Redesign of an Introduction to Computer Networks Course – Integrating TPACK with Generative AI [Research in Progress]
InSITE 2026
• 2026
• pp. 05
Aim/Purpose
The computer science degree is a combination of theory and practice, where some of the courses are mostly practical, some of the courses are mostly theoretical, and most of the courses are a combination of both. Introduction to Computer Networks (ICN) is a classic example of such a course, which includes both practical as well as theoretical material. Students learning towards a degree in computer science have difficulties understand-ing abstract concepts. The purpose of the study is to identify the issues that are creating these difficulties and to propose a framework for solutions.
Background
Students find it difficult to understand abstract problems and therefore try to map these problems to less abstract models. This study looks at ways to transform theoretical materials into more concrete examples with the expectation of enhancing the student learning experience and increasing student success.
Methodology
The TPACK framework is being used to revise the ICN course with the main emphasis on integrating technology in a pedagogically sound manner. Laboratory projects are being designed around Wireshark assignments with generative AI playing a role in helping students complete tasks with which they are having difficulties. Pre- and post-quizzes will be administered be-fore and after each activity in order to evaluate the effectiveness of the laboratory assignments while final exam scores will be compared to previous semesters in order to assess whether the revised labs contributed to greater student success in the course. In addition, student-generative AI conversation logs will be analyzed to evaluate the tool's impact on student learning by looking for common themes and patterns in their interactions.
Contribution
This study contributes an iterative practitioner-based approach for redesigning an Introduction to Computer Networks (ICN) course guided by the TPACK framework and generative AI. Moreover, we expect that our approach can be applied to other theoretical courses in higher education, where students are struggling to understand complex abstract material.
Findings
Research in progress – there are no present findings.
Recommendations for Practitioners
Research in progress – there are no present recommendations.
Recommendations for Researchers
Research in progress – there are no present recommendations.
Impact on Society
We expect that this research will help instructors identify key issues that students have with theoretical concepts and create solutions which can be implemented in the higher education classroom.
Future Research
Research in progress and not complete.
The computer science degree is a combination of theory and practice, where some of the courses are mostly practical, some of the courses are mostly theoretical, and most of the courses are a combination of both. Introduction to Computer Networks (ICN) is a classic example of such a course, which includes both practical as well as theoretical material. Students learning towards a degree in computer science have difficulties understand-ing abstract concepts. The purpose of the study is to identify the issues that are creating these difficulties and to propose a framework for solutions.
Background
Students find it difficult to understand abstract problems and therefore try to map these problems to less abstract models. This study looks at ways to transform theoretical materials into more concrete examples with the expectation of enhancing the student learning experience and increasing student success.
Methodology
The TPACK framework is being used to revise the ICN course with the main emphasis on integrating technology in a pedagogically sound manner. Laboratory projects are being designed around Wireshark assignments with generative AI playing a role in helping students complete tasks with which they are having difficulties. Pre- and post-quizzes will be administered be-fore and after each activity in order to evaluate the effectiveness of the laboratory assignments while final exam scores will be compared to previous semesters in order to assess whether the revised labs contributed to greater student success in the course. In addition, student-generative AI conversation logs will be analyzed to evaluate the tool's impact on student learning by looking for common themes and patterns in their interactions.
Contribution
This study contributes an iterative practitioner-based approach for redesigning an Introduction to Computer Networks (ICN) course guided by the TPACK framework and generative AI. Moreover, we expect that our approach can be applied to other theoretical courses in higher education, where students are struggling to understand complex abstract material.
Findings
Research in progress – there are no present findings.
Recommendations for Practitioners
Research in progress – there are no present recommendations.
Recommendations for Researchers
Research in progress – there are no present recommendations.
Impact on Society
We expect that this research will help instructors identify key issues that students have with theoretical concepts and create solutions which can be implemented in the higher education classroom.
Future Research
Research in progress and not complete.
TPACK, Wireshark, Generative AI
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