Sequence Matters but How Exactly? A Method for Evaluating Activity Sequences from Data [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (9th, Raleigh, NC, Jun 29-Jul 2, 2016).

Autor/inn/en	Doroudi, Shayan; Holstein, Kenneth; Aleven, Vincent; Brunskill, Emma
Titel	Sequence Matters but How Exactly? A Method for Evaluating Activity Sequences from Data [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (9th, Raleigh, NC, Jun 29-Jul 2, 2016).
Quelle	(2016), (8 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Sequential Learning; Data Collection; Information Retrieval; Evaluation Methods; Fractions; Intelligent Tutoring Systems; Hypothesis Testing; Learning Processes; Problem Solving; Bayesian Statistics; Performance Based Assessment; Regression (Statistics); Predictor Variables; Models; Elementary School Students; Grade 4; Grade 5; Elementary School Mathematics + Suchen Sie Ihr Suchwort? Didaktische Sequenzierung; Lernsequenz; Data capture; Datensammlung; Bruchrechnung; Intelligentes Tutorsystem; Hypothesenprüfung; Hypothesentest; Learning process; Lernprozess; Problemlösen; Leistungsermittlung; Regression; Regressionsanalyse; Prädiktor; Analogiemodell; School year 04; 4. Schuljahr; Schuljahr 04; School year 05; 5. Schuljahr; Schuljahr 05; Elementare Mathematik; Schulmathematik
Abstract	How should a wide variety of educational activities be sequenced to maximize student learning? Although some experimental studies have addressed this question, educational data mining methods may be able to evaluate a wider range of possibilities and better handle many simultaneous sequencing constraints. We introduce Sequencing Constraint Violation Analysis (SCOVA): a general method for evaluating alternative activity sequences using existing data. SCOVA can be used to explore many complex sequencing constraints, such as prerequisite relationships, blocking, interleaving, and spiraling. We demonstrate SCOVA on data collected from a fractions intelligent tutoring system (ITS). Some of our findings challenge our initial hypotheses regarding sequencing, illustrating the utility and versatility of the method. The method can also be applied to other learning environments, as long as the available data has substantial variability in students' activity sequences. [This paper was published in the "Proceedings of the 9th International Conference on Educational Data Mining," Tiffany Barnes, Min Chi, and Mingyu Feng (eds.), p70-77.] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01