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Full-Text Articles in Computer Engineering
Educational Data Mining: An Advance For Intelligent Systems In Education, Ryan Baker
Educational Data Mining: An Advance For Intelligent Systems In Education, Ryan Baker
Ryan S.J.d. Baker
Computer-based technologies have transformed the way we live, work, socialize, play, and learn. Today, the use of data collected through these technologies is supporting a second-round of transformation in all of these areas. Over the last decades, the methods of data mining and analytics have transformed field after field. Scientific fields such as physics, biology, and climate science have leveraged these methods to manage and make discoveries in previously unimaginably large datasets. The first journal devoted to data mining and analytics methods in biology, Computers in Biology and Medicine, began publication as long ago as the 1970s. In the mid-1990s …
Population Validity For Educational Data Mining Models: A Case Study In Affect Detection, Ryan Baker, Jaclyn Ocumpaugh, Sujith Gowda, Neil Heffermnan, Cristina Heffernan
Population Validity For Educational Data Mining Models: A Case Study In Affect Detection, Ryan Baker, Jaclyn Ocumpaugh, Sujith Gowda, Neil Heffermnan, Cristina Heffernan
Ryan S.J.d. Baker
ICT-enhanced research methods such as educational data mining (EDM) have allowed researchers to effectively model a broad range of constructs pertaining to the student, moving from traditional assessments of knowledge to assessment of engagement, meta-cognition, strategy, and affect. The automated detection of these constructs allows EDM researchers to develop intervention strategies that can be implemented either by the software or the teacher. It also allows for secondary analyses of the construct, where the detectors are applied to a data set that is much larger than one that could be analyzed by more traditional methods. However, in many cases, the data …
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. …