Characterizing Joint Attention Dynamics during Collaborative Problem-Solving in an Immersive Astronomy Simulation [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (15th, Durham, United Kingdom, Jul 24-27, 2022).

Autor/inn/en	Zhou, Yiqiu; Kang, Jina
Titel	Characterizing Joint Attention Dynamics during Collaborative Problem-Solving in an Immersive Astronomy Simulation [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (15th, Durham, United Kingdom, Jul 24-27, 2022).
Quelle	(2022), (8 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Astronomy; Problem Solving; Science Instruction; Cooperative Learning; Learning Processes; Artificial Intelligence; Computer Simulation; Profiles; Attention Control; Student Behavior; Behavior Patterns; Peer Relationship; Classification; Task Analysis; Computer Software; Introductory Courses; Undergraduate Students + Suchen Sie Ihr Suchwort? Astronomie; Problemlösen; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Kooperatives Lernen; Learning process; Lernprozess; Künstliche Intelligenz; Computergrafik; Computersimulation; Charakterisierung; Profilanalyse; Aufmerksamkeitstest; Student behaviour; Schülerverhalten; Peer-Beziehungen; Classification system; Klassifikation; Klassifikationssystem; Aufgabenanalyse; Einführungskurs
Abstract	The complex and dynamic nature of collaboration makes it challenging to find indicators of productive learning and quality collaboration. This exploratory study developed a collaboration metric to capture temporal patterns of joint attention (JA) based on log files generated as students interacted with an immersive astronomy simulation using augmented reality headsets and tablets. JA is defined as the ability to coordinate attention, which thus plays an important role in collaborative problem-solving to build the common ground for knowledge co-construction. We first developed a JA metric consisting of six distinct but closely relevant states as a measure of the collaboration process. We then conducted descriptive statistics to compare frequency and temporal pattern of JA states across three learning performance groups. Our results showed that high-learning-gain groups demonstrated visual coordination behaviors more frequently and utilized this collaboration strategy in the early stage. We then investigated sequences of these JA states, focusing on one key behavior: long and consistent shared view as a proxy for collaboration. This sequential analysis revealed two different collaboration profiles: attention follow-leader and turn takers, suggesting the existence of asymmetrical participation. Our findings indicate the potential of JA metric to predict overall collaboration quality, identify undesirable collaboration behaviors, and serve as an early warning to provide just-in-time guidance. [For the full proceedings, see ED623995.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01