ETDs

This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.

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A Neural Network Model for a Tutoring Companion Supporting Students in a Programming with Java Course

Description
Feedback represents a vital component of the learning process and is especially important for Computer Science students. With class sizes that are often large, it can be challenging to provide individualized feedback to students. Consistent, constructive, supportive feedback through a tutoring companion can scaffold the learning process for students.

This work…

Feedback represents a vital component of the learning process and is especially important for Computer Science students. With class sizes that are often large, it can be challenging to provide individualized feedback to students. Consistent, constructive, supportive feedback through a tutoring companion can scaffold the learning process for students.

This work contributes to the construction of a tutoring companion designed to provide this feedback to students. It aims to bridge the gap between the messages the compiler delivers, and the support required for a novice student to understand the problem and fix their code. Particularly, it provides support for students learning about recursion in a beginning university Java programming course. Besides also providing affective support, a tutoring companion could be more effective when it is embedded into the environment that the student is already using, instead of an additional tool for the student to learn. The proposed Tutoring Companion is embedded into the Eclipse Integrated Development Environment (IDE).

This thesis focuses on the reasoning model for the Tutoring Companion and is developed using the techniques of a neural network. While a student uses the IDE, the Tutoring Companion collects 16 data points, including the presence of certain key words, cyclomatic complexity, and error messages from the compiler, every time it detects an event, such as a run attempt, debug attempt, or a request for help, in the IDE. This data is used as inputs to the neural network. The neural network produces a correlating single output code for the feedback to be provided to the student, which is displayed in the IDE.

The effectiveness of the approach is examined among 38 Computer Science students who solve a programming assignment while the Tutoring Companion assists them. Data is collected from these interactions, including all inputs and outputs for the neural network, and students are surveyed regarding their experience. Results suggest that students feel supported while working with the Companion and promising potential for using a neural network with an embedded companion in the future. Challenges in developing an embedded companion are discussed, as well as opportunities for future work.
ContributorsDay, Melissa (Author) / Gonzalez-Sanchez, Javier (Thesis advisor) / Bansal, Ajay (Committee member) / Mehlhase, Alexandra (Committee member) / Arizona State University (Publisher)
Created2019