A Multi-Agent Architecture Implementation of Learning by Teaching Systems

Our group has been designing and implementing learning environments that promote deep understanding and transfer in complex domains. We have adopted the learning by teaching paradigm, and developed computer-based agents that students teach, and learn from this experience. The success of teachable agents has led us to develop a multi-agent architecture that will be used to develop extended instructional systems based on gaming environments

Teachable Agents

Teachable Agents This paper discusses Betty’s Brain, a teachable agent in the domain of river ecosystems that combines learning by teaching with self-regulation mentoring to promote deep learning and understanding. Two studies demonstrate the effectiveness of this system. The first study focused on components that define student-teacher interactions in the learning by teaching task. The second study examined the value of adding meta-cognitive strategies that governed Betty’s behavior and self-regulation hints provided by a mentor agent. The study com-pared three versions: a system where the student was tutored by a pedagogical agent (ITS), a learning by teach-ing system (LBT) , where students taught a baseline version of Betty, and received tutoring help from the men-tor, and a learning by teaching system (SRL), where Betty was enhanced to include self-regulation strategies, and the mentor provided help on domain material plus how to become better learners and better teachers. Re-sults indicate that the addition of the self-regulated Betty and the self-regulation mentor better prepared students to learn new concepts later, even when they no longer had access to the SRL environment

Case Studies in Learning by Teaching Behavioral Differences in Directed versus Guided Learning

Our studies with Betty's Brain, a learning by teaching environment, have shown that the system is effective in helping fifth grade students gain a good understanding of river ecosystem concepts. The use of self-regulation strategies demonstrated that the learning gains transferred to new domains where students worked without the self-regulation system. This paper analyzes the log files of the student activities to determine which activities in the learning environment contribute to the students developing metacognitive strategies that contribute to their preparation for future learning

Pedagogical Agents for Learning by Teaching: Teachable Agents

this article, we spotlight our agent named Betty. Students teach Betty by creating a network of entities and their relations, much like a concept map. Figure 1 provides an example. Students use a point-and-click editor to create nodes (e.g., LDL, arterial plaque) and connect pairs of nodes with links. The links are labeled (e.g., LDL builds up arterial plaque) and categorized using a pull-down menu (e.g., builds up implies and increase) (Biswas et. al, 2005). Figure 1. The Teachable Agent Betty. Students teach Betty by making a concept map. Once Betty has been taught, she can answer questions by tracing links through the concept map. At any point, students can ask Betty a question to see how well she is learning. Figure 1 shows how Betty animates her reasoning for the question, "What happens to heart disease if exercise increases?" To make it easier for the student to follow her reasoning process, Betty breaks down the explanation into parts. For example, Betty reasons that exercise increases HDL cholesterol and that exercise decreases LDL. Increasing HDL and decreasing LDL together result in decreased arterial plaque, which in turn decreases the risk of heart disease. To complement her graphical thinking, Betty unfolds her reasoning in text (lower panel). Students can observe Betty's conclusions and decide whether they need to revise what they have taught Betty. Betty can also take a quiz composed by a classroom instructor but automatically scored by the computer. So, instead of students taking the quiz, they can watch their agent perform and receive projective feedback on their own knowledge. FOUR CORE PRINCIPLES OF TEACHABLE AGENTS Betty is one instance of a Teachable Agent (for other instances, see Schwartz et. al.; in press; Blair and Schwartz, 2004). Like all our ..

Animations of thought: Interactivity in the teachable agent paradigm

Animations are a versatile media for displaying changes over time. They can show cellular processes, a billion years of continental drift, the assembly of a desk, and even the invisible shifting of political tides. Most animations depict changes to a situation, such as a desk being assembled. In this chapter, we describe a series of software environments, called Teachable Agents (TAs) that use animations in another way. Rather than displaying a situation, the TAs animate the thoughts an individual might use to reason about that situation. For example, using the same well-structured representations as experts, TAs can visually model how to reason through the causal chains of an ecosystem. This is worthwhile, because the goal of learning is often to emulate an expert’s reasoning processes, and animations of thought make that reasoning visible. For novices, learning to reason with an expert’s knowledge organization is as important as learning the bare facts themselves. We build TA systems to capitalize on the adage that an effective way to learn something is to teach it, and this framework has allowed us to introduce some uncommon uses of animation. One novelty is that students help build the animation rather than jus

Incorporating self regulated learning techniques into learning by teaching environments

This paper discusses Betty’s Brain, a teachable agent in the domain of ecosystems that combines learning by teaching with self-regulation mentoring to promote deep learning and understanding. Two studies demonstrate the effectiveness of this system. The first study focused on components that define student-teacher interactions in the learning by teaching task. The second study examined the value of adding metacognitive strategies that governed Betty’s behavior and selfregulation hints provided by a mentor agent. The study compared three versions: an intelligent tutoring version, a learning by teaching version, and a learning by teaching plus selfregulation strategies. Results indicate that the addition of the self-regulation mentor better prepared students to learn new concepts later, even when they no longer had access to the self-regulation environment

Teachable Agents: Learning by Teaching Environments for Science Domains

The crisis in science education and the need for innovative computer-based learning environments has prompted us to develop a multi-agent system, Betty’s Brain that implements the learning by teaching paradigm. The design and implementation of the system based on cognitive science and education research in constructivist, inquiry-based learning, involves an intelligent software agent, Betty, that students teach using concept map representations with a visual interface. Betty is intelligent not because she learns on her own, but because she can apply qualitative-reasoning techniques to answer questions that are directly related to what she has been taught. The results of an extensive study in a fifth grade classroom of a Nashville public school has demonstrated impressive results in terms of improved motivation and learning gains. Reflection on the results has prompted us to develop a new version of this system that focuses on formative assessment and the teaching of selfregulated strategies to improve students ’ learning, and promote better understanding and transfer.