Learning involves the integration of new information into existing knowledge. Generoting explanations to oneself (self-explaining) facilitates that integration process. Previously, self-explanation has been shown to improve the acquisition of problem-solving skills when studying worked-out examples. This study extends that finding, showing that self-explanation can also be facilitative when it is explicitly promoted, in the context of learning declarative knowledge from an expository text. Without any extensive training, 14 eighth-grade students were merely asked to self-explain after reading each line of a possage on the human circulatory system. Ten students in the control group read the same text twice, but were not prompted to self-explain. All of the students were tested for their circulatory system knowledge before and after reading the text. The prompted group had a greater gain from the pretest to the posttest. Moreover, prompted students who generated o large number of self-explanations (the high explainers) learned with greater understanding than low explainers. Understanding was assessed by answering very complex questions and inducing the function of a component when it was only implicitly stated. Understanding was further captured by a mental model onolysis of the self-explanation protocols. High explainers all achieved the correct mental model of the circulatory system, whereas many of the unprompted students as well as the low explainers did not. Three processing characteristics of self-explaining are considered as reasons for the gains in deeper understanding. Preparation of this article was supported in part by an Office of Educational Research and

Abstract: Intelligent tutoring systems (ITS) provide students with individualized instruction and coached practice in an interactive environment that fosters active learning. A different paradigm, also having the potential to significantly improve learning, is collaborative learning. Classroom learning improves significantly when students participate in structured learning activities in small groups of peers. The educational value of student collaboration has led to the development of computer-supported collaborative learning (CSCL) tools. These tools provide an exceptional learning environment but require at least two participants and do not offer students the type of individualized assistance and guidance available in an 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. The learning companion we designed encourages the student to reflect on and articulate his past actions, and to discuss his future intentions and their consequences. This paper describes the issues raised in designing a learning companion for an existing ITS, and the benefits gained from this union.

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The rapid advance of distance learning and networking technology has enabled universities and corporations to reach out and educate students across time and space barriers. Although this technology enables structured collaborative learning activities, online groups often do not enjoy the same benefits as face-to-face learners, and their instructors often do not have time to actively support and mediate the online collaboration. This article demonstrates our capacity to computationally model, analyze, and support online student interaction, in particular knowledge sharing. A unique combination of qualitative analysis and artificial intelligence methods was designed to (a) recognize when students are having trouble learning the new concepts they share with each other, and (b) understand why they are having trouble, so that we might assist an instructor or intelligent coach in mediating group knowledge sharing activities.

After their beginnings in computer-aided instruction, automated tutors have re-emerged as intelligent tutoring systems. These intelligent tutors have obtained considerable success by using results from cognitive psychology and artificial intelligence to permit non-traditional instruction which is tailored to their individual students. The success of these automated tutors is due to their precise understanding and modeling of both the student and the domain being taught. A common measure of the robustness of an automated tutor is the size of the domain that it can understand. The schema-based Prolog tutor described in this dissertation is capable of recognizing a larger class of programs than existing Prolog tutors. By using powerful generalized transformations, our Prolog tutor can generate this class of programs from a very small set of normal form programs. Thus, our Prolog tutor recognizes a larger class of programs using fewer normal form programs than existing Prolog tutors. One o...

This paper describes our work-in-progress toward developing indirect, strategic support for collaborative learners in a web-based scientific inquiry environment. Using the distribution of students' current problem solving strategies and their most likely predicted future behaviors, we plan to strategically construct collaborative learning groups containing heterogeneous combinations of various behaviors such that students with less efficient strategies will likely adopt the strategies of their more efficient peers. The objective of this research is to explore the possibility of facilitating peer interaction through strategic pairing and facilitation, with a minimal amount of direct instructional intervention.

hypothesize that computers can begin to measure affect-related expression and behavior and can eventually become adept at adjusting the presentation by varying the pace, complexity, subtlety and difficulty We are proffering a novel model by which to conceptualize the impact of emotions upon learning. We believe that there is an interplay of emotions and learning, but this interaction is far more complex than previous theories have articulated. Our model goes beyond previous research studies not just in the emotions addressed, but also in an attempt to formalize an analytical model that describes the dynamics of emotional states during model-based learning experiences, and to do so in a language that the SMET learner can come to understand and utilize. We propose to discover, describe, and evolve the cognitive and affective learning processes required for SMET learning. We then propose to incorporate these research-based findings into a testbed simulation---the Learning Compani