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Investigating effects of perceived technology-enhanced environment on self-regulated learning

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Abstract

This study examined the effects of a technology-enhanced intervention on the self-regulation of 262 eighth-grade students, employing information and communication technology (ICT) and web-based self-assessment tools set against science learning. The data were analyzed using Bayesian structural equation modeling to unravel the intricate relationships between self-regulation, self-efficacy, perceptions of ICT, and self-assessment tools. Our research findings underscored the direct and indirect impacts of self-efficacy, perceived ease of use, and perceived use of technology on self-regulation. The results revealed the predictive power of self-assessment tools in determining self-regulation outcomes, underlining the potential of technology-enhanced self-regulated learning environments. The study posited the necessity to transcend mere technology incorporation and to emphasize the inclusion of monitoring strategies explicitly designed to augment self-regulation. Interestingly, self-efficacy appeared to indirectly influence self-regulation outcomes through perceived the use of technology rather than direct influence. Analytically, this research indicated that Bayesian estimation could offer a more comprehensive insight into structural equation modeling by assessing the estimates’ uncertainty. This research substantially contributes to comprehending the influence of technology-enhanced environments on students’ self-regulated learning, stressing the importance of constructing practical tools explicitly designed to cultivate self-regulation.

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The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256. https://doi.org/10.1016/j.chb.2015.11.036

  • An, F., Xi, L., & Yu, J. (2023). The relationship between technology acceptance and self-regulated learning: The mediation roles of intrinsic motivation and learning engagement. Education and Information Technologies. https://doi.org/10.1007/s10639-023-11959-3

  • Anthonysamy, L., Koo, A. C., & Hew, S. H. (2020). Self-regulated learning strategies and non-academic outcomes in higher education blended learning environments: A one decade review. Education and Information Technologies, 25(5), 3677–3704. https://doi.org/10.1007/s10639-020-10134-2

  • Asparouhov, T., & Muthén, B. (2021). Advances in bayesian model fit evaluation for structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 28(1), 1–14. https://doi.org/10.1080/10705511.2020.1764360

  • Azevedo, R., Cromley, J. G., & Seibert, D. (2004). Does adaptive scaffolding facilitate students’ ability to regulate their learning with hypermedia? Contemporary Educational Psychology, 29(3), 344–370. https://doi.org/10.1016/j.cedpsych.2003.09.002

  • Baars, M., Vink, S., van Gog, T., de Bruin, A., & Paas, F. (2014). Effects of training self-assessment and using assessment standards on retrospective and prospective monitoring of problem solving. Learning and Instruction, 33, 92–107. https://doi.org/10.1016/j.learninstruc.2014.04.004

  • Bouffard-Bouchard, T., Parent, S., & Larivee, S. (1991). Influence of self-efficacy on self-regulation and performance among junior and senior high-school age students. International Journal of Behavioral Development, 14(2), 153–164.

  • Brown, G. T. L., & Harris, L. R. (2013). SAGE Handbook of Research on Classroom Assessment. In. SAGE Publications, Inc. https://doi.org/10.4135/9781452218649

  • Cengiz-Istanbullu, B., & Sakiz, G. (2022). Self-regulated learning strategies impact fourth-grade students’ positive outcomes in science class. Journal of Baltic Science Education, 21(2), 192–206. https://doi.org/10.33225/jbse/22.21.192

  • Chahal, J., & Rani, N. (2022). Exploring the acceptance for e-learning among higher education students in India: Combining technology acceptance model with external variables. Journal of Computing in Higher Education, 34(3), 844–867. https://doi.org/10.1007/s12528-022-09327-0

  • Chen, C. W. (2021). A study on the construction of evaluation indexes of classroom for promote self-regulated learning in junior high school [國民中學促進自主學習課堂評估指標建構之研究] (in Chinese) [Unpublished doctoral dissertation]. National Chengchi University. https://doi.org/10.6814/NCCU202100741

  • Chen, C. W. (2022). The inner treasure of cultivating self-regulated learning [陶養自主學習的內在寶藏] (in Chinese). In M.-w, & Chang (Eds.), Proactively engage in SRL, encounter quality teaching and learning [預見善教, 遇見樂學] (pp. 14–27). Education Department.

  • Cigdem, H. (2015). How does self-regulation affect computer-programming achievement in a blended context? Contemporary Educational Technology, 6(1), 19–37.

  • Dang, N. V., Chiang, J. C., Brown, H. M., & McDonald, K. K. (2018). Curricular activities that promote metacognitive skills impact lower-performing students in an introductory biology course. Journal of Microbiology & Biology Education, 19(1). https://doi.org/10.1128/jmbe.v19i1.1324

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982

  • Dignath, C., Buettner, G., & Langfeldt, H. P. (2008). How can primary school students learn self-regulated learning strategies most effectively? A meta-analysis on self-regulation training programmes. Educational Research Review, 3(2), 101–129. https://doi.org/10.1016/j.edurev.2008.02.003

  • Duane, S., Kennedy, A. D., Pendleton, B. J., & Roweth, D. (1987). Hybrid Monte Carlo. Physics Letters B, 195(2), 216–222. https://doi.org/10.1016/0370-2693(87)91197-X

  • Dunlosky, J., Hartwig, M. K., Rawson, K. A., & Lipko, A. R. (2011). Improving college students’ evaluation of text learning using idea-unit standards. Quarterly Journal of Experimental Psychology, 64(3), 467–484. https://doi.org/10.1080/17470218.2010.502239

  • Fisher, R. J. (1993). Social desirability bias and the validity of indirect questioning. Journal of Consumer Research, 20(2), 303–315. https://doi.org/10.1086/209351

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104

  • Garnier-Villarreal, M., & Jorgensen, T. D. (2020). Adapting fit indices for bayesian structural equation modeling: Comparison to maximum likelihood. Psychological Methods, 25, 46–70. https://doi.org/10.1037/met0000224

  • Gong, M., Xu, Y., & Yu, Y. (2004). An enhanced technology acceptance model for web-based learning. Journal of Information Systems Education, 15(4), 365–374. https://aisel.aisnet.org/jise/vol15/iss4/4

  • Hattie, J. (2012). Visible learning for teachers: Maximizing impact on learning. Routledge. https://doi.org/10.4324/9780203181522

  • 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, 110673. https://doi.org/10.1016/j.paid.2021.110673

  • Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

  • Ishaq, E., Bashir, S., Zakariya, R., & Sarwar, A. (2021). Technology acceptance behavior and feedback loop: Exploring reverse causality of TAM in Post-COVID-19 scenario [Hypothesis and theory]. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.682507

  • Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). Guilford Press.

  • Krebs, R., Rothstein, B., & Roelle, J. (2022). Rubrics enhance accuracy and reduce cognitive load in self-assessment. Metacognition and Learning, 17(2), 627–650. https://doi.org/10.1007/s11409-022-09302-1

  • Kruschke, J. K., & Liddell, T. M. (2018). Bayesian data analysis for newcomers. Psychonomic Bulletin & Review, 25(1), 155–177. https://doi.org/10.3758/s13423-017-1272-1

  • Lai, C. (2013). A framework for developing self-directed technology use for language learning. Language Learning & Technology, 17(2), 100–122. http://hdl.handle.net/10125/44326

  • Lai, C. L., Hwang, G. J., & Tu, Y. H. (2018). The effects of computer-supported self-regulation in science inquiry on learning outcomes, learning processes, and self-efficacy. Educational Technology Research and Development, 66(4), 863–892. https://doi.org/10.1007/s11423-018-9585-y

  • Lau, K. L., & Jong, M. S. Y. (2022). Acceptance of and self-regulatory practices in online learning and their effects on the participation of Hong Kong secondary school students in online learning. Education and Information Technologies. https://doi.org/10.1007/s10639-022-11546-y

  • Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the Technology Acceptance Model. Computers & Education, 61, 193–208. https://doi.org/10.1016/j.compedu.2012.10.001

    Article  Google Scholar 

  • Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191–204. https://doi.org/10.1016/S0378-7206(01)00143-4

    Article  Google Scholar 

  • León, J., Núñez, J. L., & Liew, J. (2015). Self-determination and STEM education: Effects of autonomy, motivation, and self-regulated learning on high school math achievement. Learning and Individual Differences, 43, 156–163. https://doi.org/10.1016/j.lindif.2015.08.017

    Article  Google Scholar 

  • Liang, X. (2020). Prior sensitivity in bayesian structural equation modeling for sparse factor loading structures. Educational and Psychological Measurement, 80(6), 1025–1058. https://doi.org/10.1177/0013164420906449

    Article  MathSciNet  Google Scholar 

  • Lim, C. P., & Chan, B. C. (2007). microLESSONS in teacher education: Examining pre-service teachers’ pedagogical beliefs. Computers & Education, 48(3), 474–494. https://doi.org/10.1016/j.compedu.2005.03.005

    Article  Google Scholar 

  • Lipko, A. R., Dunlosky, J., Hartwig, M. K., Rawson, K. A., Swan, K., & Cook, D. (2009). Using standards to improve middle school students’ accuracy at evaluating the quality of their recall. Journal of Experimental Psychology: Applied, 15(4), 307–318. https://doi.org/10.1037/a0017599

    Article  Google Scholar 

  • Ly, A., Verhagen, J., & Wagenmakers, E. J. (2016). Harold Jeffreys’s default Bayes factor hypothesis tests: Explanation, extension, and application in psychology. Journal of Mathematical Psychology, 72, 19–32. https://doi.org/10.1016/j.jmp.2015.06.004

    Article  MathSciNet  Google Scholar 

  • Ly, A., Marsman, M., & Wagenmakers, E. J. (2018). Analytic posteriors for Pearson’s correlation coefficient. Statistica Neerlandica, 72(1), 4–13. https://doi.org/10.1111/stan.12111

    Article  MathSciNet  Google Scholar 

  • Mathabathe, K. C., & Potgieter, M. (2014). Metacognitive monitoring and learning gain in foundation chemistry. Chemistry Education Research and Practice, 15(1), 94–104. https://doi.org/10.1039/c3rp00119a

    Article  Google Scholar 

  • Mayer, P., & Girwidz, R. (2019). Physics teachers’ acceptance of multimedia applications—Adaptation of the technology acceptance model to investigate the influence of TPACK on physics teachers’ acceptance behavior of multimedia applications [Original Research]. Frontiers in Education, 4,. https://doi.org/10.3389/feduc.2019.00073

  • McElreath, R. (2020). Statistical rethinking: A bayesian course with examples in R and Stan. Chapman and Hall/CRC. https://doi.org/10.1201/9780429029608

  • Merkle, E. C., & Rosseel, Y. (2018). blavaan: Bayesian structural equation models via parameter expansion. Journal of Statistical Software, 85(4), 1–30. https://doi.org/10.18637/jss.v085.i04

    Article  Google Scholar 

  • Muthén, B., & Asparouhov, T. (2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17, 313–335. https://doi.org/10.1037/a0026802

    Article  Google Scholar 

  • Osterhage, J. L., Usher, E. L., Douin, T. A., & Bailey, W. M. (2019). Opportunities for self-evaluation increase student calibration in an Introductory Biology Course. CBE—Life Sciences Education, 18(2), ar16. https://doi.org/10.1187/cbe.18-10-0202

    Article  Google Scholar 

  • Panadero, E., & Jonsson, A. (2013). The use of scoring rubrics for formative assessment purposes revisited: A review. Educational Research Review, 9, 129–144. https://doi.org/10.1016/j.edurev.2013.01.002

    Article  Google Scholar 

  • Panadero, E., Jonsson, A., & Botella, J. (2017). Effects of self-assessment on self-regulated learning and self-efficacy: Four meta-analyses. Educational Research Review, 22, 74–98. https://doi.org/10.1016/j.edurev.2017.08.004

    Article  Google Scholar 

  • Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). Ann Arbor: National Center for Research to Improve Postsecondary Teaching and Learning, The University of Michigan.

  • Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33–40. https://doi.org/10.1037/0022-0663.82.1.33

    Article  Google Scholar 

  • Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.879

    Article  Google Scholar 

  • Raaijmakers, S. F., Baars, M., Schaap, L., Paas, F., van Merriënboer, J., & van Gog, T. (2017). Training self-regulated learning skills with video modeling examples: Do task-selection skills transfer? Instructional Science, 46(2), 273–290. https://doi.org/10.1007/s11251-017-9434-0

    Article  Google Scholar 

  • Safadi, R., & Saadi, S. (2019). Learning from self-diagnosis activities when contrasting students’ own solutions with worked examples: The case of 10th graders studying geometric Optics. Research in Science Education, 51(2), 523–546. https://doi.org/10.1007/s11165-018-9806-8

    Article  Google Scholar 

  • Schunk, D. H. (1994). Self-regulation of self-efficacy and attributions in academic settings. Self-Regulation of Learning and Performance: Issues and Educational Applications, 1994, 75–99.

    Google Scholar 

  • Smid, S. C., McNeish, D., Miočević, M., & van de Schoot, R. (2020). Bayesian versus frequentist estimation for structural equation models in small sample contexts: A systematic review. Structural Equation Modeling: A Multidisciplinary Journal, 27(1), 131–161. https://doi.org/10.1080/10705511.2019.1577140

    Article  MathSciNet  Google Scholar 

  • Stanton, J. D., Neider, X. N., Gallegos, I. J., & Clark, N. C. (2015). Differences in metacognitive regulation in introductory biology students: When prompts are not enough. CBE—Life Sciences Education, 14(2). https://doi.org/10.1187/cbe.14-08-0135

  • Stanton, J. D., Dye, K. M., & Johnson, M. (2019). Knowledge of learning makes a difference: A comparison of metacognition in introductory and senior-level biology students. CBE—Life Sciences Education, 18(2), ar24. https://doi.org/10.1187/cbe.18-12-0239

    Article  Google Scholar 

  • Stephenson, C., & Isaacs, T. (2019). The role of the extended project qualification in developing self-regulated learners: Exploring students’ and teachers’ experiences. The Curriculum Journal, 30(4), 392–421. https://doi.org/10.1080/09585176.2019.1646665

    Article  Google Scholar 

  • Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302–312. https://doi.org/10.1016/j.compedu.2008.08.006

    Article  Google Scholar 

  • Tondeur, J., van Braak, J., Ertmer, P. A., & Ottenbreit-Leftwich, A. (2016). Understanding the relationship between teachers’ pedagogical beliefs and technology use in education: A systematic review of qualitative evidence. Educational Technology Research and Development, 65(3), 555–575. https://doi.org/10.1007/s11423-016-9481-2

    Article  Google Scholar 

  • van de Schoot, R., Winter, S. D., Ryan, O., Zondervan-Zwijnenburg, M., & Depaoli, S. (2017). A systematic review of bayesian articles in psychology: The last 25 years. Psychological Methods, 22, 217–239. https://doi.org/10.1037/met0000100

    Article  Google Scholar 

  • van de Schoot, R., Depaoli, S., King, R., Kramer, B., Märtens, K., Tadesse, M. G., & Yau, C. (2021). Bayesian statistics and modelling. Nature Reviews Methods Primers, 1(1), 1. https://doi.org/10.1038/s43586-020-00001-2

    Article  Google Scholar 

  • Vemu, S., Denaro, K., Sato, B. K., & Williams, A. E. (2022). Moving the needle: Evidence of an effective study strategy intervention in a community college biology course. CBE—Life Sciences Education, 21(2), ar24. https://doi.org/10.1187/cbe.21-08-0216

    Article  Google Scholar 

  • Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test*. Decision Sciences, 27(3), 451–481. https://doi.org/10.1111/j.1540-5915.1996.tb00860.x

    Article  Google Scholar 

  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

    Article  Google Scholar 

  • Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 279–306). Routledge.

  • Yang, J., Wang, Q., Wang, J., Huang, M., & Ma, Y. (2019). A study of K-12 teachers’ TPACK on the technology acceptance of E-schoolbag. Interactive Learning Environments, 29(7), 1062–1075. https://doi.org/10.1080/10494820.2019.1627560

    Article  Google Scholar 

  • Yen, M. H., Chen, S., Wang, C. Y., Chen, H. L., Hsu, Y. S., & Liu, T. C. (2018). A framework for self-regulated digital learning (SRDL). Journal of Computer Assisted Learning, 34(5), 580–589. https://doi.org/10.1111/jcal.12264

    Article  Google Scholar 

  • Yi, M. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59(4), 431–449. https://doi.org/10.1016/S1071-5819(03)00114-9

    Article  Google Scholar 

  • Zamora, Á, Suárez, J. M., & Ardura, D. (2016). Error detection and self-assessment as mechanisms to promote self-regulation of learning among secondary education students. The Journal of Educational Research, 111(2), 175–185. https://doi.org/10.1080/00220671.2016.1225657

    Article  Google Scholar 

  • Zheng, L. (2016). The effectiveness of self-regulated learning scaffolds on academic performance in computer-based learning environments: a meta-analysis. Asia Pacific Education Review, 17(2), 187–202. https://doi.org/10.1007/s12564-016-9426-9

  • Zhou, Q., Lee, C. S., & Sin, J. S. C. (2021). When social media use for formal learning is voluntary: A study of students’ use of self-regulated learning strategies. Library and Information Science Research E-Journal, 31(1). https://doi.org/10.32655/libres.2021.1.2

  • 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. Educational Technology Research and Development, 68(3), 1485–1519. https://doi.org/10.1007/s11423-020-09753-w

    Article  Google Scholar 

  • Zimmerman, B. J. (1986). Becoming a self-regulated learner: Which are the key subprocesses? Contemporary Educational Psychology, 11(4), 307–313. https://doi.org/10.1016/0361-476X(86)90027-5

    Article  Google Scholar 

  • Zimmerman, B. J. (1990). Self-regulating academic learning and achievement: The emergence of a social cognitive perspective. Educational Psychology Review, 2(2), 173–201. https://doi.org/10.1007/BF01322178

    Article  Google Scholar 

  • Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2

    Article  Google Scholar 

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Acknowledgements

We gratefully acknowledge the participants that assisted with the data collection. This study would not have been possible without their involvement.

Funding

This work was financially supported by the National Science Council of Taiwan under contracts the MOST 111-2410-H-003-032-MY3, NSTC 111-2423-H-003-004, MOST 110-2511-H-003-027-MY2 and the “Institute for Research Excellence in Learning Sciences” of National Taiwan Normal University (NTNU) from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.

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Chi-Jung Sui: conceptualization, investigation, methodology, data curation, validation, formal analysis, and writing—original draft. Miao-Hsuan Yen: conceptualization, methodology, supervision, resources, and writing—editing and review. Chun-Yen Chang: conceptualization, methodology, supervision, resources, and writing—editing and review, acting as a correspondent. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Miao-Hsuan Yen or Chun-Yen Chang.

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Sui, CJ., Yen, MH. & Chang, CY. Investigating effects of perceived technology-enhanced environment on self-regulated learning. Educ Inf Technol 29, 161–183 (2024). https://doi.org/10.1007/s10639-023-12270-x

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