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Conference Paper: A new approach for educational data analytics with wearable devices

TitleA new approach for educational data analytics with wearable devices
Authors
Keywordseducational data analytics
wearable device
optimization algorithm
Issue Date2021
PublisherIEEE, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000009
Citation
Proceedings of the 21st IEEE International Conference on Advanced Learning Technologies (ICALT), Tartu, Estonia, 12-15 July 2021, p. 138-140 How to Cite?
AbstractThe rapid development of wearable technologies has dramatically promoted the potential usages of wearable devices in educational data analytics. However, the large amount of input data and the various types of educational output labels also increase the difficulties in selecting the useful information and discovering the implicit relations between different input data. To address this issue, this paper proposed a new two-layer approach for conducting educational data analytics automatically. In this approach, there are three key components: input layer, output layer and recognition model. For the input layer, we adopted the newly proposed optimization algorithm: Adaptive Multi-Population Optimization (AMPO) to select the most related input features and suitable model structures. For the output layer, we inserted domain-specific constraints during the searching for all combinations of different output labels to discover a meaningful output strategy with a relatively higher accuracy. Based on the input elements and output strategy provided by the input layer and the output layer, the recognition model will produce the corresponding recognition accuracy. With these three components, our proposed method can find out some connotative information to provide guidance for conducting educational data analytics and drawing meaningful conclusions.
Persistent Identifierhttp://hdl.handle.net/10722/306181
ISSN
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhou, Z-
dc.contributor.authorTam, VWL-
dc.contributor.authorWong Lui, KS-
dc.contributor.authorLam, EYM-
dc.contributor.authorKong, R-
dc.contributor.authorHu, X-
dc.contributor.authorLaw, NWY-
dc.date.accessioned2021-10-20T10:19:56Z-
dc.date.available2021-10-20T10:19:56Z-
dc.date.issued2021-
dc.identifier.citationProceedings of the 21st IEEE International Conference on Advanced Learning Technologies (ICALT), Tartu, Estonia, 12-15 July 2021, p. 138-140-
dc.identifier.issn2161-3761-
dc.identifier.urihttp://hdl.handle.net/10722/306181-
dc.description.abstractThe rapid development of wearable technologies has dramatically promoted the potential usages of wearable devices in educational data analytics. However, the large amount of input data and the various types of educational output labels also increase the difficulties in selecting the useful information and discovering the implicit relations between different input data. To address this issue, this paper proposed a new two-layer approach for conducting educational data analytics automatically. In this approach, there are three key components: input layer, output layer and recognition model. For the input layer, we adopted the newly proposed optimization algorithm: Adaptive Multi-Population Optimization (AMPO) to select the most related input features and suitable model structures. For the output layer, we inserted domain-specific constraints during the searching for all combinations of different output labels to discover a meaningful output strategy with a relatively higher accuracy. Based on the input elements and output strategy provided by the input layer and the output layer, the recognition model will produce the corresponding recognition accuracy. With these three components, our proposed method can find out some connotative information to provide guidance for conducting educational data analytics and drawing meaningful conclusions.-
dc.languageeng-
dc.publisherIEEE, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000009-
dc.relation.ispartofProceedings of the 21st IEEE International Conference on Advanced Learning Technologies (ICALT)-
dc.rightsInternational Conference on Advanced Learning Technologies (ICALT) Proceedings. Copyright © IEEE, Computer Society.-
dc.rights©2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjecteducational data analytics-
dc.subjectwearable device-
dc.subjectoptimization algorithm-
dc.titleA new approach for educational data analytics with wearable devices-
dc.typeConference_Paper-
dc.identifier.emailTam, VWL: vtam@hkucc.hku.hk-
dc.identifier.emailLam, EYM: elam@eee.hku.hk-
dc.identifier.emailHu, X: xiaoxhu@hku.hk-
dc.identifier.emailLaw, NWY: nlaw@hku.hk-
dc.identifier.authorityTam, VWL=rp00173-
dc.identifier.authorityWong Lui, KS=rp00188-
dc.identifier.authorityLam, EYM=rp00131-
dc.identifier.authorityHu, X=rp01711-
dc.identifier.authorityLaw, NWY=rp00919-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICALT52272.2021.00049-
dc.identifier.scopuseid_2-s2.0-85114892775-
dc.identifier.hkuros327884-
dc.identifier.spage138-
dc.identifier.epage140-
dc.identifier.isiWOS:000719352000042-
dc.publisher.placeUnited States-

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