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Leveraging Machine-Learned Detectors Of Systematic Inquiry Behavior To Estimate And Predict Transfer Of Inquiry Skill, Ryan Baker, Michael Sao Pedro, Janice Gobert, Orlando Montalvo, Adam Nakama
Leveraging Machine-Learned Detectors Of Systematic Inquiry Behavior To Estimate And Predict Transfer Of Inquiry Skill, Ryan Baker, Michael Sao Pedro, Janice Gobert, Orlando Montalvo, Adam Nakama
Ryan S.J.d. Baker
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. …
Predicting Robust Learning With The Visual Form Of The Moment-By-Moment Learning Curve, Ryan Baker, Arnon Hershkovitz, Lisa Rossi, Adam Goldstein, Sujith Gowda
Predicting Robust Learning With The Visual Form Of The Moment-By-Moment Learning Curve, Ryan Baker, Arnon Hershkovitz, Lisa Rossi, Adam Goldstein, Sujith Gowda
Ryan S.J.d. Baker
We present a new method for analyzing a student’s learning over time, for a specific skill: analysis of the graph of the student’s moment-by-moment learning over time. Moment-bymoment learning is calculated using a data-mined model which assesses the probability that a student learned a skill or concept at a specific time during learning (Baker, Goldstein, & Heffernan, 2010, 2011). Two coders labeled data from students who used an intelligent tutoring system for college genetics, in terms of seven forms that the moment-by-moment learning curve can take. These labels are correlated to test data on the robustness of students’ learning. We …
Generalizing Automated Detection Of The Robustness Of Student Learning In An Intelligent Tutor For Genetics, Ryan Baker, Albert Corbett, Sujith Gowda
Generalizing Automated Detection Of The Robustness Of Student Learning In An Intelligent Tutor For Genetics, Ryan Baker, Albert Corbett, Sujith Gowda
Ryan S.J.d. Baker
Recently, there has been growing emphasis on supporting robust learning within intelligent tutoring systems, assessed by measures such as transfer to related skills, preparation for future learning, and longer-term retention. It has been shown that different pedagogical strategies promote robust learning to different degrees. However, the student modeling methods embedded within intelligent tutoring systems remain focused on assessing basic skill learning rather than robust learning. Recent work has proposed models, developed using educational data mining, that infer whether students are acquiring learning that transfers to related skills, and prepares the student for future learning (PFL). In this earlier work, evidence …
Towards Automatically Detecting Whether Student Learning Is Shallow, Ryan Baker, Sujith Gowda, Albert Corbett, Lisa Rossi
Towards Automatically Detecting Whether Student Learning Is Shallow, Ryan Baker, Sujith Gowda, Albert Corbett, Lisa Rossi
Ryan S.J.d. Baker
Recent research has extended student modeling to infer not just whether a student knows a skill or set of skills, but also whether the student has achieved robust learning – learning that enables the student to transfer their knowledge and prepares them for future learning (PFL). However, a student may fail to have robust learning in two fashions: they may have no learning, or they may have shallow learning (learning that applies only to the current skill, and does not support transfer or PFL). Within this paper, we present automated detectors which identify shallow learners, who are likely to need …