Research Article
BibTex RIS Cite

The role of individual differences on epistemic curiosity (EC) and self-regulated learning (SRL) during e-learning: the Turkish context

Year 2022, Volume: 9 Issue: 3, 565 - 582, 30.09.2022
https://doi.org/10.21449/ijate.907186

Abstract

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.

References

  • 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.
  • Elia, G., Solazzo, G., Lorenzo, G., & Passiante, G. (2019). Assessing learners’ satisfaction in collaborative online courses through a big data approach. Computers in Human Behavior, 92, 589-599. https://doi.org/10.1016/j.chb.2018.04.033
  • Erarslan, A., & Topkaya, E.Z. (2017). EFL students attitudes towards e-learning and effect of an online course on students success in English. The Literacy Trek, 3(2), 80-101.
  • Eren, A., & Coskun, H. (2016). Students' level of boredom, boredom coping strategies, epistemic curiosity, and graded performance. The Journal of Educational Research, 109(6), 574-588. https://doi.org/10.1080/00220671.2014.999364
  • Garcia, E., Romero, C., Ventura, S., Castro, C., & Calders, T. (2010). Association rule mining in learning management systems. In V. Kumar (Ed.). Handbook of educational data mining. (pp. 93-106). Taylor & Francis Group.
  • Gnambs, T. (2021). The development of gender differences in information and communication technology (ICT) literacy in middle adolescence. Computers in Human Behavior, 114, 1-10. https://doi.org/10.1016/j.chb.2020.106533
  • Gunnarsson, C.L. (2001). Development and assessment of students: Attitudes and achievement in a business statistics course taught online. Interactive Multimedia Electronic Journal of Computer-Enhanced Learning, 3(2).
  • Güngör, E., Yalçın, N., & Yurtay, N. (2013, Kasım, 122-127). Apriori algoritması ile teknik seçmeli ders seçim analizi [Selection Behavior Analysis of Technical Elective Courses Using Apriori Algorithm]. Pro. UZEM 2013 Ulusal Uzaktan Eğitim ve Teknolojileri Sempozyumu, Konya, Turkiye.
  • Hargittai, E., & Shafer, S. (2006). Differences in actual and perceived online skills: The role of gender. Social Science Quarterly, 87(2), 432-448. https://doi.org/10.1111/j.1540-6237.2006.00389.x
  • Haznedar, Ö., & Baran, B. (2012). Eğitim fakültesi öğrencileri için e-öğrenmeye yönelik genel bir tutum ölçeği geliştirme çalişmasi [Development of a general attitude scale towards e-learning for faculty of education students]. Eğitim Teknolojisi Kuram ve Uygulama, 2(2), 42-59.
  • Heo, M., & Toomey, N. (2020). Learning with multimedia: The effects of gender, type of multimedia learning resources, and spatial ability. Computers & Education, 146, 103747. https://doi.org/10.1016/j.compedu.2019.103747
  • Hillman, D.C., Willis, D.J., & Gunawardena, C.N. (1994). Learner interface interaction in distance education: An extension of contemporary models and strategies for practitioners. American Journal of Distance Education, 8(2), 30 42. https://doi.org/10.1080/08923649409526853
  • Howland, J.L., & Moore, J.L. (2002). Student perceptions as distance learners in Internet-based courses. Distance Education, 23(2), 183 195. https://doi.org/10.1080/0158791022000009196
  • Inokuchi, A., Washio, T., & Motoda, H. (2000, September, 13-23). An apriori-based algorithm for mining frequent substructures from graph data. Proceedings of the 2000 European symposium on the principle of data mining and knowledge discovery (PKDD’00), Lyon, France.
  • Kashdan, T.B. (2009). Curious? Discover the missing ingredient to a fulfilling life. William Morrow.
  • Lauriola, M., Litman, J.A., Mussel, P., De Santis, R., Crowson, H.M., & Hoffman, R.R. (2015). Epistemic curiosity and self-regulation. Personality and Individual Differences, 83, 202-207. https://doi.org/10.1016/j.paid.2015.04.017
  • Li, H., Wu, Y.J., & Chen, Y. (2020). Time is money: Dynamic-model-based time series data-mining for correlation analysis of commodity sales. Journal of Computational and Applied Mathematics, 370, 112659. https://doi.org/10.1016/j.cam.2019.112659
  • Liaw, S.S., & Huang, H.M. (2011, September, 28-32). A study of investigating learners’ attitudes toward e-learning. 5th International Conference on Distance Learning and Education, Paris, Fransa.
  • Litman, J. (2005). Curiosity and the pleasures of learning: Wanting and liking new information. Cognition & Emotion, 19(6), 793 814. https://doi.org/10.1080/02699930541000101
  • Litman, J.A., & Spielberger, C.D. (2003). Measuring epistemic curiosity and its diversive and specific components. Journal of Personality Assessment, 80(1), 75-86. https://doi.org/10.1207/S15327752JPA8001_16
  • Loewenstein, G. (1994). The psychology of curiosity: A review and reinterpretation. Psychological Bulletin, 116(1), 75 98. https://psycnet.apa.org/doi/10.1037/0033 2909.116.1.75
  • Luan, J. (2002). Data mining and its applications in higher education. New Directions For Institutional Research, 2002(113), 17-36.
  • Maio, G.R., Haddock, G., & Verplanken, B. (2018). The psychology of attitudes and attitude change (3rd ed.). Sage.
  • Martens, R., Bastiaens, T., & Kirschner, P.A. (2007). New learning design in distance education: The impact on student perception and motivation. Distance Education, 28(1), 81-93. https://doi.org/10.1080/01587910701305327
  • Martins, L.L., & Kellermanns, F.W. (2004). A model of business school students' acceptance of a web-based course management system. Academy of Management Learning & Education, 3(1), 7-26. https://doi.org/10.5465/amle.2004.12436815
  • McCoach, D.B. (2002). A validation study of the school attitude assessment survey. Measurement and Evaluation in Counseling and Development, 35(2), 66. https://doi.org/10.1080/07481756.2002.12069050
  • Merceron, A., Yacef, K., Romero, C., Ventura, S., & Pechenizkiy, M. (2010). Measuring correlation of strong symmetric association rules in educational data. Handbook of Educational Data Mining, 245-256.
  • Mohammadi, N., Ghorbani, V., & Hamidi, F. (2011). Effects of e-learning on language learning. Procedia Computer Science, 3, 464 468. https://doi.org/10.1016/j.procs.2010.12.078
  • Moodley, R., Chiclana, F., Caraffini, F., & Carter, J. (2020). A product-centric data mining algorithm for targeted promotions. Journal of Retailing and Consumer Services, 54, 101940. https://doi.org/10.1016/j.jretconser.2019.101940
  • Nagata, K., Washio, T., Kawahara, Y., & Unami, A. (2014). Toxicity prediction from toxicogenomic data based on class association rule mining. Toxicology Reports, 1, 1133-1142. https://doi.org/10.1016/j.toxrep.2014.10.014 Nakamura, S., Darasawang, P., & Reinders, H. (2021). The antecedents of boredom in L2 classroom learning. System, 98, 102469. https://doi.org/10.1016/j.system.2021.102469
  • Narli, S., Aksoy, E., & Ercire, Y.E. (2014). Investigation of prospective elementary mathematics teachers’ learning styles and relationships between them using data mining. International Journal of Educational Studies in Mathematics, 1(1), 37-57.
  • Nikolaki, E., Koutsouba, M., Lykesas, G., Venetsanou, F., & Savidou, D. (2017). The support and promotion of self-regulated learning in distance education. European Journal of Open, Distance and E-learning, 20(1), 1-11.
  • Odabaşı, Ç., & Yıldırım, R. (2019). Performance analysis of perovskite solar cells in 2013–2018 using machine learning tools. Nano Energy, 56, 770 791. https://doi.org/10.1016/j.nanoen.2018.11.069
  • Ong, C.S., & Lai, J.Y. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior, 22(5), 816-829. https://doi.org/10.1016/j.chb.2004.03.006
  • Özçalıcı, M. (2017). Veri madenciliğinde birliktelik kuralları ve ikinci el otomobil piyasası üzerine bir uygulama [Association Rules in Data Mining and an Application in Second Hand Car Market]. Ordu Üniversitesi Sosyal Bilimler Araştırma Dergisi, 7(1), 45-58.
  • Paul, J., & Jefferson, F. (2019). A comparative analysis of student performance in an online vs. face-to-face environmental science course from 2009 to 2016. Frontiers in Computer Science, 1,1-9. https://doi.org/10.3389/fcomp.2019.00007
  • Prathama, F., Senjaya, W.F., Yahya, B.N., & Wu, J.Z. (2021). Personalized recommendation by matrix co-factorization with multiple implicit feedback on the pairwise comparison. Computers & Industrial Engineering, 152, 107033. https://doi.org/10.1016/j.cie.2020.107033
  • Rotgans, J.I., & Schmidt, H.G. (2014). Situational interest and learning: Thirst for knowledge. Learning and Instruction, 32, 37 50. https://doi.org/10.1016/j.learninstruc.2014.01.002
  • Selim, H.M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers & Education, 49(2), 396 413. https://doi.org/10.1016/j.compedu.2005.09.004
  • Senler, B., & Sungur-Vural, S. (2012, September, 551-556). Pre-service science teachers’ use of self-regulation strategies related to their academic performance and gender. The European Conference on Educational Research (ECER), Cadiz, Spain. https://doi.org/10.1016/j.sbspro.2014.09.242
  • Suanpang, P. (2007). Students experience online learning in Thailand. In S. Hongladarom (Ed.), Computing and philosophy in Asia, (pp. 240-.252). Cambridge Scholar Publishing.
  • Tan, P.N., Steinbach, M., & Kumar, V. (2006). Introduction to data mining. Addison Wesley.
  • Tandan, M., Acharya, Y., Pokharel, S., & Timilsina, M. (2021). Discovering symptom patterns of COVID-19 patients using association rule mining. Computers in Biology and Medicine, 104249. https://doi.org/10.1016/j.compbiomed.2021.104249
  • Temple, L., & Lips, H.M. (1989). Gender differences and similarities in attitudes toward computers. Computers in Human Behavior, 5(4), 215-226. https://doi.org/10.1016/0747-5632(89)90001-0
  • Tuckman, B. (2002, August). Academic procrastinators: Their rationalizations and web-course performance. the Annual Meeting of the American Psychological Association, Chicago, IL.
  • Wang, Y.S., Wu, M.C., & Wang, H.Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92-118. https://doi.org/10.1111/j.1467-8535.2007.00809.x
  • Whitley Jr, B.E. (1997). Gender differences in computer-related attitudes and behavior: A meta-analysis. Computers in Human Behavior, 13(1), 1-22. https://doi.org/10.1016/S0747-5632(96)00026-X
  • Yükseltürk, E., & Bulut, S. (2009). Gender differences in self-regulated online learning environment. Journal of Educational Technology & Society, 12(3), 12-22.
  • Zaki, M.J., Parthasarathy, S., Ogihara, M., & Li, W. (1997). Parallel algorithms for discovery of association rules. Data Mining and Knowledge Discovery, 1(4), 343-373.
  • Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329–339. https://psycnet.apa.org/doi/10.1037/0022-0663.81.3.329
  • Zimmerman, B.J. (1994). Dimensions of academic self-regulation: A framework for education. Regulation of learning and performance. Lawrence Erlbaum.
  • Zimmerman, B.J. (2000). Attaining self-regulation: A social cognitive perspective. In Handbook of self-regulation (pp. 13-39). Academic Press.
  • Zimmerman, B.J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166-183. https://doi.org/10.3102%2F0002831207312909
  • Zimmerman, B., & Kitsantas, A. (2014). Comparing students’ self-discipline and self-regulation measures and their prediction of academic achievement. Contemporary Educational Psychology, 39(2), 145 155. https://doi.org/10.1016/j.cedpsych.2014.03.004

The role of individual differences on epistemic curiosity (EC) and self-regulated learning (SRL) during e-learning: the Turkish context

Year 2022, Volume: 9 Issue: 3, 565 - 582, 30.09.2022
https://doi.org/10.21449/ijate.907186

Abstract

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.

References

  • 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.
  • Elia, G., Solazzo, G., Lorenzo, G., & Passiante, G. (2019). Assessing learners’ satisfaction in collaborative online courses through a big data approach. Computers in Human Behavior, 92, 589-599. https://doi.org/10.1016/j.chb.2018.04.033
  • Erarslan, A., & Topkaya, E.Z. (2017). EFL students attitudes towards e-learning and effect of an online course on students success in English. The Literacy Trek, 3(2), 80-101.
  • Eren, A., & Coskun, H. (2016). Students' level of boredom, boredom coping strategies, epistemic curiosity, and graded performance. The Journal of Educational Research, 109(6), 574-588. https://doi.org/10.1080/00220671.2014.999364
  • Garcia, E., Romero, C., Ventura, S., Castro, C., & Calders, T. (2010). Association rule mining in learning management systems. In V. Kumar (Ed.). Handbook of educational data mining. (pp. 93-106). Taylor & Francis Group.
  • Gnambs, T. (2021). The development of gender differences in information and communication technology (ICT) literacy in middle adolescence. Computers in Human Behavior, 114, 1-10. https://doi.org/10.1016/j.chb.2020.106533
  • Gunnarsson, C.L. (2001). Development and assessment of students: Attitudes and achievement in a business statistics course taught online. Interactive Multimedia Electronic Journal of Computer-Enhanced Learning, 3(2).
  • Güngör, E., Yalçın, N., & Yurtay, N. (2013, Kasım, 122-127). Apriori algoritması ile teknik seçmeli ders seçim analizi [Selection Behavior Analysis of Technical Elective Courses Using Apriori Algorithm]. Pro. UZEM 2013 Ulusal Uzaktan Eğitim ve Teknolojileri Sempozyumu, Konya, Turkiye.
  • Hargittai, E., & Shafer, S. (2006). Differences in actual and perceived online skills: The role of gender. Social Science Quarterly, 87(2), 432-448. https://doi.org/10.1111/j.1540-6237.2006.00389.x
  • Haznedar, Ö., & Baran, B. (2012). Eğitim fakültesi öğrencileri için e-öğrenmeye yönelik genel bir tutum ölçeği geliştirme çalişmasi [Development of a general attitude scale towards e-learning for faculty of education students]. Eğitim Teknolojisi Kuram ve Uygulama, 2(2), 42-59.
  • Heo, M., & Toomey, N. (2020). Learning with multimedia: The effects of gender, type of multimedia learning resources, and spatial ability. Computers & Education, 146, 103747. https://doi.org/10.1016/j.compedu.2019.103747
  • Hillman, D.C., Willis, D.J., & Gunawardena, C.N. (1994). Learner interface interaction in distance education: An extension of contemporary models and strategies for practitioners. American Journal of Distance Education, 8(2), 30 42. https://doi.org/10.1080/08923649409526853
  • Howland, J.L., & Moore, J.L. (2002). Student perceptions as distance learners in Internet-based courses. Distance Education, 23(2), 183 195. https://doi.org/10.1080/0158791022000009196
  • Inokuchi, A., Washio, T., & Motoda, H. (2000, September, 13-23). An apriori-based algorithm for mining frequent substructures from graph data. Proceedings of the 2000 European symposium on the principle of data mining and knowledge discovery (PKDD’00), Lyon, France.
  • Kashdan, T.B. (2009). Curious? Discover the missing ingredient to a fulfilling life. William Morrow.
  • Lauriola, M., Litman, J.A., Mussel, P., De Santis, R., Crowson, H.M., & Hoffman, R.R. (2015). Epistemic curiosity and self-regulation. Personality and Individual Differences, 83, 202-207. https://doi.org/10.1016/j.paid.2015.04.017
  • Li, H., Wu, Y.J., & Chen, Y. (2020). Time is money: Dynamic-model-based time series data-mining for correlation analysis of commodity sales. Journal of Computational and Applied Mathematics, 370, 112659. https://doi.org/10.1016/j.cam.2019.112659
  • Liaw, S.S., & Huang, H.M. (2011, September, 28-32). A study of investigating learners’ attitudes toward e-learning. 5th International Conference on Distance Learning and Education, Paris, Fransa.
  • Litman, J. (2005). Curiosity and the pleasures of learning: Wanting and liking new information. Cognition & Emotion, 19(6), 793 814. https://doi.org/10.1080/02699930541000101
  • Litman, J.A., & Spielberger, C.D. (2003). Measuring epistemic curiosity and its diversive and specific components. Journal of Personality Assessment, 80(1), 75-86. https://doi.org/10.1207/S15327752JPA8001_16
  • Loewenstein, G. (1994). The psychology of curiosity: A review and reinterpretation. Psychological Bulletin, 116(1), 75 98. https://psycnet.apa.org/doi/10.1037/0033 2909.116.1.75
  • Luan, J. (2002). Data mining and its applications in higher education. New Directions For Institutional Research, 2002(113), 17-36.
  • Maio, G.R., Haddock, G., & Verplanken, B. (2018). The psychology of attitudes and attitude change (3rd ed.). Sage.
  • Martens, R., Bastiaens, T., & Kirschner, P.A. (2007). New learning design in distance education: The impact on student perception and motivation. Distance Education, 28(1), 81-93. https://doi.org/10.1080/01587910701305327
  • Martins, L.L., & Kellermanns, F.W. (2004). A model of business school students' acceptance of a web-based course management system. Academy of Management Learning & Education, 3(1), 7-26. https://doi.org/10.5465/amle.2004.12436815
  • McCoach, D.B. (2002). A validation study of the school attitude assessment survey. Measurement and Evaluation in Counseling and Development, 35(2), 66. https://doi.org/10.1080/07481756.2002.12069050
  • Merceron, A., Yacef, K., Romero, C., Ventura, S., & Pechenizkiy, M. (2010). Measuring correlation of strong symmetric association rules in educational data. Handbook of Educational Data Mining, 245-256.
  • Mohammadi, N., Ghorbani, V., & Hamidi, F. (2011). Effects of e-learning on language learning. Procedia Computer Science, 3, 464 468. https://doi.org/10.1016/j.procs.2010.12.078
  • Moodley, R., Chiclana, F., Caraffini, F., & Carter, J. (2020). A product-centric data mining algorithm for targeted promotions. Journal of Retailing and Consumer Services, 54, 101940. https://doi.org/10.1016/j.jretconser.2019.101940
  • Nagata, K., Washio, T., Kawahara, Y., & Unami, A. (2014). Toxicity prediction from toxicogenomic data based on class association rule mining. Toxicology Reports, 1, 1133-1142. https://doi.org/10.1016/j.toxrep.2014.10.014 Nakamura, S., Darasawang, P., & Reinders, H. (2021). The antecedents of boredom in L2 classroom learning. System, 98, 102469. https://doi.org/10.1016/j.system.2021.102469
  • Narli, S., Aksoy, E., & Ercire, Y.E. (2014). Investigation of prospective elementary mathematics teachers’ learning styles and relationships between them using data mining. International Journal of Educational Studies in Mathematics, 1(1), 37-57.
  • Nikolaki, E., Koutsouba, M., Lykesas, G., Venetsanou, F., & Savidou, D. (2017). The support and promotion of self-regulated learning in distance education. European Journal of Open, Distance and E-learning, 20(1), 1-11.
  • Odabaşı, Ç., & Yıldırım, R. (2019). Performance analysis of perovskite solar cells in 2013–2018 using machine learning tools. Nano Energy, 56, 770 791. https://doi.org/10.1016/j.nanoen.2018.11.069
  • Ong, C.S., & Lai, J.Y. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior, 22(5), 816-829. https://doi.org/10.1016/j.chb.2004.03.006
  • Özçalıcı, M. (2017). Veri madenciliğinde birliktelik kuralları ve ikinci el otomobil piyasası üzerine bir uygulama [Association Rules in Data Mining and an Application in Second Hand Car Market]. Ordu Üniversitesi Sosyal Bilimler Araştırma Dergisi, 7(1), 45-58.
  • Paul, J., & Jefferson, F. (2019). A comparative analysis of student performance in an online vs. face-to-face environmental science course from 2009 to 2016. Frontiers in Computer Science, 1,1-9. https://doi.org/10.3389/fcomp.2019.00007
  • Prathama, F., Senjaya, W.F., Yahya, B.N., & Wu, J.Z. (2021). Personalized recommendation by matrix co-factorization with multiple implicit feedback on the pairwise comparison. Computers & Industrial Engineering, 152, 107033. https://doi.org/10.1016/j.cie.2020.107033
  • Rotgans, J.I., & Schmidt, H.G. (2014). Situational interest and learning: Thirst for knowledge. Learning and Instruction, 32, 37 50. https://doi.org/10.1016/j.learninstruc.2014.01.002
  • Selim, H.M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers & Education, 49(2), 396 413. https://doi.org/10.1016/j.compedu.2005.09.004
  • Senler, B., & Sungur-Vural, S. (2012, September, 551-556). Pre-service science teachers’ use of self-regulation strategies related to their academic performance and gender. The European Conference on Educational Research (ECER), Cadiz, Spain. https://doi.org/10.1016/j.sbspro.2014.09.242
  • Suanpang, P. (2007). Students experience online learning in Thailand. In S. Hongladarom (Ed.), Computing and philosophy in Asia, (pp. 240-.252). Cambridge Scholar Publishing.
  • Tan, P.N., Steinbach, M., & Kumar, V. (2006). Introduction to data mining. Addison Wesley.
  • Tandan, M., Acharya, Y., Pokharel, S., & Timilsina, M. (2021). Discovering symptom patterns of COVID-19 patients using association rule mining. Computers in Biology and Medicine, 104249. https://doi.org/10.1016/j.compbiomed.2021.104249
  • Temple, L., & Lips, H.M. (1989). Gender differences and similarities in attitudes toward computers. Computers in Human Behavior, 5(4), 215-226. https://doi.org/10.1016/0747-5632(89)90001-0
  • Tuckman, B. (2002, August). Academic procrastinators: Their rationalizations and web-course performance. the Annual Meeting of the American Psychological Association, Chicago, IL.
  • Wang, Y.S., Wu, M.C., & Wang, H.Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92-118. https://doi.org/10.1111/j.1467-8535.2007.00809.x
  • Whitley Jr, B.E. (1997). Gender differences in computer-related attitudes and behavior: A meta-analysis. Computers in Human Behavior, 13(1), 1-22. https://doi.org/10.1016/S0747-5632(96)00026-X
  • Yükseltürk, E., & Bulut, S. (2009). Gender differences in self-regulated online learning environment. Journal of Educational Technology & Society, 12(3), 12-22.
  • Zaki, M.J., Parthasarathy, S., Ogihara, M., & Li, W. (1997). Parallel algorithms for discovery of association rules. Data Mining and Knowledge Discovery, 1(4), 343-373.
  • Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329–339. https://psycnet.apa.org/doi/10.1037/0022-0663.81.3.329
  • Zimmerman, B.J. (1994). Dimensions of academic self-regulation: A framework for education. Regulation of learning and performance. Lawrence Erlbaum.
  • Zimmerman, B.J. (2000). Attaining self-regulation: A social cognitive perspective. In Handbook of self-regulation (pp. 13-39). Academic Press.
  • Zimmerman, B.J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166-183. https://doi.org/10.3102%2F0002831207312909
  • Zimmerman, B., & Kitsantas, A. (2014). Comparing students’ self-discipline and self-regulation measures and their prediction of academic achievement. Contemporary Educational Psychology, 39(2), 145 155. https://doi.org/10.1016/j.cedpsych.2014.03.004
There are 80 citations in total.

Details

Primary Language English
Subjects Studies on Education
Journal Section Articles
Authors

Ergün Akgün 0000-0002-7271-6900

Enisa Mede 0000-0002-6555-5248

Seda Sarac 0000-0002-4598-4029

Early Pub Date August 31, 2022
Publication Date September 30, 2022
Submission Date March 31, 2021
Published in Issue Year 2022 Volume: 9 Issue: 3

Cite

APA Akgün, E., Mede, E., & Sarac, S. (2022). The role of individual differences on epistemic curiosity (EC) and self-regulated learning (SRL) during e-learning: the Turkish context. International Journal of Assessment Tools in Education, 9(3), 565-582. https://doi.org/10.21449/ijate.907186

23823             23825             23824