Real-Time Scaffolding of Students' Online Data Interpretation during Inquiry with Inq-ITS Using Educational Data Mining

Autor/inn/en	Gobert, Janice D.; Moussavi, Raha; Li, Haiying; Sao Pedro, Michael; Dickler, Rachel
Titel	Real-Time Scaffolding of Students' Online Data Interpretation during Inquiry with Inq-ITS Using Educational Data Mining
Quelle	(2018), (27 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Scaffolding (Teaching Technique); Data Interpretation; Intelligent Tutoring Systems; Science Instruction; Inquiry; Science Process Skills; Skill Development; Transfer of Training; Grade 8; Middle School Students; Instructional Effectiveness; Bayesian Statistics; Computation + Suchen Sie Ihr Suchwort? Data evaluation; Datenauswertung; Intelligentes Tutorsystem; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Kompetenzentwicklung; Qualifikationsentwicklung; Training; Transfer; Ausbildung; School year 08; 8. Schuljahr; Schuljahr 08; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Unterrichtserfolg
Abstract	This chapter addresses students' data interpretation, a key NGSS inquiry practice, with which students have several different types of difficulties. In this work, we unpack the difficulties associated with data interpretation from those associated with warranting claims. We do this within the context of Inq-ITS (Inquiry Intelligent Tutoring System), a lightweight LMS, providing computer-based assessment and tutoring for science inquiry practices/skills. We conducted a systematic analysis of a subset of our data to address whether our scaffolding is supporting students in the acquisition and transfer of these inquiry skills. We also describe an additional study, which used Bayesian Knowledge Tracing (Corbett and Anderson. User Model User- Adapt Interact 4(4):253-278, 1995), a computational approach allowing for the analysis of the fine-grained sub-skills underlying our practices of data interpretation and warranting claims. [This is a chapter in: Auer, M. E. et al. (Eds), "Cyber-Physical Laboratories in Engineering and Science Education," (p191-217). Cham, Switzerland: Springer International.] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01