Digital Value-Adding Chains in Vocational Education: Automatic Keyword Extraction from Learning Videos to Provide Learning Resource Recommendations
Pages 15 - 29
Abstract
The digital transformation of industry environments creates new demands but also opportunities for vocational education and training (VET). On the one hand, the introduction of new digital learning tools involves the risk of creating a digital parallel world. On the other hand, such tools have the potential to provide intelligent and contextualized access to information sources and learning materials. In this work, we explore approaches to provide such intelligent learning resource recommendations based on a specific learning context. Our approach aims at automatically analyzing learning videos in order to extract keywords, which in turn can be used to discover and recommend new learning materials relevant to the video. We have implemented this approach and investigated the user-perceived quality of the results in a real-world VET setting. The results indicate that the extracted keywords are in line with user-generated keywords and summarize the content of videos quite well. Also, the ensuing recommendations are perceived as relevant and useful.
References
[1]
Agrawal R, Christoforaki M, Gollapudi S, Kannan A, Kenthapadi K, and Swaminathan A Glodeanu C, Kaytoue M, and Sacarea C Mining videos from the web for electronic textbooks Formal Concept Analysis 2014 Cham Springer 219-234
[2]
Ahn JW et al. André E, Baker R, Hu X, Rodrigo M, du Boulay B, et al. Wizard’s apprentice: cognitive suggestion support for wizard-of-Oz question answering Artificial Intelligence in Education 2017 Cham Springer 630-635
[3]
Aprin F, Manske S, and Hoppe HU Herzog MA, Kubincová Z, Han P, and Temperini M SALMON: sharing, annotating and linking learning materials online Advances in Web-Based Learning – ICWL 2019 2019 Cham Springer 250-257
[4]
Auer S, Bizer C, Kobilarov G, Lehmann J, Cyganiak R, and Ives Z Aberer K DBpedia: a nucleus for a web of open data The Semantic Web 2007 Heidelberg Springer 722-735
[5]
Baeza-Yates R and Ribeiro-Neto B Modern Information Retrieval 1999 New York ACM Press
[6]
Berghaus, S., Back, A.: Disentangling the fuzzy front end of digital transformation: activities and approaches. In: Association for Information Systems (2017)
[7]
Brynjolfsson, E., McAfee, A.: Race Against the Machine: How the Digital Revolution is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy. Brynjolfsson and McAfee (2011)
[8]
Commentz-Walter B Maurer HA A string matching algorithm fast on the average Automata, Languages and Programming 1979 Heidelberg Springer 118-132
[9]
Cruz, S.M.A., Lencastre, J.A., Coutinho, C.P., José, R., Clough, G., Adams, A.: The JuxtaLearn process in the learning of maths’ tricky topics: practices, results and teacher’s perceptions (2017)
[10]
Drachsler H, Verbert K, Santos OC, and Manouselis N Ricci F, Rokach L, and Shapira B Panorama of recommender systems to support learning Recommender Systems Handbook 2015 Boston, MA Springer 421-451
[11]
Erkens, M., Daems, O., Hoppe, H.U.: Artifact analysis around video creation in collaborative STEM learning scenarios. In: 2014 IEEE 14th International Conference on Advanced Learning Technologies, pp. 388–392. IEEE (2014)
[12]
Erkens, M., Manske, S., Bodemer, D., Hoppe, H.U., Langner-Thiele, A.: Video-based competence development in chemistry vocational training. In: Proceedings of the ICCE 2019 (2019)
[13]
Ghauth KI and Abdullah NA Learning materials recommendation using good learners’ ratings and content-based filtering Educ. Technol. Res. Dev. 2010 58 6 711-727
[14]
Harbarth, L., et al.: Learning by tagging–supporting constructive learning in video-based environments. DeLFI 2018-Die 16. E-Learning Fachtagung Informatik (2018)
[15]
Hasan, K.S., Ng, V.: Automatic keyphrase extraction: a survey of the state of the art. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1262–1273) (2014)
[16]
Hecking, T., Dimitrova, V., Mitrovic, A., Ulrich Hoppe, U.: Using network-text analysis to characterise learner engagement in active video watching. In: ICCE 2017 Main Conference Proceedings, pp. 326–335. Asia-Pacific Society for Computers in Education (2017)
[17]
Hecking, T., Hoppe, H.U.: A network based approach for the visualization and analysis of collaboratively edited texts. In: VISLA@ LAK, pp. 19–23 (2015)
[18]
Henriette, E., Feki, M., Boughzala, I.: The shape of digital transformation: a systematic literature review. In: MCIS 2015 Proceedings, pp. 431–443 (2015)
[19]
Hirsch-Kreinsen H Digitization of industrial work: development paths and prospects J. Labour Market Res. 2016 49 1 1-14
[20]
Jäschke R, Marinho L, Hotho A, Schmidt-Thieme L, and Stumme G Kok JN, Koronacki J, Lopez de Mantaras R, Matwin S, Mladenič D, and Skowron A Tag recommendations in folksonomies Knowledge Discovery in Databases: PKDD 2007 2007 Heidelberg Springer 506-514
[21]
Kopeinik S, Kowald D, and Lex E Verbert K, Sharples M, and Klobučar T Which algorithms suit which learning environments? a comparative study of recommender systems in tel Adaptive and Adaptable Learning 2016 Cham Springer 124-138
[22]
Landis J and Koch G The measurement of observer agreement for categorical data Biometrics 1977 33 1 159-174
[23]
Malzahn N, Hartnett E, Llinás P, Hoppe HU, et al. Li Y et al. A smart environment supporting the creation of juxtaposed videos for learning State-of-the-Art and Future Directions of Smart Learning 2016 Singapore Springer 461-470
[24]
Manske, S., Hoppe, H.U.: The concept cloud: supporting collaborative knowledge construction based on semantic extraction from learner-generated artefacts. In: International Conference on Advanced Learning Technologies (ICALT 2016) (2016)
[25]
Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: DBpedia spotlight: shedding light on the web of documents. In: Proceedings of the 7th international conference on semantic systems, pp. 1–8 (2011)
[26]
Mitrovic, A., Dimitrova, V., Lau, L., Weerasinghe, A., Mathews, M.: Supporting constructive video-based learning: requirements elicitation from exploratory studies. In: International Conference on Artificial Intelligence in Education, pp. 224–237 (2017)
[27]
Parviainen P, Tihinen M, Kääriäinen J, and Teppola S Tackling the digitalization challenge: how to benefit from digitalization in practice Int. J. Inf. Syst. Proj. Manage. 2017 5 1 63-77
[28]
Pazzani MJ and Billsus D Brusilovsky P, Kobsa A, and Nejdl W Content-based recommendation systems The Adaptive Web 2007 Heidelberg Springer 325-341
[29]
Pu, P., Chen, L., Hu, R.: A user-centric evaluation framework for recommender systems. In: Proceedings of the 5th ACM Conference on Recommender Systems, pp. 157–164 (2011)
[30]
Taskin Y, Hecking T, Hoppe HU, Dimitrova V, and Mitrovic A Scheffel M, Broisin J, Pammer-Schindler V, Ioannou A, and Schneider J Characterizing comment types and levels of engagement in video-based learning as a basis for adaptive nudging Transforming Learning with Meaningful Technologies 2019 Cham Springer 362-376
[31]
Taskin Y, Hecking T, and Hoppe HU Cherifi H, Gaito S, Mendes JS, Moro E, and Rocha LM ESA-T2N: a novel approach to network-text analysis Complex Networks and Their Applications VIII 2020 Cham Springer 129-139
[32]
Zaíane, O.R.: Building a recommender agent for e-learning systems. In: International Conference on Computers in Education, Proceedings, pp. 55–59. IEEE (2002)
[33]
Zhao, J., Bhatt, C., Cooper, M., Shamma, D.A.: Flexible learning with semantic visual exploration and sequence-based recommendation of MOOC videos. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–13 (2018)
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Published In
Sep 2020
507 pages
ISBN:978-3-030-57716-2
DOI:10.1007/978-3-030-57717-9
© Springer Nature Switzerland AG 2020.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 14 September 2020
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