Skip to main content
SpringerLink
Log in

An improved adaptive personalization model for instructional video-based e-learning environments

  • Published:
Journal of Computers in Education Aims and scope Submit manuscript

Abstract

Due to the unexpected COVID-19 pandemic, video-based e-learning environments for programming education have disrupted traditional classroom teaching methods. The major drawbacks of these environments are that they never consider the individual differences and personal traits of the learner while building a challenging course like programming, having high dropout and failure rates. To address this issue, this paper proposed a learning style-enabled novel rule-based personalized instructional video delivery model for programming education. The model used the following four learning parameters for delivering the instructional videos: (a) most recent instructional video, (b) assessment score, (c) complexity level, and (d) weight (variance of two recent assessments) score. This work was designed using a paired pre-test–post-test experimental approach with first-year undergraduate students. For the experimental evaluation, students were randomly classified into three groups. Learner scores and feedback were taken as evaluation metrics. Results revealed that the proposed model-driven group showed significant improvements in knowledge acquisition, grade, and positive feedback compared to the other groups. Hence, the proposed model is highly recommended for traditional programming e-learning environments to deliver personalized instructional videos based on learners’ receptive pace, cognitive level, and learning preference.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Algorithm 1
Algorithm 2
Algorithm 3
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Data availability

The grades and feedback of the students are not available due to data privacy reasons.

Abbreviations

ANCOVA:

Analysis of covariance

CSE:

Computer Science and Engineering

KNN:

K-nearest neighbors algorithm

LO:

Learning object

MAE:

Mean absolute error

MSE:

Mean squared error

RMSE:

Root mean squared error

SVM:

Support vector machines algorithm

VARK:

Visual Auditory Read/write and Kinesthetic

VPTH:

Vista Phonic Textual and Hands-on

References

  • Abeysekera, L., & Dawson, P. (2015). Motivation and cognitive load in the flipped classroom: Definition, rationale and a call for research. Higher Education Research & Development, 34(1), 1–14. https://doi.org/10.1080/07294360.2014.934336

    Article  Google Scholar 

  • Aboagye, E., Yawson, J. A., & Appiah, K. N. (2021). Covid-19 and e-learning: The challenges of students in tertiary institutions. Social Education Research. https://doi.org/10.37256/ser.122020422

    Article  Google Scholar 

  • Alam, T. M., Mushtaq, M., Shaukat, K., Hameed, I. A., Umer Sarwar, M., & Luo, S. (2021). A novel method for performance measurement of public educational institutions using machine learning models. Applied Sciences. https://doi.org/10.3390/app11199296

    Article  Google Scholar 

  • Alammary, A. (2019). Blended learning models for introductory programming courses: A systematic review. PLoS ONE, 14(9), e0221765. https://doi.org/10.1371/journal.pone.0221765

    Article  Google Scholar 

  • Alammary, A., Sheard, J., & Carbone, A. (2014). Blended learning in higher education: Three different design approaches. Australasian Journal of Educational Technology. https://doi.org/10.14742/ajet.693

  • Ali, N., Eassa, F., & Hamed, E. (2019). Personalized learning style for adaptive elearning system. International Journal of Advanced Trends in Computer Science and Engineering, 8(1), 223–230. https://doi.org/10.30534/ijatcse/2019/4181.12019

    Article  Google Scholar 

  • Al-Jarf, R. (2022). YouTube videos as a resource for self-regulated pronunciation practice in EFL distance learning environments. Journal of English Language Teaching and Applied Linguistics, 4(2), 44–52. https://doi.org/10.32996/jeltal.2022.4.2.4

  • Alshammari, M. (2016). Adaptation based on learning style and knowledge level in e-learning systems (unpublished doctoral dissertation). University of Birmingham.

  • Altamimi, A. M., Azzeh, M., & Albashayreh, M. (2022). Predicting students’ learning styles using regression techniques. arXiv Preprint. arXiv:2209.12691

  • Alzain, A., Clark, S., Ireson, G., & Jwaid, A. (2018a). Adaptive education based on learning styles: Are learning style instruments precise enough? International Journal of Emerging Technologies in Learning, 13(09), 41–52. https://doi.org/10.3991/ijet.v13i09.8554

    Article  Google Scholar 

  • Alzain, A., Clark, S., Ireson, G., & Jwaid, A. (2018b). Learning personalization based on learning style instruments. Advances in Science, Technology and Engineering Systems Journal (ASTESJ), 3(3), 108–115. https://doi.org/10.25046/aj030315

    Article  Google Scholar 

  • Beldagli, B., & Adiguzel, T. (2010). Illustrating an ideal adaptive e-learning: A conceptual framework. Procedia-Social and Behavioral Sciences, 2(2), 5755–5761. https://doi.org/10.1016/j.sbspro.2010.03.939

    Article  Google Scholar 

  • Belt, E. S., & Lowenthal, P. R. (2023). Synchronous video-based communication and online learning: An exploration of instructors’ perceptions and experiences. Education and Information Technologies, 28(5), 4941–4964. https://doi.org/10.1007/s10639-022-11360-6

    Article  Google Scholar 

  • Bostrom, R. P., Olfman, L., & Sein, M. K. (1990). The importance of learning style in end-user training. Mis Quarterly. https://doi.org/10.2307/249313

    Article  Google Scholar 

  • Breslyn, W., & Green, A. E. (2022). Learning science with YouTube videos and the impacts of covid-19. Disciplinary and Interdisciplinary Science Education Research, 4(1), 1–20. https://doi.org/10.1186/s43031-022-00051-4

    Article  Google Scholar 

  • Cahapay, M. (2020). Rethinking education the new normal post-covid-era: A curriculum studies perspective. B. in 19 Aquademia, 4(2), ep20018.

    Article  Google Scholar 

  • Capone, R. (2022). Blended learning and student-centered active learning environment: A case study with stem undergraduate students. Canadian Journal of Science, Mathematics and Technology Education, 22(1), 210–236. https://doi.org/10.1007/s42330-022-00195-5

    Article  Google Scholar 

  • Chen, S. Y., & Wang, J.-H. (2021). Individual differences and personalized learning: A review and appraisal. Universal Access in the Information Society, 20, 833–849. https://doi.org/10.1007/s10209-020-00753-4

    Article  Google Scholar 

  • Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243. https://doi.org/10.1080/00461520.2014.965823

    Article  Google Scholar 

  • DeCapua, A., & Marshall, H. W. (2015). Implementing a mutually adaptive learning paradigm in a community-based adult ESL literacy class. In Low educated second language and literacy acquisition. Proceedings of the ninth symposium (pp. 151–171).

  • Demaidi, M. N., Qamhieh, M., & Afeefi, A. (2019). Applying blended learning in programming courses. IEEE Access, 7, 156824–156833. https://doi.org/10.1109/access.2019.2949927

    Article  Google Scholar 

  • Deperlioglu, O., & Kose, U. (2013). The effectiveness and experiences of blended learning approaches to computer programming education. Computer Applications in Engineering Education, 21(2), 328–342. https://doi.org/10.1002/cae.20476

    Article  Google Scholar 

  • Dugard, P., & Todman, J. (1995). Analysis of pre-test–post-test control group designs in educational research. Educational Psychology, 15(2), 181–198. https://doi.org/10.1080/0144341950150207

    Article  Google Scholar 

  • Dutsinma, L. I. F., & Temdee, P. (2020). VARK learning style classification using decision tree with physiological signals. Wireless Personal Communications, 115, 2875–2896. https://doi.org/10.1007/s11277-020-07196-3

    Article  Google Scholar 

  • El Bachari, E., Abdelwahed, E., & El Adnani, M. (2010). Design of an adaptive e-learning model based on learner’s personality. Ubiquitous Computing and Communication Journal, 5(3), 1–8.

    Google Scholar 

  • El-Sabagh, H. A. (2021). Adaptive e-learning environment based on learning styles and its impact on development students’ engagement. International Journal of Educational Technology in Higher Education, 18(1), 1–24.

    Article  Google Scholar 

  • Felder, R. M., & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Engineering Education, 78(7), 674–681.

    Google Scholar 

  • Fleming, N. (2022). The VARK questionnaire—How do you learn best? Retrieved August 14, 2022, from https://vark-learn.com/the-vark-questionnaire/

  • Fleming, N., & Mills, C. (2001). VARK: A guide to learning styles. Retrieved November 30, 2022, from http://vark-learn.com/

  • Forehand, M. (2010). Bloom’s taxonomy. Emerging Perspectives on Learning, Teaching, and Technology, 41(4), 47–56.

    Google Scholar 

  • Friesen, N. (2012). Report: Defining blended learning, 130. August 2012. https://www.learningspaces.org/papers/Defining_Blended_Learning_NF.pdf

  • Gambo, F. L., Wajiga, G. M., Garba, E. J., & Aliyu, A. (2021). A deep learning solution for learning style detection using cognitive-affective features. Computer Science, 3, 1.

  • Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95–105. https://doi.org/10.1016/j.iheduc.2004.02.001

    Article  Google Scholar 

  • Gordillo, A., López-Fernández, D., & Tovar, E. (2022). Comparing the effectiveness of video-based learning and game-based learning using teacher-authored video games for online software engineering education. IEEE Transactions on Education, 65(4), 524–532. https://doi.org/10.1109/te.2022.3142688

    Article  Google Scholar 

  • Graf, S., Viola, S. R., Leo, T., & Kinshuk. (2007). In-depth analysis of the Felder-Silverman learning style dimensions. Journal of Research on Technology in Education, 40(1), 79–93. https://doi.org/10.1080/15391523.2007.10782498

    Article  Google Scholar 

  • Grinsztajn, L., Oyallon, E., & Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on typical tabular data? Advances in Neural Information Processing Systems, 35, 507–520.

    Google Scholar 

  • Hedberg, E., & Ayers, S. (2015). The power of a paired t-test with a covariate. Social Science Research, 50, 277–291. https://doi.org/10.1016/j.ssresearch.2014.12.004

    Article  Google Scholar 

  • Honey, P., & Mumford, A. (1992). The manual of learning styles maidenhead. P. Honey.

    Google Scholar 

  • Hsu, F.-H., Lin, I.-H., Yeh, H.-C., & Chen, N.-S. (2022). Effect of Socratic Reflection Prompts via video-based learning system on elementary school students’ critical thinking skills. Computers & Education, 183, 104497. https://doi.org/10.1016/j.compedu.2022.104497

    Article  Google Scholar 

  • Hsu, H., & Lachenbruch, P. A. (2014). Paired t test. Wiley StatsRef: Statistics Reference Online. https://doi.org/10.1002/9781118445112.stat05929

    Article  Google Scholar 

  • Hwang, G.-J., Sung, H.-Y., Hung, C.-M., & Huang, I. (2013). A learning style perspective to investigate the necessity of developing adaptive learning systems. Journal of Educational Technology & Society, 16(2), 188–197.

    Google Scholar 

  • Jaleel, S., & Thomas, A. M. (2019). Learning styles: Theories and implications for teaching learning. Horizon Research Publishing.

    Google Scholar 

  • Kawale, S. R., Laxmi, M. P. D., Shekhar, R., Sarma, P., Lingeshwaran, N., & Rao, B. K. (2022). Development of an e-learning system based on artificial intelligence. Journal of Positive School Psychology, 6(8), 4038–4049.

    Google Scholar 

  • Khongpit, V., Sintanakul, K., & Nomphonkrang, T. (2018). The VARK learning style of the university student in computer course. International Journal of Learning and Teaching, 4(2), 102–106. https://doi.org/10.18178/ijlt.4.2.102-106

    Article  Google Scholar 

  • Kilbury, M., Böhnke, A., & Thiel, F. (2023). Producing staged videos for teacher education: Development and content validation of video scripts on the topic of handling classroom disruptions. Education Sciences, 13(1), 56. https://doi.org/10.3390/educsci13010056

    Article  Google Scholar 

  • Kleftodimos, A., & Evangelidis, G. (2016). Using open source technologies and open internet resources for building an interactive video based learning environment that supports learning analytics. Smart Learning Environments, 3, 1–23. https://doi.org/10.1186/s40561-016-0032-4

    Article  Google Scholar 

  • Kolekar, S. V., Pai, R. M., & Manohara Pai, M. M. (2017). Prediction of learner’s profile based on learning styles in adaptive e-learning system. International Journal of Emerging Technologies in Learning. https://doi.org/10.3991/ijet.v12i06.6579

    Article  Google Scholar 

  • Kori, K., Pedaste, M., Tonisson, E., Palts, T., Altin, H., Rantsus, R., & Rüütmann, T. (2015). First-year dropout in ICT studies. In 2015 IEEE global engineering education conference (EDUCON) (pp. 437–445).

  • Krishnamoorthy, D., & Lokesh, D. (2020). Process of building a dataset and classification of VARK learning styles with machine learning and predictive analytics models. Journal of Contemporary Issues in Business and Government, 26(2), 903–910. https://doi.org/10.47750/cibg.2020.26.02.128

    Article  Google Scholar 

  • Kustitskaya, T. A., Kytmanov, A. A., & Noskov, M. V. (2022). Early student-at-risk detection by current learning performance and learning behavior indicators. Cybernetics and Information Technologies, 22(1), 117–133. https://doi.org/10.2478/cait-2022-0008

    Article  Google Scholar 

  • Laaser, W., & Toloza, E. A. (2017). The changing role of the educational video in higher distance education. The International Review of Research in Open and Distributed Learning, 18(2), 1–2. https://doi.org/10.19173/irrodl.v18i2.3067

    Article  Google Scholar 

  • Liang, J. S. (2012). The effects of learning styles and perceptions on application of interactive learning guides for web-based courses. In 23rd annual conference of the Australasian Association for Engineering Education 2012: Profession of engineering education: Advancing teaching, research and careers (pp. 712–721).

  • Liao, C.-W., Chen, C.-H., & Shih, S.-J. (2019). The interactivity of video and collaboration for learning achievement, intrinsic motivation, cognitive load, and behavior patterns in a digital game-based learning environment. Computers & Education, 133, 43–55. https://doi.org/10.1016/j.compedu.2019.01.013

    Article  Google Scholar 

  • Lin, Y.-T., Yeh, M.K.-C., & Tan, S.-R. (2022). Teaching programming by revealing thinking process: Watching experts’ live coding videos with reflection annotations. IEEE Transactions on Education, 65(4), 617–627. https://doi.org/10.1109/te.2022.3155884

    Article  Google Scholar 

  • Lynch, T., & Ghergulescu, I. (2016). An evaluation framework for adaptive and intelligent tutoring systems. In E-learn: World conference on e-learning in corporate, government, healthcare, and higher education (pp. 1385–1390).

  • Macrides, E., Miliou, O., & Angeli, C. (2022). Programming in early childhood education: A systematic review. International Journal of Child-Computer Interaction, 32, 100396. https://doi.org/10.1016/j.ijcci.2021.100396

    Article  Google Scholar 

  • Mahnane, L., Tayeb, L. M., & Trigano, P. (2013). A model for an adaptive e-learning hypermedia system. International Journal of Information and Communication Technology Education (IJICTE), 9(4), 21–39.

    Article  Google Scholar 

  • Mayer, R. E. (2008). Applying the science of learning: Evidence-based principles for the design of multimedia instruction. American Psychologist, 63(8), 760. https://doi.org/10.1037/0003-066x.63.8.760

    Article  Google Scholar 

  • Megahed, N. A., & Ghoneim, E. M. (2022). E-learning ecosystem metaphor: Building sustainable education for the post-covid-19 era. International Journal of Learning Technology, 17(2), 133–153. https://doi.org/10.1504/ijlt.2022.10049986

    Article  Google Scholar 

  • Mseleku, Z. (2020). A literature review of e-learning and e-teaching in the era of covid-19 pandemic. Sage.

    Google Scholar 

  • Naqeeb, H., & Awad, A. (2011). Learning styles as perceived by learners of English as a foreign language in the English language center of the Arab American university—Jenin, Palestine. An-Najah University Journal for Research - B (Humanities), 25(8), 2231–2256. https://doi.org/10.35552/0247-025-008-010

    Article  Google Scholar 

  • Noetel, M., Griffith, S., Delaney, O., Sanders, T., Parker, P., del Pozo Cruz, B., & Lonsdale, C. (2021). Video improves learning in higher education: A systematic review. Review of Educational Research, 91(2), 204–236. https://doi.org/10.31234/osf.io/kynez

    Article  Google Scholar 

  • Nuankaew, P., Nuankaew, W., Phanniphong, K., Imwut, S., & Bussaman, S. (2019). Students model in different learning styles of academic achievement at the University of Phayao, Thailand. International Journal of Emerging Technologies in Learning (Online), 14(12), 133. https://doi.org/10.3991/ijet.v14i12.10352

    Article  Google Scholar 

  • Qazdar, A., Cherkaoui, C., Er-Raha, B., & Mammass, D. (2015). AeLF: Mixing adaptive learning system with learning management system. International Journal of Computer Applications, 119(15), 1–8. https://doi.org/10.5120/21140-4171

    Article  Google Scholar 

  • Ragan, L. C., Cavanagh, T. B., Schroeder, R., & Thompson, K. (2023). Supporting faculty success in online learning: Requirements for individual and institutional leadership. In Leading the e-learning transformation of higher education (pp. 116–137). Routledge.

  • Rao, C. S., & Arunachalam, A. (2021). Ensemble based learning style identification using VARK. NVEO-Natural Volatiles & Essential OILS Journal-NVEO, 8(5), 4550–4559.

  • Rao, V. (2019). Blended learning: A new hybrid teaching methodology. Online Submission, 3(13), 1–6.

  • Rasheed, F., & Wahid, A. (2021). Learning style detection in e-learning systems using machine learning techniques. Expert Systems with Applications, 174, 114774. https://doi.org/10.1016/j.eswa.2021.114774

    Article  Google Scholar 

  • Richter, E., Hußner, I., Huang, Y., Richter, D., & Lazarides, R. (2022). Video-based reflection in teacher education: Comparing virtual reality and real classroom videos. Computers & Education, 190, 104601. https://doi.org/10.1016/j.compedu.2022.104601

    Article  Google Scholar 

  • Rolfe, V. E., & Gray, D. (2011). Are multimedia resources effective in life science education? A meta-analysis. Bioscience Education, 18(1), 1–14. https://doi.org/10.3108/beej.18.5

    Article  Google Scholar 

  • Sablić, M., Mirosavljevi’c, A., & Škugor, A. (2021). Video-based learning (VBL)-past, present and future: An overview of the research published from 2008 to 2019. Technology, Knowledge and Learning, 26(4), 1061–1077. https://doi.org/10.1007/s10758-020-09455-5

    Article  Google Scholar 

  • Sanal Kumar, T. S., & Thandeeswaran, R. (2023). A general model for an instructional video-based personalized programming learning environment and its practical implications. In 2023 12th international conference on advanced computing (ICOAC) (pp. 1–6).

  • Sayed, W. S., Noeman, A. M., Abdellatif, A., Abdelrazek, M., Badawy, M. G., Hamed, A., & El-Tantawy, S. (2023). Ai-based adaptive personalized content presentation and exercises navigation for an effective and engaging e-learning platform. Multimedia Tools and Applications, 82(3), 3303–3333. https://doi.org/10.1007/s11042-022-13076-8

    Article  Google Scholar 

  • Scagnoli, N. I., Choo, J., & Tian, J. (2019). Students’ insights on the use of video lectures in online classes. British Journal of Educational Technology, 50(1), 399–414.

    Article  Google Scholar 

  • Schneider, S., Nebel, S., Beege, M., & Rey, G. D. (2018). The autonomy-enhancing effects of choice on cognitive load, motivation and learning with digital media. Learning and Instruction, 58, 161–172. https://doi.org/10.1016/j.learninstruc.2018.06.006

    Article  Google Scholar 

  • Shi, L., Cristea, A. I., Foss, J. G., Al Qudah, D., & Qaffas, A. (2013). A social personalized adaptive e-learning environment: A case study in topolor. IADIS International Journal on WWW/Internet, 11(3), 1–17. https://doi.org/10.1109/icalt.2013.92

    Article  Google Scholar 

  • Sidi, Y., Shamir-Inbal, T., & Eshet-Alkalai, Y. (2023). From face-to-face to online: Teachers’ perceived experiences in online distance teaching during the covid-19 pandemic. Computers & Education, 201, 104831.

    Article  Google Scholar 

  • Singh, J., Evans, E., Reed, A., Karch, L., Qualey, K., Singh, L., & Wiersma, H. (2022). Online, hybrid, and face-to-face learning through the eyes of faculty, students, administrators, and instructional designers: Lessons learned and directions for the post-vaccine and post-pandemic/covid-19 world. Journal of Educational Technology Systems, 50(3), 301–326. https://doi.org/10.1177/00472395211063754

    Article  Google Scholar 

  • Tchamova, A., Dezert, J., Bocheva, N., Konstantinova, P., Genova, B., & Stefanova, M. (2021). A study on human learning ability during classification of motion and colour visual cues and their combination. Cybernetics and Information Technologies, 21(1), 73–86. https://doi.org/10.2478/cait-2021-0006

    Article  Google Scholar 

  • Troussas, C., Chrysafiadi, K., & Virvou, M. (2021a). Personalized tutoring through a stereotype student model incorporating a hybrid learning style instrument. Education and Information Technologies, 26, 2295–2307. https://doi.org/10.1007/s10639-020-10366-2

    Article  Google Scholar 

  • Troussas, C., Krouska, A., & Sgouropoulou, C. (2021b). A novel teaching strategy through adaptive learning activities for computer programming. IEEE Transactions on Education, 64(2), 103–109. https://doi.org/10.1109/TE.2020.3012744

    Article  Google Scholar 

  • Van Breukelen, G. J. (2006). ANCOVA versus change from baseline had more power in randomized studies and more bias in nonrandomized studies. Journal of Clinical Epidemiology, 59(9), 920–925. https://doi.org/10.1016/j.jclinepi.2006.02.007

    Article  Google Scholar 

  • Wachtler, J., Hubmann, M., Zöhrer, H., & Ebner, M. (2016). An analysis of the use and effect of questions in interactive learning-videos. Smart Learning Environments, 3(1), 1–16. https://doi.org/10.1186/s40561-016-0033-3

    Article  Google Scholar 

  • Wan, S., & Niu, Z. (2018). An e-learning recommendation approach based on the self-organization of learning resource. Knowledge-Based Systems, 160, 71–87. https://doi.org/10.1016/j.knosys.2018.06.014

    Article  Google Scholar 

  • Wong, J. K., Oladinrin, O. T., Ho, C. M., Guilbert, E., & Kam, R. (2022). Assessment of video-based e-learning in a construction measurement course. International Journal of Construction Management, 22(1), 1–7. https://doi.org/10.1080/15623599.2018.1435152

    Article  Google Scholar 

  • York, R. O. (2016). Statistics for human service evaluation. Sage Publications.

    Google Scholar 

  • Zhang, H. (2017). Accommodating different learning styles in the teaching of economics: With emphasis on Fleming and Mill’s sensory-based learning style typology. Applied Economics and Finance, 4(1), 72–83. https://doi.org/10.11114/aef.v4i1.192151

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank all the students and faculty members who participated in the data collection.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. School of Computer Science Engineering and Information Systems, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India

    T S Sanal Kumar & R. Thandeeswaran

Authors
  1. T S Sanal Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Thandeeswaran
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors read and approved the final manuscript.

Corresponding author

Correspondence to R. Thandeeswaran.

Ethics declarations

Competing interests

No potential competing interest.

Consent for participant

Students’ consent was recorded electronically while attempting the e-learning course.

Ethical declaration

We hereby declare that this manuscript is the result of our independent creation under the reviewer’s comments. Except for the quoted contents, this manuscript does not contain any research achievements that have been published or written by other individuals or groups. We are the only authors of this manuscript. The legal responsibility of this statement shall be borne by us.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Algorithm 4.

Algorithm 4
figure dfigure dfigure d

Algorithm for path generation

Full size image

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sanal Kumar, T.S., Thandeeswaran, R. An improved adaptive personalization model for instructional video-based e-learning environments. J. Comput. Educ. (2024). https://doi.org/10.1007/s40692-023-00310-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40692-023-00310-x

Keywords

Search

Navigation