ANALYZING COGNITIVE ABILITY AND ACADEMIC PERFORMANCE TO IDENTIFY STUDENT PROFILES: A DATA-DRIVEN APPROACH FOR EDUCATIONAL SUPPORT
Abstract
The connection of cognitive ability and educational success and efforts to elicit vivid images of students using data-drivenapproach. The analysis uses a sample size of 200 student data that include Intelligence Quotient (IQ) that is a cognitiveability measure and Cumulative Grade Point Average (CGPA) that is an academic performance measure.The associationbetween the variables was investigated using descriptive analysis, correlation analysis and K-means clustering was usedto determine significant student groups. The results indicate that there is a moderate or strong positive relationshipbetween IQ and CGPA, which means that the higher is the cognitive ability, the more positive is its academic performance.Nevertheless, the fact that there is significant variability indicates that academic performance is affected by other factorsother than intelligence. The clustering analysis revealed four different student profiles, such as high achievers, underperforming high potential students, compensatory learners, and students at risk of academic failure. These findings pointto the significance of embracing evidence-based methods to learn about the diversity of students and assist them withspecific educational interventions. The research shows that despite the few variables, valuable lessons can be learned toinfluence the instructional practicesand improve learning outcomes. Findings also aid in the field of research in educationby highlighting the incorporation of the analytical techniques in enhancing teaching and learning processes.
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References
Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st
century higher education: A review and synthesis. Telematics and Informatics, 37, 13-49.
Alghamdi, A. S., & Rahman, A. (2023). Data mining approach to predict success of secondary school students: A
Saudi Arabian case study. Education Sciences, 13(3), 293.
Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students' performance using
educational data mining. Computers & education, 113, 177-194.
Avella, J. T., Kebritchi, M., Nunn, S. G., & Kanai, T. (2016). Learning analytics methods, benefits, and challenges in
higher education: A systematic literature review. Online Learning, 20(2), 13-29.
Bakhshinategh, B., Zaiane, O. R., ElAtia, S., & Ipperciel, D. (2018). Educational data mining applications and tasks:
A survey of the last 10 years. Education and Information Technologies, 23(1), 537-553.
Conijn, R., Snijders, C., Kleingeld, A., & Matzat, U. (2016). Predicting student performance from LMS data: A
comparison of 17 blended courses using Moodle LMS. IEEE Transactions on Learning Technologies, 10(1), 17-29.
Costa, A., & Faria, L. (2018). Implicit theories of intelligence and academic achievement: A meta-analytic
review. Frontiers in psychology, 9, 829.
Gamazo, A., & Martínez-Abad, F. (2020). An exploration of factors linked to academic performance in PISA 2018
through data mining techniques. Frontiers in Psychology, 11, 575167.
Gitinabard, N., Xu, Y., Heckman, S., Barnes, T., & Lynch, C. F. (2019). How widely can prediction models be
generalized? Performance prediction in blended courses. IEEE Transactions on Learning Technologies, 12(2), 184
Hernández-de-Menéndez, M., Morales-Menendez, R., Escobar, C. A., & Ramírez Mendoza, R. A. (2022). Learning
analytics: state of the art. International Journal on Interactive Design and Manufacturing (IJIDeM), 16(3), 1209-1230.
Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics to support study success in higher education: a
systematic review. Educational technology research and development, 68(4), 1961-1990.
Kumar,
S.
(2022).
CGPA,
IQ,
and
placement
dataset
[Data
set].
Kaggle.
https://www.kaggle.com/datasets/kuchhbhi/cpga-iq-placement
Lin-Siegler, X., Dweck, C. S., & Cohen, G. L. (2016). Instructional interventions that motivate classroom
learning. Journal of Educational Psychology, 108(3), 295.
MacCann, C., Jiang, Y., Brown, L., Double, K., Bucich, M., & Minbashian, A. (2019). Post-Print manuscript:
Emotional Intelligence Predicts Academic Performance: A Meta-Analysis.
Md, S., & Krishnamoorthy, S. (2022). Student performance prediction, risk analysis, and feedback based on context
bound cognitive skill scores. Education and Information Technologies, 27(3), 3981-4005.
Paunesku, D., Walton, G. M., Romero, C., Smith, E. N., Yeager, D. S., & Dweck, C. S. (2015). Mind-set interventions
are a scalable treatment for academic underachievement. Psychological science, 26(6), 784-793.
Perera, H. N., & DiGiacomo, M. (2015). The role of trait emotional intelligence in academic performance during the
university transition: An integrative model of mediation via social support, coping, and adjustment. Personality and
individual differences, 83, 208-213.
Perez Sanchez, C. J., Calle-Alonso, F., & Vega-Rodríguez, M. A. (2022). Learning analytics to predict students’
performance: A case study of a neurodidactics-based collaborative learning platform. Education and Information
Technologies, 27(9), 12913-12938.
Roth, B., Becker, N., Romeyke, S., Schäfer, S., Domnick, F., & Spinath, F. M. (2015). Intelligence and school grades:
A meta-analysis. Intelligence, 53, 118-137.
Semeraro, C., Giofrè, D., Coppola, G., Lucangeli, D., & Cassibba, R. (2020). The role of cognitive and non-cognitive
factors in mathematics achievement: The importance of the quality of the student-teacher relationship in middle
school. Plos one, 15(4), e0231381.
Soares, D. L., Lemos, G. C., Primi, R., & Almeida, L. S. (2015). The relationship between intelligence and academic
achievement throughout middle school: The role of students' prior academic performance. Learning and Individual
differences, 41, 73-78.
Yadav, R. S. (2020). Application of hybrid clustering methods for student performance evaluation. International
Journal of Information Technology, 12(3), 749-756.
Zhang, Y., Yun, Y., An, R., Cui, J., Dai, H., & Shang, X. (2021). Educational data mining techniques for student
performance prediction: method review and comparison analysis. Frontiers in psychology, 12, 698490.
