An important goal of formal education is to prepare students for future learning when there is no longer classroom supervision. To continue learning, people need to learn to self-assess their own progress and understanding. We are investigating the social basis of self-assessment for learning. Specifically, we hypothesize that, under identifiable conditions, assessing others can support self-assessments that promote content learning plus the ability to develop self-assessment strategies that can be applied in the future. In this project, we take advantage of Teachable Agent technologies where students learn by teaching computer agents through the use of well-formed visual representations. Teachable Agents, using simple artificial intelligence techniques, can then reason based on what they have been taught. This creates optimal conditions for self-assessment, because students' assessments of their agents' performance is also an assessment of their own knowledge. The work occurs in the context of teaching the key ecosystem concepts of interdependence and balance to middle school students. Students will first create Teachable Agents that are linked to their curriculum on pond and river ecosystems, and use this learning experience to create a new Teachable Agent that can sustain multiple fish in a home aquarium system. Students will also use the Teachable Agents in a new homework model that leverages current trends in home computer use and connects learning in formal and informal settings; students log on, chat with one another, and their agents interact with another in an on-line virtual environment. Overall, the proposed project joins three important strands of research assisted by advanced technology tools: The learning of dynamic processes in science; the social basis of self-assessments for learning; and, the improvement of connections between formal and informal learning settings.
