Fifteen years ago, research started on SQL-Tutor, the first constraint-based tutor. The initial efforts were focused on evaluating constraint-based Modeling (CBM), its effectiveness and applicability to various instru...
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Fifteen years ago, research started on SQL-Tutor, the first constraint-based tutor. The initial efforts were focused on evaluating constraint-based Modeling (CBM), its effectiveness and applicability to various instructional domains. Since then, we extended CBM in a number of ways, and developed many constraint-based tutors. Our tutors teach both well-and ill-defined domains and tasks, and deal with domain-and meta-level skills. We have supported mainly individual learning, but also the acquisition of collaborative skills. Authoring support for constraint-based tutors is now available, as well as mature, well-tested deployment environments. Our current research focuses on building affect-sensitive and motivational tutors. Over the period of fifteen years, CBM has progressed from a theoretical idea to a mature, reliable and effective methodology for developing effective tutors.
The little previous research comparing Student errors across schools indicates that student "bugs" do not transfer - that is, the distribution of students' systematic errors in one school does not signif...
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ISBN:
(纸本)9781586037642
The little previous research comparing Student errors across schools indicates that student "bugs" do not transfer - that is, the distribution of students' systematic errors in one school does not significantly match those in other schools. The issue has practical implications as cognitive (or "model-tracing") tutors rely on the modeling of student errors in order to provide targeted remediation. In this study we examine the responses of students at three schools to a middle-school mathematics problem. We find the same error is the most common error across all schools, and this single error accounts for some half of all incorrect responses at each school. The top five errors are similar across schools and account for some 2/3 of errors at each school. We conclude that in this example, there appears to be considerable overlap Of Student errors across schools.
Intelligent tutoring systems become increasingly common in assisting human learners, but they are often aimed at isolated domain tasks without creating a scaffolding system from lower- to higher-level cognitive skills...
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ISBN:
(纸本)9783030804213;9783030804206
Intelligent tutoring systems become increasingly common in assisting human learners, but they are often aimed at isolated domain tasks without creating a scaffolding system from lower- to higher-level cognitive skills. We designed and implemented an intelligent tutoring system CompPrehension aimed at the comprehension level of Bloom's taxonomy that often gets neglected in favour of the higher levels. The system features plugin-based architecture, easing adding new domains and learning strategies;using formal models and software reasoners to solve the problems and judge the answers;and generating explanatory feedback and follow-up questions to stimulate the learners' thinking. The architecture and workflow are shown. We demonstrate the process of interacting with the system in the Control Flow Statements domain. The advantages and limits of the developed system are discussed.
Tutorial dialogues are considered as one of the critical factors contributing to the effectiveness of human one-on-one tutoring. We discuss how we evaluated the effectiveness of a general model of adaptive tutorial di...
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ISBN:
(纸本)9783642218682;9783642218699
Tutorial dialogues are considered as one of the critical factors contributing to the effectiveness of human one-on-one tutoring. We discuss how we evaluated the effectiveness of a general model of adaptive tutorial dialogues in both an ill-defined and a well-defined task. The first study involved dialogues in database design, an ill-defined task. The control group participants received non-adaptive dialogues regardless of their knowledge level and explanation skills. The experimental group participants received adaptive dialogues that were customised based on their student models. The performance on pre- and post-tests indicate that the experimental group participants learned significantly more than their peers. The second study involved dialogues in data normalization, a well-defined task. The performance of the experimental group increased significantly between pre- and post-test, while the improvement of the control group was not significant. The studies show that the model is applicable to both ill- and well-defined tasks, and that they support learning effectively.
Previous research on adaptive educational systems has shown that allowing the student to view their student model is useful in the learning process. Open student models help support meta-cognitive processes, such as s...
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Previous research on adaptive educational systems has shown that allowing the student to view their student model is useful in the learning process. Open student models help support meta-cognitive processes, such as self-assessment and reflection, and at the same time increase the student's trust in the system. Negotiable student models take this a step further, and allow students to negotiate and potentially modify their model. Very few negotiable student models have been implemented, and only in relatively simple systems, not integrated into a complex Intelligent Tutoring System (ITS). Therefore, it is not clearly known whether negotiable student models pose a significant advantage over simpler open student models. This research implements a basic negotiable student model into a version of a complex and internationally deployed ITS. Subjective evaluation is performed, and shows promising results. Participants felt the negotiable student model was both useful for learning, and enjoyable to use. With a few improvements, this negotiable student model implementation could be used in a wide-scale objective analysis to help determine the usefulness of negotiable student models.
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