The Predictiveness of PFA Is Improved by Incorporating the Learner's Correct Response Time Fluctuation [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023).

Autor/inn/en	Chu, Wei; Pavlik, Philip I., Jr.
Titel	The Predictiveness of PFA Is Improved by Incorporating the Learner's Correct Response Time Fluctuation [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023).
Quelle	(2023), (7 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Reaction Time; Accuracy; Models; Predictor Variables; Factor Analysis; Learning Management Systems; Learning Analytics; Psychological Patterns; Theories; Recall (Psychology); Probability; Learning Trajectories; Guessing (Tests); Learning Processes; Item Analysis; Item Response Theory; Comparative Analysis; Multiple Choice Tests; Chinese; Japanese; Language Tests; Pronunciation; English; Vocabulary; Cloze Procedure; Goodness of Fit + Suchen Sie Ihr Suchwort? Reaktionsvermögen; Analogiemodell; Prädiktor; Faktorenanalyse; Theory; Theorie; Abberufung; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Erraten; Learning process; Lernprozess; Itemanalyse; Item-Response-Theorie; Multiple choice examinations; Multiple-choice tests, Multiple-choice examinations; Multiple-Choice-Verfahren; China; Chinesen; Japaner; Japanisch; Language test; Sprachtest; Aussprache; English language; Englisch; Wortschatz; Lückentext
Abstract	In adaptive learning systems, various models are employed to obtain the optimal learning schedule and review for a specific learner. Models of learning are used to estimate the learner's current recall probability by incorporating features or predictors proposed by psychological theory or empirically relevant to learners' performance. Logistic regression for knowledge tracing has been used widely in modern learner performance modeling. Notably, the learning history included in such models is typically confined to learners' prior accuracy performance without paying attention to learners' response time (RT), such as the performance factors analysis (PFA) model. However, RT and accuracy may give us a more comprehensive picture of a learner's learning trajectory. For example, without considering RT, we cannot estimate whether the learner's performance has reached the automatic or fluent level since these criteria are not accuracy based. Therefore, in the current research, we propose and test new RT-related features to capture learners' correct RT fluctuations around their estimated ideal fluent RT. Our results indicate that the predictiveness of the standard PFA model can be increased by up to 10% for our test data after incorporating RT-related features, but the complexity of the question format constrains the improvement during practice. If the question is of low complexity and the observed accuracy of the learner can be influenced by guessing, which results in the imprecision measured by accuracy, then the RT-related features provide additional predictive power. In other words, RT-related features are informative when accuracy alone does not completely reflect learners' learning processes. [For the complete proceedings, see ED630829.] (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