The role of individual differences on epistemic curiosity (EC) and self-regulated learning (SRL) during e-learning: the Turkish context
Yıl 2022,
Cilt: 9 Sayı: 3, 565 - 582, 30.09.2022
Ergün Akgün
,
Enisa Mede
,
Seda Sarac
Öz
This study aims to examine the relations and associations between gender, epistemic curiosity (EC), self-regulated learning (SRL), and attitudes toward e-learning in higher education students. The participants were 2438 (862 males, 1576 females) undergraduate students enrolled in a Turkish university. The regression analysis findings showed that although the effect size was low, attitudes towards e-learning can be predicted significantly by gender, EC, and SRL. Datasets are further analyzed using data mining. The findings of the association rule mining revealed that gender plays an influential role. Several association rules among EC, SRL, and attitudes towards e-learning were detected for female students. The results provide recommendations about using data mining as a statistical method in educational and psychological research.
Kaynakça
- Acun, N., Kapıkıran, Ş., & Kabasakal, Z. (2013). Merak ve keşfetme ölçeği II: Açımlayıcı ve doğrulayıcı faktör analizleri ve güvenirlik çalışması.[Trait Curiosity and Exploration Inventory-II: Exploratory and Confirmatory Factor Analysis and Its Reliability] Türk Psikoloji Yazıları, 16(31), 74-85.
- Agrawal, R., & Srikant, R. (1994, September, 487-489). Fast algorithms for mining association rules. Proc. of the 20th VLDB Conference, San Francisco, USA.
- Aixia, D., & Wang, D. (2011). Factors influencing learner attitudes toward e-learning and development of e-learning environment based on the integrated e-learning platform. International Journal of e-Education, e-Business, e-Management and e-Learning, 1(3), 264-268.
- Altun, T., Akyıldız, S., Gülay, A., & Özdemir, C. (2021). Investigating education faculty students’ views about asynchronous distance education practices during Covid-19 ısolation period. Psycho-Educational Research Reviews, 10(1), 34–45.
- Andrade, M.S., & Bunker, E.L. (2011). The role of SRL and TELEs in distance education: Narrowing the gap. In Fostering self-regulated learning through ICT (pp. 105-121). IGI Global. https://doi.org/10.4018/978-1-61692-901-5.ch007
- Aran, O., Bozki̇r, A., Gok, B., & Yagci̇, E. (2019). Analyzing the views of teachers and prospective teachers on information and communication technology via descriptive data mining. International Journal of Assessment Tools in Education, 6(2), 314-329. https://doi.org/10.21449/ijate.537877
- Arora, R.K., & Badal, D. (2014). Mining association rules to improve academic performance. International Journal of Computer Science and Mobile Computing, 3(1), 428-433.
- Ayık, Y.Z., Özdemir, A., & Yavuz, U. (2007). Lise türü ve lise mezuniyet başarisinin, kazanilan fakülte ile ilişkisinin veri madenciliği tekniği ile analizi. [Analysis of the relationship of high school type and high school graduation success with the faculty entered by data mining technique] Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 10(2), 441-454.
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- Baradwaj B.K., & Pal, S. (2012). Mining educational data to analyze students’ performance. arXiv preprint arXiv:1201.3417. https://doi.org/10.48550/arXiv.1201.3417
- Bashir, H., & Bashir, L. (2016). Investigating the relationship between self-regulation and spiritual intelligence of higher secondary school students. Indian Journal of Health and Wellbeing, 7(3), 327.
- Bastiaens, T.J., & Martens, R.L. (2000). Conditions for web-based learning with real events. In Instructional and cognitive impacts of web-based education (pp. 1-31). IGI Global. https://doi.org/10.4018/978-1-878289-59-9.ch001
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- Borgelt, C., & Kruse, R. (2002). Induction of association rules: Apriori implementation. In Compstat (pp. 395-400). Physica-Verlag Heidelberg.
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- Chen, S., Yuan, Y., Luo, X.R., Jian, J., & Wang, Y. (2021). Discovering group-based transnational cyber fraud actives: A polymethodological view. Computers & Security, 102217. https://doi.org/10.1016/j.cose.2021.102217
- Colley, A., & Comber, C. (2003). Age and gender differences in computer use and attitudes among secondary school students: what has changed?. Educational Research, 45(2), 155-165. https://doi.org/10.1080/0013188032000103235
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- Çakır, E., Fışkın, R., & Sevgili, C. (2021). Investigation of tugboat accidents severity: An application of association rule mining algorithms. Reliability Engineering & System Safety, 209, 107470. https://doi.org/10.1016/j.ress.2021.107470
- Çalışkan, S., & Sezgin-Selçuk, G. (2010). Üniversite öğrencilerinin Fizik problemlerinde lullandıkları özdüzenleme stratejileri: Cinsiyet ve üniversite etkileri [Self-regulated strategies used by undergraduate students in physics problems: effects of gender and university]. Dokuz Eylül Üniversitesi Buca Eğitim Fakültesi Dergisi, 27(1), 50-62.
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The role of individual differences on epistemic curiosity (EC) and self-regulated learning (SRL) during e-learning: the Turkish context
Yıl 2022,
Cilt: 9 Sayı: 3, 565 - 582, 30.09.2022
Ergün Akgün
,
Enisa Mede
,
Seda Sarac
Öz
This study aims to examine the relations and associations between gender, epistemic curiosity (EC), self-regulated learning (SRL), and attitudes toward e-learning in higher education students. The participants were 2438 (862 males, 1576 females) undergraduate students enrolled in a Turkish university. The regression analysis findings showed that although the effect size was low, attitudes towards e-learning can be predicted significantly by gender, EC, and SRL. Datasets are further analyzed using data mining. The findings of the association rule mining revealed that gender plays an influential role. Several association rules among EC, SRL, and attitudes towards e-learning were detected for female students. The results provide recommendations about using data mining as a statistical method in educational and psychological research.
Kaynakça
- Acun, N., Kapıkıran, Ş., & Kabasakal, Z. (2013). Merak ve keşfetme ölçeği II: Açımlayıcı ve doğrulayıcı faktör analizleri ve güvenirlik çalışması.[Trait Curiosity and Exploration Inventory-II: Exploratory and Confirmatory Factor Analysis and Its Reliability] Türk Psikoloji Yazıları, 16(31), 74-85.
- Agrawal, R., & Srikant, R. (1994, September, 487-489). Fast algorithms for mining association rules. Proc. of the 20th VLDB Conference, San Francisco, USA.
- Aixia, D., & Wang, D. (2011). Factors influencing learner attitudes toward e-learning and development of e-learning environment based on the integrated e-learning platform. International Journal of e-Education, e-Business, e-Management and e-Learning, 1(3), 264-268.
- Altun, T., Akyıldız, S., Gülay, A., & Özdemir, C. (2021). Investigating education faculty students’ views about asynchronous distance education practices during Covid-19 ısolation period. Psycho-Educational Research Reviews, 10(1), 34–45.
- Andrade, M.S., & Bunker, E.L. (2011). The role of SRL and TELEs in distance education: Narrowing the gap. In Fostering self-regulated learning through ICT (pp. 105-121). IGI Global. https://doi.org/10.4018/978-1-61692-901-5.ch007
- Aran, O., Bozki̇r, A., Gok, B., & Yagci̇, E. (2019). Analyzing the views of teachers and prospective teachers on information and communication technology via descriptive data mining. International Journal of Assessment Tools in Education, 6(2), 314-329. https://doi.org/10.21449/ijate.537877
- Arora, R.K., & Badal, D. (2014). Mining association rules to improve academic performance. International Journal of Computer Science and Mobile Computing, 3(1), 428-433.
- Ayık, Y.Z., Özdemir, A., & Yavuz, U. (2007). Lise türü ve lise mezuniyet başarisinin, kazanilan fakülte ile ilişkisinin veri madenciliği tekniği ile analizi. [Analysis of the relationship of high school type and high school graduation success with the faculty entered by data mining technique] Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 10(2), 441-454.
- Baker, R.S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3 17. https://doi.org/10.5281/zenodo.3554657
- Baradwaj B.K., & Pal, S. (2012). Mining educational data to analyze students’ performance. arXiv preprint arXiv:1201.3417. https://doi.org/10.48550/arXiv.1201.3417
- Bashir, H., & Bashir, L. (2016). Investigating the relationship between self-regulation and spiritual intelligence of higher secondary school students. Indian Journal of Health and Wellbeing, 7(3), 327.
- Bastiaens, T.J., & Martens, R.L. (2000). Conditions for web-based learning with real events. In Instructional and cognitive impacts of web-based education (pp. 1-31). IGI Global. https://doi.org/10.4018/978-1-878289-59-9.ch001
- Berlyne, D.E. (1966). Curiosity and exploration. Science, 153(3731), 25 33. https://doi.org/10.1126/science.153.3731.25
- Berlyne, D.E. (1954). A theory of human curiosity. British Journal of Psychology, 45, 180–191.
- Bhuasiri, W., Xaymoungkhoun, O., Zo, H., Rho, J.J., & Ciganek, A.P. (2012). Critical success factors for e-learning in developing countries: A comparative analysis between ICT experts and faculty. Computers & Education, 58(2), 843 855. https://doi.org/10.1016/j.compedu.2011.10.010
- Borgelt, C., & Kruse, R. (2002). Induction of association rules: Apriori implementation. In Compstat (pp. 395-400). Physica-Verlag Heidelberg.
- Brin, S., Motwani, R., Ullman, J.D., & Tsur, S. (1997, June, 255-264). Dynamic itemset counting and implication rules for market basket data. Proceedings of the 1997 ACM SIGMOD international conference on Management of data, New York, USA. https://doi.org/10.1145/253260.253325
- Cazan, A.M. (2012). Self-regulated learning strategies–predictors of academic adjustment. Procedia Social and Behavioral Sciences, 33, 104 108. https://doi.org/10.1016/j.sbspro.2012.01.092
- Chen, M. (1986). Gender and computers: The beneficial effects of experience on attitudes. Journal of Educational Computing Research, 2(3), 265 282. https://doi.org/10.2190%2FWDRY-9K0F-VCP6-JCCD
- Chen, S., Yuan, Y., Luo, X.R., Jian, J., & Wang, Y. (2021). Discovering group-based transnational cyber fraud actives: A polymethodological view. Computers & Security, 102217. https://doi.org/10.1016/j.cose.2021.102217
- Colley, A., & Comber, C. (2003). Age and gender differences in computer use and attitudes among secondary school students: what has changed?. Educational Research, 45(2), 155-165. https://doi.org/10.1080/0013188032000103235
- Cömert, Z., & Akgün, E. (2021). Game preferences of K-12 level students: analysis and prediction using the association rule. Ilkogretim Online, 20(1), 435-455. http://doi.org/10.17051/ilkonline.2021.01.039
- Çakır, E., Fışkın, R., & Sevgili, C. (2021). Investigation of tugboat accidents severity: An application of association rule mining algorithms. Reliability Engineering & System Safety, 209, 107470. https://doi.org/10.1016/j.ress.2021.107470
- Çalışkan, S., & Sezgin-Selçuk, G. (2010). Üniversite öğrencilerinin Fizik problemlerinde lullandıkları özdüzenleme stratejileri: Cinsiyet ve üniversite etkileri [Self-regulated strategies used by undergraduate students in physics problems: effects of gender and university]. Dokuz Eylül Üniversitesi Buca Eğitim Fakültesi Dergisi, 27(1), 50-62.
- Dan, O., Leshkowitz, M., & Hassin, R.R. (2020). On clickbaits and evolution: Curiosity from urge and interest. Current Opinion in Behavioral Sciences, 35, 150-156. https://doi.org/10.1016/j.cobeha.2020.09.009
- Delavari, N., Phon-Amnuaisuk, S., & Beikzadeh, M.R. (2008). Data mining application in higher learning institutions. Informatics in Education-International Journal, 7, 31-54.
- Duru, E., Balkıs, M., Buluş, M., & Duru, S. (2009, October, 57-73). Öğretmen adaylarında akademik erteleme eğiliminin yordanmasında öz düzenleme, akademik başarı ve demografik değişkenlerin rolü [The role of self-regulation, academic achievement and demographic variables in the prediction of academic procrastination in teacher candidates]. 18th Educational Sciences Congress, İzmir, Turkiye.
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