one’s beliefs in one’s own abilities. Students with similar intelligence scores, with identical values and the same prior attainment will see improved academic outcomes by believing in their own competencies and abilities ( Steinmayr et al. 2019a ). If one concludes that academic performance is determined by a multitude of variables including psychological factors that influence student response to overcome setbacks, the evidence points to intelligence as a predictor of success, but also, as this research shows, to a positive mindset in relation to one’s own intelligence and academic abilities. This positive mindset will also be established by the context in which their academic life takes place, i.e., society, beliefs, values, education system, etc. ( Aditomo 2015 ; Hong et al. 1999 ). Therefore, the results of this study point the way to implement interventions aimed at improving the students’ own beliefs about their subject-specific mastery skills.

Finally, we conclude with the need to expand the study in order to limit the term intelligence. What would its general structure be, and how do the different types of intelligence add significance to the general and traditional concept? What conceptual divergences exist between the different theories? Do all these concepts have the same impact on new or repeated learning, on general and specific?

Acknowledgments

The authors would like to thank the University of Zaragoza for their support in this research.

Funding Statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author Contributions

Conceptualization, A.Q.-R. and R.L.-B.; methodology, A.Q.-R.; software, R.L.-B.; validation, R.C.-L., P.U. and C.S.; formal analysis, R.L.-B.; investigation, R.L.-B.; resources, A.Q.-R.; data curation, A.Q.-R.; writing—original draft preparation, R.L.-B.; writing—review and editing, A.Q.-R.; visualization, R.C.-L.; supervision, C.S.; project administration, P.U. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

• Aditomo Anindito. Students’ response to academic setback: “Growth mindset” as a buffer against demotivation. International Journal of Educational Psychology. 2015; 4 :198–222. doi: 10.17583/ijep.2015.1482. [ CrossRef ] [ Google Scholar ]
• Alquichire R Shirley Lorena, Arrieta R Juan Carlos. Relación entre habilidades de pensamiento crítico y rendimiento académico. Voces y Silencios. Revista Latinoamericana de Educación. 2018; 9 :28–52. doi: 10.18175/vys9.1.2018.03. [ CrossRef ] [ Google Scholar ]
• Amin Hafeez Ullah, Malik Aamir Saeed, Kamel Nidal, Chooi Weng-Tink, Hussain Muhammad. P300 correlates with learning & memory abilities and fluid intelligence. Journal of Neuroengineering and Rehabilitation. 2015; 12 :87. doi: 10.1186/s12984-015-0077-6. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ariza Carla Patricia, Toncel Luis Ángel Rueda, Blanchar Jainer Sardoth. El rendimiento académico: Una problemática compleja. Revista Boletín Redipe. 2018; 7 :137–41. [ Google Scholar ]
• Ausina Juan Botella, Sánchez-Meca Julio. Meta-Análisis en Ciencias Sociales y de la Salud. Síntesis; Madrid: 2015. [ Google Scholar ]
• Balkis Murat. Desmotivación académica e intención de abandono escolar: El papel mediador del logro académico y el absentismo. Asia Pacific Journal of Education. 2018; 38 :257–70. doi: 10.1080/02188791.2018.1460258. [ CrossRef ] [ Google Scholar ]
• Blankson A. Nayena, Gudmundson Jessica A., Kondeh Memuna. Cognitive predictors of kindergarten achievement in African American children. Journal of Educational Psychology. 2019; 111 :1273–83. doi: 10.1037/edu0000346. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bonett Douglas G. Robust Confidence Interval for a Ratio of Standard Deviations. Applied Psychological Measurements. 2006; 30 :432–39. doi: 10.1177/0146621605279551. [ CrossRef ] [ Google Scholar ]
• Botella Juan, Gambara Hilda. Qué es el Meta-Análisis. Biblioteca Nueva; Madrid: 2002. [ Google Scholar ]
• Buckle Sarah K., Lancaster Sandra, Powell Martine B., Higgins Daryl J. The relationship between child sexual abuse and academic achievement in a sample of adolescent psychiatric inpatients. Child Abuse & Neglect. 2005; 29 :1031–47. doi: 10.1016/j.chiabu.2004.12.013. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Carroll John. ¿Qué es la inteligencia? In: Sternberg Robert J., Detterman Douglas K., editors. ¿Qué es la Inteligencia? Enfoque Actual de su Naturaleza y Definición. Pirámide; Madrid: 1992. pp. 69–72. [ Google Scholar ]
• Castejon Juan L., Perez Antonio M., Gilar Raquel. Confirmatory factor analysis of Project Spectrum activities. A second-order g factor or multiple intelligences? Intelligence. 2010; 38 :481–96. doi: 10.1016/j.intell.2010.07.002. [ CrossRef ] [ Google Scholar ]
• Cattell Raymond B. Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology. 1963; 54 :1. doi: 10.1037/h0046743. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chen Jason A., Tutwiler M. Shane. Implicit theories of ability and self-efficacy. Zeitschrift für Psychologie. 2017; 225 :127–36. doi: 10.1027/2151-2604/a000289. [ CrossRef ] [ Google Scholar ]
• Chen Wei-Wen, Wong Yi-Lee. What my parents make me believe in learning: The role of filial piety in Hong Kong students’ motivation and academic achievement. International Journal of Psychology. 2014; 49 :249–56. doi: 10.1002/ijop.12014. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cheshire Michelle H., Strickland Haley P., Carter Melondie R. Comparing traditional measures of academic success with emotional intelligence scores in nursing students. Asia-Pacific Journal of Oncology Nursing. 2015; 2 :99–106. doi: 10.4103/2347-5625.154090. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chew Boon How, Zain Azhar Md, Hassan Faezah. Emotional intelligence and academic performance in first and final year medical students: A cross-sectional study. BMC Medical Education. 2013; 13 :44. doi: 10.1186/1472-6920-13-44. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cortés Pascual Alejandra, Muñoz Nieves Moyano, Robres Alberto Quílez. Relación entre funciones ejecutivas y rendimiento académico en educación primaria: Revisión y metanálisis. Frente. Psicol. 2019; 10 :1582. doi: 10.3389/fpsyg.2019.01582. [ CrossRef ] [ Google Scholar ]
• Deary Ian J., Strand Steve, Smith Pauline, Fernandes Cres. Intelligence and educational achievement. Intelligence. 2007; 35 :13–21. doi: 10.1016/j.intell.2006.02.001. [ CrossRef ] [ Google Scholar ]
• Dersimonian Rebecca, Laird Nan. Meta-analysis in clinical trials. Controlled Clinical Trials. 1986; 7 :177–88. doi: 10.1016/0197-2456(86)90046-2. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dinger Felix C., Dickhäuser Oliver, Spinath Birgit, Steinmayr Ricarda. Antecedents and consequences of students’ achievement goals: A mediation analysis. Learning and Individual Differences. 2013; 28 :90–101. doi: 10.1016/j.lindif.2013.09.005. [ CrossRef ] [ Google Scholar ]
• Diseth Åge, Meland Eivind, Breidablik Hans Johan. Self-beliefs among students: Grade level and gender differences in self-esteem, self-efficacy and implicit theories of intelligence. Learning and Individual Differences. 2014; 35 :1–8. doi: 10.1016/j.lindif.2014.06.003. [ CrossRef ] [ Google Scholar ]
• Egger Matthias, Smith George Davey, Phillips Andrew N. Meta-analysis: Principles and procedures. Britihs Medical Journal BMJ. 1997; 315 :1533–37. doi: 10.1136/bmj.315.7121.1533. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• El Jaziz Anas, Lotfi Said, Ahami Ahmad O. T. Interrelationship of physical exercise, perceptual discrimination and academic achievement variables in high school students. Ann Ig. 2020; 32 :528–40. doi: 10.7416/ai.2020.2373. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Enea-Drapeau Claire, Carlier Michèle, Huguet Pascal. Implicit theories concerning the intelligence of individuals with Down syndrome. PLoS ONE. 2017; 12 :e0188513. doi: 10.1371/journal.pone.0188513. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Engin Melih. Analysis of Students’ Online Learning Readiness Based on Their Emotional Intelligence Level. Universal Journal of Educational Research. 2017; 5 :32–40. doi: 10.13189/ujer.2017.051306. [ CrossRef ] [ Google Scholar ]
• Erath Stephen A., Tu Kelly M., Buckhalt Joseph A., El-Sheikh Mona. Associations between children’s intelligence and academic achievement: The role of sleep. Journal of Sleep Research. 2015; 24 :510–13. doi: 10.1111/jsr.12281. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fayombo Grace A. Relating emotional intelligence to academic achievement among university students in Barbados. [(accessed on 5 January 2020)]; The International Journal of Emotional Education. 2012 4 :43–54. Available online: https://www.um.edu.mt/library/oar/handle/123456789/6141 [ Google Scholar ]
• Friese Malte, Frankenbach Julius. p-Hacking and publication bias interact to distort meta-analytic effect size estimates. Psychological Methods. 2020; 25 :456–71. doi: 10.1037/met0000246. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gardner Howard. The Mind’s New Science: A History of the Cognitive Revolutión. Basic Books; New York: 1985. [ Google Scholar ]
• Geary David C. Cognitive predictors of achievement growth in mathematics: A 5-year longitudinal study. Developmental Psychology. 2011; 47 :1539–52. doi: 10.1037/a0025510. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gioia Gerard A., Isquith Peter, Guy Steven C., Kenworthy Lauren. In: Santamaria (Adapters), BRIEF-2. Evaluación Conductual de la Función Ejecutiva. Maldonado María Jesús, Fournier María de la Concepción, Martínez-Arias Rosario, González-Marqués Javier, Espejo-Saavedra Juan Manuel, Santamaría Pablo., editors. TEA Ediciones; Madrid: 2017. [ Google Scholar ]
• Goleman Daniel. Inteligencia Emocional. Kairós; Barcelona: 1996. [ Google Scholar ]
• Goleman Daniel. La Práctica de la Inteligencia Emocional. Kairós; Barcelona: 1999. [ Google Scholar ]
• González Gustavo, Solano Alejandro Castro, González Federico. Perfiles aptitudinales, estilos de pensamiento y rendimiento académico. [(accessed on 4 January 2020)]; Anuario de Investigaciones. 2008 15 :33–41. Available online: https://www.redalyc.org/pdf/3691/369139944035.pdf [ Google Scholar ]
• Higgins Julian P. T., Green Sally. Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0. The Cochrane Collaboration; London: 2011. [(accessed on 4 January 2020)]. Available online: www.cochrane-handbook.org [ Google Scholar ]
• Higgins Julian P. T., Ramsay Craig, Reeves Barnaby C., Deeks Jonathan J., Shea Beverley, Valentine Jeffrey C., Tugwell Peter, Wells George. Issues relating to study design and risk of bias when including non-randomized studies in systematic reviews on the effects of interventions. Research Synthesis Methods. 2013; 4 :12–25. doi: 10.1002/jrsm.1056. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hong Ying-yi, Chiu Chi-yue, Dweck Carol S., Lin Derrick M-S., Wan Wendy. Implicit theories, attributions, and coping: A meaning system approach. Journal of Personality and Social Psychology. 1999; 77 :588–99. doi: 10.1037/0022-3514.77.3.588. [ CrossRef ] [ Google Scholar ]
• Hunter John E., Schmidt Frank L. Methods of Meta-Analysis: Correcting Error and Bias in Research Findings. Sage; New York: 2004. [ Google Scholar ]
• Jak Suzanne, Cheung Mike W-L. Meta-analytic structural equation modeling with moderating effects on SEM parameters. Psychological Methods. 2020; 25 :430–55. doi: 10.1037/met0000245. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• King Ronnel B., McInerney Dennis M., Watkins David A. How you think about your intelligence determines how you feel in school: The role of theories of intelligence on academic emotions. Learning and Individual Differences. 2012; 22 :814–19. doi: 10.1016/j.lindif.2012.04.005. [ CrossRef ] [ Google Scholar ]
• Kiuru Noona, Salmela-Aro Katariina, Nurmi Jari-Erik, Zettergren Peter, Andersson Håkan, Bergman Lars. Best friends in adolescence show similar educational careers in early adulthood. Journal of Applied Developmental Psychology. 2012; 33 :102–11. doi: 10.1016/j.appdev.2011.12.001. [ CrossRef ] [ Google Scholar ]
• Kornilova Tatiana V., Chumakova Maria A., Krasavtseva Yulia V. Emotional intelligence, patterns for coping with decisional conflict, and academic achievement in cross-cultural perspective (evidence from selective Russian and Azerbaijani student populations) [(accessed on 4 January 2020)]; Psychology in Russia: State of the Art. 2018 11 :114–33. doi: 10.11621/pir.2018.0209. Available online: http://psychologyinrussia.com/volumes/index.php?article=7230 [ CrossRef ] [ Google Scholar ]
• Kornilova Tatiana V., Kornilov Sergey A., Chumakova Maria A. Subjective evaluations of intelligence and academic self-concept predict academic achievement: Evidence from a selective student population. Learning and Individual Differences. 2009; 19 :596–608. doi: 10.1016/j.lindif.2009.08.001. [ CrossRef ] [ Google Scholar ]
• Kriegbaum Katharina, Jansen Malte, Spinath Birgit. Motivation: A predictor of PISA’s mathematical competence beyond intelligence and prior test achievement. Learning and Individual Differences. 2015; 43 :140–48. doi: 10.1016/j.lindif.2015.08.026. [ CrossRef ] [ Google Scholar ]
• Kuncel Nathan R., Hezlett Sarah A., Ones Deniz S. Academic performance, career potential, creativity, and job performance: Can one construct predict them all? Journal of Personality and Social Psychology. 2004; 86 :148–61. doi: 10.1037/0022-3514.86.1.148. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Laidra Kaia, Pullmann Helle, Allik Jüri. Personality and intelligence as predictors of academic achievement: A cross-sectional study from elementary to secondary school. Personality and Individual Differences. 2007; 42 :441–51. doi: 10.1016/j.paid.2006.08.001. [ CrossRef ] [ Google Scholar ]
• Li Ping, Zhou Nan, Zhang Yuchi, Xiong Qing, Nie Ruihong, Fang Xiaoyi. Incremental theory of intelligence moderated the relationship between prior achievement and school engagement in Chinese high school students. Frontiers in Psychology. 2017; 8 :1703. doi: 10.3389/fpsyg.2017.01703. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lotz Christin, Schneider Rebecca, Sparfeldt Jörn R. Differential relevance of intelligence and motivation for grades and competence tests in mathematics. Learning and Individual Differences. 2018; 65 :30–40. doi: 10.1016/j.lindif.2018.03.005. [ CrossRef ] [ Google Scholar ]
• Luo Dasen, Thompson Lee A., Detterman Douglas K. The criterion validity of tasks of basic cognitive processes. Intelligence. 2006; 34 :79–120. doi: 10.1016/j.intell.2004.12.003. [ CrossRef ] [ Google Scholar ]
• MacCann Carolyn, Jiang Yixin, Brown Luke E. R., Double Kit S, Bucich Micaela, Minbashian Amirali. Emotional intelligence predicts academic performance: A meta-analysis. Psychological Bulletin. 2020; 146 :150. doi: 10.1037/bul0000219. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Martín Andrés A., Luna del Castillo J. D. D. Bioestadística para las Ciencias de la Salud. Capitel Ediciones, SL; Madrid: 2004. [ Google Scholar ]
• Molano Olga Lucía. Identidad cultural un concepto que evoluciona. [(accessed on 5 January 2020)]; Revista Opera. 2007 7 :69–84. Available online: https://dialnet.unirioja.es/servlet/articulo?codigo=4020258 [ Google Scholar ]
• Molero-Puertas Pilar, Zurita-Ortega Félix, Chacón-Cuberos Ramón, Castro-Sánchez Manuel, Ramírez-Granizo Irwin, Valero-González Gabriel. La inteligencia emocional en el ámbito educativo: Un metaanálisis. Anales de Psicología/Annals of Psychology. 2020; 36 :84–91. doi: 10.6018/analesps.345901. [ CrossRef ] [ Google Scholar ]
• Monir Zeinab M., El-Din Ebtissam M. Salah, El-Alameey Inas R., Yamamah Gamal A., Megahed Hala S., Salem Samar M., Ibrahim Tarek S. Academic Achievement and Psychosocial Profile of Egyptian Primary School Children in South Sinai. Open Access Macedonian Journal of Medical Sciences. 2016; 4 :624–29. doi: 10.3889/oamjms.2016.111. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Moreau David, Gamble Beau. Conducting a meta-analysis in the age of open science: Tools, tips, and practical recommendations. Advance online publication. Psychological Methods. 2020; 27 :426–32. doi: 10.1037/met0000351. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Müllensiefen Daniel, Harrison Peter, Caprini Francesco, Fancourt Amy. Investigating the importance of self-theories of intelligence and musicality for students’ academic and musical achievement. Frontiers in Psychology. 2015; 6 :1702. doi: 10.3389/fpsyg.2015.01702. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Navarro Rubén Edel. El rendimiento académico: Concepto, investigación y desarrollo. [(accessed on 5 January 2020)]; REICE. Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación. 2003 1 :1–16. Available online: https://revistas.uam.es/index.php/reice/article/view/5354/5793 [ Google Scholar ]
• Nieto Martín Santiago. Hacia una teoría sobre el rendimiento académico en enseñanza primaria a partir de la investigación empírica: Datos preliminares. Teoría de la Educación. Revista Interuniversitaria. 2008; 20 :249–74. doi: 10.14201/ted.992. [ CrossRef ] [ Google Scholar ]
• Nisbett Richard E., Aronson Joshua, Blair Clancy, Dickens William, Flynn James, Halpern Diane F., Turkheimer Eric. Intelligence: New findings and theoretical developments. American Psychologist. 2012; 67 :130–59. doi: 10.1037/a0026699. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Okwuduba Emmanuel Nkemakolam, Nwosu Kingsley Chinaza, Okigbo Ebele Chinelo, Samuel Naomi Nkiru, Achugbu Chinwe. Impact of intrapersonal and interpersonal emotional intelligence and self-directed learning on academic performance among pre-university science students. Heliyon. 2021; 7 :e06611. doi: 10.1016/j.heliyon.2021.e06611. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Perera Harsha N., DiGiacomo Michelle. The relationship of trait emotional intelligence with academic performance: A meta-analytic review. Learning and Individual Differences. 2013; 28 :20–33. doi: 10.1016/j.lindif.2013.08.002. [ CrossRef ] [ Google Scholar ]
• Pintrich Paul R. The role of metacognitive knowledge in learning, teaching, and assessing. Theory into Practice. 2002; 41 :219–25. doi: 10.1207/s15430421tip4104_3. [ CrossRef ] [ Google Scholar ]
• Plomin Robert, Deary Ian J. Genetics and intelligence differences: Five special findings. Molecular Psychiatry. 2015; 20 :98–108. doi: 10.1038/mp.2014.105. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Priess-Groben Heather A., Hyde Janet Shibley. Implicit theories, expectancies, and values predict mathematics motivation and behavior across high school and college. Journal of Youth and Adolescence. 2017; 46 :1318–32. doi: 10.1007/s10964-016-0579-y. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• PRISMA Grupo PRISMA-P. Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015: Elaboration and explanation. British Medical Journal BMJ. 2015; 349 :g7647. doi: 10.1136/bmj.g7647. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Quílez-Robres Alberto, González-Andrade Alejandro, Ortega Zaira, Santiago-Ramajo Sandra. Intelligence quotient, short-term memory and study habits as academic achievement predictors of elementary school: A follow-up study. Studies in Educational Evaluation. 2021a; 70 :101020. doi: 10.1016/j.stueduc.2021.101020. [ CrossRef ] [ Google Scholar ]
• Quílez-Robres Alberto, Moyano Nieves, Cortés-Pascual Alejandra. Motivational, Emotional, and Social Factors Explain Academic Achievement in Children Aged 6–12 Years: A Meta-Analysis. Education Sciences. 2021b; 11 :513. doi: 10.3390/educsci11090513. [ CrossRef ] [ Google Scholar ]
• Rabbitt Patrick, Lowe Christine. Patterns of cognitive ageing. Psychological Research. 2000; 63 :308–16. doi: 10.1007/s004269900009. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ren Xuezhu, Schweizer Karl, Wang Tengfei, Xu Fen. The prediction of students’ academic performance with fluid intelligence in giving special consideration to the contribution of learning. Advances in Cognitive Psychology. 2015; 11 :97–105. doi: 10.5709/acp-0175-z. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rhodes Emma, Devlin Kathryn N., Steinberg Laurence, Giovannetti Tania. Grit in adolescence is protective of late-life cognition: Non-cognitive factors and cognitive reserve. Aging, Neuropsychology, and Cognition. 2017; 24 :321–32. doi: 10.1080/13825585.2016.1210079. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Richardson Michelle, Abraham Charles, Bond Rod. Psychological Correlates of University Students’ Academic Performance: A Systematic Review and Meta-Analysis. Psychological Bulletin. 2012; 138 :353–87. doi: 10.1037/a0026838. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Robins Richard W., Trzesniewski Kali H. Self-esteem development across the lifespan. Current Directions in Psychological Science. 2005; 14 :158–62. doi: 10.1111/j.0963-7214.2005.00353.x. [ CrossRef ] [ Google Scholar ]
• Rodic Maja, Zhou Xinlin, Tikhomirova Tatiana, Wei Wei, Malykh Sergei, Ismatulina Victoria, Sabirova Elena, Davidova Yulia, Tosto Maria Grazia, Lemelin Jean-Pascal, et al. Cross-cultural investigation into cognitive underpinnings of individual differences in early arithmetic. Developmental Science. 2015; 18 :165–74. doi: 10.1111/desc.12204. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Romero Carissa, Master Allison, Paunesku Dave, Dweck Carol S., Gross James J. Academic and emotional functioning in middle school: The role of implicit theories. Emotion. 2014; 14 :227–34. doi: 10.1037/a0035490. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Salovey Peter, Mayer John D. Emotional Intelligence. Imagination, Cognition and Personality. 1990; 9 :185–211. doi: 10.2190/DUGG-P24E-52WK-6CDG. [ CrossRef ] [ Google Scholar ]
• Sanchez-Ruiz Maria-Jose, Mavroveli Stella, Poullis Joseph. Trait emotional intelligence and its links to university performance: An examination. Personality and Individual Differences. 2013; 54 :658–62. doi: 10.1016/j.paid.2012.11.013. [ CrossRef ] [ Google Scholar ]
• Sarver Dustin E., Rapport Mark D., Kofler Michael J., Scanlan Sean W., Raiker Joseph S., Altro Thomas A., Bolden Jennifer. Attention problems, phonological short-term memory, and visuospatial short-term memory: Differential effects on near-and long-term scholastic achievement. Learning and Individual Differences. 2012; 22 :8–19. doi: 10.1016/j.lindif.2011.09.010. [ CrossRef ] [ Google Scholar ]
• Schellenberg E. Glenn. Examining the association between music lessons and intelligence. British Journal Psychology. 2011; 102 :283–302. doi: 10.1111/j.2044-8295.2010.02000.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Serpell Roberto. Inteligencia y cultura. In: Sternberg Roberto J., editor. Manual de Inteligencia. Cambridge University Press; Cambridge: 2000. pp. 549–77. [ CrossRef ] [ Google Scholar ]
• Singh Yashpal, Makharia Archita, Sharma Abhilasha, Agrawal Kruti, Varma Gowtham, Yadav Tarun. A study on different forms of intelligence in Indian school-going children. Industrial Psychiatry Journal. 2017; 26 :71–76. doi: 10.4103/ipj.ipj_61_16. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Steinmayr Ricarda, Spinath Birgit. The importance of motivation as a predictor of school achievement. Learning and Individual Differences. 2009; 19 :80–90. doi: 10.1016/j.lindif.2008.05.004. [ CrossRef ] [ Google Scholar ]
• Steinmayr Ricarda, Weidinger Anne F., Schwinger Malte, Spinath Birgit. The importance of students’ motivation for their academic achievement–replicating and extending previous findings. Frontiers in Psychology. 2019a; 10 :1730. doi: 10.3389/fpsyg.2019.01730. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Steinmayr Ricarda, Wirthwein Linda, Modler Laura, Barry Margaret M. Development of subjective well-being in adolescence. International Journal of Environmental Research and Public Health. 2019b; 16 :3690. doi: 10.3390/ijerph16193690. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sternberg Robert J. Beyond IQ: A Triarchic Theory of Human Intelligence. Cambridge University Press; Cambridge: 1985. [ Google Scholar ]
• Sternberg Robert J. A theory of adaptive intelligence and its relation to general intelligence. Journal of Intelligence. 2019; 7 :23. doi: 10.3390/jintelligence7040023. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sternberg Robert J., Grigorenko Elena L. Intelligence and culture: How culture shapes what intelligence means, and the implications for a science of well–being. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences. 2004; 359 :1427–34. doi: 10.1098/rstb.2004.1514. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sternberg Robert J., Grigorenko Elena L., Bundy Donald A. The predictive value of IQ. [(accessed on 5 January 2020)]; Merrill-Palmer Quarterly. 2001 47 :1–41. doi: 10.1353/mpq.2001.0005. Available online: https://www.jstor.org/stable/23093686 [ CrossRef ] [ Google Scholar ]
• Sternberg Robert, Grigorenko Elena, Ferrando Mercedes, Hernández Daniel, Ferrándiz Carmen, Bermejo Rosario. Enseñanza de la inteligencia exitosa para alumnos superdotados y talentos. [(accessed on 5 January 2020)]; Revista Electrónica Interuniversitaria de Formación del Profesorado. 2010 13 :111–18. Available online: https://www.redalyc.org/pdf/2170/217014922011.pdf [ Google Scholar ]
• Tikhomirova Tatiana, Malykh Artem, Malykh Sergey. Predicting academic achievement with cognitive abilities: Cross-sectional study across school education. Behavioral Sciences. 2020; 10 :158. doi: 10.3390/bs10100158. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Usán Supervía Pablo, Quílez Robres Alberto. Emotional Regulation and Academic Performance in the Academic Context: The Mediating Role of Self-Efficacy in Secondary Education Students. International Journal of Environmental Research and Public Health. 2021; 18 :5715. doi: 10.3390/ijerph18115715. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Verbitskaya Ludmila A., Zinchenko Yury P., Malykh Sergey B., Gaidamashko Igor V., Kalmyk Olga A., Tikhomirova Tatiana N. Cognitive Predictors of Success in Learning Russian Among Native Speakers of High School Age in Different Educational Systems. [(accessed on 4 January 2020)]; Psychology in Russia. State of the Art. 2020 13 :2–15. doi: 10.11621/pir.2020.0201. Available online: http://psychologyinrussia.com/volumes/pdf/2020_2/Psychology_2_2020_2-15_Verbitskaya.pdf [ CrossRef ] [ Google Scholar ]
• Visser Beth A., Ashton Michael C., Vernon Philip A. Beyond g: Putting multiple intelligences theory to the test. Intelligence. 2006; 34 :487–502. doi: 10.1016/j.intell.2006.02.004. [ CrossRef ] [ Google Scholar ]
• Von Stumm Sopfhie, Ackerman Phillip L. Inversión e intelecto: Una revisión y meta-análisis. Psychological Bulletin. 2013; 139 :841–69. doi: 10.1037/a0030746. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wigfield Allan, Tonks Stephen, Klauda Susan Lutz. Expectancy-value theory. In: Wentzel Kathryn R., Miele David B., editors. Handbook of Motivation in School. 2nd ed. Routledge; London: 2016. pp. 55–74. [ Google Scholar ]
• Willoughby Michael T., Magnus Brooke, Vernon-Feagans Lynne, Blair Clancy B., Family Life Project Investigators Developmental delays in executive function from 3 to 5 years of age predict kindergarten academic readiness. Journal of Learning Disabilities. 2017; 50 :359–72. doi: 10.1177/0022219415619754. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zhoc Karen C. H., Chung Tony S. H., King Ronnel B. Emotional intelligence (EI) and self-directed learning: Examining their relation and contribution to better student learning outcomes in higher education. British Educational Research Journal. 2018; 44 :982–1004. doi: 10.1002/berj.3472. [ CrossRef ] [ Google Scholar ]

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

•  We're Hiring!
•  Help Center

Multiple Intelligences

• Most Cited Papers
• Most Downloaded Papers
• Newest Papers
• Save to Library
• Last »
• Education Follow Following
• CMC Follow Following
• Intelligence Follow Following
• Emotional intelligence Follow Following
• Learning Organization Follow Following
• Research in Higher Education Follow Following
• Learning Sciences Follow Following
• Multiple Intelligences in EFL Follow Following
• Double Consciousness Follow Following
• Learning and Teaching Follow Following

Enter the email address you signed up with and we'll email you a reset link.

• Academia.edu Publishing
•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Microsoft Defender Threat Intelligence

Unmask and neutralize modern adversaries and cyberthreats such as ransomware.

Uncover your adversaries

Expose and eliminate modern cyberthreats and their infrastructure using dynamic threat intelligence.

Identify cyberattackers and their tools

Understand your adversaries and their online infrastructures to identify your potential cyberthreat exposures using a complete map of the internet.

Accelerate cyberthreat detection and remediation

Discover the full scope of a cyberattack. Understand an online adversary’s entire toolkit, prevent access by all their machines and known entities, and continuously block IP addresses or domains.

Enhance your security tools and workflows

Extend the reach and visibility of your existing security investments. Gain more context and understanding of cyberthreats with Microsoft Defender XDR, Microsoft Sentinel, and Copilot for Security.

Learn how Defender Threat Intelligence enables security professionals to directly access, ingest, and act upon our powerful repository of threat intelligence built from 78 trillion signals and more than 10,000 multidisciplinary experts worldwide.

Capabilities

Uncover and help eliminate cyberthreats with Defender Threat Intelligence. 

Get continuous threat intelligence

Get a complete view of the internet and track day-to-day changes. Create threat intelligence for your own business to understand and reduce exposure.

Expose adversaries and their methods

Understand the group behind an online attack, their methods, and how they typically operate.

Enhance alert investigations

Enrich Microsoft Sentinel and Defender XDR incident data with finished and raw threat intelligence to understand and uncover the full scale of a cyberthreat or cyberattack.

Accelerate incident response

Investigate and remove malicious infrastructure such as domains and IPs and all the known tools and resources operated by a cyberattacker or cyberthreat family.

Hunt cyberthreats as a team

Easily collaborate on investigations across teams using the Defender Threat Intelligence workbench and share knowledge of cyberthreat actors, tooling, and infrastructure with projects and intelligence profiles.

Expand prevention and improve security posture

Automatically uncover malicious entities and help stop outside cyberthreats by blocking internal resources from accessing dangerous internet resources.

File and URL (detonation) intelligence

Submit a file or URL to instantly know its reputation. Enrich security incidents with in-context threat intelligence.

Microsoft Copilot for Security is now generally available

Use natural language queries to investigate incidents with Copilot, now with integrations across the Microsoft Security suite of products.

How to use Microsoft Defender Threat Intelligence

Microsoft tracks more than 78 trillion signals daily, helping security teams identify vulnerabilities with greater efficacy and stay ahead of today's cyberthreats.

Microsoft Defender Threat Intelligence (MDTI) contains a repository of raw and finished Microsoft threat intelligence. This intelligence helps professionals analyze and act upon the trillions of security signals collected by Microsoft and processed by security experts and machine learning. Powerful finished intelligence provides the latest on cyberthreat actors and their tools, tactics, and procedures. Unique security data sets show the infrastructure connections across the global cyberthreat landscape to uncover an organization’s vulnerabilities and enable teams to investigate the tools and systems used in cyberattacks. Defender Threat Intelligence complements the Microsoft Security suite and enhances SIEM, XDR, and AI solutions with powerful threat intelligence context.

Unified security operations platform

Secure your digital estate with the only security operations (SecOps) platform that unifies the full capabilities of extended detection and response (XDR) and security information and event management (SIEM).

Unified portal

Detect and disrupt cyberthreats in near real time and streamline investigation and response.

Microsoft Defender XDR

Achieve unified security and visibility across your clouds, platforms, and endpoints.

Microsoft Sentinel

Aggregate security data and correlate alerts from virtually any source with cloud-native SIEM.

Explore Defender Threat Intelligence licenses

Defender threat intelligence—standard version.

Use this free version of Defender Threat Intelligence to address global cyberthreats.

Free version includes:

Public indicators of compromise (IOCs)

Open-source intelligence (OSINT)

Common vulnerabilities and exposures (CVEs) database

Articles and analysis from Microsoft Threat Intelligence ( limited 1 )

Defender Threat Intelligence datasets ( limited 2 )

Intelligence Profiles ( limited 3 )

Defender Threat Intelligence—premium version

Get full access to the operational, strategic, and tactical intelligence in the Defender Threat Intelligence content library and investigative workbench. 

Premium version includes:

Articles and analysis from Microsoft Threat Intelligence 

Defender Threat Intelligence datasets

Intelligence Profiles

Microsoft IOCs

Microsoft-enriched OSINT

URL and file intelligence

Related products

Use best-in-class Microsoft security products to help prevent and detect cyberattacks across your organization.

See and stop cyberthreats across your entire enterprise with intelligent security analytics.

Microsoft Defender for Cloud

Increase protection in your multicloud and hybrid environments.

Microsoft Defender External Attack Surface Management

Understand your security posture beyond the firewall.

Additional resources

Read the threat intelligence blog.

Learn about the new threat intelligence offerings from Microsoft.

Help protect your business with threat intelligence

Learn how to use internet threat intelligence to protect your organization against cyberattacks.

Best practices and implementation

Get started with threat intelligence solutions for your organization today.

Visit the Microsoft Defender Threat Intelligence blog

Learn from Defender Threat Intelligence experts, see what's new, and let us hear from you.

Protect everything

Make your future more secure. Explore your security options today.

• [1] Get OSINT and access select featured articles.
• [2] Get the most recent two weeks of select datasets.
• [3] Preview select Intel Profiles.

Follow Microsoft

• Chat with sales

Available M-F 6 AM to 6 PM PT.

The state of AI in 2023: Generative AI’s breakout year

You have reached a page with older survey data. please see our 2024 survey results here ..

The latest annual McKinsey Global Survey  on the current state of AI confirms the explosive growth of generative AI (gen AI) tools . Less than a year after many of these tools debuted, one-third of our survey respondents say their organizations are using gen AI regularly in at least one business function. Amid recent advances, AI has risen from a topic relegated to tech employees to a focus of company leaders: nearly one-quarter of surveyed C-suite executives say they are personally using gen AI tools for work, and more than one-quarter of respondents from companies using AI say gen AI is already on their boards’ agendas. What’s more, 40 percent of respondents say their organizations will increase their investment in AI overall because of advances in gen AI. The findings show that these are still early days for managing gen AI–related risks, with less than half of respondents saying their organizations are mitigating even the risk they consider most relevant: inaccuracy.

The organizations that have already embedded AI capabilities have been the first to explore gen AI’s potential, and those seeing the most value from more traditional AI capabilities—a group we call AI high performers—are already outpacing others in their adoption of gen AI tools. 1 We define AI high performers as organizations that, according to respondents, attribute at least 20 percent of their EBIT to AI adoption.

The expected business disruption from gen AI is significant, and respondents predict meaningful changes to their workforces. They anticipate workforce cuts in certain areas and large reskilling efforts to address shifting talent needs. Yet while the use of gen AI might spur the adoption of other AI tools, we see few meaningful increases in organizations’ adoption of these technologies. The percent of organizations adopting any AI tools has held steady since 2022, and adoption remains concentrated within a small number of business functions.

Table of Contents

• It’s early days still, but use of gen AI is already widespread
• Leading companies are already ahead with gen AI
• AI-related talent needs shift, and AI’s workforce effects are expected to be substantial
• With all eyes on gen AI, AI adoption and impact remain steady

About the research

1. it’s early days still, but use of gen ai is already widespread.

The findings from the survey—which was in the field in mid-April 2023—show that, despite gen AI’s nascent public availability, experimentation with the tools  is already relatively common, and respondents expect the new capabilities to transform their industries. Gen AI has captured interest across the business population: individuals across regions, industries, and seniority levels are using gen AI for work and outside of work. Seventy-nine percent of all respondents say they’ve had at least some exposure to gen AI, either for work or outside of work, and 22 percent say they are regularly using it in their own work. While reported use is quite similar across seniority levels, it is highest among respondents working in the technology sector and those in North America.

Organizations, too, are now commonly using gen AI. One-third of all respondents say their organizations are already regularly using generative AI in at least one function—meaning that 60 percent of organizations with reported AI adoption are using gen AI. What’s more, 40 percent of those reporting AI adoption at their organizations say their companies expect to invest more in AI overall thanks to generative AI, and 28 percent say generative AI use is already on their board’s agenda. The most commonly reported business functions using these newer tools are the same as those in which AI use is most common overall: marketing and sales, product and service development, and service operations, such as customer care and back-office support. This suggests that organizations are pursuing these new tools where the most value is. In our previous research , these three areas, along with software engineering, showed the potential to deliver about 75 percent of the total annual value from generative AI use cases.

In these early days, expectations for gen AI’s impact are high : three-quarters of all respondents expect gen AI to cause significant or disruptive change in the nature of their industry’s competition in the next three years. Survey respondents working in the technology and financial-services industries are the most likely to expect disruptive change from gen AI. Our previous research shows  that, while all industries are indeed likely to see some degree of disruption, the level of impact is likely to vary. 2 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. Industries relying most heavily on knowledge work are likely to see more disruption—and potentially reap more value. While our estimates suggest that tech companies, unsurprisingly, are poised to see the highest impact from gen AI—adding value equivalent to as much as 9 percent of global industry revenue—knowledge-based industries such as banking (up to 5 percent), pharmaceuticals and medical products (also up to 5 percent), and education (up to 4 percent) could experience significant effects as well. By contrast, manufacturing-based industries, such as aerospace, automotives, and advanced electronics, could experience less disruptive effects. This stands in contrast to the impact of previous technology waves that affected manufacturing the most and is due to gen AI’s strengths in language-based activities, as opposed to those requiring physical labor.

Responses show many organizations not yet addressing potential risks from gen AI

According to the survey, few companies seem fully prepared for the widespread use of gen AI—or the business risks these tools may bring. Just 21 percent of respondents reporting AI adoption say their organizations have established policies governing employees’ use of gen AI technologies in their work. And when we asked specifically about the risks of adopting gen AI, few respondents say their companies are mitigating the most commonly cited risk with gen AI: inaccuracy. Respondents cite inaccuracy more frequently than both cybersecurity and regulatory compliance, which were the most common risks from AI overall in previous surveys. Just 32 percent say they’re mitigating inaccuracy, a smaller percentage than the 38 percent who say they mitigate cybersecurity risks. Interestingly, this figure is significantly lower than the percentage of respondents who reported mitigating AI-related cybersecurity last year (51 percent). Overall, much as we’ve seen in previous years, most respondents say their organizations are not addressing AI-related risks.

2. Leading companies are already ahead with gen AI

The survey results show that AI high performers—that is, organizations where respondents say at least 20 percent of EBIT in 2022 was attributable to AI use—are going all in on artificial intelligence, both with gen AI and more traditional AI capabilities. These organizations that achieve significant value from AI are already using gen AI in more business functions than other organizations do, especially in product and service development and risk and supply chain management. When looking at all AI capabilities—including more traditional machine learning capabilities, robotic process automation, and chatbots—AI high performers also are much more likely than others to use AI in product and service development, for uses such as product-development-cycle optimization, adding new features to existing products, and creating new AI-based products. These organizations also are using AI more often than other organizations in risk modeling and for uses within HR such as performance management and organization design and workforce deployment optimization.

AI high performers are much more likely than others to use AI in product and service development.

Another difference from their peers: high performers’ gen AI efforts are less oriented toward cost reduction, which is a top priority at other organizations. Respondents from AI high performers are twice as likely as others to say their organizations’ top objective for gen AI is to create entirely new businesses or sources of revenue—and they’re most likely to cite the increase in the value of existing offerings through new AI-based features.

As we’ve seen in previous years , these high-performing organizations invest much more than others in AI: respondents from AI high performers are more than five times more likely than others to say they spend more than 20 percent of their digital budgets on AI. They also use AI capabilities more broadly throughout the organization. Respondents from high performers are much more likely than others to say that their organizations have adopted AI in four or more business functions and that they have embedded a higher number of AI capabilities. For example, respondents from high performers more often report embedding knowledge graphs in at least one product or business function process, in addition to gen AI and related natural-language capabilities.

While AI high performers are not immune to the challenges of capturing value from AI, the results suggest that the difficulties they face reflect their relative AI maturity, while others struggle with the more foundational, strategic elements of AI adoption. Respondents at AI high performers most often point to models and tools, such as monitoring model performance in production and retraining models as needed over time, as their top challenge. By comparison, other respondents cite strategy issues, such as setting a clearly defined AI vision that is linked with business value or finding sufficient resources.

The findings offer further evidence that even high performers haven’t mastered best practices regarding AI adoption, such as machine-learning-operations (MLOps) approaches, though they are much more likely than others to do so. For example, just 35 percent of respondents at AI high performers report that where possible, their organizations assemble existing components, rather than reinvent them, but that’s a much larger share than the 19 percent of respondents from other organizations who report that practice.

Many specialized MLOps technologies and practices  may be needed to adopt some of the more transformative uses cases that gen AI applications can deliver—and do so as safely as possible. Live-model operations is one such area, where monitoring systems and setting up instant alerts to enable rapid issue resolution can keep gen AI systems in check. High performers stand out in this respect but have room to grow: one-quarter of respondents from these organizations say their entire system is monitored and equipped with instant alerts, compared with just 12 percent of other respondents.

3. AI-related talent needs shift, and AI’s workforce effects are expected to be substantial

Our latest survey results show changes in the roles that organizations are filling to support their AI ambitions. In the past year, organizations using AI most often hired data engineers, machine learning engineers, and Al data scientists—all roles that respondents commonly reported hiring in the previous survey. But a much smaller share of respondents report hiring AI-related-software engineers—the most-hired role last year—than in the previous survey (28 percent in the latest survey, down from 39 percent). Roles in prompt engineering have recently emerged, as the need for that skill set rises alongside gen AI adoption, with 7 percent of respondents whose organizations have adopted AI reporting those hires in the past year.

The findings suggest that hiring for AI-related roles remains a challenge but has become somewhat easier over the past year, which could reflect the spate of layoffs at technology companies from late 2022 through the first half of 2023. Smaller shares of respondents than in the previous survey report difficulty hiring for roles such as AI data scientists, data engineers, and data-visualization specialists, though responses suggest that hiring machine learning engineers and AI product owners remains as much of a challenge as in the previous year.

Looking ahead to the next three years, respondents predict that the adoption of AI will reshape many roles in the workforce. Generally, they expect more employees to be reskilled than to be separated. Nearly four in ten respondents reporting AI adoption expect more than 20 percent of their companies’ workforces will be reskilled, whereas 8 percent of respondents say the size of their workforces will decrease by more than 20 percent.

Looking specifically at gen AI’s predicted impact, service operations is the only function in which most respondents expect to see a decrease in workforce size at their organizations. This finding generally aligns with what our recent research  suggests: while the emergence of gen AI increased our estimate of the percentage of worker activities that could be automated (60 to 70 percent, up from 50 percent), this doesn’t necessarily translate into the automation of an entire role.

AI high performers are expected to conduct much higher levels of reskilling than other companies are. Respondents at these organizations are over three times more likely than others to say their organizations will reskill more than 30 percent of their workforces over the next three years as a result of AI adoption.

4. With all eyes on gen AI, AI adoption and impact remain steady

While the use of gen AI tools is spreading rapidly, the survey data doesn’t show that these newer tools are propelling organizations’ overall AI adoption. The share of organizations that have adopted AI overall remains steady, at least for the moment, with 55 percent of respondents reporting that their organizations have adopted AI. Less than a third of respondents continue to say that their organizations have adopted AI in more than one business function, suggesting that AI use remains limited in scope. Product and service development and service operations continue to be the two business functions in which respondents most often report AI adoption, as was true in the previous four surveys. And overall, just 23 percent of respondents say at least 5 percent of their organizations’ EBIT last year was attributable to their use of AI—essentially flat with the previous survey—suggesting there is much more room to capture value.

Organizations continue to see returns in the business areas in which they are using AI, and they plan to increase investment in the years ahead. We see a majority of respondents reporting AI-related revenue increases within each business function using AI. And looking ahead, more than two-thirds expect their organizations to increase their AI investment over the next three years.

The online survey was in the field April 11 to 21, 2023, and garnered responses from 1,684 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 913 said their organizations had adopted AI in at least one function and were asked questions about their organizations’ AI use. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

The survey content and analysis were developed by Michael Chui , a partner at the McKinsey Global Institute and a partner in McKinsey’s Bay Area office, where Lareina Yee is a senior partner; Bryce Hall , an associate partner in the Washington, DC, office; and senior partners Alex Singla and Alexander Sukharevsky , global leaders of QuantumBlack, AI by McKinsey, based in the Chicago and London offices, respectively.

They wish to thank Shivani Gupta, Abhisek Jena, Begum Ortaoglu, Barr Seitz, and Li Zhang for their contributions to this work.

This article was edited by Heather Hanselman, an editor in the Atlanta office.

Explore a career with us

Related articles.

The economic potential of generative AI: The next productivity frontier

What is generative AI?

Exploring opportunities in the generative AI value chain

• Skip to main content
• Skip to search
• Skip to footer

Products and Services

Now available: ccna v1.1 exam topics.

CCNA certification

Validate your knowledge and skills in network fundamentals and access, IP connectivity, IP services, security fundamentals, and more. Take your IT career in any direction by earning a Cisco Certified Network Associate (CCNA) certification.

Your career in networking begins with CCNA

Take your IT career in any direction by earning a CCNA. CCNA validates a broad range of fundamentals for all IT careers - from networking technologies, to security, to software development - proving you have the skills businesses need to meet market demands.

Networking fundamentals

Showcase your knowledge of networking equipment and configuration. Be able to troubleshoot connectivity issues and effectively manage networks.

IP Services

Demonstrate your ability to configure routing for different IP versions and describe the purpose of redundancy protocols. Be able to interpret the components of a routing table.

Security fundamentals

Understand threats and ways to prevent them. Identify key elements of a security program, like user awareness and training. Demonstrate practical skills like setting up secure access to devices and networks.

Understand how automation affects network management, and compare traditional networks with controller-based networking. Leverage APIs, and understand configuration management tools.

CCNA Certification

How it works, no formal prerequisites.

CCNA is an asset to IT professionals of all experience levels, but learners often benefit from one or more years of experience implementing and administering Cisco solutions.

Example learner profiles

• Individuals looking to move into the IT field
• IT professionals looking to stand out in the job market
• IT professionals looking to enrich their current roles with additional networking skills

Getting started

To earn this certification, you’ll need to pass a single required exam.

A variety of resources are available to help you study - from guided learning to self-study and a community forum.

Unlock your career potential

Because CCNA covers so many IT fundamentals, it’s a great way to stand out no matter where your career takes you.

Potential roles

Network engineer.

Apply a range of technologies to connect, secure, and automate complex networks.

Network administrator

Install, maintain, monitor, and troubleshoot networks and keep them secure.

Help desk administrator

Diagnose and troubleshoot technical issues for clients and employees.

Alumni testimonials

Ccna moved elvin up the career ladder.

"Passing that CCNA exam triggered a chain of events I could never have predicted. First, I was a student, then a teacher, then a Cisco instructor, and I eventually became a Cisco VIP."

Elvin Arias Soto, CloudOps engineer

CCNA, CCDP, CCDA, CCNP, CCIE

Certifications give Kevin instant credibility at work

"People always want to know who they're talking to. They want to know if you’re qualified. Certifications give you instant credibility."

Kevin Brown, CyberOps analyst

CCNA, CyberOps Associate

Ben made a career change with a Cisco certification

"I chose to pursue Cisco certifications because I knew it would put me in the best position to start a career in networking."

Ben Harting, Configuration engineer

Maintain your certification

Your certification is valid for three years. You can renew with Continuing Education credits or retake exams before they expire.

CCNA essentials webinar series

Learn what to expect from the CCNA exam, and chart your path to certification success.

CCNA certification guide

Get familiar with Cisco’s learning environment, find study resources, and discover helpful hints for earning your CCNA.

CCNA Prep Program

Packed with 50+ hours of resources, webinars, and practice quizzes, CCNA Prep On Demand is your ultimate study buddy.

Enhance your learning journey

Stay up to date.

Get the latest news about Cisco certifications, plus tools and insights to help you get where you want to go.

CCNA community

Not sure where to begin? Head to the Cisco CCNA community to get advice and connect with experts.