The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
The impacts of constructivist learning design and learning analytics on students’ engagement and self-regulation
Open accessAbstract
So far, the synergistic relationship between learning design and learning analytics and its impact on learners’ educational practices have been investigated. However, little work has been done to show how integrated learning design based on constructivism learning theory and learning analytics can affect learners’ engagement and self-regulation. Thus, this study aimed to investigate the effect of the Constructivist Learning Design and Learning Analytics (CLDLA) Model on learners’ engagement and self-regulation. In this experimental study, 50 graduate students at Iranian’ Allameh Tabataba’i University were asked to participate in a Moodle course called “Teaching Skills” and fill out Agentic Engagement Scale and Self-Regulation Questionnaires in two steps as a pre-test and post-test measurements. Analysis of the data indicated that the CLDLA model has a positive impact on learners’ engagement and self-regulation. Based on the results, recommendations for further research and educational practices are suggested.
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“Similarly, Banihashem et al. (2022) employed a combination of LMS data, surveys, and psychometric scales to measure different dimensions of engagement, including behavioural, cognitive, and emotional aspects.”
From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Banihashem et al., 2022
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“Similarly, interventions based on emotional and cognitive engagement data enhanced self-regulation and motivation (Banihashem et al., 2022).”
From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Banihashem et al., 2022
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“However, these were rare, with relatively few studies using qualitative data collection and analysis methods (Banihashem et al., 2022;Foster & Francis, 2020).”
From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Banihashem et al., 2022
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
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“As previous LA and educational technology reviews have found (e.g., Bergdahl et al., 2020;Bond et al., 2020;Banihashem et al., 2022), there is an underrepresentation of African and South and Central American research, although this might be due in some part to the search strategy employed for this review.”
From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Banihashem et al., 2022
Experimental — accuracy not yet measuredIn the citing paper
“This hierarchy of steps incorporated into our conceptual framework is in line with the findings of prior studies (e.g. Banihashem et al., 2022; Chatti et al., 2012; Elias, 2011, pp. 1 -22; Clow, 2012).”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Banihashem et al., 2022
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Cites
First principles of instruction
Merrill, M. D. (2002) · Educational Technology Research and Development · doi:10.1007/bf02505024 · Cited by 2,092
Executive functions and self-regulation
Hofmann, W., Schmeichel, B. J., & Baddeley, A. D. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.01.006 · Cited by 1,965
Broadbent, J., & Poon, W. L. (2015) · The Internet and Higher Education · doi:10.1016/j.iheduc.2015.04.007 · Cited by 1,692 · cited 1×
“Prior research showed that engagement and self-regulated learning are positively associated with students’ academic success in online courses (Abubakar et al., 2017; Broadbent & Poon, 2015; Cho & Shen, 2013; Kahn et al., 2017; Pardo et al., 2016).”
MethodAgency as a fourth aspect of students’ engagement during learning activities
Reeve, J., & Tseng, C.-M. (2011) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2011.05.002 · Cited by 1,618
Martin, F., & Bolliger, D. U. (2018) · Online Learning · doi:10.24059/olj.v22i1.1092 · Cited by 1,435
Innovations in Clinical Practice : A Source Book
VandeCreek, L. D., & Jackson, T. L. (1992) · Cited by 1,020
Gašević, D., Dawson, S., Rogers, T., & Gašević, D. (2015) · The Internet and Higher Education · doi:10.1016/j.iheduc.2015.10.002 · Cited by 767
Supporting Self-Regulated Learning in Online Learning Environments and MOOCs: A Systematic Review
Wong, J., Baars, M., Davis, D., Van Der Zee, T., Houben, G.-J., & Paas, F. (2019) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2018.1543084 · Cited by 631
Using learning analytics to scale the provision of personalised feedback
Pardo, A., Jovanovic, J., Dawson, S., Gašević, D., & Mirriahi, N. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12592 · Cited by 425
Self-regulation in online learning
Cho, M.-H., & Shen, D. (2013) · Distance Education · doi:10.1080/01587919.2013.835770 · Cited by 380
Research works printed but not linked: 8
- Abubakar A., 2017, Journal of Education and Social Sciences, 8, 5
- Banihashem, S. K. (2020). Development and validation of learning environment design model based on the constructivism theory in higher education with a focus on learning analytics [Unpublished doctoral dissertation]. Allameh Tabataba’i University.
- Bybee R. W., 2006, Colorado Springs, 80918
- Chaubey A., 2015, IJSTE-International Journal of Science Technology & Engineering, 2, 158
- Heller, N. (2020). Pervasive learning analytics for fostering learners’ self-regulation [doctoral dissertation, ludwig maximilian university of munich]. Ludwig Maximilian University of Munich. https://edoc.ub.uni-muenchen.de/26428/
- Hodson D., 1998, School Science Review, 79, 33
- Jonassen D., 1999, Instructional-design theories and models: A new paradigm of instructional theory, 215
- Mameli C., 2017, TPM: Testing, Psychometrics, Methodology in Applied Psychology, 24
Cited by
Sorted by citations
Banihashem, S. K., Noroozi, O., van Ginkel, S., Macfadyen, L. P., & Biemans, H. J. A. (2022) · Educational Research Review · doi:10.1016/j.edurev.2022.100489 · Cited by 246
Self-citationSharma, S., Mittal, P., Kumar, M., & Bhardwaj, V. (2025) · Discover Sustainability · doi:10.1007/s43621-025-01094-z · Cited by 165
Unpacking student engagement in higher education learning analytics: a systematic review
Bergdahl, N., Bond, M., Sjöberg, J., Dougherty, M., & Oxley, E. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00493-y · Cited by 138
Fan, L., Deng, K., & Liu, F. (2025) · Scientific Reports · doi:10.1038/s41598-025-06930-w · Cited by 133
Online peer feedback in higher education: A synthesis of the literature
Kerman, N. T., Banihashem, S. K., Karami, M., Er, E., van Ginkel, S., & Noroozi, O. (2024) · Education and Information Technologies · doi:10.1007/s10639-023-12273-8 · Cited by 109
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Metadata
- Authors
- Banihashem, S. K., Farrokhnia, M., Badali, M., & Noroozi, O.
- Affiliations
- Allameh Tabataba'i University (IR)
- Wageningen University & Research (NL)
- Tarbiat Modares University (IR)
- Corresponding author
- Banihashem, Seyyed Kazem
- Venue
- Innovations in Education and Teaching International
- Publisher
- Informa UK Limited
- Published
- 2021-02-26
- Volume
- 59
- Issue
- 4
- Pages
- 442-452
- ISSN
- 1470-3297 (ISSN-L)
- 1470-3300
- OpenAlex
- W3134746875
- Cited by
- 82
- References
- 38
- Citations by year
- 2026: 17 · 2025: 10 · 2024: 23 · 2023: 19 · 2022: 10 · 2021: 3
- FWCI
- 11.64
- Type
- journal article
- Language
- en
- Open access
- green
- research.wur.nl
- Locations
- Innovations in Education and Teaching International (publishedVersion)
- DSpace (Open University in the Netherlands) (publishedVersion)
- Socio-Environmental Systems Modeling (acceptedVersion)
- DSpace (Open University in the Netherlands) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Education › Online and Blended Learning
- Keywords
- learning analytics, student engagement, student self-regulation, Moodle, graduate students
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