Unified Model for Learning Style Recommendation
Keywords:Association Evaluation, Association Rules, Guideline, Learning Styles, Moderation Analysis, Style-fit Strategy
Studying computer programming requires not only an understanding of theories and concepts but also coding adeptness. Success in studying or conducting such a course is definitely a challenge. This paper proposes a systematic learning style recommendation. The model is designed to evaluate students’ attributes and ongoing or formative learning outcomes for suggesting the effective style-fit strategy that facilitates learners to enhance their learning performances in terms of knowledge and skill. A two-stage association analysis was designed and conducted on a dataset collected from IT major students who enrolled in the Introduction to Computer Programming course. The first stage of association rules is to analyze and discover important relationships amongst learning styles, students’ attribute, and learning performance. The second stage of moderation analysis is then applied to probe the moderation effect of the different learning preferences on the relationship between student attributes and learning achievement. Experiments expose many insights, for example, mathematics and logical thinking are powerful assets of success in computer programming study. Association rules can effectively identify associations of learning styles and the learning performance in terms of knowledge or skills. By moderation analysis, students in the “Excellent” cluster have a broad learning style than other students. Two types of significant moderators, the universal and specific, exemplify how lecturers can flexibly post style-fit teaching strategies for a class-wide and specific group, respectively.
C. Watson and F. W. B. Li, “Failure rates in introductory programming revisited,” in Proceedings of the 2014 conference on Innovation & technology in computer science education, 2014, pp. 39–44.
J. Bennedsen and M. E. Caspersen, “Failure rates in introductory programming,” ACM SIGcSE Bull., vol. 39, no. 2, pp. 32–36, 2007.
G. Bain and I. Barnes, “Why is programming so hard to learn?,” in Proceedings of the 2014 conference on Innovation & technology in computer science education, 2014, p. 356.
M. Guzdial, “Why is it so hard to learn to program,” Mak. Softw. What Really Work. Why We Believe It. O’Reilly Media, pp. 111–124, 2010.
C. Romero and S. Ventura, “Data mining in education,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 3, no. 1, pp. 12–27, 2013.
D. Perera, J. Kay, I. Koprinska, K. Yacef, and O. R. Zaïane, “Clustering and sequential pattern mining of online collaborative learning data,” IEEE Trans. Knowl. Data Eng., vol. 21, no. 6, pp. 759–772, 2008.
C.-M. Chen, M.-C. Chen, and Y.-L. Li, “Mining key formative assessment rules based on learner profiles for web-based learning systems,” in Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007), 2007, pp. 584–588.
Y. Psaromiligkos, M. Orfanidou, C. Kytagias, and E. Zafiri, “Mining log data for the analysis of learners’ behaviour in web-based learning management systems,” Oper. Res., vol. 11, no. 2, pp. 187–200, 2011.
N. B. A. Normadhi, L. Shuib, H. N. M. Nasir, A. Bimba, N. Idris, and V. Balakrishnan, “Identification of personal traits in adaptive learning environment: Systematic literature review,” Comput. Educ., vol. 130, pp. 168–190, 2019.
S. Sfenrianto, Y. B. Hartarto, H. Akbar, M. Mukhtar, E. Efriadi, and M. Wahyudi, “An adaptive learning system based on knowledge level for English learning,” Int. J. Emerg. Technol. Learn., vol. 13, no. 12, pp. 191–200, 2018.
T.-C. Yang, G.-J. Hwang, and S. J.-H. Yang, “Development of an adaptive learning system with multiple perspectives based on students’ learning styles and cognitive styles,” J. Educ. Technol. Soc., vol. 16, no. 4, pp. 185–200, 2013.
S. Bergin and R. Reilly, “Programming: factors that influence success,” in ACM SIGCSE Bulletin, 2005, vol. 37, no. 1, pp. 411–415.
S. G. A. van Herpen, M. Meeuwisse, W. H. A. Hofman, S. E. Severiens, and L. R. Arends, “Early predictors of first-year academic success at university: pre-university effort, pre-university self-efficacy, and pre-university reasons for attending university,” Educ. Res. Eval., vol. 23, no. 1–2, pp. 52–72, Feb. 2017.
U. Ninrutsirikun, B. Watanapa, C. Arpnikanondt, and N. Phothikit, “Effect of the Multiple Intelligences in multiclass predictive model of computer programming course achievement,” in IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2017.
B. C. Wilson and S. Shrock, “Contributing to success in an introductory computer science course: a study of twelve factors,” in ACM SIGCSE Bulletin, 2001, vol. 33, no. 1, pp. 184–188.
P. Byrne and G. Lyons, “The effect of student attributes on success in programming,” in ACM SIGCSE Bulletin, 2001, vol. 33, no. 3, pp. 49–52.
K. Davis, J. Christodoulou, S. Seider, and H. E. Gardner, “The theory of multiple intelligences,” 2011.
T. D. Ford, “Barriers to Computer Programming Student Success: A Quantitative Study of Community College Students in Southwest Missouri.” Lindenwood University, 2015.
A. Adorjan and I. F. de Kereki, “Multiple Intelligence approach and competencies applied to Computer Science 1,” in 2013 IEEE Frontiers in Education Conference (FIE), 2013, pp. 1170–1172.
U. Ninrutsirikun, H. Imai, B. Watanapa, and C. Arpnikanondt, “Principal Component Clustered Factors for Determining Study Performance in Computer Programming Class,” Wirel. Pers. Commun., pp. 1–20, 2020.
T. Gnambs, “What makes a computer wiz? Linking personality traits and programming aptitude,” J. Res. Pers., vol. 58, pp. 31–34, 2015.
Z. Karimi, A. Baraani-Dastjerdi, N. Ghasem-Aghaee, and S. Wagner, “Using personality traits to understand the influence of personality on computer programming: An empirical study,” J. Cases Inf. Technol., vol. 18, no. 1, pp. 28–48, 2016.
P. Honey and A. Mumford, Learning styles questionnaire. Organization Design and Development, Incorporated, 1989.
M. Schneider and F. Preckel, “Variables associated with achievement in higher education: A systematic review of meta-analyses.,” Psychol. Bull., vol. 143, no. 6, p. 565, 2017.
A. E. Poropat, “A meta-analysis of the five-factor model of personality and academic performance.,” Psychol. Bull., vol. 135, no. 2, p. 322, 2009.
M. C. O’Connor and S. V Paunonen, “Big Five personality predictors of post-secondary academic performance,” Pers. Individ. Dif., vol. 43, no. 5, pp. 971–990, 2007.
M. R. Lavery, P. Acharya, S. A. Sivo, and L. Xu, “Number of predictors and multicollinearity: What are their effects on error and bias in regression?,” Commun. Stat. Comput., vol. 48, no. 1, pp. 27–38, 2019.
S. K. Pinto, R. Mansfield, M. Jacobs, and D. Rubin, “Predictive model augmentation by variable transformation.” Google Patents, 01-Jun-2010.
S. Oxman, W. Wong, and D. V. X. Innovations, “White paper: Adaptive learning systems,” Integr. Educ. Solut., pp. 6–7, 2014.
E. Popescu, “Diagnosing students’ learning style in an educational hypermedia system,” in Cognitive and emotional processes in Web-based education: Integrating human factors and personalization, IGI Global, 2009, pp. 187–208.
S. Ouf, M. A. Ellatif, S. E. Salama, and Y. Helmy, “A proposed paradigm for smart learning environment based on semantic web,” Comput. Human Behav., vol. 72, pp. 796–818, 2017.
Y. Akbulut and C. S. Cardak, “Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011,” Comput. Educ., vol. 58, no. 2, pp. 835–842, 2012.
M. Yilmaz-Soylu and B. Akkoyunlu, “The Effect of Learning Styles on Achievement in Different Learning Environments.,” Turkish Online J. Educ. Technol., vol. 8, no. 4, pp. 43–50, 2009.
H. Pashler, M. McDaniel, D. Rohrer, and R. Bjork, “Learning styles: Concepts and evidence,” Psychol. Sci. public Interes., vol. 9, no. 3, pp. 105–119, 2008.
R. M. Felder and L. K. Silverman, “Learning and teaching styles in engineering education,” Eng. Educ., vol. 78, no. 7, pp. 674–681, 1988.
D. A. Kolb, The Kolb learning style inventory. Hay Resources Direct Boston, MA, 2007.
M. Ehrman and R. Oxford, “Adult language learning styles and strategies in an intensive training setting,” Mod. Lang. J., vol. 74, no. 3, pp. 311–327, 1990.
R. Dunn, “Understanding the Dunn and Dunn learning styles model and the need for individual diagnosis and prescription,” Reading, Writing, Learn. Disabil., vol. 6, no. 3, pp. 223–247, 1990.
R. M. Felder and R. Brent, “Understanding student differences,” J. Eng. Educ., vol. 94, no. 1, pp. 57–72, 2005.
B. A. Soloman and R. M. Felder, “Index of learning styles questionnaire,” NC State Univ. Available online http//www. engr. ncsu. edu/learningstyles/ilsweb. html (last Visit. 14.05. 2010), vol. 70, 2005.
J. Allert, “Learning style and factors contributing to success in an introductory computer science course,” in IEEE International Conference on Advanced Learning Technologies, 2004. Proceedings., 2004, pp. 385–389.
A. T. Chamillard and D. Karolick, “Using learning style data in an introductory computer science course,” in The proceedings of the thirtieth SIGCSE technical symposium on Computer science education, 1999, pp. 291–295.
A. Gomes and A. J. Mendes, “Learning to program-difficulties and solutions,” in International Conference on Engineering Education–ICEE, 2007, vol. 2007.
L. Thomas, M. Ratcliffe, J. Woodbury, and E. Jarman, “Learning styles and performance in the introductory programming sequence,” ACM SIGCSE Bull., vol. 34, no. 1, pp. 33–37, 2002.
H. M. Truong, “Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities,” Comput. Human Behav., vol. 55, pp. 1185–1193, 2016.
H. Fasihuddin, G. Skinner, and R. Athauda, “A Framework to Personalise Open Learning Environments by Adapting to Learning Styles.,” in CSEDU (1), 2015, pp. 296–305.
G. E. Evans and M. G. Simkin, “What best predicts computer proficiency?,” Commun. ACM, vol. 32, no. 11, pp. 1322–1327, 1989.
S. Bergin and R. Reilly, “The influence of motivation and comfort-level on learning to program,” 2005.
E. Chandra and K. Nandhini, “Knowledge mining from student data,” Eur. J. Sci. Res., vol. 47, no. 1, pp. 156–163, 2010.
V. Kumar et al., “An Approach to Measure Coding Competency Evolution,” in Smart Learning Environments, Springer, 2015, pp. 27–43.
G. R. Bergersen, J. E. Hannay, D. I. K. Sjøberg, T. Dyba, and A. Karahasanovic, “Inferring skill from tests of programming performance: Combining time and quality,” in 2011 International Symposium on Empirical Software Engineering and Measurement, 2011, pp. 305–314.
J. P. Campbell, R. A. McCloy, S. H. Oppler, and C. E. Sager, “A theory of performance: In N. Schmitt & WC Borman (Eds.), Personnel Selection in Organizations (pp. 35–70).” San Francisco: Jossey-Bass, 1993.
G. P. Latham and C. C. Pinder, “Work motivation theory and research at the dawn of the twenty-first century,” Annu. Rev. Psychol., vol. 56, pp. 485–516, 2005.
C.-P. Lai and J.-R. Lu, “Evaluating the efficiency of currency portfolios constructed by the mining association rules,” Asia Pacific Manag. Rev., vol. 24, no. 1, pp. 11–20, 2019.
R. Agrawal, T. Imieliñski, and A. Swami, “Mining association rules between sets of items in large databases,” in Acm sigmod record, 1993, vol. 22, no. 2, pp. 207–216.
K. Becker, C. G. Ghedini, and E. L. Terra, “Using KDD to analyze the impact of curriculum revisions in a Brazilian university,” in Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, 2000, vol. 4057, pp. 412–419.
N. Selmoune and Z. Alimazighi, “A decisional tool for quality improvement in higher education,” in 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications, 2008, pp. 1–6.
L. Zhang, X. Liu, and X. Liu, “Personalized instructing recommendation system based on web mining,” in 2008 The 9th International Conference for Young Computer Scientists, 2008, pp. 2517–2521.
H. Ba-Omar, I. Petrounias, and F. Anwar, “A framework for using web usage mining to personalise e-learning,” in Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007), 2007, pp. 937–938.
C. Romero, A. Zafra, J. M. Luna, and S. Ventura, “Association rule mining using genetic programming to provide feedback to instructors from multiple-choice quiz data,” Expert Syst., vol. 30, no. 2, pp. 162–172, 2013.
O. R. Zaíane, “Building a recommender agent for e-learning systems,” in International Conference on Computers in Education, 2002. Proceedings., 2002, pp. 55–59.
J. Lu, “A personalized e-learning material recommender system,” in International Conference on Information Technology and Applications, 2004.
E. García, C. Romero, S. Ventura, and T. Calders, “Drawbacks and solutions of applying association rule mining in learning management systems,” in Proceedings of the International Workshop on Applying Data Mining in e-Learning (ADML 2007), Crete, Greece, 2007, pp. 13–22.
A. Merceron and K. Yacef, “Interestingness measures for association rules in educational data,” in Educational Data Mining 2008, 2008.
A. F. Hayes, Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Publications, 2017.
W. F. Chaplin, “The next generation of moderator research in personality psychology,” J. Pers., vol. 59, no. 2, pp. 143–178, 1991.
A. F. Hayes, “PROCESS: A versatile computational tool for observed variable mediation, moderation, and conditional process modeling.” University of Kansas, KS, 2012.
J. Wakefield and J. K. Frawley, “How does students’ general academic achievement moderate the implications of social networking on specific levels of learning performance?,” Comput. Educ., vol. 144, p. 103694, 2020.
D. R. Sanchez, M. Langer, and R. Kaur, “Gamification in the classroom: Examining the impact of gamified quizzes on student learning,” Comput. Educ., vol. 144, p. 103666, 2020.
E. R. Fyfe and B. Rittle-Johnson, “Feedback both helps and hinders learning: The causal role of prior knowledge.,” J. Educ. Psychol., vol. 108, no. 1, p. 82, 2016.
E. R. Fyfe, B. Rittle-Johnson, and M. S. DeCaro, “The effects of feedback during exploratory mathematics problem solving: Prior knowledge matters.,” J. Educ. Psychol., vol. 104, no. 4, p. 1094, 2012.
S. Timmers, M. Valcke, K. De Mil, and W. R. G. Baeyens, “The impact of computer supported collaborative learning on internship outcomes of pharmacy students,” Interact. Learn. Environ., vol. 16, no. 2, pp. 131–141, 2008.
C.-C. LINa, Y.-T. WUb, and T.-Y. CHENG, “Online knowledge-structure-based adaptive science learning: Integrates adaptive dynamic assessment into adaptive learning.”
M. K. Khribi, M. Jemni, and O. Nasraoui, “Automatic recommendations for e-learning personalization based on web usage mining techniques and information retrieval,” in 2008 Eighth IEEE International Conference on Advanced Learning Technologies, 2008, pp. 241–245.
M. S. Ibrahim and M. Hamada, “Adaptive learning framework,” in 2016 15th International Conference on Information Technology Based Higher Education and Training (ITHET), 2016, pp. 1–5.
B. Vesin, M. Ivanović, A. KlašNja-MilićEvić, and Z. Budimac, “Protus 2.0: Ontology-based semantic recommendation in programming tutoring system,” Expert Syst. Appl., vol. 39, no. 15, pp. 12229–12246, 2012.
B. Vesin, M. Ivanović, A. Klašnja-Milićević, and Z. Budimac, “Ontology-based architecture with recommendation strategy in java tutoring system,” Comput. Sci. Inf. Syst., vol. 10, no. 1, pp. 237–261, 2013.
O. Conlan, V. Wade, C. Bruen, and M. Gargan, “Multi-model, metadata driven approach to adaptive hypermedia services for personalized elearning,” in International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, 2002, pp. 100–111.
U. Ninrutsirikun, B. Watanapa, C. Arpnikanondt, and V. Watananukoon, “A Unified Framework for Student Cluster Grouping with Learning Preference Associative Detection for Enhancing Students’ Learning Outcomes in Computer Programming Courses,” in 2018 Global Wireless Summit (GWS), 2018, pp. 266–271.
B. Mark, Theory of Knowledge: Structures and Processes, vol. 5. World scientific, 2016.
I. Katsov, Introduction to Algorithmic Marketing: Artificial Intelligence for Marketing Operations. Ilia Katcov, 2017.
R. L. Ebel, “Marks and marking systems,” IEEE Trans. Educ., vol. 17, no. 2, pp. 76–92, 1974.
O. P. John and S. Srivastava, “The Big Five trait taxonomy: History, measurement, and theoretical perspectives,” Handb. Personal. Theory Res., vol. 2, no. 1999, pp. 102–138, 1999.
G. James, D. Witten, T. Hastie, and R. Tibshirani, An introduction to statistical learning, vol. 112. Springer, 2013.
V. Fonti and E. Belitser, “Feature selection using lasso,” VU Amsterdam Res. Pap. Bus. Anal., 2017.
M. I. Lopez, J. M. Luna, C. Romero, and S. Ventura, “Classification via clustering for predicting final marks based on student participation in forums.,” Int. Educ. Data Min. Soc., 2012.
Y. S. Koh and N. Rountree, “Rare Association Rule Mining: An Overview,” in Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection, IGI Global, 2010, pp. 1–14.
R. M. Felder and J. Spurlin, “Applications, reliability and validity of the index of learning styles,” Int. J. Eng. Educ., vol. 21, no. 1, pp. 103–112, 2005.
R. M. Felder and B. A. Soloman, “Learning styles and strategies.” 2000.
I. A. Zualkernan, J. Allert, and G. Z. Qadah, “Learning styles of computer programming students: a Middle Eastern and American comparison,” IEEE Trans. Educ., vol. 49, no. 4, pp. 443–450, 2006.
M. M. Quinn, T. Smith, E. L. Kalmar, and J. M. Burgoon, “What type of learner are your students? Preferred learning styles of undergraduate gross anatomy students according to the index of learning styles questionnaire,” Anat. Sci. Educ., vol. 11, no. 4, pp. 358–365, 2018.