Exploring Policies for Dynamically Teaming up Students through Log Data Simulation [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (14th, Online, Jun 29-Jul 2, 2021).

Autor/inn/en	Yang, Kexin Bella; Echeverria, Vanessa; Wang, Xuejian; Lawrence, LuEttaMae; Holstein, Kenneth; Rummel, Nikol; Aleven, Vincent
Titel	Exploring Policies for Dynamically Teaming up Students through Log Data Simulation [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (14th, Online, Jun 29-Jul 2, 2021).
Quelle	(2021), (12 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Grouping (Instructional Purposes); Cooperative Learning; Teaching Methods; Kindergarten; Elementary Secondary Education; Educational Policy; Intelligent Tutoring Systems; Learning Analytics; Computer Simulation; Middle School Students; Policy Analysis; Comparative Analysis; Heterogeneous Grouping; Student Characteristics; Knowledge Level + Suchen Sie Ihr Suchwort? Grouping; Gruppenbildung; Kooperatives Lernen; Teaching method; Lehrmethode; Unterrichtsmethode; Politics of education; Bildungspolitik; Intelligentes Tutorsystem; Computergrafik; Computersimulation; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Politikfeldanalyse; Wissensbasis
Abstract	Constructing effective and well-balanced learning groups is important for collaborative learning. Past research explored how group formation policies affect learners' behaviors and performance. With the different classroom contexts, many group formation policies work in theory, yet their feasibility is rarely investigated in authentic class sessions. In the current work, we define "feasibility" as the ratio of students being able to find available partners that satisfy a given group formation policy. Informed by user-centered research in K-12 classrooms, we simulated pairing policies on historical data from an intelligent tutoring system (ITS), a process we refer to as "SimPairing." As part of the process for designing a pairing orchestration tool, this study contributes insights into the feasibility of four dynamic pairing policies, and how the feasibility varies depending on parameters in the pairing policies or different classes. We found that on average, dynamically pairing students based on their in-the-moment wheel-spinning status can pair most struggling students, even with moderate constraints of restricted pairings. In addition, we found there is a trade-off between the required knowledge heterogeneity and policy feasibility. Furthermore, the feasibility of pairing policies can vary across different classes, suggesting a need for customization regarding pairing policies. [For the full proceedings, see ED615472.] (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	2022/1/01