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Work in Progress: Knowledge Networks and Computer-Assisted Learning

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2020 ASEE Virtual Annual Conference Content Access


Virtual On line

Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

Computers in Education Division Technical Session 3: Digital Learning Part I

Tagged Division

Computers in Education

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Paper Authors


Edward E. Anderson Texas Tech University

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Professor Edward E. Anderson is a faculty member of the Texas Tech University Department of Mechanical Engineering where he is a Ray Butler Distinguished Educator and Piper Professor Award recipient. Since returning to the faculty after several different administrative assignments, including Departmental Chairman, Assistant Dean, and Director of the TTU Teaching, Learning and Technology Center, he has focused upon engineering student learning research with an eye upon how to use these findings to improve traditional and computer-based learning. Recently, he received the Premier Award for excellence in engineering education courseware.

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Experts and learners organize knowledge into networks of knowledge bits (nodes) which are interconnected by relational links. This paper discusses a network model used by teachers and learners for a knowledge domain (say thermodynamics) consisting of knowledge nodes and links like curriculum and course structures and links. Course structures tend to first focus on knowledge clumps (say ideal gases) and individual nodes (say gas constants) and the links interconnecting these nodes in a forward-directed, prerequisite manner. On the other hand, novice learners focus on individual nodes (say definition of entropy) and then look back for links to prerequisite nodes (say definition of heat transfer) as they build their own knowledge networks. Ex-pert forward-directed network search strategies are compared to novice backward-directed strategies in this paper.

The goal of an expert AI search is to find a problem solution given inputs to the network while the goal of the learner is to find the perquisite knowledge required to master a knowledge node. Expert search strategies are then amenable to expert system AI strategies such as Baysian statistics. cosine simularity and fuzzy logic. Learners seek to master the individual nodes and the relational links between nodes so that they can construct their own expert knowledge network. The strategies for meeting the learner goals are then completely different from those of the expert and not suited to well-known AI searching methods.

This paper also discusses the requirements of an AI system designed to assist the learner in meeting their goals as efficiently as possible. A brute force AI system which assists learners with building links and their knowledge net-work is discussed in this paper.

Anderson, E. E. (2020, June), Work in Progress: Knowledge Networks and Computer-Assisted Learning Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--35667

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