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| dc.contributor.author | Haque, Md. Samsul | |
| dc.date.accessioned | 2025-03-14T05:15:03Z | |
| dc.date.available | 2025-03-14T05:15:03Z | |
| dc.date.issued | 2024-06-12 | |
| dc.identifier.citation | Al Mamun, M. A., & Lawrie, G. (2023). Student-content interactions: Exploring behavioural engagement with self-regulated inquiry-based online learning modules. Smart Learning Environments, 10(1), 1–31. https://doi.org/10.1186/s40561-022-00221-x Ananda, P. N. (2023). Validity and Practicality of E-Module Model Inquiry Based Online Learning to Improve Student Competence. 9(4), 2010–2017. https://doi.org/10.29303/jppipa.v9i4.3563 Andrew, M., Taylorson, J., Langille, D. J., Grange, A., & Williams, N. (2018). Student attitudes towards technology and their preferences for learning tools/devices at two universities in the UAE. Journal of Information Technology Education: Research, 17, 309–344. https://doi.org/10.28945/4111 Ardies, J., De Maeyer, S., Gijbels, D., & van Keulen, H. (2015). Students attitudes towards technology. International Journal of Technology and Design Education, 25(1), 43–65. https://doi.org/10.1007/s10798-014-9268-x Ashtari, S., & Eydgahi, A. (2017). Student perceptions of cloud applications effectiveness in higher education. Journal of Computational Science, 23, 173–180. https://doi.org/10.1016/j.jocs.2016.12.007 Bakker, A. B., Sanz Vergel, A. I., & Kuntze, J. (2015). Student engagement and performance: A weekly diary study on the role of openness. Motivation and Emotion, 39(1), 49–62. https://doi.org/10.1007/s11031-014-9422-5 Bandura, A. (2006). Toward a Psychology of Human Agency. Perspectives on Psychological Science, 1(2), 164–180. https://doi.org/10.1111/j.1745-6916.2006.00011.x Borawska-Kalbarczyk, K., Tołwińska, B., & Korzeniecka-Bondar, A. (2018). From smart teaching to smart learning in the fast-changing digital world. Didactics of Smart Pedagogy: Smart Pedagogy for Technology Enhanced Learning, 23–39. https://doi.org/10.1007/978-3-030-01551-0_2 Branch, R. M. (2004). Problem-Based Learning: What and How Do Students Learn? Educational Psychology Review, 16(3), 235–266. Brezovszky, B., McMullen, J., Veermans, K., Hannula-Sormunen, M. M., Rodríguez-Aflecht, G., Pongsakdi, N., Laakkonen, E., & Lehtinen, E. (2019). Effects of a mathematics game- 39 based learning environment on primary school students’ adaptive number knowledge. Computers and Education, 128(February 2017), 63–74. https://doi.org/10.1016/j.compedu.2018.09.011 Broadbent, J., Panadero, E., & Fuller-Tyszkiewicz, M. (2020). Effects of mobile-app learning diaries vs online training on specific self-regulated learning components. Educational Technology Research and Development, 68(5), 2351–2372. https://doi.org/10.1007/s11423-020-09781-6 Buckner, E., & Kim, P. (2014). Integrating technology and pedagogy for inquiry-based learning: The Stanford Mobile Inquiry-based Learning Environment (SMILE). Prospects, 44(1), 99–118. https://doi.org/10.1007/s11125-013-9269-7 Carlson, J., & O’Cass, A. (2010). Exploring the relationships between e-service quality, satisfaction, attitudes and behaviours in content-driven e-service web sites. Journal of Services Marketing, 24(2), 112–127. https://doi.org/10.1108/08876041011031091 Carter, R. A., Rice, M., Yang, S., & Jackson, H. A. (2020). Self-regulated learning in online learning environments: strategies for remote learning. Information and Learning Science, 121(5–6), 311–319. https://doi.org/10.1108/ILS-04-2020-0114 Chan, J. W. W., & Pow, J. W. C. (2020). The role of social annotation in facilitating collaborative inquiry-based learning. Computers and Education, 147(December 2019), 103787. https://doi.org/10.1016/j.compedu.2019.103787 Chang, C. Y., Panjaburee, P., Lin, H. C., Lai, C. L., & Hwang, G. H. (2022). Effects of online strategies on students’ learning performance, self-efficacy, self-regulation and critical thinking in university online courses. Educational Technology Research and Development, 70(1), 185–204. https://doi.org/10.1007/s11423-021-10071-y Chen, K. Z., & Li, S. C. (2021). Sequential, typological, and academic dynamics of self regulated learners: Learning analytics of an undergraduate chemistry online course. Computers and Education: Artificial Intelligence, 2, 100024. https://doi.org/10.1016/j.caeai.2021.100024 Chen, Q. L., & Zhou, Z. H. (2016). Unusual formations of superoxo heptaoxomolybdates from peroxo molybdates. Inorganic Chemistry Communications, 67(3), 95–98. https://doi.org/10.1016/j.inoche.2016.03.015 40 Davis, F. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. Management Information Systems Research Center, University of Minnesota, pp. 319-340. Davis, F. D. (1989). Technology Acceptance Model : TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption, 219. Dörrenbächer-Ulrich, L., Dilhuit, S., & Perels, F. (2024). Investigating the relationship between self-regulated learning, metacognition, and executive functions by focusing on academic transition phases: a systematic review. Current Psychology, 43(18), 16045–16072. https://doi.org/10.1007/s12144-023-05551-8 Dziuban, C. D., & Florida, C. (2004). Ble nde d Le a rning. Ertmer, P. A. (2005). Teacher pedagogical beliefs: The final frontier in our quest for technology inteErtmer, P. A. (2005). Teacher pedagogical beliefs: The final frontier in our quest for technology integration? Educational Technology Research and Development, 53(4), 25– 39. h. Educational Technology Research and Development, 53(4), 25–39. Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059 Getenet, S., Cantle, R., Redmond, P., & Albion, P. (2024). Students’ digital technology attitude, literacy and self-efficacy and their effect on online learning engagement. International Journal of Educational Technology in Higher Education, 21(1). https://doi.org/10.1186/s41239-023-00437-y Hinostroza, J. E., Armstrong-Gallegos, S., & Villafaena, M. (2024). Roles of digital technologies in the implementation of inquiry-based learning (IBL): A systematic literature review. Social Sciences and Humanities Open, 9(November 2023), 100874. https://doi.org/10.1016/j.ssaho.2024.100874 Hong, J. C., Lee, Y. F., & Ye, J. H. (2021). Procrastination predicts online self-regulated learning and online learning ineffectiveness during the coronavirus lockdown. Personality and Individual Differences, 174(January), 110673. https://doi.org/10.1016/j.paid.2021.110673 Jin, S. H., Im, K., Yoo, M., Roll, I., & Seo, K. (2023). Supporting students’ self-regulated 41 learning in online learning using artificial intelligence applications. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239- 023-00406-5 Kahu, E. R. (2013). Framing student engagement in higher education. Studies in Higher Education, 38(5), 758–773. https://doi.org/10.1080/03075079.2011.598505 Karabenick, S. A., & Zusho, A. (2015). Examining approaches to research on self-regulated learning: conceptual and methodological considerations. Metacognition and Learning, 10(1), 151–163. https://doi.org/10.1007/s11409-015-9137-3 Khalil, M. (2022). Mobile Multimodal Learning Analytics Conceptual Framework to Support Student Self-Regulated Learning (MOLAM). In SpringerBriefs in Open and Distance Education (Issue May). Springer Nature Singapore. https://doi.org/10.1007/978-981-19- 0786-9_5 Kong, L. N., Qin, B., Zhou, Y. qing, Mou, S. yu, & Gao, H. M. (2014). The effectiveness of problem-based learning on development of nursing students’ critical thinking: A systematic review and meta-analysis. International Journal of Nursing Studies, 51(3), 458–469. https://doi.org/10.1016/j.ijnurstu.2013.06.009 Kraglund-Gauthier, W. (2015). Book Review- The New Digital Shoreline: How Web 2.0 and Millennials are Revolutionizing Higher Education. Journal of Teaching and Learning with Technology, 4(1), 80–82. https://doi.org/10.14434/jotlt.v4n1.13497 Krejcie, R., V.Morgan, & W., D. (1996). (1970) “Determining sample Size for Research Activities”, Educational and Psychological Measurement. International Journal of Employment Studies, 18(1), 89–123. Kubicek, J. P. (2014). Inquiry-based learning, the nature of science, and computer technology: New possibilities in science education. Canadian Journal of Learning and Technology, 31(1), 1–8. Kuh, G. D. (2009). The national survey of student engagement: Conceptual and empirical foundations. New Directions for Institutional Research, 2009(141), 5–20. https://doi.org/10.1002/ir.283 Li, S., Chen, G., Xing, W., Zheng, J., & Xie, C. (2020). Longitudinal clustering of students’ self-regulated learning behaviors in engineering design. Computers and Education, 42 153(November 2019), 103899. https://doi.org/10.1016/j.compedu.2020.103899 Luo, Y., Ye, Q., & Meng, L. (2017). An Empirical Research on Influence Factor of College Students’ Continued Intentions of Online Self-Regulated Learning Based on the Model of ECM and TAM. 99, 521–528. https://doi.org/10.2991/icsshe-17.2017.132 Ma, Q., & Liu, L. (2011). The Technology Acceptance Model. Advanced Topics in End User Computing, Volume 4, January 2005. https://doi.org/10.4018/9781591404743.ch006.ch000 Mamun, M. A. Al. (2022). Fostering self-regulation and engaged exploration during the learner-content interaction process: the role of scaffolding in the online inquiry-based learning environment. Interactive Technology and Smart Education, 19(4), 482–509. https://doi.org/10.1108/ITSE-11-2021-0195 Marikyan, D., & Papagiannidis, S. (2023). Technology Acceptance Model: A review. TheoryHub Book, 1–17. https://open.ncl.ac.uk Masrom, M. (2007). Technology acceptance model and E-learning. 12th International Conference on Education, May, 21–24. Mcclintock, C. C., & Brannon, D. (1979). Applying the Logic of Sample Suiveys to Qual itative Case Studies : The Case Cluster Method * Maynard-Moody. Administrative Science Quarterly, 24(December), 612–629. Mo, S. (2011). An Exploratory Study Of Intrinsic & Extrinsic Motivators And Student Performance In An Auditing Course. American Journal of Business Education (AJBE), 4(2), 19–26. https://doi.org/10.19030/ajbe.v4i2.3558 Muhiyuddin, G., & Benazirabad, S. (2023). Impact Of Technology On S tudent ’ s Engagement In Different Dimensions : Cognitive , Behavioral , Reflective And Social Engagement. August. Mulisa, F. (2022). When Does a Researcher Choose a Quantitative, Qualitative, or Mixed Research Approach? Interchange, 53(1), 113–131. https://doi.org/10.1007/s10780-021- 09447-z Nassuora, A. (2013). Students Acceptance of Mobile Learning for Higher Education in Saudi Arabia. International Journal of Learning Management Systems, 1(1), 1–9. https://doi.org/10.12785/ijlms/010101 43 Nightingale, S., Spiby, H., Sheen, K., & Slade, P. (2018). LJMU Research Online ｍ. Tourism Recreation Research, 19. http://researchonline.ljmu.ac.uk/id/eprint/8705/ Olakanmi, E. E., & Gumbo, M. T. (2017). The effects of self-regulated learning training on students’ metacognition and achievement in chemistry. International Journal of Innovation in Science and Mathematics Education, 25(2), 34–48. Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. In Frontiers in Psychology (Vol. 8, Issue APR, pp. 1–28). https://doi.org/10.3389/fpsyg.2017.00422 Paul R Pintrich, M. Z. M. B. (2000). The Role of Goal Orientation in Self-Regulated Learning. Handbook of Self-Regulation, 451–502. http://www.sciencedirect.com/science/article/pii/B9780121098902500433 Persico, D., & Steffens, K. (2017). Self-regulated learning in technology enhanced learning environments. In Technology Enhanced Learning: Research Themes (Issue January). https://doi.org/10.1007/978-3-319-02600-8_11 Ramdass, D., & Zimmerman, B. J. (2011). Developing Self-Regulation Skills: The Important Role of Homework. Journal of Advanced Academics, 22(2), 194–218. https://doi.org/10.1177/1932202X1102200202 Rhema, A., & Miliszewska, I. (2014). Analysis of Student Attitudes towards E-learning: The Case of Engineering Students in Libya. Issues in Informing Science and Information Technology, 11. Issues in Informing Science and Information Technology, 11, 169–190. http://iisit.org/Vol11/IISITv11p169-190Rhema0471.pdf Romero, L., Saucedo, C., Caliusco, M. L., & Gutiérrez, M. (2019). Supporting self-regulated learning and personalization using ePortfolios: a semantic approach based on learning paths. International Journal of Educational Technology in Higher Education, 16(1), 1– 16. https://doi.org/10.1186/s41239-019-0146-1 Ross, S. M., & Morrison, J. R. (2014). Measuring meaningful outcomes in consequential contexts: Searching for a happy medium in educational technology research (Phase II). Journal of Computing in Higher Education, 26(1), 4–21. https://doi.org/10.1007/s12528- 013-9074-6 Scardamalia, M. (2002). Collective cognitive responsibility for the advancement of knowledge. 44 Liberal Education in a Knowledge Society, 67–98. Schindler, L. A., Burkholder, G. J., Morad, O. A., & Marsh, C. (2017). Computer-based technology and student engagement: a critical review of the literature. International Journal of Educational Technology in Higher Education, 14(1). https://doi.org/10.1186/s41239-017-0063-0 Šerbetar, I., & Sedler, I. (2009). Assessing Reliability of a Multi-Dimensional Scale. Revija Za Elementarno Izobraževanje Št., XIX(September), 9–11. Sitzmann, T., & Ely, K. (2011). A Meta-Analysis of Self-Regulated Learning in Work-Related Training and Educational Attainment: What We Know and Where We Need to Go. Psychological Bulletin, 137(3), 421–442. https://doi.org/10.1037/a0022777 Sun, J. C. Y., & Rueda, R. (2012). Situational interest, computer self-efficacy and self regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191–204. https://doi.org/10.1111/j.1467- 8535.2010.01157.x Sun, J. C. Y., Wu, Y. T., & Lee, W. I. (2017). The effect of the flipped classroom approach to OpenCourseWare instruction on students’ self-regulation. British Journal of Educational Technology, 48(3), 713–729. https://doi.org/10.1111/bjet.12444 Syahada, T., & Sari, S. Y. (2024). Journal of English Language Teaching The Effectiveness of English Score Application on Pre-Teaching Activities to Improve Vocabulary Mastery at SMAN 8 Padang. 13(1), 63–71. https://doi.org/10.24036/jelt.v13i1.127223 Tabuenca, B., Kalz, M., Drachsler, H., & Specht, M. (2015). Time will tell: The role of mobile learning analytics in self-regulated learning. Computers and Education, 89, 53–74. https://doi.org/10.1016/j.compedu.2015.08.004 Taherdoost, H., & Group, H. (2017). Validity and Reliability of the Research Instrument ; How to Test the Validation of a Questionnaire / Survey in a Researchfile:///C:/Users/admin/Desktop/RISACHI REPORT 2021/reference B/2190- 8050-1-PB-1 SOCIO.pdf. International Journal of Sport, Exercise & Training Sciences, 5(3), 27–36. Trowler, V. and P., Engle, R. a., Conant, F. R., Trowler, V. and P., Organization, W. H., Ramsden, A. P., Callender, C., Skinner, E. A., Belmont, M. J., Ramsden, A. P., & 45 Callender, C. (2010). Student Engagement Evidence Summary Department of Educational Research University of Lancaster Deliverable 2 for the Higher Education Academy Student Engagement Project. Univ Lancaster, 85(June), 1–33. http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Global+Health+Obser vatory#0%5Cnhttp://americandemocracy.illinoisstate.edu/documents/democratic engagement-white-paper-2_13_09.pdf Tsitouras, A., Tsivilis, S., & Kakali, G. (2014). A Taguchi Approach for the Synthesis Optimization of Metakaolin Based Geopolymers. In 13th International Ceramics Congress - Part F (Vol. 92). https://doi.org/10.4028/www.scientific.net/ast.92.44 Walkington, C., & Bernacki, M. L. (2019). Personalizing Algebra to Students’ Individual Interests in an Intelligent Tutoring System: Moderators of Impact. International Journal of Artificial Intelligence in Education, 29(1), 58–88. https://doi.org/10.1007/s40593-018- 0168-1 Wang, F., Kinzie, M. B., McGuire, P., & Pan, E. (2010). Applying technology to inquiry-based learning in early childhood education. Early Childhood Education Journal, 37(5), 381– 389. https://doi.org/10.1007/s10643-009-0364-6 Wong, J., Khalil, M., Baars, M., de Koning, B. B., & Paas, F. (2019). Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course. Computers and Education, 140(May), 103595. https://doi.org/10.1016/j.compedu.2019.103595 Yusuf, M. O., & Balogun, M. R. (2020). Student-Teachers’ Competence and Attitude towards Information and Communication Technology: A Case Study in a Nigerian University. Contemporary Educational Technology, 2(1), 18–36. https://doi.org/10.30935/cedtech/6041 Zepke, N. (2014). Student engagement research in higher education: Questioning an academic orthodoxy. Teaching in Higher Education, 19(6), 697–708. https://doi.org/10.1080/13562517.2014.901956 Zhu, Y., Zhang, J. H., Au, W., & Yates, G. (2020). University students’ online learning attitudes and continuous intention to undertake online courses: a self-regulated learning perspective. In Educational Technology Research and Development (Vol. 68, Issue 3). Springer US. https://doi.org/10.1007/s11423-020-09753-w 46 Zimmerman. (2009). Self-Regulation Where Metacognition and Motivation Intersect. 270 Madison Ave, New York: Routledge. Zimmerman, B. J. (2015). Self-Regulated Learning: Theories, Measures, and Outcomes. In International Encyclopedia of the Social & Behavioral Sciences: Second Edition (Second Edi, Vol. 21). Elsevier. https://doi.org/10.1016/B978-0-08-097086-8.26060-1 | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/2401 | |
| dc.description | Supervised by Mr. Muhammad Rashedul Huq Shamim, Associate Professor, Department of Technical and Vocational Education (TVE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh This thesis is submitted in partial fulfillment of the requirement for the Degree of Master of Science in Technical and Vocational Education with specialization in Electrical and Electronics Engineering at the Department of Technical and Vocational Education (TVE) Islamic University of Technology (IUT) Dhaka, Bangladesh June, 2024 | en_US |
| dc.description.abstract | In the current evolving educational landscape, technology plays an important role in enhancing learning experiences. Among its transformative applications, online learning stands out as a revolutionary force that is reshaping traditional classroom dynamics. This study exploring the engineering students’ attitudes towards self-regulated, inquiry-based online learning (SRIBOL), which is a specific aspect of online education. Through a quantitative approach, the study employed a survey questionnaire to collect data from 397 students across three engineering universities in Bangladesh: one international university, one public university and one private university. Data collection utilized a convenient sampling technique, while analysis was conducted using a one-way ANOVA. The instrument was validated by validity analysis using Cohen’s Kappa index, which indicated strong face validity with a Kappa value of 0.82. Moreover, reliability analysis using Cronbach’s alpha demonstrated good internal consistency with a Kappa value of 0.82. Moreover, reliability analysis using Cronbach Alpha demonstrated good internal consistency with an alpha value of 0.75. The findings revealed a significant variation in engineering students’ perceptions based on their field of specialization, suggesting that personalized experiences in SRIBOL could cater to their specific needs. For example, Mechanical and Chemical Engineering (MCE) students may benefit more from simulations and software tools, while Computer Science Engineering (CSE) students may find greater value in self-regulated inquiry-based online learning activities. This emphasizes the variability in engineering students’ perceptions depending on their area of study | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Department of Technical and Vocational Education(TVE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh | en_US |
| dc.subject | Self-regulated online learning, Inquiry-based learning, Technology Acceptance Model, Attitudes on technology, Experience on Technology. | en_US |
| dc.title | Explore Engineering Students’ Attitude Towards Self Regulated Inquiry-Based Online Learning | en_US |
| dc.type | Thesis | en_US |
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