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Principal Component Clustered Factors for Determining Study Performance in Computer Programming Class

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Abstract

Studying computer programming requires not only an understanding of theories and concepts, but also coding pragmatism. Success in studying or conducting such a course is definitely a challenge. This paper proposes a model that transforms students’ attributes (including the cognitive and non-cognitive abilities, and traditional lagging measures of academic background) into a set of principal components (PCs). As opposed to traditional approaches, the proposed model optimally extracts the orthogonal PCs to form a basis for determining the studying performance of students in terms of declarative knowledge and procedural proficiency (or skill). The obtained relationship model yields two contributive values (1) an optimal set of determinants, in the form of students’ clusters, to determine study performance and (2) the fully preserved interpretability of the original attributes of students in each PC. The experiment was conducted using 115 complete datasets of IT major students who enrolled the Introduction to Computer Programming course. The Best Subset Selection and LASSO algorithms were deployed to find the optimal set of features. The effectiveness of the model was validated by multiple linear regression to predict the performance in terms of knowledge and skills with an accuracy of 76.52%, and 70.44%, respectively. Insights into the interpretability of student clusters are provided.

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References

  1. Bennedsen, J., & Caspersen, M. E. (2007). Failure rates in introductory programming. AcM SIGcSE Bulletin, 39(2), 32.

    Article  Google Scholar 

  2. Watson, C., & Li, F. W. (2014). Failure rates in introductory programming revisited. In Proceedings of the 2014 conference on Innovation and technology in computer science education (pp. 39–44). ACM.

  3. Ninrutsirikun, U., Watanapa, B., Arpnikanondt, C., & Watananukoon, V. (2018). A unified framework for student cluster grouping with learning preference associative detection for enhancing students’ learning outcomes in computer programming courses. In The 6th global wireless summit (GWS-2018) (pp. 266–271). IEEE.

  4. Mark, B. (2016). Theory of knowledge: Structures and processes (Vol. 5). Singapore: World scientific.

    Google Scholar 

  5. Berkowitz, M., & Stern, E. (2018). Which cognitive abilities make the difference? Predicting academic achievements in advanced stem studies. Journal of Intelligence, 6(4), 48.

    Article  Google Scholar 

  6. Shahiri, A. M., Husain, W., et al. (2015). A review on predicting student’s performance using data mining techniques. Procedia Computer Science, 72, 414.

    Article  Google Scholar 

  7. Bowles, S., & Gintis, H. (1976). Education, inequality, and the meritocracy. In Schooling in the Capitalist America: Educational reform and the contradictions of economic life (pp. 102–124).

  8. Zhou, K. (2017). Non-cognitive skills: Potential candidates for global measurement. European Journal of Education, 52(4), 487.

    Article  Google Scholar 

  9. Khan, Z. N. (2009). Cognitive and non-cognitive characteristics as determinants of success in professional courses at undergraduate stage. Online Submission, 5(3), 212.

    Google Scholar 

  10. Bergin, S., & Reilly, R. (2005). Programming: factors that influence success. ACM SIGCSE bulletin (pp. 411–415). New York: ACM.

    Google Scholar 

  11. van Herpen, S. G., Meeuwisse, M., Hofman, W. A., Severiens, S. E., & Arends, L. R. (2017). Early predictors of first-year academic success at university: Pre-university effort, pre-university self-efficacy, and pre-university reasons for attending university. Educational Research and Evaluation, 23(1–2), 52.

    Article  Google Scholar 

  12. Geiser, S., & Santelices, M. V. (2007). Validity of high-school grades in predicting student success beyond the freshman year: High-school record vs. standardized tests as indicators of four-year college outcomes. Research & Occasional Paper Series: CSHE.6.07 (2007)

  13. Wilson, B. C., & Shrock, S. (2001). Contributing to success in an introductory computer science course: a study of twelve factors. In ACM SIGCSE bulletin (pp. 184–188). New York: ACM.

  14. Byrne, P., & Lyons, G. (2001). The effect of student attributes on success in programming. ACM SIGCSE Bulletin, 33(3), 49.

    Article  Google Scholar 

  15. Wang, H. Y., Huang, I., & Hwang, G. J. (2016). Comparison of the effects of project-based computer programming activities between mathematics-gifted students and average students. Journal of Computers in Education, 3(1), 33.

    Article  Google Scholar 

  16. Ninrutsirikun, U., Watanapa, B., Arpnikanondt, C., & Phothikit, N. (2016). Effect of the multiple intelligences in multiclass predictive model of computer programming course achievement. In 2016 IEEE region 10 conference (TENCON) (pp. 297–300). IEEE.

  17. Thai-Nghe, N., Busche, A., & Schmidt-Thieme, L. (2009). Improving academic performance prediction by dealing with class imbalance. In 2009 Ninth international conference on intelligent systems design and applications (pp. 878–883). IEEE.

  18. Trakulphadetkrai, N. V. (2011). Thailand: Educational equality and quality. Education in South-East Asia, pp. 197–219.

  19. Gardner, H. (2011). Frames of mind: The theory of multiple intelligences. London: Hachette.

    Google Scholar 

  20. Wang, H. (2017). Research on multiple intelligences theory and its enlightenment to higher education. Research on Modern Higher Education, 3(unknown), 121.

    Article  Google Scholar 

  21. Baş, G., & Beyhab, Ö. (2017). Effects of multiple intelligences supported project-based learning on students’ achievement levels and attitudes towards english lesson. International Electronic Journal of Elementary Education, 2(3), 365.

    Google Scholar 

  22. Lazear, D. (2000). The intelligent curriculum: Using multiple intelligences to develop your students’ full potential.. Kern County: ERIC.

  23. Armstrong, T. (2009). Multiple intelligences in the classroom (Ascd).

  24. Davis, K., Christodoulou, J., Seider, S., & Gardner, H. E. (2011). The theory of multiple intelligences. In The Cambridge handbook of intelligence (pp. 485–503)

  25. Bilgin, I., Karakuyu, Y., & Ay, Y. (2015). The effects of project based learning on undergraduate students’ achievement and self-efficacy beliefs towards science teaching. Eurasia Journal of Mathematics, Science and Technology Education, 11(3), 469.

    Article  Google Scholar 

  26. Kezar, A. (2001). Theory of multiple intelligences: Implications for higher education. Innovative Higher Education, 26(2), 141.

    Article  Google Scholar 

  27. Gardner, H. (1994). Multiple intelligences: The theory in practice. 1993, Múltiples inteligencias: La Teoría en Práctica.

  28. Ford, T. D. (2015). Barriers to computer programming student success: A quantitative study of community college students in southwest missouri. Ph.D. thesis, Lindenwood University.

  29. Bogarín, A., Romero, C., Cerezo, R., & Sánchez-Santillán, M. (2014). Clustering for improving educational process mining. In Proceedings of the fourth international conference on learning analytics and knowledge (pp. 11–15). ACM.

  30. Chamorro-Premuzic, T., & Furnham, A. (2014). Personality and intellectual competence. In Personality and intellectual competence. London: Psychology Press.

  31. O’Connor, M. C., & Paunonen, S. V. (2007). Big five personality predictors of post-secondary academic performance. Personality and Individual differences, 43(5), 971.

    Article  Google Scholar 

  32. Schmitt, D. P., Allik, J., McCrae, R. R., & Benet-Martínez, V. (2007). The geographic distribution of big five personality traits: Patterns and profiles of human self-description across 56 nations. Journal of Cross-Cultural Psychology, 38(2), 173.

    Article  Google Scholar 

  33. McCrae, R. R., & John, O. P. (1992). An introduction to the five-factor model and its applications. Journal of Personality, 60(2), 175.

    Article  Google Scholar 

  34. Poropat, A. E. (2009). A meta-analysis of the five-factor model of personality and academic performance. Psychological Bulletin, 135(2), 322.

    Article  Google Scholar 

  35. Schneider, M., & Preckel, F. (2017). Variables associated with achievement in higher education: A systematic review of meta-analyses. Psychological Bulletin, 143(6), 565.

    Article  Google Scholar 

  36. Rothstein, M. G., Paunonen, S. V., Rush, J. C., & King, G. A. (1994). Personality and cognitive ability predictors of performance in graduate business school. Journal of Educational Psychology, 86(4), 516.

    Article  Google Scholar 

  37. Chamorro-Premuzic, T., & Furnham, A. (2003). Personality predicts academic performance: Evidence from two longitudinal university samples. Journal of Research in Personality, 37(4), 319.

    Article  Google Scholar 

  38. Evans, G. E., & Simkin, M. G. (1989). What best predicts computer proficiency? Communications of the ACM, 32(11), 1322.

    Article  Google Scholar 

  39. Chandra, E., & Nandhini, K. (2010). Knowledge mining from student data. European Journal of Scientific Research, 47(1), 156.

    Google Scholar 

  40. Kumar, V., Somasundaram, T., Harris, S., Boulanger, D., Seanosky, J., Paulmani, G., et al. (2015). An approach to measure coding competency evolution. In Smart learning environments (pp. 27–43). Berlin: Springer.

  41. Bergersen, G. R., & Gustafsson, J. E. (2011). Programming skill, knowledge, and working memory among professional software developers from an investment theory perspective. Journal of Individual Differences, 32(4), 201–209.

    Article  Google Scholar 

  42. Campbell, J. P., McCloy, R. A., Oppler, S. H., & Sager, C. E. (1993). A theory of performance. Personnel Selection in Organizations, 3570, 35.

    Google Scholar 

  43. Bergersen, G. R., Hannay, J. E., Sjoberg, D. I., Dyba, T., & Karahasanovic, A. (2011). Inferring skill from tests of programming performance: Combining time and quality. In 2011 international symposium on empirical software engineering and measurement (pp. 305–314). IEEE.

  44. Latham, G. P., & Pinder, C. C. (2005). Work motivation theory and research at the dawn of the twenty-first century. Annual Review of Psychology, 56, 485.

    Article  Google Scholar 

  45. Feng, Q., Hannig, J., & Marron, J. (2016). A note on automatic data transformation. Stat, 5(1), 82.

    Article  Google Scholar 

  46. Pinto, S. K., Mansfield, R., Jacobs, M., & Rubin, D. (2010). Predictive model augmentation by variable transformation. US Patent 7,730,003.

  47. Guyon, I., & Elisseeff, A. (2006). An introduction to feature extraction. In Feature extraction (pp. 1–25). Berlin: Springer.

  48. Nagy, M., & Molontay, R. (2018). Predicting dropout in higher education based on secondary school performance. In 2018 IEEE 22nd international conference on intelligent engineering systems (INES) (pp. 000,389–000,394). IEEE.

  49. Shaleena, K., & Paul, S. (2015). Data mining techniques for predicting student performance. In 2015 IEEE international conference on engineering and technology (ICETECH) (pp. 1–3). IEEE.

  50. Pechenizkiy, M., Tsymbal, A., & Puuronen, S. (2004). PCA-based feature transformation for classification: Issues in medical diagnostics. In Proceedings of the 17th IEEE symposium on computer-based medical systems (pp. 535–540). IEEE.

  51. Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202.

    Article  MathSciNet  Google Scholar 

  52. Gniazdowski, Z. (2017). New interpretation of principal components analysis, arXiv preprint arXiv:1711.10420.

  53. Meyer, R., & Krueger, D. (2001). Minitab guide to statistics. Upper Saddle River: Prentice Hall.

    Google Scholar 

  54. Ebel, R. L. (1972). Essentials of educational measurement. Upper Saddle River: Prentice-Hall.

    Google Scholar 

  55. Borghans, L., Duckworth, A. L., Heckman, J. J., & Ter Weel, B. (2008). The economics and psychology of personality traits. Journal of Human Resources, 43(4), 972.

    Article  Google Scholar 

  56. John, O. P., Srivastava, S., et al. (1999). The big five trait taxonomy: History, measurement, and theoretical perspectives. Handbook of Personality: Theory and Research, 2(1999), 102.

    Google Scholar 

  57. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112). Berlin: Springer.

    Book  Google Scholar 

  58. Fonti, V., & Belitser, E. (2017). Feature selection using lasso. VU Amsterdam Research Paper in Business Analytics, 30, 1–25.

    Google Scholar 

  59. George, D. (2011). SPSS for windows step by step: A simple study guide and reference, 17.0 update, 10/e. Bengaluru: Pearson Education India.

    Google Scholar 

  60. Calcagno, V., de Mazancourt, C., et al. (2010). glmulti: An R package for easy automated model selection with (generalized) linear models. Journal of Statistical Software, 34(12), 1.

    Article  Google Scholar 

  61. Friedman, J., Hastie, T., & Tibshirani, R. (2009). glmnet: Lasso and elastic-net regularized generalized linear models. R Package version 1.4 (2009).

  62. Dumfart, B., & Neubauer, A. C. (2016). Conscientiousness is the most powerful noncognitive predictor of school achievement in adolescents. Journal of individual Differences, 37(1), 8–15.

    Article  Google Scholar 

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Acknowledgements

The authors would like to express their sincere gratitude to all volunteers, lecturers, and staff at the School of Information Technology (SIT), King Mongkut’s University of Technology, Thonburi (KMUTT) for assisting in data collection process. The main author would like to express her grateful appreciation and thanks to Petchra Pra Jom Klao Ph.D. Research Scholarship for granting a full Ph.D. scholarship. She also wants to thank the School of Information Technology (SIT) for all support.

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Authors and Affiliations

  1. Data Science and Engineering Laboratory (D-Lab), School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, 10140, Thailand

    Unhawa Ninrutsirikun, Bunthit Watanapa & Chonlameth Arpnikanondt

  2. Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo, Hokkaido, 060-0814, Japan

    Hideyuki Imai

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  1. Unhawa Ninrutsirikun
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Correspondence to Unhawa Ninrutsirikun.

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Ninrutsirikun, U., Imai, H., Watanapa, B. et al. Principal Component Clustered Factors for Determining Study Performance in Computer Programming Class. Wireless Pers Commun 115, 2897–2916 (2020). https://doi.org/10.1007/s11277-020-07194-5

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