Supervised machine learning in multimodal learning analytics for estimating success in project-based learning

↓ Direkt till sidans innehåll
↓ Direkt till sidans sekundära innehåll (sidomenyn)

Search: WFRF:(Spikol Daniel 1965 ) > (2015-2019) > Supervised machine ...

Details
MARC

Spikol, Daniel,1965-Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT),Disciplinary literacy and inclusive teaching (author)

Supervised machine learning in multimodal learning analytics for estimating success in project-based learning

Article/chapterEnglish2018

Publisher, publication year, extent ...

2018-05-15
John Wiley & Sons,2018
printrdacarrier

Numbers

LIBRIS-ID:oai:DiVA.org:mau-16003
https://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-16003URI
https://doi.org/10.1111/jcal.12263DOI

Supplementary language notes

Language:English
Summary in:English

Part of subdatabase

SwePubSwePub

Classification

Subject category:ref swepub-contenttype
Subject category:art swepub-publicationtype

Notes

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.

Subject headings and genre

machine learning
multimodal learning analytics
project-based learning

Added entries (persons, corporate bodies, meetings, titles ...)

Ruffaldi, EmanueleScuola Superiore Sant'Anna, Italy (author)
Dabisias, GiacomoScuola Superiore Sant'Anna, Italy (author)
Cukurova, MutluUCL Knowledge Lab, University College London, United Kingdom (author)
Malmö universitetInstitutionen för datavetenskap och medieteknik (DVMT) (creator_code:org_t)

Related titles

In:Journal of Computer Assisted Learning: John Wiley & Sons34:4, s. 366-3770266-49091365-2729

Internet link

Find in a library

Journal of Computer Assisted Learning (Search for host publication in LIBRIS)

To the university's database

Find more in SwePub

By the author/editor: Spikol, Daniel, ...; Ruffaldi, Emanue ...; Dabisias, Giacom ...; Cukurova, Mutlu

Articles in the publication: Journal of Compu ...

By the university: Malmö University

Search outside SwePub

Extend your search to:: Google; Google Book Search; Google Scholar

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

LIBRIS.kb.se