Ensembling predictions of student post-test scores for an intelligent tutoring system.

________________________________________________________________________ Over the last few decades, there have been a rich variety of approaches towards modeling student knowledge and skill within interactive learning environments. There have recently been several empirical comparisons as to which types of student models are better at predicting future performance, both within and outside of the interactive learning environment. A recent paper (Baker et al., in press) considers whether ensembling can produce better prediction than individual models, when ensembling is performed at the level of predictions of performance within the tutor. However, better performance was not achieved for predicting the post-test. In this paper, we investigate ensembling at the post-test level, to see if this approach can produce better prediction of post-test scores within the context of a Cognitive Tutor for Genetics. We find no improvement for ensembling over the best individual models and we consider possible explanations for this finding, including the limited size of the data set

Towards an Understanding of Affect and Knowledge from Student Interaction with an Intelligent Tutoring System

ABS TRACT. Csikszentmihalyi's Flow theory states that a balance between challenge and skill leads to high engagement, overwhelming challenge leads to anxiety or frustration, and insufficient challenge leads to boredom. In this p aper, we test this theory within the context of student interaction with an intelligent tutoring system. Automated detectors of student affect and knowledge were developed, validated, and applied to a large data set. The results did not match Flow theory: boredom was more common for poorly -known material, and frustration was common both for very difficult material and very easy material. These results suggest that design for optimal engagement within online learning may require further study of the factors leading students to become bored on difficult material, and frustrated on very well-known material

S.M.: Predicting Robust Learning with the Visual Form of the Moment-by-Moment Learning Curve

Abstract 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 find that different visual forms are correlated with very different learning outcomes. This work suggests that analysis of moment-by-moment learning curves may be able to shed light on the implications of students' different patterns of learning over time