Detecting Credit-Seeking Behavior with Programmed Instruction Framesets in a Formal Languages Course
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. A student might behave to some degree in either or both ways with given content. In this work, we attempt to detect the degr...
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| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Education Sciences |
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| Online Access: | https://www.mdpi.com/2227-7102/15/4/439 |
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| author | Yusuf Elnady Mohammed Farghally Mostafa Mohammed Clifford A. Shaffer |
| author_facet | Yusuf Elnady Mohammed Farghally Mostafa Mohammed Clifford A. Shaffer |
| author_sort | Yusuf Elnady |
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| description | 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. A student might behave to some degree in either or both ways with given content. In this work, we attempt to detect the degree to which either behavior takes place and investigate relationships with student performance. Our testbed is an eTextbook for teaching Formal Languages, an advanced Computer Science course. This eTextbook uses Programmed Instruction framesets (slideshows with frequent questions interspersed to keep students engaged) to deliver a significant portion of the material. We analyze session interactions to detect credit-seeking incidents in two ways. We start with an unsupervised machine learning model that clusters behavior in work sessions based on sequences of user interactions. Then, we perform a fine-grained analysis where we consider the type of each question presented within the frameset (these can be multi-choice, single-choice, or T/F questions). Our study involves 219 students, 224 framesets, and 15,521 work sessions across three semesters. We find that credit-seeking behavior is correlated with lower learning outcomes for students. We also find that the type of question is a key factor in whether students use credit-seeking behavior. The implications of our research suggest that educational software should be designed to minimize opportunities for credit-seeking behavior and promote genuine engagement with the material. |
| format | Article |
| id | doaj-art-0cb9e68d62ee4449a0ade7e5c935e0f0 |
| institution | OA Journals |
| issn | 2227-7102 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Education Sciences |
| spelling | doaj-art-0cb9e68d62ee4449a0ade7e5c935e0f02025-08-20T02:28:28ZengMDPI AGEducation Sciences2227-71022025-03-0115443910.3390/educsci15040439Detecting Credit-Seeking Behavior with Programmed Instruction Framesets in a Formal Languages CourseYusuf Elnady0Mohammed Farghally1Mostafa Mohammed2Clifford A. Shaffer3Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USADepartment of Computer Science, Virginia Tech, Blacksburg, VA 24061, USADepartment of Computer Science and Engineering, University at Buffalo, Buffalo, NY 14260, USADepartment of Computer Science, Virginia Tech, Blacksburg, VA 24061, USAWhen 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. A student might behave to some degree in either or both ways with given content. In this work, we attempt to detect the degree to which either behavior takes place and investigate relationships with student performance. Our testbed is an eTextbook for teaching Formal Languages, an advanced Computer Science course. This eTextbook uses Programmed Instruction framesets (slideshows with frequent questions interspersed to keep students engaged) to deliver a significant portion of the material. We analyze session interactions to detect credit-seeking incidents in two ways. We start with an unsupervised machine learning model that clusters behavior in work sessions based on sequences of user interactions. Then, we perform a fine-grained analysis where we consider the type of each question presented within the frameset (these can be multi-choice, single-choice, or T/F questions). Our study involves 219 students, 224 framesets, and 15,521 work sessions across three semesters. We find that credit-seeking behavior is correlated with lower learning outcomes for students. We also find that the type of question is a key factor in whether students use credit-seeking behavior. The implications of our research suggest that educational software should be designed to minimize opportunities for credit-seeking behavior and promote genuine engagement with the material.https://www.mdpi.com/2227-7102/15/4/439programmed instructionstudent engagementautomated assessmenteTextbookcredit-seeking behaviorformal languages |
| spellingShingle | Yusuf Elnady Mohammed Farghally Mostafa Mohammed Clifford A. Shaffer Detecting Credit-Seeking Behavior with Programmed Instruction Framesets in a Formal Languages Course Education Sciences programmed instruction student engagement automated assessment eTextbook credit-seeking behavior formal languages |
| title | Detecting Credit-Seeking Behavior with Programmed Instruction Framesets in a Formal Languages Course |
| title_full | Detecting Credit-Seeking Behavior with Programmed Instruction Framesets in a Formal Languages Course |
| title_fullStr | Detecting Credit-Seeking Behavior with Programmed Instruction Framesets in a Formal Languages Course |
| title_full_unstemmed | Detecting Credit-Seeking Behavior with Programmed Instruction Framesets in a Formal Languages Course |
| title_short | Detecting Credit-Seeking Behavior with Programmed Instruction Framesets in a Formal Languages Course |
| title_sort | detecting credit seeking behavior with programmed instruction framesets in a formal languages course |
| topic | programmed instruction student engagement automated assessment eTextbook credit-seeking behavior formal languages |
| url | https://www.mdpi.com/2227-7102/15/4/439 |
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