Guojing Zhou

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Two factors characterize a good feature selection algorithm: its accuracy and stability. This paper aims at introducing a new approach to stable feature selection algorithms. The innovation of this paper centers on a class of stable feature selection algorithms called feature weighting as regularized energy-based learning (FREL). Stability properties of(More)
Research shows that expert-crafted worked examples can have a positive effect on student performance. To investigate the potential for data-driven worked examples to achieve similar results, we generated worked examples for the Deep Thought logic tutor, and conducted an experiment to assess their impact on performance. Students who received data-driven(More)
In this paper, we explore the impact of two types of instructional interventions, worked examples and problem solving, at two levels of granularity: problems and steps. This study drew on an existing Intelligent Tutoring System (ITS) for Probability called Pyrenees and involved 266 students who were randomly assigned to five conditions. All students(More)
In this study we explored the impact of student versus tutor pedagogical decision-making on learning. More specifically, we examined what would happen if we let students decide how to handle the next task: to view it as a worked example or to solve it as a problem solving. We examined this impact at two levels of task granularity: problem vs. step. This 2×(More)
In this study, we explored the impact of the decision agency (Student vs. Tutor) and granularity (Problem vs. Step) across students with different levels of incoming competence (High vs. Low). Students were randomly assigned to four conditions and split into High and Low groups based on their pre-test scores. All students used the same Intelligent Tutoring(More)
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