Predicting engagement in computer-supported collaborative learning groups using natural language processing
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
20.03.2024
Typ der Arbeit
Studiengang
Typ
06 - Präsentation
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Themenheft
DOI der Originalpublikation
Reihe / Serie
Reihennummer
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
Patentnummer
Verlag / Herausgebende Institution
Verlagsort / Veranstaltungsort
Regensburg
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Collaborative group engagement is a key factor of success in learning groups. This work explores the development of an innovative natural language processing method for predicting collaborative group engagement. To this end, we identified linguistic markers based on an established observation-based scheme for rating collaborative group engagement, such as, semantic similarity to task instructions, verbal mimicry, sentiment, and use of jargon. We evaluated the predictive power of the linguistic markers on the data of an observational study in which 38 learning groups were instructed to perform a collaborative learning task. Overall, the data consisted of 2588 expert ratings on collaborative group engagement. We relied on machine learning to the predict collaborative group engagement ratings using informative subsets of linguistic markers. The results showed above-baseline predictive accuracy for all four dimensions of collaborative group engagement. Moreover, the analysis of feature importance points to quantity of utterances, responsiveness and uniformity of participation as the most important markers for collaborative group engagement. By harnessing natural language processing, this work extends traditional qualitative analysis and delivers nuanced quantitative metrics suitable for capturing the complexity and dynamics of contemporary Computer Supported Collaborative Learning (CSCL) environments. Thereby, it contributes to the evolving landscape of CSCL research and demonstrates the potential of novel analytic techniques to support and enrich qualitative analysis in multiple domains.
Schlagwörter
Natural Language Processing, Collaborative Group Engagement, Computer-supported collaborative learning
Fachgebiet (DDC)
150 - Psychologie
Veranstaltung
TeaP 2024 Tagung experimentell arbeitender Psycholog:innen
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
17.03.2024
Enddatum der Konferenz
20.03.2024
Datum der letzten Prüfung
ISBN
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Publikationsstatus
Begutachtung
Keine Begutachtung
Open Access-Status
Lizenz
Zitation
JEITZINER, Loris Tizian, Lisa PANETH, Oliver RACK, Carmen ZAHN und Dirk WULFF, 2024. Predicting engagement in computer-supported collaborative learning groups using natural language processing. TeaP 2024 Tagung experimentell arbeitender Psycholog:innen. Regensburg. 20 März 2024. Verfügbar unter: https://irf.fhnw.ch/handle/11654/46073