scholarly journals On-Line Student Modeling for Coached Problem Solving Using Bayesian Networks

User Modeling ◽  
1997 ◽  
pp. 231-242 ◽  
Author(s):  
Cristina Conati ◽  
Abigail S. Gertner ◽  
Kurt VanLehn ◽  
Marek J. Druzdzel
Keyword(s):  
Problem Solving ◽  
Bayesian Networks ◽  
Student Modeling ◽  
On Line

Cognitive control

2021 ◽  
pp. 93-116
Author(s):  
John Toner ◽  
Barbara Gail Montero ◽  
Aidan Moran
Keyword(s):  
Executive Function ◽  
Problem Solving ◽  
Cognitive Control ◽  
Empirical Evidence ◽  
Skill Acquisition ◽  
Mental Representations ◽  
Phenomenological Analysis ◽  
Cognitive Responses ◽  
Expert Performance ◽  
On Line

What role might intuition and deliberation play during the performance of well-learned skills? Dreyfus and Dreyfus’ (1986) influential phenomenological analysis of skill-acquisition proposes that expert performance is guided by non-cognitive responses which are fast, effortless, and intuitive in nature. Although Dreyfus and Dreyfus (1986) recognize that, on occasions (e.g. when performance goes awry for some reason), a form of ‘detached deliberative rationality’ may be used by experts to improve their performance, they see no role for calculative problem solving or deliberation (i.e. drawing on rules or mental representations) when performance is going well. The current chapter counters this argument by drawing on empirical evidence and phenomenological description to argue that skilled performers use cognitive control (an executive function) across a range of sporting situations (i.e. in training, pre-performance routines, on-line skill execution) in order to maintain and enhance performance proficiency.

scholarly journals Student Modeling: Supporting Personalized Instruction, from Problem Solving to Exploratory Open Ended Activities

AI Magazine ◽  
2013 ◽  
Vol 34 (3) ◽  
pp. 13-26 ◽  
Author(s):  
Cristina Conati ◽  
Samad Kardan
Keyword(s):  
Problem Solving ◽  
Intelligent Tutoring Systems ◽  
Educational Games ◽  
Student Modeling ◽  
Student Needs ◽  
Personalized Instruction ◽  
Tutoring Systems ◽  
Student Models ◽  
Target Skills ◽  
Interactive Simulations

The field of intelligent tutoring systems has successfully delivered techniques and applications to provide personalized coaching and feedback for problem solving in a variety of domains. The core of this personalized instruction is a student model; the ITS component in charge of assessing student traits and states relevant to tailor the tutorial interaction to specific student needs during problem solving. There are however, other educational activities that can help learners acquire the target skills and abilities at different stages of learning including, among others, exploring interactive simulations and playing educational games. This article describes research on creating student models that support personalization for these novel types of interactions, their unique challenges, and how AI and machine learning can help.

Experimental Analysis of Man-Computer Problem-Solving

1970 ◽  
Vol 12 (2) ◽  
pp. 187-201 ◽  
Author(s):  
H. Sackmanh
Keyword(s):  
Problem Solving ◽  
Experimental Studies ◽  
Experimental Methods ◽  
Time Sharing ◽  
Computer Communication ◽  
On Line ◽  
The Subject ◽  
Computer Problem ◽  
Human Problem ◽  
Cross Fertilization

Experimental methods and findings in human problem-solving using on-line and off-line computer systems are reviewed. For historical and technical reasons the field of applied man-computer communication has not been the subject of extensive scientific study. The advent of time-sharing systems in the last decade produced an initial body of empirical data from user statistics and experimental studies comparing time-sharing with batch-processing. This body of data is reviewed for its implications to the controversy over batch and time-sharing systems and to the understanding of human behavior in the man-computer setting. Although the available experimental data are meager and tentative, it is already apparent that behavioral principles of human problem-solving and learning theory can account for many of the trends observed. In turn, the theories can be enriched by new leads stemming from studies of man-computer dialog. A plea is made for interdisciplinary cross-fertilization between behavioral and computer sciences to bridge the humanistic lag in man-computer communication.

Development of Bayesian Networks From Use Case Diagrams for Managing the Learner Model

2018 ◽  
pp. 48-63
Keyword(s):  
Problem Solving ◽  
Computational Modeling ◽  
Bayesian Networks ◽  
Unified Modeling Language ◽  
Modeling Language ◽  
Use Case ◽  
Learner Modeling ◽  
Learner Model ◽  
Unified Modeling ◽  
Use Case Diagram

First of all, and to clarify the purpose, it seems important to say that the work presented in this chapter lies within the framework of learner modeling in an adaptive system understood as computational modeling of the learner. One must also state that Bayesian networks are effective tools for learner modeling under uncertainty. They have been successfully used in many systems, with different objectives, from the assessment of knowledge of the learner to the recognition of the plan followed in problem solving. The main objective of this chapter is to develop a Bayesian networks for modeling the learner from the use case diagram of the unified modeling language. The prototypes and diagrams presented in this chapter are arguments in favor of the objective. The network obtained also promotes reusing learner modeling through similar systems.

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