When students use an online eTextbook with content and interactive graded exercises, they often display aspects of two types of behavior: credit-seeking, and knowledge-seeking. Any given student might behave to some degree in either way in a given assignment. In this work, we look at multiple aspects of detecting the degree to which either behavior is taking place, and investigate relationships to student performance. In particular, we focus on an eTextbook used for teaching Formal Languages, an advanced computer science course. This eTextbook is using Programmed Instruction (PI) framesets to deliver the material. We take two approaches to analyze session interactions in order to detect credit-seeking incidents.
We first start with a coarse-grained approach by presenting an unsupervised model that clusters the behavior in the work sessions based on the sequence of different interactions that happens during them. Then we perform a fine-grained analysis where we consider the type of each question in the frameset, which can be a multi-choice, single-choice, or T/F question. We show that credit-seeking behavior is negatively affecting the learning outcome of the students. We also find that the type of the PI frame is a key factor in drawing students more into the credit-seeking behavior to finish the PI framesets quickly. We implement three machine learning models that predict students' midterm and overall semester grades based on their amount of credit-seeking behavior on the PI framesets. Finally, we provide a semisupervised learning model to aid in the work session labeling process. / Master of Science / Students frequently exhibit features of two types of behavior when using an online eTextbook with content and interactive graded exercises: credit-seeking and knowledge-seeking.
When solving homework or studying a material, students can behave in either manner to some extent. In this research, we study links between student performance and different elements of recognizing the degree to which either behavior is occurring. We concentrate on an eTextbook used to teach an advanced computer science course, Formal Languages and Automata, using a teaching paradigm called Programmed Instruction (PI). In order to detect credit-seeking instances, we use two ways to study students' behavior in the Programmed Instruction sessions. We begin with a coarse-grained approach by building a model that can categorize work sessions into two groups based on the interactions that occur throughout them. Then we do a fine-grained analysis in which we analyze the question types in the framesets and their effect on the students' behavior. We show that credit-seeking behavior has a negative effect on students' learning outcomes. We discovered that the PI frame type is an important factor in enticing students to engage in credit-seeking behavior in an attempt to finish PI framesets fast. Finally, we present three predictive models that can forecast the students' midterm and total semester grades based on their credit-seeking behavior on the Programmed Instruction framesets.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/110408 |
| Date | 02 June 2022 |
| Creators | Elnady, Yusuf Fawzy |
| Contributors | Computer Science, Shaffer, Clifford A., Edwards, Stephen H., Farghally, Mohammed Fawzi Seddik |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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