Learning Styles and Academic Performance: What Research Shows
This paper reviews four scholarly studies on learning styles and their relationship to academic performance in higher education. It examines Vermunt's Inventory of Learning Styles as applied to British social science students, Furnham's findings on conscientiousness and intelligence as predictors of college performance, the role of active and multimodal teaching in economics courses, and Oxford University research that qualifies the benefits of deep learning by highlighting the influence of personality, socioeconomic background, and instructional quality. Together, the studies reveal that while deep learning is generally favored over surface learning, academic success is shaped by a complex interplay of individual characteristics, motivation, teaching approaches, and environmental factors.
- Introduction: Overview of four studies on learning styles
- Deep Learning vs. Surface Learning: Vermunt's Inventory applied to British students
- Three Approaches to Learning: Deep, Achieving, and Surface: Conscientiousness and intelligence as performance predictors
- Active Teaching Methods in Economics: Multimodal instruction to boost economics engagement
- Counter-Argument: The Limits of Deep Learning: Oxford study highlights personality and environment factors
- Conclusion: Academic success shaped by multiple interacting factors
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What makes this paper effective
- Clearly organizes each study into its own section, making the comparative structure easy to follow.
- Balances supportive evidence with a counter-argument section, demonstrating awareness of scholarly debate rather than one-sided advocacy.
- Uses direct quotations strategically to anchor analytical claims in the source material.
Key academic technique demonstrated
The paper demonstrates an effective literature synthesis technique: each source is introduced with its research context (sample size, institution, methodology), a key finding is extracted and quoted, and a brief analytical comment connects the finding to the paper's central theme. This pattern creates a consistent, readable review structure that is easy to replicate in undergraduate literature-based essays.
Structure breakdown
The paper opens with a brief framing introduction, then moves through four source-based sections in a logical sequence: first establishing that deep learning is preferable to surface learning (Boyle et al.), then refining the picture with personality and intelligence variables (Furnham), then broadening scope to teaching methods (Boatman et al.), and finally complicating the deep-learning consensus with a counter-argument (Mellanby et al.). A Works Cited list closes the paper in MLA-adjacent format, though in-text citations follow an author-date pattern closer to APA.
Introduction
There has been a great deal of scholarly research on how students learn and how certain learning styles affect academic outcomes. This paper reviews four scholarly research studies that describe different learning styles and the academic achievements associated with them.
Deep Learning vs. Surface Learning
Several learning styles were examined in a research project involving 273 social science students at a British university. The styles observed — part of Vermunt's Inventory of Learning Styles — included "deep learning," "self-regulated learning," "intrinsic motivation," and "constructivist conception of learning." In the research, these four styles were considered preferable to "surface learning," "teacher-centered learning," "extrinsic motivation," and "objectivist conception of learning" (Boyle et al., 2003).
The most effective learning style in terms of positive academic outcomes was "deep learning," which requires the student to "understand meaning by working out relationships between concepts, relating new material to previously known information" (Boyle, p. 268). Surface learners, by contrast, tend to rely on memorization to retain knowledge, primarily motivated by the goal of earning good grades and, eventually, securing employment (Boyle, p. 268).
Three Approaches to Learning: Deep, Achieving, and Surface
Psychology professor Adrian Furnham conducted research to identify which learning methods best support student achievement. In his study, 178 psychology students at University College London completed six tests upon entering the university. One year later, they were given comprehensive essay-based exams. Results showed that "Conscientiousness and General Intelligence" were the only significant predictors of academic performance (Furnham, 2012).
One notable finding was that intelligence accounts for only 5 to 20% of the factors involved in measuring learning ability. Three variables played a substantial role in learning outcomes: conscientiousness, deep learning, and personality — or, as Furnham frames it, "intelligence and personality" (Furnham, p. 120). Students who were "very focused on the relevant (examinable) aspects of the topic and those who were pragmatic and extrinsically (not intrinsically) motivated" tended to perform best (Furnham, p. 124).
Conclusion
Taken together, these four studies confirm that while deep learning is broadly beneficial, academic success depends on a complex interplay of learning style, personality, motivation, teaching quality, and home environment. No single factor operates in isolation, and effective higher education must account for the diverse characteristics students bring to the classroom.
Works Cited
Boatman, K., Courtney, R., and Lee, W. (2008). "See How They Learn": The Impact of Faculty and Student Learning Styles on Student Performance in Introductory Economics. The American Economist, 52(1), 39–52.
Boyle, E. A., Duffy, T., and Dunleavy, K. (2003). Learning styles and academic outcome: The validity and utility of Vermunt's Inventory of Learning Styles in a British higher education setting. British Journal of Educational Psychology, 73, 267–290.
Furnham, A. (2012). Learning Style, Personality Traits and Intelligence as Predictors of College Academic Performance. Individual Differences Research, 10(3), 117–128.
Mellanby, J., Cortina-Borja, M., and Stein, J. (2009). Deep learning questions can help selection of high ability candidates for universities. Higher Education, 57, 587–608.
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