Diagnosing collaboration in practice-based learning: Equality and intra-individual variability of physical interactivity

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Collaborative problem solving (CPS), as a teaching and learning approach, is considered to have the potential to improve some of the most important skills to prepare students for their future. CPS often differs in its nature, practice, and learning outcomes from other kinds of peer learning approaches, including peer tutoring and cooperation; and it is important to establish what identifies collaboration in problem-solving situations. The identification of indicators of collaboration is a challenging task. However, students physical interactivity can hold clues of such indicators. In this paper, we investigate two non-verbal indexes of student physical interactivity to interpret collaboration in practice-based learning environments: equality and intra-individual variability. Our data was generated from twelve groups of three Engineering students working on open-ended tasks using a learning analytics system. The results show that high collaboration groups have member students who present high and equal amounts of physical interactivity and low and equal amounts of intra-individual variability.

OriginalsprogEngelsk
TitelData Driven Approaches in Digital Education - 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, Proceedings
RedaktørerJulien Broisin, Elise Lavoue, Hendrik Drachsler, Katrien Verbert, Mar Perez-Sanagustin
Antal sider13
ForlagSpringer Verlag,
Publikationsdato2017
Sider30-42
ISBN (Trykt)9783319666099
DOI
StatusUdgivet - 2017
Eksternt udgivetJa
Begivenhed12th European Conference on Technology Enhanced Learning, EC-TEL 2017 - Tallinn, Estland
Varighed: 12 sep. 201715 sep. 2017

Konference

Konference12th European Conference on Technology Enhanced Learning, EC-TEL 2017
LandEstland
ByTallinn
Periode12/09/201715/09/2017
NavnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind10474 LNCS
ISSN0302-9743

ID: 256267429