EXPLORING THE IMPACT OF COGNITIVE, AFFECTIVE, AND PERSONALITY DIFFERENCES ON LEARNING PROCESSES
Keywords:
Keywords: individual differences, learning processes, cognitive style, metacognition, self-regulated learning, working memory capacityAbstract
Individual differences play a critical role in shaping how learners engage with
and internalize new information during various stages of the learning process. This
study investigates the extent to which cognitive, affective, and personality-related
individual differences predict distinct learning processes—namely encoding, rehearsal,
elaboration, and metacognitive regulation. Employing a mixed-methods design, 180
undergraduate participants completed standardized measures of working memory
capacity, cognitive style, learning motivation, and trait anxiety. Quantitative data were
analyzed using structural equation modeling to examine direct and indirect effects of
individual differences on learning outcomes. Complementing this, think-aloud
protocols from a purposive subsample of 30 students were thematically coded to
identify strategy use during problem-solving tasks. Results reveal that (a) higher
working memory capacity and reflective cognitive styles are positively associated with
deeper elaboration strategies, (b) intrinsic motivation and low anxiety levels predict
more frequent metacognitive monitoring and regulation, and (c) personality traits
linked to conscientiousness moderate the relationship between cognitive style and
rehearsal strategies. Qualitative themes illustrate how learners adapt their study tactics
in real time, confirming and extending the quantitative model. These findings
underscore the necessity of tailoring instructional design to accommodate
multidimensional individual differences, suggesting that adaptive scaffolding and
metacognitive prompts can enhance learning efficiency. Implications for educators
include integrating diagnostic assessments of learner profiles and embedding process-
oriented interventions to foster self-regulated learning.
References
References:
1. Allinson, C. W., & Hayes, J. (1996). The Cognitive Style Index: A measure of
intuition–analysis for organizational research. Journal of Management Studies,
33(1), 119–135.
2. Ashcraft, M. H., & Kirk, E. P. (2001). The relationships among working memory,
math anxiety, and performance. Journal of Experimental Psychology: General,
130(2), 224–237.
3. Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system
and its control processes. In K. W. Spence & J. T. Spence (Eds.), The psychology
of learning and motivation (Vol. 2, pp. 89–195). Academic Press.
4. Baddeley, A. (2000). The episodic buffer: A new component of working memory?
Trends in Cognitive Sciences, 4(11), 417–423.
5. Chamorro-Premuzic, T., & Furnham, A. (2003). Personality predicts academic
performance: Evidence from two longitudinal university samples. Journal of
Research in Personality, 37(4), 319–338.
6. Craik, F. I. M., & Lockhart, R. S. (1972). Levels of processing: A framework for
memory research. Journal of Verbal Learning and Verbal Behavior, 11(6), 671–
684.
7. Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed
methods research (3rd ed.). Sage.
8. Crowne, D. P., & Marlowe, D. (1960). A new scale of social desirability
independent of psychopathology. Journal of Consulting Psychology, 24(4), 349–
352.
9. Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits:
Human needs and the self‐determination of behavior. Psychological Inquiry,
11(4), 227–268.
10. Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, phone, mail,
and mixed‐mode surveys: The tailored design method (4th ed.). Wiley.
11. Dunlosky, J., & Metcalfe, J. (2009). Metacognition. Sage.
12. Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: Verbal reports as data
(Rev. ed.). MIT Press.
13. Evans, C. (2008). The effectiveness of metacognitive strategy instruction on
learning to learn. Educational Psychology Review, 20(3), 283–298.
14. Evans, C., & Waring, M. (2012). The impact of cognitive style on learning and
training. Journal of Applied Psychology, 97(2), 374–385.
15. Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and
cognitive performance: Attentional control theory. Emotion, 7(2), 336–353.
16. Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage.
17. Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of
cognitive‐developmental inquiry. American Psychologist, 34(10), 906–911.
18. Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough?
Field Methods, 18(1), 59–82.
19. Hallgren, K. A. (2012). Computing inter‐rater reliability for observational data:
An overview and tutorial. Tutorials in Quantitative Methods for Psychology, 8(1),
23–34.
20. Jefferson, G. (2004). Glossary of transcript symbols with an introduction. In G.
H. Lerner (Ed.), Conversation analysis: Studies from the first generation (pp. 13–
31). John Benjamins.
21. Kline, R. B. (2015). Principles and practice of structural equation modeling (4th
ed.). Guilford Press.
22. Little, R. J. A. (1988). A test of missing completely at random for multivariate
data with missing values. Journal of the American Statistical Association,
83(404), 1198–1202.
23. Miles, M. B., Huberman, A. M., & Saldaña, J. (2014). Qualitative data analysis:
A methods sourcebook (3rd ed.). Sage.
24. Mueller, S. T., & Piper, B. J. (2014). The Psychology Experiment Building
Language (PEBL) and PEBL test battery. Behavior Research Methods, 46(2),
427–432.
25. O’Connor, M. C., & Paunonen, S. V. (2007). Big Five personality predictors of
post‐secondary academic performance. Personality and Individual Differences,
43(5), 971–990.
