Bridging learning analytics and Cognitive Computing for Big Data classification in micro-learning video collections.
Abstract Moving towards the next generation of personalized learning environments requires intelligent approaches powered by analytics for advanced learning contexts with enriched digital content. Micro-Learning through Massive Open Online Courses is riding the wave of popularity as a novel paradigm...
| Publicado en: | Computers in Human Behavior Vol. 92; pp. 468 - 478 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Elsevier B.V.
Mar2019
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=134185571&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 134185571 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 07475632 JC4 jtl: Computers in Human Behavior issn: 07475632 maglogo: N pubinfo: dt: Mar2019 vid: 92 pid: 2410 pub: Elsevier B.V. artinfo: ui: 134185571 10.1016/j.chb.2018.03.004 ppf: 468 ppct: 10 formats: tig: atl: Bridging learning analytics and Cognitive Computing for Big Data classification in micro-learning video collections. aug: au: Dessì, Danilo Fenu, Gianni Marras, Mirko Reforgiato Recupero, Diego affil: Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, Italy su: Cognition Computer assisted instruction Semantics Audiovisual materials Automation Learning strategies Natural language processing Educational outcomes Massive open online courses Data analytics sug: subj: Cognition Computer assisted instruction Semantics Audiovisual materials Automation Learning strategies Natural language processing Educational outcomes Massive open online courses Data analytics keyword: Big Data technologies Cognitive Computing Learning Analytics Micro-learning video Multi-class classification Video classification Big Data technologies Cognitive Computing Learning Analytics Micro-learning video Multi-class classification Video classification ab: Abstract Moving towards the next generation of personalized learning environments requires intelligent approaches powered by analytics for advanced learning contexts with enriched digital content. Micro-Learning through Massive Open Online Courses is riding the wave of popularity as a novel paradigm for delivering short educational videos in small pre-organized chunks over time, so that learners can get knowledge in a manageable way. However, with the ever-increasing number of videos, it has become challenging to arrange and search them according to specific categories. In this paper, we get around the problem by bridging Learning Analytics and Cognitive Computing to analyze the content of large video collections, going over traditional term-based methods. We propose an efficient and effective approach to automatically classify a collection of educational videos on pre-existing categories which uses (i) a Speech-to-Text tool to get video transcripts, (ii) Natural Language Processing and Cognitive Computing methods to extract semantic concepts and keywords from video transcripts for their representation, and (iii) Apache Spark as Big Data technology for scalability. Several classifiers are trained on the feature vectors extracted by Cognitive Computing tools. Then, we compared our approach with other combinations of state-of-the-art feature types and classifiers over a large-scale dataset we collected from Coursera. Considering the experimental results, we expect our approach can facilitate the development of Learning Analytics tools powered by Cognitive Computing to support content managers on micro-learning video management while improving how learners search videos. Highlights • An efficient and effective approach to classify micro-learning videos is proposed. • Cognitive Computing is leveraged to extract features from micro-learning videos. • Using concepts and keywords as features improves overall classification performance. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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