Schema and emotion in memory retrieval following video-based learning: An artificial intelligence study
Open accessAbstract
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.
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AI systems that integrate schema and affect attention and memory retrieval might enable perceptual learning.
“AI systems that integrate schema and affect attention and memory retrieval might enable perceptual learning, indicated by [198].”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [198]
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- 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
- Ekman, P. (Ed.). (1973). Darwin and facial expression: A century of research in review (1st ed.). Academic Press.
- 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
- Fleming, M. (1987). Displays and communication. Instructional Technology Foundations , 233-260. https://doi.org/10.4324/9781315060248 · DOI
- 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.
- 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
- 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
- Kant, I. (1899). Critique of pure reason (J. M. D. Meiklejohn, Trans.). Willey Book. https://doi.org/10.1037/11654-000 · DOI
- 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
- 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
- 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
- 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
- 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
Halkiopoulos, C., & Gkintoni, E. (2024) · Electronics · doi:10.3390/electronics13183762 · Cited by 328 · see citing works
“AI systems that integrate schema and affect attention and memory retrieval might enable perceptual learning, indicated by [198].”
Gkintoni, E., Antonopoulou, H., Sortwell, A., & Halkiopoulos, C. (2025) · Brain Sciences · doi:10.3390/brainsci15020203 · Cited by 309 · see citing works
Vidanaralage, A. J., Dharmaratne, A. T., & Haque, S. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100109 · Cited by 30 · see citing works
Self-citationGeorge-Reyes, C. E., Rumbaut-Rangel, D., Fernández, M. B., & Oliva-Córdova, L. M. (2026) · Information · doi:10.3390/info17060616 · Cited by 4 · see citing works
영어읽기전략 동영상 교재 사용이 중학생들의 영어읽기전략과 독해력에 미치는 영향
이, 경., 김, 수., & 임, 옥. (2023) · Modern English Education · doi:10.18095/meeso.2023.24.1.186 · Cited by 1 · see citing works
Ecampus's Role in Supporting Learning in Higher Education
Zulfadli, Z., & Khairat, A. (2023) · ALSYSTECH Journal of Education Technology · doi:10.58578/alsystech.v1i1.1371
“The research conducted has the aim of being able to find out how effective internet ecampus media is at a tertiary institution (Li et al., 2021; Vidanaralage et al., 2022).”
BackgroundThe impact of teaching with schemas on structural word challenges
Samreen, S., Bi, N., & Khajanchi, Y. (2023) · Multidisciplinary Science Journal · doi:10.31893/multiscience.2023ss0217
Advanced Technologies for Skill Enhancement and Talent Recognition
Kumarasinghe, D. P., Jayasuriya, S. H., Semini, B. V. S., et al. (2025) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet63943.2025.11472168
“This research demonstrates the strength of speech recognition technology supporting speech intervention by offering immediate feedback and precise corrections [22][23].”
Self-citation
- 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
- diamond
- doi.org
- Licence
- 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
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