Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
A Quality-Aware Web API Recommender System for Mashup Development Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Towards the Readiness of Learning Analytics Data for Micro Learning

Authors : Jiayin Lin, Geng Sun, Jun Shen, Tingru Cui, Ping Yu, Dongming Xu, Li Li, Ghassan Beydoun

Published in: Services Computing – SCC 2019

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

With the development of data mining and machine learning techniques, data-driven based technology-enhanced learning (TEL) has drawn wider attention. Researchers aim to use established or novel computational methods to solve educational problems in the ‘big data’ era. However, the readiness of data appears to be the bottleneck of the TEL development and very little research focuses on investigating the data scarcity and inappropriateness in the TEL research. This paper is investigating an emerging research topic in the TEL domain, namely micro learning. Micro learning consists of various technical themes that have been widely studied in the TEL research field. In this paper, we firstly propose a micro learning system, which includes recommendation, segmentation, annotation, and several learning-related prediction and analysis modules. For each module of the system, this paper reviews representative literature and discusses the data sources used in these studies to pinpoint their current problems and shortcomings, which might be debacles for more effective research outcomes. Accordingly, the data requirements and challenges for learning analytics in micro learning are also investigated. From a research contribution perspective, this paper serves as a basis to depict and understand the current status of the readiness of data sources for the research of micro learning.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
previous chapter An Empirical Investigation of Real-World QoS of Web Services
next chapter Personalized Service Recommendation Based on User Dynamic Preferences
Footnotes
Literature
1.
Ferguson, R.: The state of learning analytics in 2012: a review and future challenges. Knowledge Media Institute, Technical report KMI-2012-01 (2012)
2.
Sun, G., et al.: MLaaS: a cloud-based system for delivering adaptive micro learning in mobile MOOC learning. IEEE Trans. Serv. Comput. 11(2), 292–305 (2018)CrossRef
3.
Hendez, M., Achour, H.: Keywords extraction for automatic indexing of e-learning resources. In: 2014 World Symposium on Computer Applications & Research (WSCAR). IEEE (2014)
4.
Du, X., Zhang, F., Zhang, M., Xu, S., Liu, M.: Research on result integration mechanism based on crowd wisdom to achieve the correlation of resources and knowledge points. In: Wu, T.-T., Huang, Y.-M., Shadieva, R., Lin, L., Starčič, A.I. (eds.) ICITL 2018. LNCS, vol. 11003, pp. 568–577. Springer, Cham (2018). https://​doi.​org/​10.​1007/​978-3-319-99737-7_​60CrossRef
5.
Verbert, K., et al.: Context-aware recommender systems for learning: a survey and future challenges. IEEE Trans. Learn. Technol. 5(4), 318–335 (2012)CrossRef
6.
7.
Chen, W., et al.: A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web 17(2), 271–284 (2014).CrossRef
8.
Yang, H., Meinel, C.: Content based lecture video retrieval using speech and video text information. IEEE Trans. Learn. Technol. 2, 142–154 (2014)CrossRef
9.
Dessì, D., et al.: Bridging learning analytics and cognitive computing for big data classification in micro-learning video collections. Comput. Hum. Behav. (2018, in Press)
10.
Kim, J., et al.: Understanding in-video dropouts and interaction peaks inonline lecture videos. In: Proceedings of the first ACM conference on Learning@ scale conference. ACM (2014)
11.
Risko, E.F., et al.: The collaborative lecture annotation system (CLAS): a new TOOL for distributed learning. IEEE Trans. Learn. Technol. 6(1), 4–13 (2013)CrossRef
12.
Welinder, P., et al.: The multidimensional wisdom of crowds. In: Advances in Neural Information Processing Systems (2010)
13.
Cernea, D., Del Moral, E., Gayo, J.: SOAF: semantic indexing system based on collaborative tagging. Interdisc. J. E-Learn. Learn. Obj. 4(1), 137–149 (2008)
14.
15.
Shu, J., et al.: A content-based recommendation algorithm for learning resources. Multimedia Syst. 24(2), 163–173 (2018)CrossRef
16.
Ziegler, C.-N., et al.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web. ACM (2005)
17.
Zhao, Q., Zhang, Y., Chen, J.: An improved ant colony optimization algorithm for recommendation of micro-learning path. In: 2016 IEEE International Conference on Computer and Information Technology (CIT). IEEE (2016)
18.
Chen, M., et al.: Recommendation of learning path using an improved ACO based on novel coordinate system. In: 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE (2017)
19.
Zhou, Y., et al.: Personalized learning full-path recommendation model based on LSTM neural networks. Inf. Sci. 444, 135–152 (2018)CrossRef
20.
Wu, D., Lu, J., Zhang, G.: A fuzzy tree matching-based personalized e-learning recommender system. IEEE Trans. Fuzzy Syst. 23(6), 2412–2426 (2015)CrossRef
21.
Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4), 19 (2016)
22.
Fenza, G., Orciuoli, F., Sampson, D.G.: Building adaptive tutoring model using artificial neural networks and reinforcement learning. In: 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT). IEEE (2017)
23.
Al-Hmouz, A., et al.: Modeling and simulation of an adaptive neuro-fuzzy inference system (ANFIS) for mobile learning. IEEE Trans. Learn. Technol. 5(3), 226–237 (2012)CrossRef
24.
Dorça, F.A., et al.: An approach for automatic and dynamic analysis of learning objects repositories through ontologies and data mining techniques for supporting personalized recommendation of content in adaptive and intelligent educational systems. In: 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT). IEEE (2017)
25.
Yang, T.-Y., et al.: Behavior-based grade prediction for MOOCs via time series neural networks. IEEE J. Sel. Topics Signal Process. 11(5), 716–728 (2017)
26.
Brinton, C.G., et al.: Mining MOOC clickstreams: on the relationship between learner behavior and performance. arXiv preprint arXiv:​1503.​06489 (2015)
27.
Brinton, C.G., Chiang, M.: MOOC performance prediction via clickstream data and social learning networks. In: 2015 IEEE Conference on Computer Communications (INFOCOM). IEEE (2015)
28.
29.
30.
Kórösi, G., et al.: Clickstream-based outcome prediction in short video MOOCs. In: 2018 International Conference on Computer, Information and Telecommunication Systems (CITS). IEEE (2018)
31.
Shridharan, M.: et al.: Predictive learning analytics for video-watching behavior in MOOCs. In: 2018 52nd Annual Conference on Information Sciences and Systems (CISS). IEEE (2018)
32.
Sinha, T., et al.: Your click decides your fate: inferring information processing and attrition behavior from MOOC video clickstream interactions. arXiv preprint arXiv:​1407.​7131 (2014)
33.
34.
Lopez, G., et al.: Google BigQuery for education: framework for parsing and analyzing edX MOOC data. In: Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale. ACM (2017)
Metadata
Title
Towards the Readiness of Learning Analytics Data for Micro Learning
Authors
Jiayin Lin
Geng Sun
Jun Shen
Tingru Cui
Ping Yu
Dongming Xu
Li Li
Ghassan Beydoun
Copyright Year
2019
Book
Services Computing – SCC 2019

Print ISBN: 978-3-030-23553-6

Electronic ISBN: 978-3-030-23554-3

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-23554-3_5

Premium Partner

    Image Credits