nach oben

Erschienen in:

2020 | OriginalPaper | Buchkapitel

A Data-Driven Student Model to Provide Adaptive Support During Video Watching Across MOOCs

verfasst von : Sébastien Lallé, Cristina Conati

Erschienen in: Artificial Intelligence in Education

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

MOOCs have great potential to innovate education, but lack of personalization. In this paper, we show how FUMA, a data-driven framework for student modeling and adaptation, can help understand how to provide personalized support to MOOCs students, specifically targeting video watching behaviors. We apply FUMA across several MOOCs to show how to: (i) discover video watching behaviors that can be detrimental for or conductive to learning; (ii) use these behaviors to detect ineffective learners at different weeks of MOOCs usage. We discuss how these behaviors can be used to define personalized support to effective MOOC video usage regardless of the target course.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Neural Multi-task Learning for Teacher Question Detection in Online Classrooms

Nächstes Kapitel Transfer of Automated Performance Feedback Models to Different Specimens in Virtual Reality Temporal Bone Surgery

Li, W., Gao, M., Li, H., Xiong, Q., Wen, J., Wu, Z.: Dropout prediction in MOOCs using behavior features and multi-view semi-supervised learning. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 3130–3137. IEEE (2016)

Nagrecha, S., Dillon, J.Z., Chawla, N.V.: MOOC dropout prediction: lessons learned from making pipelines interpretable. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 351–359. International World Wide Web Conferences Steering Committee (2017)

Zhao, C., Yang, J., Liang, J., Li, C.: Discover learning behavior patterns to predict certification. In: 2016 11th International Conference on Computer Science & Education (ICCSE), pp. 69–73. IEEE (2016)

Wang, Z., Zhu, J., Li, X., Hu, Z., Zhang, M.: Structured knowledge tracing models for student assessment on Coursera. In: 2016 Proceedings of the Third ACM Conference on Learning@ Scale, pp. 209–212. ACM (2016)

Pardos, Z.A., Bergner, Y., Seaton, D.T., Pritchard, D.E.: Adapting Bayesian knowledge tracing to a massive open online course in edX. In: EDM, vol. 13, pp. 137–144 (2013)

Liu, S., Hu, Z., Peng, X., Liu, Z., Cheng, H.N., Sun, J.: Mining learning behavioral patterns of students by sequence analysis in cloud classroom. Int. J. Distance Educ. Technol. (IJDET) 15, 15–27 (2017)CrossRef

Boroujeni, M.S., Dillenbourg, P.: Discovery and temporal analysis of latent study patterns in MOOC interaction sequences. In: Proceedings of the 8th International Conference on Learning Analytics and Knowledge, pp. 206–215. ACM (2018)

Liu, T., Xiu, L.L.: Finding out reasons for low completion in MOOC environment: an explicable approach using hybrid data mining methods. In: 2017 Proceedings of the International Conference on Modern Education and Information Technology, pp. 376–384. DEStech (2017)

Rosen, Y., Rushkin, I., Ang, A., Federicks, C., Tingley, D., Blink, M.J.: Designing adaptive assessments in MOOCs. In: 2017 Proceedings of the Fourth ACM Conference on Learning@ Scale, pp. 233–236. ACM (2017)

10.

Ketamo, H.: Learning fingerprint: adaptive tutoring for MOOCs. In: EdMedia: World Conference on Educational Media and Technology. Association for the Advancement of Computing in Education (AACE) (2014)

11.

Brinton, C.G., Rill, R., Ha, S., Chiang, M., Smith, R., Ju, W.: Individualization for education at scale: MIIC design and preliminary evaluation. IEEE Trans. Learn. Technol. 8, 136–148 (2015)CrossRef

12.

Sonwalkar, N.: The first adaptive MOOC: a case study on pedagogy framework and scalable cloud architecture—part I. In: MOOCs Forum, pp. 22–29. Mary Ann Liebert, Inc., New Rochelle (2013)

13.

Bhatt, C., Cooper, M., Zhao, J.: SeqSense: video recommendation using topic sequence mining. In: Schoeffmann, K., et al. (eds.) MMM 2018. LNCS, vol. 10705, pp. 252–263. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73600-6_22CrossRef

14.

Cooper, M., Zhao, J., Bhatt, C., Shamma, D.A.: MOOCex: exploring educational video via recommendation. In: Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, pp. 521–524. ACM, New York (2018). https://doi.org/10.1145/3206025.3206087

15.

Conati, C., Kardan, S.: Student modeling: Supporting personalized instruction, from problem solving to exploratory open ended activities. AI Mag. 34, 13–26 (2013)CrossRef

16.

Kardan, S., Conati, C.: A framework for capturing distinguishing user interaction behaviors in novel interfaces. In: EDM, pp. 159–168 (2011)

17.

Kardan, S., Conati, C.: Providing adaptive support in an interactive simulation for learning: an experimental evaluation. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 3671–3680. ACM, Seoul (2015)

18.

Fratamico, L., Conati, C., Kardan, S., Roll, I.: Applying a framework for student modeling in exploratory learning environments: comparing data representation granularity to handle environment complexity. Int. J. Artif. Intell. Educ. 27, 320–352 (2017). https://doi.org/10.1007/s40593-016-0131-yCrossRef

19.

Yousef, A.M.F., Chatti, M.A., Schroeder, U., Wosnitza, M.: What drives a successful MOOC? An empirical examination of criteria to assure design quality of MOOCs. In: 2014 IEEE 14th International Conference on Advanced Learning Technologies (ICALT), pp. 44–48. IEEE (2014)

20.

Kim, J., Guo, P.J., Seaton, D.T., Mitros, P., Gajos, K.Z., Miller, R.C.: Understanding in-video dropouts and interaction peaks in online lecture videos. In: Proceedings of the First ACM Conference on Learning @ Scale Conference, pp. 31–40. ACM, New York (2014). https://doi.org/10.1145/2556325.2566237

21.

Guo, P.J., Kim, J., Rubin, R.: How video production affects student engagement: an empirical study of MOOC videos. In: Proceedings of the First ACM Conference on Learning @ Scale Conference, pp. 41–50. ACM, New York (2014). https://doi.org/10.1145/2556325.2566239

22.

Mitrovic, A., Dimitrova, V., Lau, L., Weerasinghe, A., Mathews, M.: Supporting constructive video-based learning: requirements elicitation from exploratory studies. In: André, E., Baker, R., Hu, X., Rodrigo, M., du Boulay, B. (eds.) AIED 2017. LNCS (LNAI), vol. 10331, pp. 224–237. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61425-0_19CrossRef

23.

Li, N., Kidziński, Ł., Jermann, P., Dillenbourg, P.: MOOC video interaction patterns: what do they tell us? In: Conole, G., Klobučar, T., Rensing, C., Konert, J., Lavoué, É. (eds.) EC-TEL 2015. LNCS, vol. 9307, pp. 197–210. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24258-3_15CrossRef

24.

Athira, L., Kumar, A., Bijlani, K.: Discovering learning models in MOOCs using empirical data. In: Shetty, N.R., Prasad, N.H., Nalini, N. (eds.) Emerging Research in Computing, Information, Communication and Applications, pp. 551–567. Springer, New Delhi (2015). https://doi.org/10.1007/978-81-322-2550-8_53CrossRef

25.

Wen, M., Rosé, C.P.: Identifying latent study habits by mining learner behavior patterns in massive open online courses. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 1983–1986. ACM (2014)

26.

Brinton, C.G., Buccapatnam, S., Chiang, M., Poor, H.V.: Mining MOOC clickstreams: video-watching behavior vs. in-video quiz performance. IEEE Trans. Sig. Process. 64, 3677–3692 (2016). https://doi.org/10.1109/TSP.2016.2546228MathSciNetCrossRefMATH

27.

Brinton, C.G., Chiang, M.: MOOC performance prediction via clickstream data and social learning networks. In: 2015 IEEE Conference on Computer Communications (INFOCOM), pp. 2299–2307 (2015). https://doi.org/10.1109/INFOCOM.2015.7218617

28.

Sinha, T., Jermann, P., Li, N., Dillenbourg, P.: Your click decides your fate: inferring information processing and attrition behavior from MOOC video clickstream interactions. ArXiv preprint arXiv:1407.7131 (2014)

29.

Whitehill, J., Mohan, K., Seaton, D., Rosen, Y., Tingley, D.: MOOC dropout prediction: how to measure accuracy? In: 2017 Proceedings of the Fourth ACM Conference on Learning @ Scale, pp. 161–164. Association for Computing Machinery, Cambridge (2017). https://doi.org/10.1145/3051457.3053974

30.

Gardner, J., Yang, Y., Baker, R.S., Brooks, C.: Modeling and experimental design for MOOC dropout prediction: a replication perspective. In: International Educational Data Mining Society (2019)

31.

Gardner, J., Brooks, C., Andres, J.M., Baker, R.: Replicating MOOC predictive models at scale. In: Proceedings of the Fifth Annual ACM Conference on Learning at Scale, pp. 1–10. Association for Computing Machinery, London (2018). https://doi.org/10.1145/3231644.3231656

32.

Gardner, J., Brooks, C., Baker, R.: Evaluating the fairness of predictive student models through slicing analysis. In: Proceedings of the 9th International Conference on Learning Analytics & Knowledge, pp. 225–234. Association for Computing Machinery, Tempe (2019). https://doi.org/10.1145/3303772.3303791

33.

Kim, J., Guo, P.J., Cai, C.J., Li, S.-W., Gajos, K.Z., Miller, R.C.: Data-driven interaction techniques for improving navigation of educational videos. In: Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology, pp. 563–572 (2014)

34.

Mitrovic, A., Gordon, M., Piotrkowicz, A., Dimitrova, V.: Investigating the effect of adding nudges to increase engagement in active video watching. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds.) AIED 2019. LNCS (LNAI), vol. 11625, pp. 320–332. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23204-7_27CrossRef

35.

Davis, D., Jivet, I., Kizilcec, R.F., Chen, G., Hauff, C., Houben, G.-J.: Follow the successful crowd: raising MOOC completion rates through social comparison at scale. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 454–463. ACM (2017)

36.

Davis, D., Triglianos, V., Hauff, C., Houben, G.-J.: SRLx: a personalized learner interface for MOOCs. In: Pammer-Schindler, V., Pérez-Sanagustín, M., Drachsler, H., Elferink, R., Scheffel, M. (eds.) EC-TEL 2018. LNCS, vol. 11082, pp. 122–135. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98572-5_10CrossRef

37.

Lallé, S., Conati, C.: A framework to counteract suboptimal user-behaviors in exploratory learning environments: an application to MOOCs. In: Proceedings of the 12th Workshop on Plan, Activity, and Intent Recognition, pp. 1–8. AAAI Press, Honolulu (2019)

38.

Andres, J.M.L., Baker, R.S., Gašević, D., Siemens, G., Crossley, S.A., Joksimović, S.: Studying MOOC completion at scale using the MOOC replication framework. In: Proceedings of the 8th International Conference on Learning Analytics and Knowledge, pp. 71–78. ACM, Sydney (2018). https://doi.org/10.1145/3170358.3170369

39.

Field, A.: Discovering Statistics Using IBM SPSS Statistics. Sage Publications Ltd., London (2012)

40.

Rastrollo-Guerrero, J.L., Gómez-Pulido, J.A., Durán-Domínguez, A.: Analyzing and predicting students’ performance by means of machine learning: a review. Appl. Sci. 10, 1042 (2020). https://doi.org/10.3390/app10031042CrossRef

41.

Kloft, M., Stiehler, F., Zheng, Z., Pinkwart, N.: Predicting MOOC dropout over weeks using machine learning methods. In: Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs, pp. 60–65 (2014)

42.

Hong, B., Wei, Z., Yang, Y.: Discovering learning behavior patterns to predict dropout in MOOC. In: 2017 12th International Conference on Computer Science and Education (ICCSE), pp. 700–704 (2017). https://doi.org/10.1109/ICCSE.2017.8085583

43.

Putnam, V., Conati, C.: Exploring the need for explainable artificial intelligence (XAI) in intelligent tutoring systems (ITS). In: IUI Workshops (2019)

Titel: A Data-Driven Student Model to Provide Adaptive Support During Video Watching Across MOOCs
verfasst von: Sébastien Lallé
Cristina Conati
Verlag: Springer International Publishing
Buch: Artificial Intelligence in Education
Print ISBN: 978-3-030-52236-0

Electronic ISBN: 978-3-030-52237-7

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-52237-7_23

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner