Detecting Credit-Seeking Behavior on Programmed Instruction Framesets

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Virginia Tech

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.

Formal Languages, Programmed Instruction, Automated Assessment, Learning Analytics, Cluster Analysis, OpenDSA, OpenFLAP, Computer Science Education, Student Behavior, Credit-Seeking, Actively-Learning