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DISCOVERING HIDDEN COGNITIVE SKILL DEPENDENCIES BETWEEN KNOWLEDGE UNITS USING MARKOV COGNITIVE KNOWLEDGE STATE NETWORK (MCKSN)

Nafa, Fatema B
http://rave.ohiolink.edu/etdc/view?acc_num=kent1553688024482058

Abstract Details

2019, PHD, Kent State University, College of Arts and Sciences / Department of Computer Science.
Bloom’s Taxonomy(BT) theory provides a framework for transferring the learning process from quantity to quality. Bloom’s taxonomy can be applied to learn the curriculum objectives of a course in terms of the cognitive level of understanding, designing the learning materials based on learner cognitive level, and the assessment of learning outcomes. However, no research has been done to automate the comprehension process to apply Bloom’s taxonomy. The focus of this dissertation is to infer automatically Cognitive Skill Dependencies (CSD) among concepts in the knowledge-units. The cognitive skill dependencies are represented by adapting Bloom’s taxonomy to recognize concepts in Computer Science textbooks. The dissertation develops a new probability-based statistical model termed Markov Cognitive Knowledge State Network (MCKSN). The key components of the model include the following: reordering Bloom levels to be more flexible for Computer Science; generating a graph-based representation for the domain-concepts; and verbs classification based on cognitive levels using three techniques: WordNet, VerbNet, and Singular Value Decomposition. An experiment was conducted on Introduction to Algorithms, a standard popular textbook used in Computer Science. To evaluate the MCKSN model, ground truth was used. The ground truth is established by asking students to infer Bloom’s taxonomy among concepts. The results of the experiment verify that the MCKSN model is suitable to discover the cognitive skill dependencies among concepts compared with students’ evaluations.
Javed Khan, Dr (Advisor)
Austin Melton, Dr (Committee Member)
Feodor Dragan, Dr (Committee Member)
Arvind Bansal, Dr (Committee Member)
Kambiz Ghazinour, Dr (Committee Member)
Katherine Rawson, Dr (Committee Member)
Phillip Hamrick, Dr (Committee Member)
186 p.
Cognitive Psychology; Computer Science

Recommended Citations

Citations

  • Nafa, F. B. (2019). DISCOVERING HIDDEN COGNITIVE SKILL DEPENDENCIES BETWEEN KNOWLEDGE UNITS USING MARKOV COGNITIVE KNOWLEDGE STATE NETWORK (MCKSN) [Doctoral dissertation, Kent State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=kent1553688024482058

    APA Style (7th edition)

  • Nafa, Fatema. DISCOVERING HIDDEN COGNITIVE SKILL DEPENDENCIES BETWEEN KNOWLEDGE UNITS USING MARKOV COGNITIVE KNOWLEDGE STATE NETWORK (MCKSN). 2019. Kent State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=kent1553688024482058.

    MLA Style (8th edition)

  • Nafa, Fatema. "DISCOVERING HIDDEN COGNITIVE SKILL DEPENDENCIES BETWEEN KNOWLEDGE UNITS USING MARKOV COGNITIVE KNOWLEDGE STATE NETWORK (MCKSN)." Doctoral dissertation, Kent State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=kent1553688024482058

    Chicago Manual of Style (17th edition)

kent1553688024482058
441
© 2019, some rights reserved.Creative Commons License DISCOVERING HIDDEN COGNITIVE SKILL DEPENDENCIES BETWEEN KNOWLEDGE UNITS USING MARKOV COGNITIVE KNOWLEDGE STATE NETWORK (MCKSN) by Fatema B Nafa is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Kent State University and OhioLINK.