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ERIC Number: ED528502
Record Type: Non-Journal
Publication Date: 2011
Pages: 6
Abstractor: ERIC
Reference Count: 11
ISBN: N/A
ISSN: N/A
Using Machine-Learned Detectors to Assess and Predict Students' Inquiry Performance
Gobert, Janice D.; Baker, Ryan; Pedro, Michael Sao
Society for Research on Educational Effectiveness
The authors present work towards automatically assessing data collection behaviors as middle school students engage in inquiry within a physics microworld. In this study, the authors used machine learned models that can detect when students test their articulated hypotheses, design controlled experiments, and engage in planning behaviors using their inquiry support tools. They compared two approaches, an averaging-based method that assumes static skill level and Bayesian Knowledge Tracing, on their efficacy at predicting skill before a student engages in an inquiry activity and on predicting performance on a paper-style multiple choice test of inquiry and a transfer task requiring data collection skills. Their data were collected in a rural town in Central Massachusetts. Participants were 134 eighth grade students, ranging in age from 12-14 years, from a public middle school in Central Massachusetts. Their findings provide some evidence that the skills for successfully engaging in authentic inquiry and answering equivalent paper test-style questions are related (Black, 1999; Pellegrino, 2001). Furthermore, their findings support the notion that authentic skill learned in one context can be applied to other domains, as shown by the significant correlation between performance in designing controlled experiments in two domains. As such, these models have considerable potential to enable future "discovery with models" analyses (cf. Baker, 2010) that can shed light on the relationship between a student's mastery of systematic experimentation strategies and their domain learning. Additional research will be needed to determine if these findings are robust over different student populations and if the feature set and associated detectors are general enough (cf. Ghazarian & Noorhosseini, 2010) to be applicable to microworlds in other scientific domains. It will also be important to determine if these relationships will hold after incorporating scaffolding, thus giving students a better opportunity to both perform well despite incomplete knowledge, and to acquire these skills while using the microworld. (Contains 2 tables.)
Society for Research on Educational Effectiveness. 2040 Sheridan Road, Evanston, IL 60208. Tel: 202-495-0920; Fax: 202-640-4401; e-mail: inquiries@sree.org; Web site: http://www.sree.org
Publication Type: Reports - Research
Education Level: Middle Schools
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: Society for Research on Educational Effectiveness (SREE)
Identifiers - Location: Massachusetts