Leveraging Machine-Learned Detectors of Systematic Inquiry Behavior to Estimate and Predict Transfer of Inquiry Skill

Autor/inn/en	Sao Pedro, Michael A.; Baker, Ryan S. J. d.; Gobert, Janice D.; Montalvo, Orlando; Nakama, Adam
Titel	Leveraging Machine-Learned Detectors of Systematic Inquiry Behavior to Estimate and Predict Transfer of Inquiry Skill
Quelle	(2013), (61 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Artificial Intelligence; Inquiry; Middle School Students; Physical Sciences; Science Process Skills; Student Behavior; Identification; Automation; Predictive Validity; Grade 8; Computer Assisted Instruction; Transfer of Training; Prediction; Markov Processes; Massachusetts + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Natural sciences; Naturwissenschaften; Naturwissenschaft; Student behaviour; Schülerverhalten; Identifikation; Identifizierung; School year 08; 8. Schuljahr; Schuljahr 08; Computer based training; Computerunterstützter Unterricht; Training; Transfer; Ausbildung; Vorhersage; Markowscher Prozess; Master-Studiengang
Abstract	We present work toward automatically assessing and estimating science inquiry skills as middle school students engage in inquiry within a physical science microworld. Towards accomplishing this goal, we generated machine-learned models that can detect when students test their articulated hypotheses, design controlled experiments, and engage in planning behaviors using two inquiry support tools. Models were trained using labels generated through a new method of manually hand-coding log files, "text replay tagging". This approach led to detectors that can automatically and accurately identify these inquiry skills under student-level cross-validation. The resulting detectors can be applied at run-time to drive scaffolding intervention. They can also be leveraged to automatically score all practice attempts, rather than hand-classifying them, and build models of latent skill proficiency. As part of this work, we also compared two approaches for doing so, Bayesian Knowledge-Tracing and an averaging approach that assumes static inquiry skill level. These approaches were compared on their efficacy at predicting skill before a student engages in an inquiry activity, predicting performance on a paper-style multiple choice test of inquiry, and predicting performance on a transfer task requiring data collection skills. Overall, we found that both approaches were effective at estimating student skills within the environment. Additionally, the models' skill estimates were significant predictors of the two types of inquiry transfer tests. [This paper was published in "User Modeling and User-Adapted Interaction" v23 p1-39 2013.] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01