Tutoring systems

Design and development methods:

1. needs assesment
2. cognitive task analysis - how domain experts think when solving problems
3. initial tutor implementation
4. evaluation

An intelligent tutor system should enable the student to work to the successful conclusion of problem solving.

• Represent student competence as a production set.
• Communicate the goal structure underlying the problem solving.
• Provide instruction in the problem solving context.
• Promote an abstract understanding of the problem-solving knowledge.
• Minimize working memory load.
• Provide immediate feedback on errors.
• Adjust the grain size of instruction with learning.
• Facilitate successive approximations to the target skill

4 Components architecture:

1. domain model (expert knowledge): contains the concepts, rules, and problem-solving strategies of the domain to be learned
2. student model: gather explicit and implicit data about the student, must use this data to create a representation of the student knowledge and learning process, must use this to select optimal pedagogical strategies. 1) Corrective: to help eradicate bugs in the student’s knowledge; 2) Elaborative: to help correct ‘incomplete’ student knowledge; 3) Strategic: to help initiate significant changes in the tutorial strategy other than the tactical decisions of 1 and 2 above; 3) Diagnostic: to help diagnose bugs in the student’s knowledge; 5) Predictive: to help determine the student’s likely response to tutorial actions; 6) Evaluative: to help assess the student or the ITS
3. tutoring model: receives input from the domain and student models and makes decisions about tutoring strategies and actions
4. interface component

References

https://en.wikipedia.org/wiki/Intelligent_tutoring_system#cite_ref-Nkambou_29-1 https://www.youtube.com/watch?v=B1wBwVA90M4