Cited by 8Memory Processes and InfluencesVisual and Cognitive Learning ProcessesInnovative Teaching Methods

Schema and emotion in memory retrieval following video-based learning: An artificial intelligence study

Open access
Vidanaralage, Junius, Dharmaratne, Anuja, Haque, Shamsul·2022·Australasian Journal of Educational Technology

Abstract

Adapting innovative educational technologies to bolster students’ academic learning is increasing rapidly. This study explored schema congruent and incongruent participants behaviour when experiencing video-based materials as the medium of learning within the frame of a flipped learning environment. The participants watched an educational learning video on a given topic and completed memory retention tests in different time variations: immediate and delayed. Additionally, an artificial intelligence-based emotion analysis examined the emotional valency of participants during two phases: study phase and test phase. The experiment comprised 16 healthy young adult volunteers (8 schema congruent, 8 schema incongruent; 9 males [56.25%], 7 females [43.75%]; age range 20–34 years, mean age 27.31 years, SD = 2.87 years). A combination of statistics-based and AI-based analysis evaluated the effectiveness of video-based learning in terms of retrieval accuracy, response time and emotional valence. The findings indicate that retrieval accuracy for the schema incongruent group was better than schema congruent. Response time for schema congruent group was quicker than schema incongruent. Both groups exhibited more negative emotions during the study phase but more positive emotions during the test phase. Implications for practice or policy: Acceptance testing of video-based learning in tertiary education for different schema groups of students by assessing their emotional state helps educators to enhance pedagogy. Nourishing positive learning experiences from videos and questionnaires should be the goal, considered at the design stage for courses that rely on video-based materials. Adaptation of video-based learning strategy is more instructionally efficient and scalable for academic institutions and educators during a pandemic situation.

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 · 10.14742/ajet.7473

Cites

71 of 71 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?

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: 14

  1. Cervi, C. R., Brock, L. A., Galante, R., & de Oliveira, J. P. M. (2013). A computational tool to analyze the evolution of student learning. In Proceedings of the 5th International Conference on Education and New Learning Technologies (pp. 301-308). IATED. https://library.iated.org/view/cervi2013aco
  2. Dawkins, M. S. (2000). Animal minds and animal emotions. American Zoologist, 40 (6), 883-888. https://doi.org/10.1093/icb/40.6.883 · DOI
  3. Ekman, P. (Ed.). (1973). Darwin and facial expression: A century of research in review (1st ed.). Academic Press.
  4. Findlay-Thompson, S., & Mombourquette, P. (2014). Evaluation of a flipped classroom in an undergraduate business course. Business Education & Accreditation, 6 (1), 63-71. https://ssrn.com/abstract=2331035
  5. Fleming, M. (1987). Displays and communication. Instructional Technology Foundations , 233-260. https://doi.org/10.4324/9781315060248 · DOI
  6. Huang, G., Mattar, M., Berg, T., & Learned Miller, E. (2008, October). Labeled faces in the wild: A database for studying face recognition in unconstrained environments [Workshop presentation]. Faces in 'real-life' images: Detection, alignment, and recognition, France.
  7. Huang, R., Spector, J. M., & Yang, J. (2019). Educational technology: A primer for the 21st centuary . Springer. https://doi.org/10.1007/978-981-13-6643-7 · DOI
  8. Izard, C. E. (2013). Patterns of emotions: A new analysis of anxiety and depression Press. https://doi.org/10.1016/c2013-0-10907-5 · DOI
  9. Kant, I. (1899). Critique of pure reason (J. M. D. Meiklejohn, Trans.). Willey Book. https://doi.org/10.1037/11654-000 · DOI
  10. Laura, C., & Olivier, R. (Eds.). (2020). Introduction: Why Plato comes first. In Emotions in Plato (Vol. 4, pp. 1-14). Brill. https://doi.org/10.1163/9789004432277\_002 · DOI
  11. Richey, R. C., Klein, J. D., & Tracey, M. W. (2010). The instructional design knowledge base: Theory, research, and practice . Routledge. https://doi.org/10.4324/9780203840986 · DOI
  12. Thong, L. P., Stewart, C., Arnab, S., & Lameras, P. (2016). Virtual designer: Digital role-playing game for knowledge transferal in design education. In T. Connolly & L. Boyle (Eds.), Proceedings of the 10th European Conference on Games Based Learning (Vol. 1, pp. 862-869). http://toc.proceedings.com/31939webtoc.pdf
  13. Willey, K., & Gardner. (2014). Combining flipped instruction and multiple perspectives to develop cognitive and affective processes. In Proceedings of the SEFI Annual Conference . European Society for Engineering Education. https://www.sefi.be/wp-content/uploads/2017/10/0092.pdf
  14. Please cite as : Vidanaralage, A. J., Dharmaratne, A. T., & Haque, S. (2022). Schema and emotion in memory retrieval following video-based learning: An artificial intelligence study. Australasian Journal of Educational Technology, 38 (5), 109-132. https://doi.org/10.14742/ajet.7473

Cited by

Sorted by citations

Authors
Vidanaralage, J., Dharmaratne, A., & Haque, S.
Affiliations
Monash University (AU)
Venue
Australasian Journal of Educational Technology
Published
2022-08-03
Pages
109-132
ISSN
  • 1449-3098 (ISSN-L)
  • 1449-5554
OpenAlex
W4289544654
Cited by
8
References
71
Citations by year
2026: 1 · 2025: 2 · 2024: 1 · 2023: 3 · 2022: 1
FWCI
0.90
Type
journal article
Language
en
Open access
Locations
  • Australasian Journal of Educational Technology (publishedVersion, cc-by-nc-nd)
  • Monash University Research Portal (Monash University) (submittedVersion, cc-by-nc-nd)
Funders
Monash University
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
  • Social Sciences › Education › Innovative Teaching Methods
Keywords
video-based learning, memory retrieval, emotion recognition, flipped learning, memory retention, emotional valence, retrieval accuracy, reaction time (psychology), higher education

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