Corpus ID: 35481470

How quickly can wheel spinning be detected?

  title={How quickly can wheel spinning be detected?},
  author={Noboru Matsuda and Sanjay Chandrasekaran and John C. Stamper},
We have developed a wheel spinning detector for cognitive tutors that uses a simplified method compared to existing wheel spinning detectors. The detector reads a sequence of the correctness of applying particular skill performed by a student using the cognitive tutor. The response sequence is first fed to Bayesian knowledge tracing to compute a sequence of probability of mastery at each time a skill was applied. The detector uses a neural-network model to make a binary classification for a… Expand
Identification of Wheel Spinning Cases while Learning and Retaining a Skill in Intelligent Tutoring Systems
Application of the state-of-the-art machine learning approaches such as deep learning and random forest are investigated on the extracted features for modeling wheel spinning cases and demonstrate that Random Forest model can predict mastery or wheel spinning at an early stage with an AUC of 0.87. Expand
Early Detection of Wheel-Spinning in ASSISTments
This paper extends on prior work to develop wheel-spinning detectors in the ASSISTments learning system that distinguish between non-persistence, productive persistence and wheel- Spinning, and shows that early differentiation between wheel-Spinning and productive persistence is feasible. Expand
Towards Suggesting Actionable Interventions for Wheel Spinning Students
The method trains a model to predict wheelspinning and utilizes a popular tool in interpretable machine learning, Shapley values, to provide individualized credit attribution over the features of the model, including actionable features like possible gaps in prerequisites. Expand
Wheel-Spinning Models in a Novice Programming Context
Five wheel-spinning models were developed in the context of novice programming that had acceptable performance with data from 114 Filipino students using a meaningful gamification system and following intuition, the number of problems correctly solved was negatively correlated with wheel- Spinning, while overall number of Problems attempted and consecutive mistakes were indicative of wheel-Spinning. Expand
Decision Tree Modeling of Wheel-Spinning and Productive Persistence in Skill Builders.
Research on non-cognitive factors has shown that persistence in the face of challenges plays an important role in learning. However, recent work on wheel-spinning, a type of unproductive persistenceExpand
Developing Early Detectors of Student Attrition and Wheel Spinning Using Deep Learning
This paper applies a transfer learning methodology using deep learning and traditional modeling techniques to study high and low representations of unproductive persistence, and finds that models developed to detect each within and across-assignment stopout and wheel spinning are able to learn sets of features that generalize to predict the other. Expand
Where’s the Reward?
A review of the variety of attempts to use RL for instructional sequencing finds that reinforcement learning has been most successful in cases where it has been constrained with ideas and theories from cognitive psychology and the learning sciences. Expand
Characterizing Productive Perseverance Using Sensor-Free Detectors of Student Knowledge, Behavior, and Affect
This dissertation explores the development of tools that incorporate student models that study learning through the use of advancements in student modeling and deep learning methodologies to support teachers in taking action in real classrooms to promote productive approaches to learning. Expand
Seven-Year Longitudinal Implications of Wheel Spinning and Productive Persistence
It is found that productive persistence during middle school mathematics is associated with a higher probability of college enrollment, and that wheelspinning during middleSchool mathematics is not statistically significantly associated with college enrollment in either direction. Expand
Cognitive Tutors Produce Adaptive Online Course: Inaugural Field Trial
An adaptive online course is developed on the Open Learning Initiative OLI platform by integrating four new instances of cognitive tutors into an existing OLI course, and the results show that the proposed adaptive onlinecourse technology is robust enough to be used in actual classroom with mixed effect for learning. Expand


Wheel-Spinning: Students Who Fail to Master a Skill
It is shown that if a student does not master a skill in ASSISTments or the Cognitive Tutor quickly, the student is likely to struggle and will probably never master the skill. Expand
Knowledge tracing: Modeling the acquisition of procedural knowledge
An effort to model students' changing knowledge state during skill acquisition and a series of studies is reviewed that examine the empirical validity of knowledge tracing and has led to modifications in the process. Expand
The Behavior of Tutoring Systems
  • K. VanLehn
  • Computer Science
  • Int. J. Artif. Intell. Educ.
  • 2006
Although tutoring systems differ widely in their task domains, user interfaces, software structures, knowledge bases, etc., their behaviors are in fact quite similar. Expand
Cognitive Tutor: Applied research in mathematics education
The theoretical background of this approach to cognitive models of mathematics, which have become a basis for middle- and high-school curricula, and how embedding a well specified theory in instructional software allows us to dynamically evaluate the effectiveness of the authors' instruction at a more detailed level than was previously possible. Expand
The Behavior of Tutoring Systems. International Journal of Artificial Intelligence in Education 16
  • Proceedings of the 9th International Conference on Educational Data
  • 2006