Improving the engineering of model tracing based intelligent program diagnosis

1997 ◽  
Vol 144 (3) ◽  
pp. 149 ◽  
Author(s):  
H.A. Ramadhan
Keyword(s):  
Model Tracing

Fading and Deepening: The Next Steps for Andes and Other Model-Tracing Tutors

2000 ◽  
pp. 474-483 ◽  
Author(s):  
Kurt VanLehn ◽  
Reva Freedman ◽  
Pamela Jordan ◽  
Charles Murray ◽  
Remus Osan ◽  
...  
Keyword(s):  
Model Tracing

Cognitive Demand of Model Tracing Tutor Tasks: Conceptualizing and Predicting How Deeply Students Engage

2015 ◽  
Vol 20 (3) ◽  
pp. 317-337 ◽  
Author(s):  
Aaron M. Kessler ◽  
Mary Kay Stein ◽  
Christian D. Schunn
Keyword(s):  
Cognitive Demand ◽  
Model Tracing

scholarly journals Intelligent Feedback on Hypothesis Testing

2020 ◽  
Vol 30 (4) ◽  
pp. 616-636
Author(s):  
Sietske Tacoma ◽  
Bastiaan Heeren ◽  
Johan Jeuring ◽  
Paul Drijvers
Keyword(s):  
Hypothesis Testing ◽  
Intelligent Tutoring System ◽  
Control Group ◽  
First Year ◽  
Construction Tasks ◽  
Model Tracing ◽  
Constraint Based Modeling ◽  
The Individual ◽  
Support Students

AbstractHypothesis testing involves a complex stepwise procedure that is challenging for many students in introductory university statistics courses. In this paper we assess how feedback from an Intelligent Tutoring System can address the logic of hypothesis testing and whether such feedback contributes to first-year social sciences students’ proficiency in carrying out hypothesis tests. Feedback design combined elements of the model-tracing and constraint-based modeling paradigms, to address both the individual steps as well as the relations between steps. To evaluate the feedback, students in an experimental group (N = 163) received the designed intelligent feedback in six hypothesis-testing construction tasks, while students in a control group (N = 151) only received stepwise verification feedback in these tasks. Results showed that students receiving intelligent feedback spent more time on the tasks, solved more tasks and made fewer errors than students receiving only verification feedback. These positive results did not transfer to follow-up tasks, which might be a consequence of the isolated nature of these tasks. We conclude that the designed feedback may support students in learning to solve hypothesis-testing construction tasks independently and that it facilitates the creation of more hypothesis-testing construction tasks.

Information Technologies for Learning Principles of Fault-Tolerant Systems

2019 ◽  
pp. 331-357
Author(s):  
Juan Pablo Martínez Bastida ◽  
Olena Havrylenko ◽  
Andrey Chukhray
Keyword(s):  
Information Technologies ◽  
Fault Tolerant ◽  
Learner Model ◽  
Self Regulated Learning ◽  
Cognitive Tutor ◽  
Cognitive States ◽  
Fidelity Assessment ◽  
Model Tracing ◽  
Fault Tolerant Systems ◽  
Open Learner Model

In this chapter, the authors present a methodology for developing a model-tracing cognitive tutor. The methodology is based on Bayesian probabilistic networks for generating pedagogical interventions. The presented probabilistic model increases fidelity assessment due to its ability of independently diagnosing the degree of mastery for every knowledge component involved in students' actions; fidelity assessment in education is the ability to represent students' cognitive states as close as possible for analysis and evaluation. The cognitive tutor was developed to promote a self-regulated learning approach with an open learner model. The open learner model let students change the learning flow by changing the assigned tasks. The authors explain in detail the structural construction and employed algorithms for developing a model-tracing cognitive tutor in the domain of fault-tolerant systems. Preliminary results and future work are also discussed to assess effectiveness of the proposed approach and its implication in actual educational programs.

scholarly journals Rule-based Cognitive Modeling and Model Tracing in a Math Story Problem Tutor

2017 ◽  
Vol 12 (04) ◽  
pp. 111
Author(s):  
Nabila Khodier ◽  
Hanan Elazhary ◽  
Nayer Wanas
Keyword(s):  
Problem Solving ◽  
Word Problems ◽  
Cognitive Modeling ◽  
Symbolic Form ◽  
Web Based ◽  
Problem Solving Skills ◽  
Relevant Feedback ◽  
Tutoring Systems ◽  
Story Problem ◽  
Model Tracing

Intelligent Tutoring Systems (ITSs) are intended to help in tutoring the students in specific domains typically by improving their problem solving skills. An important aspect of such ITSs is their ability to solve the generated problems in the same way that the student would in addition to interpreting the student actions to provide relevant feedback and help. Cognitive models that mimic the way knowledge is represented in human minds are excellent means toward achieving this goal. This paper discusses cognitive modelling in the MAth Story problem Tutor (MAST). MAST is a Web-based ITS that can generate probability story problems of different contexts, types and difficulty levels. The paper also discusses the model tracing approach of MAST to interpret the student actions in symbolizing the word problems and estimating the required probabilities to provide relevant feedback and help. A major contribution of the paper is in considering the symbolization of the probability word problems to convert them to the symbolic form and tracing the students errors in this process. As an example, the paper considers the context of rolling a die and tossing a coin. Evaluation results have shown the ability of MAST to considerably improve the probability story problem solving skills of the students.

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