Supervised machine learning in multimodal learning analytics for estimating success in project‐based learning.
Abstract: Multimodal learning analytics provides researchers new tools and techniques to capture different types of data from complex learning activities in dynamic learning environments. This paper investigates the use of diverse sensors, including computer vision, user‐generated content, and data...
| Publicado en: | Journal of Computer Assisted Learning Vol. 34; no. 4; pp. 366 - 378 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Aug2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=130898735&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130898735 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Aug2018 vid: 34 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 130898735 130898735 130898735 10.1111/jcal.12263 130898735 ppf: 366 ppct: 12 formats: tig: atl: Supervised machine learning in multimodal learning analytics for estimating success in project‐based learning. aug: au: Spikol, Daniel Ruffaldi, Emanuele Dabisias, Giacomo Cukurova, Mutlu affil: Malmö University, Sweden sug: subj: Computer-Assisted Instruction Learning Methods Data Analytics Human Programming Languages Female Male Engineering Education Task Performance and Analysis Coding Motion Analysis Systems Machine Learning Female Male ab: Abstract: Multimodal learning analytics provides researchers new tools and techniques to capture different types of data from complex learning activities in dynamic learning environments. This paper investigates the use of diverse sensors, including computer vision, user‐generated content, and data from the learning objects (physical computing components), to record high‐fidelity synchronised multimodal recordings of small groups of learners interacting. We processed and extracted different aspects of the students' interactions to answer the following question: Which features of student group work are good predictors of team success in open‐ended tasks with physical computing? To answer this question, we have explored different supervised machine learning approaches (traditional and deep learning techniques) to analyse the data coming from multiple sources. The results illustrate that state‐of‐the‐art computational techniques can be used to generate insights into the "black box" of learning in students' project‐based activities. The features identified from the analysis show that distance between learners' hands and faces is a strong predictor of students' artefact quality, which can indicate the value of student collaboration. Our research shows that new and promising approaches such as neural networks, and more traditional regression approaches can both be used to classify multimodal learning analytics data, and both have advantages and disadvantages depending on the research questions and contexts being investigated. The work presented here is a significant contribution towards developing techniques to automatically identify the key aspects of students success in project‐based learning environments, and to ultimately help teachers provide appropriate and timely support to students in these fundamental aspects. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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