Modeling and understanding students' off-task behavior in intelligent tutoring systems

@article{Baker2007ModelingAU,
  title={Modeling and understanding students' off-task behavior in intelligent tutoring systems},
  author={R. Baker},
  journal={Proceedings of the SIGCHI Conference on Human Factors in Computing Systems},
  year={2007}
}
  • R. Baker
  • Published 29 April 2007
  • Computer Science
  • Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
We present a machine-learned model that can automatically detect when a student using an intelligent tutoring system is off-task, i.e., engaged in behavior which does not involve the system or a learning task. [...] Key Method We use this model in combination with motivational and attitudinal instruments, developing a profile of the attitudes and motivations associated with off-task behavior, and compare this profile to the attitudes and motivations associated with other behaviors in intelligent tutoring…Expand

Figures, Tables, and Topics from this paper

Automatic Detection of Off-Task Behaviors in Intelligent Tutoring Systems with Machine Learning Techniques
TLDR
An extensive set of experiment results demonstrates the power of using multiple types of evidence, the personalized model, and the robust Ridge Regression algorithm to address data sparseness problem. Expand
Learning to Identify Students' Off-Task Behavior in Intelligent Tutoring Systems
TLDR
A machine learning model that can automatically identify off-task behaviors of students while using an intelligent tutoring system is proposed and a robust Ridge Regression algorithm is designed to estimate model parameters. Expand
Differences Between Intelligent Tutor Lessons, and the Choice to Go Off-Task
TLDR
Surprisingly, the best model predicting off-task behavior from lesson features contains only one feature: lessons that involve equation-solving. Expand
Data-driven causal modeling of “ gaming the system ” and off-task behavior in Cognitive Tutor Algebra
“Gaming the system” and off-task behavior in intelligent tutoring systems (ITSs) have been found to be negatively associated with student learning outcomes. We summarize recent work to determineExpand
Student Off-Task Behavior in Computer-Based Learning in the Philippines: Comparison to Prior Research in the USA
Background Off-task behavior can be defined as any behavior that does not involve the learning task or material, or where learning from the material is not the primary goal. One suggested path forExpand
Predicting College Enrollment from Student Interaction with an Intelligent Tutoring System in Middle School
TLDR
This paper predicts college attendance from detectors of specific aspects of student learning and engagement in the context of 3,747 students using the ASSISTment system in New England, producing detection that is both successful and potentially more actionable than previous approaches. Expand
Predicting Individualized Learner Models Across Tutor Lessons
TLDR
It is found that best-fitting student parameters trained on previous lessons do not directly transfer to new lessons; however, one can effectively predict the student parameters for the new lesson by using features derived from prior lessons, and prior to tutor text-reading transaction data. Expand
Learning analytics in outer space: a Hidden Naïve Bayes model for automatic student off-task behavior detection
TLDR
A novel machine learning model is described which automatically detects students' off-task behavior as students interact with a learning system, ASSISTments, based solely on log file data. Expand
Design Recommendations for Adaptive Intelligent Tutoring Systems Learner Modeling ( Volume I ) 155 CHAPTER 14 ‒ Assessing the Disengaged Behaviors of Learners
In recent years, an increasing number of models have been published that can infer if a learner is behaviorally disengaged while working within an interactive learning environment, and can conductExpand
Automatically Detecting a Student's Preparation for Future Learning: Help Use is Key
We present an automated detector that can predict a student’s later performance on a paper test of preparation for future learning, a post-test involving learning new material to solve problemsExpand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 34 REFERENCES
Off-task behavior in the cognitive tutor classroom: when students "game the system"
TLDR
Analysis of students who choose to game the system suggests that learned helplessness or performance orientation might be better accounts for why students choose this behavior than lack of interest in the material. Expand
Designing intelligent tutors that adapt to when students game the system
Students use intelligent tutors and other types of interactive learning environments in a considerable variety of ways. In this thesis, I detail my work to understand, automatically detect, andExpand
Detecting Student Misuse of Intelligent Tutoring Systems
TLDR
A machine-learned Latent Response Model is presented that can identify if a student is gaming the system in a way that leads to poor learning and will be useful both for re-designing tutors to respond appropriately to gaming, and for understanding the phenomenon of gaming better. Expand
Cognitive Tutors: Lessons Learned
TLDR
The 10-year history of tutor development based on the advanced computer tutoring (ACT) theory is reviewed, finding that a new system for developing and deploying tutors is being built to achieve the National Council of Teachers of Mathematics (NCTM) standards for high-school mathematics in an urban setting. Expand
Do Performance Goals Lead Students to Game the System?
TLDR
It is found that the frequency of gaming the system does not correlate to a known measure of performance goals, and instead, gaming is correlated to disliking computers and the tutor. Expand
Toward Tutoring Help Seeking: Applying Cognitive Modeling to Meta-cognitive Skills
TLDR
A preliminary model of help-seeking behavior is presented that will provide the basis for a Help-Seeking Tutor Agent and found that students frequently avoided using help when it was likely to be of benefit and often acted in a quick, possibly undeliberate manner. Expand
Engagement tracing: using response times to model student disengagement
  • J. Beck
  • Psychology, Computer Science
  • AIED
  • 2005
TLDR
This paper explores student disengagement and proposes an approach, engagement tracing, for detecting whether a student is engaged in answering questions, based on item response theory, which is sensitive enough to detect variations in student engagement within a single tutoring session. Expand
Automatic Recognition of Learner Groups in Exploratory Learning Environments
TLDR
The value of a data-based approach for recognizing learners as an alternative to knowledge-based approaches that tend to be complex and time-consuming even for domain experts, especially in highly unstructured ELEs are shown. Expand
Informing the Detection of the Students' Motivational State: An Empirical Study
TLDR
An empirical study is presented which provided a considerable amount of knowledge regarding motivation diagnosis and was formalised in order to create a set of motivation diagnosis rules that can be incorporated into a prototype tutoring system. Expand
Integrating Affect Sensors in an Intelligent Tutoring System
This project augments an existing intelligent tutoring system (AutoTutor) that helps learners construct explanations by interacting with them in natural language and helping them use simulationExpand
...
1
2
3
4
...