Affective Teachable Agent in VLE: A Goal Oriented Approach.
ABSTRACT Teachable agent is a type of pedagogical agent which instantiates Learning-by-Teaching theory through simulating a "naive" learner in order to motivate students to teach it. This paper discusses existing teachable agent systems and proposes an affective teachable agent based on goal oriented modeling in Virtual Learning Environment (VLE). The proposed agent is more initiative and believable as it is able to behave according to its own goals accompanied with expressions of emotions. A use case study illustrates the effectiveness of this approach.
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Conference Proceeding: Mining Student Behavior Models in Learning-by-Teaching Environments.[show abstract] [hide abstract]
ABSTRACT: This paper discusses our approach to building models and analyzing student behaviors in different versions of our learning by teaching environment where students learn by teaching a computer agent named Betty using a visual concept map representation. We have run studies in fifth grade classrooms to compare the different versions of the system. Students' interactions on the sys- tem, captured in log files represent their performance in generating the causal concept map structures and their activities in using the different tools provided by the system. We discuss methods for analyzing student behaviors and linking them to student performance. At the core of this approach is a hidden Markov model methodology that builds students' behavior models from data collected in the log files. We discuss our modeling algorithm and the interpretation of the models.Educational Data Mining 2008, The 1st International Conference on Educational Data Mining, Montreal, Québec, Canada, June 20-21, 2008. Proceedings; 01/2008
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ABSTRACT: Using hidden Markov models (HMMs) and traditional behavior anal- ysis, we have examined the effect of metacognitive prompting on students’ learning in the context of our computer-based learning-by-teaching environ- ment. This paper discusses our analysis techniques, and presents evidence that HMMs can be used to effectively determine students’ pattern of activities. The results indicate clear differences between different interventions, and links be- tween students learning performance,and their interactions with the system. Keywords: Learning by Teaching environments, Metacognition, BehaviorIntelligent Tutoring Systems, 9th International Conference, ITS 2008, Montreal, Canada, June 23-27, 2008, Proceedings; 01/2008
Conference Proceeding: Learning by Teaching SimStudent: Technical Accomplishments and an Initial Use with Students.[show abstract] [hide abstract]
ABSTRACT: The purpose of the current study is to test whether we could create a system where students can learn by teaching a live machine-learning agent, called SimStudent. SimStudent is a computer agent that interactively learns cognitive skills through its own tutored-problem solving experience. We have developed a game-like learning environment where students learn algebra equations by tutoring SimStudent. While Simulated Students, Teachable Agents and Learning Companion systems have been created, our study is unique that it genuinely learns skills from student input. This paper describes the overview of the learning environment and some results from an evaluation study. The study showed that after tutoring SimStudent, the students improved their performance on equation solving. The number of correct answers on the error detection items was also significantly improved. On average students spent 70.0 minutes on tutoring SimStudent and used an average of 15 problems for tutoring.Intelligent Tutoring Systems, 10th International Conference, ITS 2010, Pittsburgh, PA, USA, June 14-18, 2010, Proceedings, Part I; 01/2010