Recently, there has been considerable interest in understanding the relationship between student affect and cognition. This research is facilitated by the advent of automated sensor-free detectors that have been designed to " infer " affect from the logs of student interactions within a learning environment. Such detectors allow for fine-grained analysis of… (More)
ASTUS is an Intelligent Tutoring System (ITS) framework for problem solving domains. In this chapter we present a study we performed to evaluate the strengths and weaknesses of ASTUS compared to the well-known Cognitive Tutor Authoring Tools (CTAT) framework. To challenge their capacity to handle a comprehensive model of a well-defined task, we built a… (More)
In this paper, we present progress towards a longitudinal study of the post‐course career advancement of MOOC learners. We present initial results and analysis plans for how to link this to in‐course behavior, towards better understanding the goals of all MOOC learners.
We investigated the interplay between confusion and in-game behavior among students using Newton's Playground (NP), a computer game for physics. We gathered data from 48 public high school students in the Philippines. Upon analyzing quantitative field observations and interaction logs generated by NP, we found that confusion among students was negatively… (More)
Modeling learners is a fundamental part of intelligent tutoring systems. It allows tutors to provide personalized feedback and to assess the learners' mastery over a task domain. One aspect often overlooked is the modeling of erroneous behaviors that can be used to provide error specific feedback. This is especially true for model-tracing tutors that… (More)
ASTUS is an authoring framework designed to create model-tracing tutors with similar efforts to those needed to create Cognitive Tutors. Its knowledge representation system was designed to model the teacher's point of view of the task and to be manipulated by task independent processes such as the automatic generation of sophisticated pedagogical feedback.… (More)
Gaming the system, a behavior where students disengage from a learning environment and attempt to succeed by exploiting properties of the system, has been shown to be associated with lower learning. Machine learned and knowledge engineered models have been created to identify gaming behaviors, but few efforts have been made to precisely identify how experts… (More)
To reduce the prohibitive efforts associated to problem-solving ITS, a domain-independent framework can be developed. In this paper, we compare ASTUS to the MTT architecture with examples drawn from a scatter plot tutor. We show that both approaches are similar, but that an ASTUS tutor can exhibit a more sophisticated tutoring behavior thanks to its "… (More)