The Institute of Education Sciences (IES) provides funding opportunities for researchers to bring their knowledge of learning, cognitive science, and technology to bear on education practice. This panel describes opportunities available through the National Center for Education Research and the National Center for Special Education Research.

The relationship between equilibrium convergence to a uniform quality distribution and price is investigated in the Q-model, a self-organizing, evolutionary computational matching model of a fixed-price post-secondary higher education created by Ortmann and Slobodyan (2006). The Q-model is replicated with price equaling 100% its Ortmann and Slobodyan (2006) value, Varying the fixed price between 0% and 200% reveals thresholds at which the Q-model reaches different market clustering configurations. Results indicate structural market robustness to prices less than 100% and high sensitivity to prices greater than 100%.

Unfortunately, many students stop using these beneficial learning practices as soon Presented by Stephen Grossberg, Department of Cognitive as the metatutoring ceases. Apparently, the metatutors were and Neural Systems, Center for Adaptive Systems, and Center nagging rather than convincing. This talk will present a of Excellence for Learning in Education, Science, and study of Pyrenees, a metatutor that coaches students to focus Technology, Boston University, Boston, MA 02215 on learning domain principles rather than solutions to A deep and rational understanding of the factors that influence examples. It was convincing, in that students who were effective education and learning technologies depends taught probability with Pyrenees used principle-based problem on a corresponding understanding of how the brain in health solving on post-test more so than students taught by Andes, and disease controls learned behaviors. There has been a which did not focus students on principles. Moreover, revolution in discovering new computational paradigms, organizational when all students were transferred to Andes for learning principles, mechanisms, and models of how of physics, those who were metatutored used the principlefocused learning processes enable brains to give rise to minds.

Narrative is emerging as an effective medium for contextualizing learning. Narrative-centered learning environments provide engaging, story-centric virtual spaces that offer guided learning and problem-solving opportunities. Students’ ability to self-regulate learning can significantly impact performance in these environments, and, in general, is critical for academic achievement. This paper explores the relationship between narrative-centered learning and self-regulation. Connections are drawn between the salient characteristics of narrative-centered learning environments and principles for promoting and enhancing self-regulated learning in science education. These relationships are further explicated through an examination of the CRYSTAL ISLAND learning environment. The paper explores the hypothesis that narrative-centered learning environments are particularly well suited for simultaneously promoting learning, engagement, and self-regulation.

This paper describes the DynaLearn project, which seeks to address contemporary problems in science education by integrating well established, but currently independent technological developments, and utilize the added value that emerges. Specifically, diagrammatic representations are used for learners to articulate, analyse and communicate ideas, and thereby construct their conceptual knowledge. Ontology mapping is used to find and match co-learners working on similar ideas to provide individualised and mutually benefiting learning opportunities. Virtual characters are used to make the interaction engaging and motivating. The development of the workbench is tuned to fit key topics from environmental science curricula, and evaluated and further improved in the context of existing curricula using case studies. Through this approach, the DynaLearn project will deliver an individualised and engaging cognitive tool for acquiring conceptual knowledge that fits the true nature of this expertise.

Uncertainty in natural language dialogue is often treated through stochastic models. Some of the authors already presented TutorJ that is an Intelligent Tutoring System, whose interaction with the user is very intensive, and makes use of both dialogic and graphical modality. When managing the interaction, the system needs to cope with uncertainty due to the understanding of the user's needs and wishes. In this paper we present the extended version of TutorJ, focusing on the new features added to its chatbot module. These features allow to merge deterministic and probabilistic reasoning in dialogue management, and in writing the rules of the system's procedural memory.

Design-based learning (DBL) has many affordances for promoting deep and lasting learning of both content and complex skills. However, careful orchestration and scaffolding are usually needed to achieve its full potential. In this paper, we describe our efforts at implementing a software suite to meet the cognitive and meta-cognitive needs of learners engaged in DBL. In Study 1, our software suite gave learners the opportunity to design in simulation, to run experiments to learn the effects of variables, and it scaffolded science explanation construction. Through our analysis of study 1 we identified both cognitive and metacognitive needs that the software did not provide for. To meet these additional requirements, we added an interactive science resource and a case library to the software to provide multi-representational content material, to facilitate exploration, and to invite metacognitive reflection needed to do well at learning through design. Learners recognized what they did not understand, took initiative to explore those science concepts, and applied them in novel ways. We present here our analysis of the kinds of metacognitive help learners need to productively learn from design activities and some ways of providing that help. Our conclusion is that cognitive aid without related metacognitive aid is insufficient in a DBL environment.

This paper presents a methodology to acquire new knowledge in TutorJ using external information sources. TutorJ is an ITS whose architecture is inspired to the HIPM cognitive model, while meta-cognition principles have been used to design the knowledge acquisition process. The system behavior is intended to increase its own knowledge as a consequence of the interaction with users. The implemented methodology uses external links and services to capture new knowledge from contents related to discussion topics and transforms these contents into structured knowledge that is stored inside an ontology. The purpose of the proposed methodology is to lower the effort of system scaffolding creation and to increase the level of interaction with users. The focus is on self-regulated learners while meta-cognitive strategies have to bee defined to adapt and to increase the effectiveness of tutoring actions.

We hypothesize that enhancing computer tutors to respond to student uncertainty over and above correctness is one method for increasing both student learning and self-monitoring abilities. We explore this hypothesis using data from an experiment with a wizarded spoken tutorial dialogue system, where tutor responses to uncertain and/or incorrect student answers were manipulated. Our results suggest that monitoring and responding to student uncertainty has the potential to improve both cognitive and metacognitive student abilities.

We have developed computer environments that support learning by teaching and the use of self regulated learning (SRL) skills through interactions with virtual agents. More specifically, students teach a computer agent, Betty, and can monitor her progress by asking her questions and getting her to take quizzes. The system provides SRL support via dialog-embedded prompts by Betty, the teachable agent, and Mr. Davis, the mentor agent. Our primary goals have been to support learning in complex science domains and facilitate development of metacognitive skills. More recently, we have also employed sequence analysis schemes and hidden Markov model (HMM) methods for assigning context to and deriving aggregated student behavior sequences from activity data. These techniques allow us to go beyond analyses of individual behaviors, instead examining how these behaviors cohere in larger patterns. We discuss the information derived from these models, and draw inferences on students’ use of self-regulated learning strategies.