Affective States and State Tests: Investigating How Affect and Engagement during the School Year Predict End-of-Year Learning Outcomes

Autor/inn/en	Pardos, Zachary A.; Baker, Ryan S. J. D.; San Pedro, Maria O. C. Z.; Gowda, Sujith M.; Gowda, Supreeth M.
Titel	Affective States and State Tests: Investigating How Affect and Engagement during the School Year Predict End-of-Year Learning Outcomes
Quelle	In: Journal of Learning Analytics, 1 (2014) 1, S.107-128 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Affective Behavior; Student Behavior; Learner Engagement; Web Based Instruction; Educational Technology; Technology Uses in Education; Tutoring; High Stakes Tests; Mathematics Tests; Longitudinal Studies; Probability; Problem Solving; Correlation; Scaffolding (Teaching Technique); Models; Predictor Variables; Standardized Tests; Scores; Performance; Mathematics Instruction; Middle School Students; Urban Schools; Observation; Coding; Massachusetts; Massachusetts Comprehensive Assessment System + Suchen Sie Ihr Suchwort? Affective disturbance; Active behaviour; Affektive Störung; Student behaviour; Schülerverhalten; Web Based Training; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Förderkonzept; Nachhilfeunterricht; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Problemlösen; Korrelation; Analogiemodell; Prädiktor; Standadised tests; Standardisierter Test; Achievement; Leistung; Mathematics lessons; Mathematikunterricht; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Urban area; Urban areas; School; Schools; Stadtregion; Stadt; Schule; Beobachtung; Codierung; Programmierung; Master-Studiengang
Abstract	In this paper, we investigate the correspondence between student affect and behavioural engagement in a web-based tutoring platform throughout the school year and learning outcomes at the end of the year on a high-stakes mathematics exam in a manner that is both longitudinal and fine-grained. Affect and behaviour detectors are used to estimate student affective states and behaviour based on post-hoc analysis of tutor log-data. For every student action in the tutor, the detectors give us an estimated probability that the student is in a state of boredom, engaged concentration, confusion, or frustration, and estimates of the probability that the student is exhibiting off-task or gaming behaviours. We used data from the ASSISTments math tutoring system and found that boredom during problem solving is negatively correlated with performance, as expected; however, boredom is positively correlated with performance when exhibited during scaffolded tutoring. A similar pattern is unexpectedly seen for confusion. Engaged concentration and, surprisingly, frustration are both associated with positive learning outcomes. In a second analysis, we build a unified model that predicts student standardized examination scores from a combination of student affect, disengaged behaviour, and performance within the learning system. This model achieves high overall correlation to standardized exam score, showing that these types of features can effectively infer longer-term learning outcomes. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01