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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 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)
Completion rates for massive open online classes (MOOCs) are notoriously low, but learner intent is an important factor. By studying students who drop out despite their intent to complete the MOOC, it may be possible to develop interventions to improve retention and learning outcomes. Previous research into predicting MOOC completion has focused on(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)
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)
Completion rates for massive open online classes (MOOCs) are notoriously low. Identifying student patterns related to course completion may help to develop interventions that can improve retention and learning outcomes in MOOCs. Previous research predicting MOOC completion has focused on click-stream data, student demographics, and natural language(More)
Computational models that automatically detect learners' affective states are powerful tools for investigating the interplay of affect and learning. Over the past decade, affect detectors—which recognize learners' affective states at run-time using behavior logs and sensor data—have advanced substantially across a range of K-12 and postsecondary education(More)
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)
Increased attention to the relationships between affect and learning has led to the development of machine-learned models that are able to identify students' affective states in computerized learning environments. Data for these affect detectors have been collected from multiple modalities including physical sensors, dialogue logs, and logs of students'(More)
Model-tracing tutors (MTTs) have proven effective for the tutoring of well-defined tasks, but the pedagogical interventions they produce are limited and usually require the inclusion of pedagogical content, such as text message templates, in the model of the task. The capability to generate pedagogical content would be beneficial to MTT frameworks, as it(More)