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Overview of the 10th International Conference on Intelligent Tutoring Systems ITS2010

Svetlana Stoyanchev

SLTC Newsletter, July 2010

One-on-one tutoring allows students to receive personalized attention and is found more effective than classroom learning. In a classroom, where one teacher presents material to 20 or 30 students, each student receives less personalized attention. Providing a personal tutor for each student is expensive and infeasible. Can automatic tutoring systems provide instructions, support, and feedback to a student and play the role of a one-on-one tutor? This and other educational system-related questions were addressed at the tenth conference on Intelligent Tutoring Systems (ITS) that took place in June, 2010 in Pittsburgh, USA.

Automatic Tutoring

Automatic tutoring systems are interactive applications that guide students through lessons and exercises, provide feedback, hints, and encouragement. Tutoring systems aim to improve student's learning by providing personal attention as a one-on-one tutor. Researchers at ITS presented techniques for modelling virtual tutors, learn tutoring strategies from human tutors, and evaluate effect of automatic tutoring approaches. The presented tutoring systems focused on different student levels, from elementary school to college, as well as medical students and soldiers.

The ITS conference started in 1988 and has been steadily gaining popularity. Currently the conference takes place every 2 years. The ITS2010 edition featured 62 oral paper presentations and 74 posters, including poster presentations for the Young Researchers Track, and a demo session with 17 demonstrations. Five invited speakers included psychologists and computer scientists from industry and academia.

Emotion

The role of detecting and modelling emotions during tutoring was one of the topics emphasised at the ITS2010. In his invited speech, Dr. Stacy Marsella from the Institute for Creative Technologies, University of Southern California, spoke about the role of emotional modelling in systems with virtual agents. He described an approach to modelling emotion in a virtual agent that allows the agent to adapt to the environment by dynamically changing its goal, belief, and intention and modelling human coping behaviour. Dr. Beverly Park Woolf, an invited speaker from the University of Massachusetts, Amherst, described the emergence of social and caring computer tutors, which respond to both affect and cognition.

"Affective" tutoring systems try to detect the user's emotional state and change their behaviour to adapt to the user's state. The conference presented two sessions on "affect" and one on "metacognition". The presentations discussed relation between affective feedback and student's learning (D'Mello et al.), psychological state (Pour et al.), and self esteem (Jraidi and Frasson), link between psychological state and cognition during learning (Lehman et al.), between emotions, motivation, and performance (A. Chauncey and R. Azevedo).

Tutoring and DialogueTutoring and Dialogue

Tutoring system are dialogue systems: they understand user's input, determine the next dialogue move, and generate the system's move. System's effectiveness (and hence student's knowledge gain) depend on the appropriateness of the dialogue move chosen by the system. The tutoring dialogue strategy determines whether at a particular point of the interaction, a system should give student a hint, repeat a question, ask a suggestive question, give the user an encouragement, or switch to a new topic. Two of the conference sessions presented evaluation and analysis of human of pedagogical strategies in human tutors and evaluation of strategies in tutoring systems. Natural language interactive system use machine learning for automatic detection of a system strategy (K. Boyer et al., M. Chi et al.). K. Forbes-Riley and D. Litman automatically detect metacognitive information, such as student's confidence and use this information in their system's dialogue move modelling.

Games and Virtual Agents

Virtual humans are animated life-like characters that interact with users using a combination of speech and non-verbal behaviour. Some tutoring systems employ virtual agents to incorporate visual modality and personify tutoring systems. For a system that teaches intercultural negotiation skills (Ogan et al.) or a deception detection (H. C. Lane et al), a virtual agent is an essential component as students learn by observing virtual agent's behaviour. Using gaming environment for teaching is also gaining popularity in the tutoring domain. The ITS conference featured two sessions on intelligent games. In games, student cheating becomes possible, hence one of the sessions at ITS focused on ways of detecting users that try to "game" the system.

Evaluation

Most of the evaluation of tutoring systems involves measure of students' learning progress by comparing students' performance on pretest and posttest. Another approach to focus on long term student's learning (R. Gluga et al.).

Tutoring and Speech

Incorporating speech into tutoring system gives a user a more natural interface. However, speech recognition errors as in other dialogue systems present a challenge to this task. Using speech in tutoring system is especially difficult as the state space and vocabulary are larger than in traditional 'slot-filling' dialogue systems. Intelligent tutoring is a socially important domain that combines a range of research fields including Psychology, Artificial Intelligence, Dialogue, Multimodal Communication, and Speech Technology presenting open challenges to speech researchers.

If you have comments, corrections, or additions to this article, please contact the author: Svetlana Stoyanchev, s.stoyanchev [at] open [dot] ac [dot] uk.

Svetlana Stoyanchev is Research Associate at the Open University. Her interests are in dialogue, question generation, question answering, and information presentation.