CoChat
Sign Up
Cited by 101Online Learning and AnalyticsInnovative Teaching and Learning MethodsOnline and Blended Learning

The impacts of constructivist learning design and learning analytics on students’ engagement and self-regulation

Open access
Banihashem, Seyyed Kazem, Farrokhnia, Mohammadreza, Badali, Mehdi, Noroozi, Omid·2022·Innovations in Education and Teaching International
DOI 10.1080/14703297.2021.1890634
Open this paper in CoChat — free accountOpen PDF

Abstract

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/14703297.2021.1890634

Cites

38 of 38 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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.

    “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

    The Student engagement section says the study used a scale to assess behavioural, cognitive, and emotional engagement. The Measurement tools and Analysis sections also describe LMS activity data and a self-reported questionnaire, but the questionnaire collected background information and the LMS data were analysed separately; the copy does not show that all three sources measured those engagement dimensions. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Interventions based on emotional and cognitive engagement data enhanced self-regulation and motivation.

    “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

    The Results and RQ2 sections report improved self-regulation in the constructivist learning-design and learning-analytics condition. The Student engagement section says emotional and cognitive engagement were measured, but the paper does not say the intervention was based specifically on those data. The Analysis section identifies engagement and self-regulation as outcomes; it does not report an improvement in motivation. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Studies using qualitative data collection and analysis methods were rare.

    “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

    The Conclusion says this study relied mostly on Moodle and survey data because it had limited access to qualitative data. Neither that passage nor the Introduction establishes that studies using qualitative data collection and analysis methods were rare across the field; searches for that prevalence claim found no such passage in our copy. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • There is an underrepresentation of African and South and Central American research in HELA research, although this might be due in part to the search strategy employed for this review.

    “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

    The Introduction and Discussion address learning analytics, engagement, and self-regulation; the Conclusion discusses limitations of a single-course study. Our copy does not address the geographic distribution of HELA research. Searches for African, South and Central American research and for geographic coverage and review search strategy found no supporting passage. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • The hierarchy of steps incorporated into the conceptual framework is in line with the findings of prior studies.

    “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

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 4

  1. Abubakar, A., Abubakar, Y., & Itse, J. B. (2017). Students' engagement in relationship to academic performance. Journal of Education and Social Sciences, 8(1), 5-9. https://www.jesoc.com/wp- content/uploads/2017/11/JESOC8_201.pdf
  2. 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.
  3. Chaubey, A., & Bhattacharya, B. (2015). Learning management system in higher education. IJSTE- International Journal of Science Technology & Engineering, 2(3), 158-162. https://ijste.org/Article. php?manuscript=IJSTEV2I3050
  4. Jonassen, D. (1999). Designing constructivist learning environments. In C. Reigeluth (Ed.), Instructional-design theories and models: A new paradigm of instructional theory (pp. 215-239). University Park: Pennsylvania State University.

Cited by

Sorted by citations

Related

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
ORCID
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
DOI
10.1080/14703297.2021.1890634
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review