Octav Popescu

The Beginning of a Beautiful Friendship? Intelligent Tutoring Systems and MOOCs

A key challenge in ITS research and development is to support tutoring at scale, for example by embedding tutors in MOOCs. An obstacle to at-scale deployment is that ITS architectures tend to be complex, not easily deployed in browsers without significant server-side processing, and not easily embedded in a learning management system (LMS). We present a case study in which a widely used ITS authoring tool suite, CTAT/TutorShop, was modified so that tutors can be embedded in MOOCs. Specifically, the inner loop (the example-tracing tutor engine) was moved to the client by reimplementing it in JavaScript, and the tutors were made compatible with the LTI e-learning standard. The feasibility of this general approach to ITS/MOOC integration was demonstrated with simple tutors in an edX MOOC “Data Analytics and Learning.”

Evaluating the Effectiveness of a Tutorial Dialogue System for Self-Explanation

Previous research has shown that self-explanation can be supported effectively in an intelligent tutoring system by simple means such as menus. We now focus on the hypothesis that natural language dialogue is an even more effective way to support self-explanation. We have developed the Geometry Explanation Tutor, which helps students to state explanations of their problem-solving steps in their own words. In a classroom study involving 71 advanced students, we found that students who explained problem-solving steps in a dialogue with the tutor did not learn better overall than students who explained by means of a menu, but did learn better to state explanations. Second, examining a subset of 700 student explanations, students who received higher-quality feedback from the system made greater progress in their dialogues and learned more, providing some measure of confidence that progress is a useful intermediate variable to guide further system development. Finally, students who tended to reference specific problem elements in their explanations, rather than state a general problem-solving principle, had lower learning gains than other students. Such explanations may be indicative of an earlier developmental level.

Pilot-Testing a Tutorial Dialogue System That Supports Self-Explanation

Previous studies have shown that self-explanation is an effective metacognitive strategy and can be supported effectively by intelligent tutoring systems. It is plausible however that students may learn even more effectively when stating explanations in their own words and when receiving tutoring focused on their explanations. We are developing the Geometry Explanation Tutor in order to test this hypothesis. This system helps students, through a restricted form of dialogue, to construct general explanations of problem-solving steps in their own words. We conducted a pilot study in which the tutor was used for two class periods in a junior high school. The data from this study suggest that the techniques that we chose to implement the dialogue system, namely a knowledge-based approach to natural language understanding and classification of student explanations, are up to the task. There are a number of ways in which the system could be improved within the current architecture.

Embedding Intelligent Tutoring Systems in MOOCs and e-Learning Platforms

Intelligent tutoring systems ITS and MOOCs tend to have complementary pedagogical approaches, but their combination is rarely if ever seen. A key obstacle may be technical integration. We present a generalizable case study of extending ITS authoring technology to make tutors easily embeddable into a variety of MOOC/e-learning platforms and run on a range of web-enabled devices. We enhanced the domain-independent Cognitive Tutor Authoring Tools CTAT to enable integration of CTAT tutors into multiple environments. A salient lesson learned is that use of widely-used web-based technologies HTML and JavaScript may be a major factor in ITS uptake. Also, we found that embedding tutors into existing LMS is challenging, but environment-specific changes can be isolated in a generalizable manner.

An Adaptive Coach for Invention Activities

A focus in recent AIED research is to create adaptive support for learners in inquiry learning environments. However, only few examples of such support have been demonstrated. Our work focuses on Invention activities, inquiry activities in which students generate representations that explain data presented as contrasting cases. To help teachers implement these activities in their classrooms, we have created and pilot-tested a dedicated adaptive computer coach (the Invention Coach) and are currently evaluating it in a classroom study. The Coach’s pedagogical strategy balances structuring and problematizing, unlike many ITSs, which favor structuring. The Coach is implemented in CTAT as a model-tracing tutor, with a rule-based model that captures its pedagogical coaching strategy, designed in part based on data from human tutors. We describe the Invention Coach and its pedagogical model. We present evidence from our pilot tests that illustrate the tutor’s versatility and provide preliminary evidence of its effectiveness. The contributions of the work are: identifying an adaptive coaching strategy for Invention tasks that balances structuring and problematizing, and an automated coach for a successful instructional method (Invention) for which few tutors have been built.

Opening Up an Intelligent Tutoring System Development Environment for Extensible Student Modeling

ITS authoring tools make creating intelligent tutoring systems more cost effective, but few authoring tools make it easy to flexibly incorporate an open-ended range of student modeling methods and learning analytics tools. To support a cumulative science of student modeling and enhance the impact of real-world tutoring systems, it is critical to extend ITS authoring tools so they easily accommodate novel student modeling methods. We report on extensions to the CTAT/Tutorshop architecture to support a plug-in approach to extensible student modeling, which gives an author full control over the content of the student model. The extensions enhance the range of adaptive tutoring behaviors that can be authored and support building external, student- or teacher-facing real-time analytics tools. The contributions of this work are: (1) an open architecture to support the plugging in, sharing, re-mixing, and use of advanced student modeling techniques, ITSs, and dashboards; and (2) case studies illustrating diverse ways authors have used the architecture.