Abstract
Learning videos enjoy great popularity in a digitalized world, especially since their use is usually possible regardless of time and location. Learners use this advantage mainly in self-study. Supervision, as for example in classroom teaching, is rather difficult and learners are usually left to their own devices when learning obstacles arise. However, not treating learning obstacles can have serious consequences, ranging from a gradual loss of the learners’ motivation to the termination of the learning project. Consequently, learning obstacles must be identified and treated in order to support an efficient learning process. Fortunately, a digital learning environment opens up many opportunities to support learners automatically. This paper explains an approach to identify potential learning obstacles in video learning based on indirect feedback. The first part of the approach to removing learning obstacles in learning videos is based on an analysis of learners’ click interaction within a video to identify potential problem areas. Building on this, the second part provides first thematically relevant ad hoc video recommendations for the potentially identified learning obstacle. In order to verify whether there is actually a learning obstacle, the third part explicitly induces learners to give indirect or direct feedback on whether the recommendations have helped them and, consequently, whether they have removed an actual learning obstacle.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dede, C.: The evolution of distance education: emerging technologies and distributed learning. Am. J. Distance Educ. (1996)
Giannakos, M.N., Aalberg, T., Divitini, M., Jaccheri, L., Mikalef, P., Pappas, I.O., Sindre, G.: Identifying dropout factors in information technology education: a case study. In: Proceedings of 2017 IEEE Global Engineering Education Conference (EDUCON), pp. 1187–1194. IEEE, Piscataway, NJ (2017)
Lehmann, A.: Problem tagging and solution-based video recommendations in learning video environments. In: Ashmawy, A.K., Schreiter, S. (eds.) Proceedings of 2019 IEEE Global Engineering Education Conference (EDUCON), pp. 365–373. IEEE, Piscataway, New Jersey (2019)
Belarbi, N., Chafiq, N., Talbi, M., Namir, A., Benlahmar, E.: User profiling in a SPOC: a method based on user video clickstream analysis. Int. J. Emerg. Technol. Learn. (2019)
Sinha, T., Jermann, P., Li, N., Dillenbourg, P.: Your click decides your fate: inferring information processing and attrition behavior from MOOC video clickstream interactions. In: Rose, C., Siemens, G. (eds.) Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs, pp. 3–14. Association for Computational Linguistics, Stroudsburg, PA, USA
Manouselis, N.: Recommender Systems for Learning. Springer Briefs in Electrical and Computer Engineering. Springer, New York (2013)
Teusner, R., Hille, T., Staubitz, T.: Effects of automated interventions in programming assignments. In: Luckin, R., Klemmer, S., Koedinger, K., Koedinger, K.R. (eds.) Proceedings of the Fifth Annual ACM Conference on Learning at Scale, pp. 1–10. ACM, New York, NY (2018)
Lehmann, A.: Adaptive starting points in video learning environments for new learners based on video and topic tree relations. In: Auer, M.E., Rüütmann, T. (eds.) Educating Engineers for Future Industrial Revolutions, vol. 1329. Advances in Intelligent Systems and Computing, pp. 808–818. Springer International Publishing, Cham (2021)
Stuckenschmidt, H.: Ontologien. Konzepte, Technologien und Anwendungen. Informatik im Fokus. Springer, Berlin Heidelberg, Berlin, Heidelberg (2009)
Middleton, S.E., de Roure, D., Shadbolt, N.R.: Ontology-based recommender systems. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies. International Handbooks on Information Systems, pp. 477–498. Springer Berlin Heidelberg, Berlin, Heidelberg (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lehmann, A., Landes, D. (2024). Tackling Learning Obstacles in Learning Videos by Thematic Ad-Hoc Recommendations. In: Auer, M.E., Cukierman, U.R., Vendrell Vidal, E., Tovar Caro, E. (eds) Towards a Hybrid, Flexible and Socially Engaged Higher Education. ICL 2023. Lecture Notes in Networks and Systems, vol 899. Springer, Cham. https://doi.org/10.1007/978-3-031-51979-6_49
Download citation
DOI: https://doi.org/10.1007/978-3-031-51979-6_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-51978-9
Online ISBN: 978-3-031-51979-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)