According to current theories of cognitive skill acquisition, new problem solving rules are constructed by proceduralization, production compounding, chunking, syntactic generalization, and a variety of other mechanisms.All these mechanisms are assumed to run rather quickly, so a rule's acquisition should be a matter of a few seconds at most. Such "learning events" might be visible in protocol data. This paper discusses a method for locating the initial use of a rule in protocol data. The method is applied to protocols of subjects learning three tasks: a river crossing puzzle, the Tower of Hanoi, and a topic in college physics. Rules were discovered at the rate of about one every half hour. Most rules required several learning events before they were used consistently, which is not consistent with the one-trial learning predicted by explanation-based learning methods. Some observed patterns of learning events were consistent with a learning mechanism based on syntactic generalization of rules. Although most rules seem to have been acquired at impasses-occasions when the subject docs not know what to do next there were clear cases of rules being learned without visible signs of an impasse, which does not support the popular hypothesis that all learning occurs at impasses.

When a small group of students collaborate, learning gains are often proportional to the amount of co-construction in theirdialogue. Co-construction (also called transactivity or co-explaining) is an observable behavior that meets two criteria:students add task content to the dialogue (i.e., they construct) and their construction builds off their partners contributions.Unfortunately, co-construction is uncommon. In our studies of students collaborating face-to-face in middle school mathclassrooms, less than 5% of their spoken dialogue was classified as co-construction. In order to increase the frequencyof co-construction and raise learning gains, prior work has inserted prompts into text-based dialogue, but our FACTsystem is alone in trying to use prompting to improve spoken dialogues in classrooms. Results on the accuracy of FACTscollaboration detectors will be presented along with results from a pilot test of its prompting in 5 middle school classrooms.

This paper describes a prototype of a simulated physics student that learns by interacting with a human tutor. The system solves physics problems while showing its work on a workstation screen, and the tutor can intervene at certain points during problem-solving to advise the simulated student This prototype constitutes an initial cognitive task analysis of the skill of learning from a tutor, which prescribes several tutoring practices that appear to be plausible for both human and computer tutors.

Interpreting the student's actions and inferring the student's solution plan during problem solving is one of the main challenges of tutoring based on cognitive apprenticeship, especially in domains with large solution spaces. We present a student modeling framework that performs probabilistic plan recognition by integrating in a Bayesian network knowledge about the available plans and their structure and knowledge about the student's actions and mental state. Besides predictions about the most probable plan followed, the Bayesian network provides probabilistic knowledge tracing, that is assessment of the student's domain knowledge. We show how our student model can be used to tailor scaffolding and fading in cognitive apprenticeship. In particular, we describe how the information in the student model and knowledge about the structure of the available plans can be used to devise heuristics to generate effective hinting strategies when the student needs help.