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Learner Modeling for Integration Skills in Programming

Huang, Yun (2018) Learner Modeling for Integration Skills in Programming. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Mastery development requires not only acquiring component skills, but also practicing their integration into more complex skills. When learning programming, an example is to first learn += and loops, then learn how to combine them into a loop that sums a sequence of numbers. The existence of integration skills has been supported by cognitive science research, yet it has rarely been considered in learner modeling, the key component for adaptive assistance in an intelligent tutoring system (ITS). Without this, early assertions of mastery in ITSs after only basic component skill practice or practice in limited contexts may be merely indicating shallow learning.

My dissertation introduces integration skills, widely acknowledged by cognitive science research, into learner modeling. To demonstrate this, I chose program comprehension with a complex integrative nature. To provide grounds for skill modeling, I applied a Difficulty Factors Assessment (DFA) approach (from cognitive science) and identified integration skills along with generalizable integration difficulty factors in common basic programming patterns. I used the DFA data to inform the construction of the learner model, CKM-HI, which incorporates integration skills in a hierarchical structure in a Bayesian network (BN). Compared with other machine learning approaches, BN naturally utilizes domain knowledge and maintains interpretable knowledge states for adaptation decisions. To address the limitation of prediction metrics to evaluate such multi-skill learner models, I proposed and applied a multifaceted evaluation framework. Data-driven evaluations on a real-world dataset show that CKM-HI is superior to two popular multi-skill learner models, CKM and WKT, regarding predictive performance, parameter plausibility, and expected instructional effectiveness. To evaluate its real-world impact, I built a program comprehension ITS driven by learner modeling and a classroom study deploying this system suggests that CKM-HI could lead to better learning than the CKM model.

My dissertation work is the first to systematically demonstrate the value of integration skill modeling, and offers novel integration-level learner modeling and multifaceted evaluation approaches applicable to a broader context. Further, my work contributes recent ITS infrastructure and techniques to programming education, and also contributes an example of taking an interdisciplinary approach to ITS research.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Huang, Yunyuh43@pitt.eduYUH43
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairBrusilovsky, Peterpeterb@pitt.edupeterb
Committee MemberDruzdzel, Marekdruzdzel@pitt.edudruzdzel
Committee MemberSchunn, Christianschunn@pitt.eduschunn
Committee MemberKoedinger,
Date: 27 September 2018
Date Type: Publication
Defense Date: 5 July 2018
Approval Date: 27 September 2018
Submission Date: 10 August 2018
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 172
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Intelligent Systems
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Learner Model, Intelligent Tutoring Systems, Educational Data Mining, Cognitive Model, User Studies, Computer Science Education
Date Deposited: 27 Sep 2018 19:17
Last Modified: 27 Sep 2018 19:17

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