Bradley A. Goodman

Promoting Effective Peer Interaction in an Intelligent Collaborative Learning System

Placing students in a group and assigning them a task does not guarantee that the students will engage in effective collaborative learning behavior. The collaborative learning model described in this paper identifies the specific characteristics exhibited by effective collaborative learning teams, and based on these characteristics, suggests strategies for promoting effective peer interaction. The model is designed to help an intelligent collaborative learning system recognize and target group interaction problem areas. Once targeted, the system can take actions to help students collaborate more effectively with their peers, maximizing individual student and group learning.

On the interaction between plan recognition and intelligent interfaces

Plan recognition is an active research area in automatic reasoning, as well as a promising approach to engineering interfaces that can exploit models of users plans and goals. Much research in the field has focused on the development of plan recognition algorithms to support particular user/system interactions, such as found in naturally occurring dialogues. However, two questions have typically remained unexamined: 1) exactly what kind of interface tasks can knowledge of a users plans be used to support across communication modalities, and 2) how can such tasks in turn constrain development of plan recognition algorithms? In this paper we present a concrete exploration of these issues. In particular, we provide an assessment of plan recognition, with respect to the use of plan recognition in enhancing user interfaces. We clarify how use of a user model containing plans makes interfaces more intelligent and interactive (by providing an intelligent assistant that supports such tasks as advice generation, task completion, context-sensitive responses, error detection and recovery). We then show how interface tasks in turn provide constraints that must be satisfied in order for any plan recognizer to construct and represent a plan in ways that efficiently support these tasks. Finally, we survey how interfaces are fundamentally limited by current plan recognition approaches, and use these limitations to identify and motivate current research. Our research is developed in the context of CHECS, a plan-based design interface.

Using Dialogue Features to Predict Trouble During Collaborative Learning

A web-based, collaborative distance-learning system that will allow groups of students to interact with each other remotely and with an intelligent electronic agent that will aid them in their learning has the potential for improving on-line learning. The agent would follow the discussion and interact with the participants when it detects learning trouble of some sort, such as confusion about the problem they are working on or a participant who is dominating the discussion or not interacting with the other participants. In order to recognize problems in the dialogue, we investigated conversational elements that can be utilized as predictors for effective and ineffective interaction between human students. These elements can serve as the basis for student and group models. In this paper, we discuss group interaction during collaborative learning, our representation of participant dialogue, and the statistical models we are using to determine the role being played by a participant at any point in the dialogue and the effectiveness of the group. We also describe student and group models that can be built using conversational elements and discuss one set that we built to illustrate their potential value in collaborative learning.

Plan recognition for intelligent interfaces

A domain-independent assessment of plan recognition, with respect to its use in enhancing user interfaces, is presented. Plan recognition is a research area in automatic reasoning and is a promising approach to engineering better interfaces. Plan recognition makes interfaces more intelligent and interactive by providing an intelligent assistant that supports such tasks as advice generation, task completion, context-sensitive responses and error detection and recovery. How such tasks in turn provide representation and reasoning constraints that must be satisfied in order for the plan recognizer to efficiently support them is described. It is also shown how interfaces are fundamentally limited by current plan and recognition approaches. These limitations can be used to direct current research towards creating a new generation of plan-recognition systems.<<ETX>>

Towards intelligent agents for collaborative learning: recognizing the roles of dialogue participants

Our goal is to build and evaluate a web-based, collaborative distance-learning system that will allow groups of students to interact with each other remotely and with an intelligent agent that will aid them in their learning. The agent will follow the discussion and interact when it detects learning trouble of some sort, such as confusion about the problem they are working on or a participant who is dominating the discussion or not interacting with the other participants. In order to recognize problems in the dialogue, we are first examining the role that a participant is playing as the dialogue progresses. In this paper we discuss group interaction during collaborative learning, our representation of participant roles, and the statistical model we are using to determine the role being played by a participant at any point in the dialogue.

Encouraging Student Reflection and Articulation Using a Learning Companion: A Commentary.

Our 1998 paper “Encouraging Student Reflection and Articulation using a Learning Companion” (Goodman et al. 1998) was a stepping stone in the progression of learning companions for intelligent tutoring systems (ITS). A simulated learning companion, acting as a peer in an intelligent tutoring environment ensures the availability of a collaborator and encourages the student to learn collaboratively, while drawing upon the instructional advantages that ITSs provide. This paper is a commentary on our 1998 paper, reflecting on that research and some of the subsequent relevant research by others and us since then in Learning Companions, Intelligent Tutoring Systems, and Collaborative Learning.