Perspectives on Computer Science Education

A special issue of Education Sciences (ISSN 2227-7102). This special issue belongs to the section "Technology Enhanced Education".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 611

Special Issue Editor


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Guest Editor
Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
Interests: digital education; learning analytics; educational data mining; computer science ethics

Special Issue Information

Dear Colleagues,

This Special Issue focuses on recent advancements in CS education and educators’ perspectives on improving students’ learning of core CS topics. CS education is rapidly evolving, and the need for innovative educational strategies and pedagogical interventions is becoming increasingly critical. This Special Issue aims to explore and disseminate research that contributes to the understanding and improvement of CS education across various contexts. 

We invite educators to submit original research papers that address one or more of the following themes:

  1. Educational Interventions:
    • Innovative approaches to teaching core CS topics;
    • Implementation and evaluation of new teaching methodologies or technologies in CS education;
    • Case studies of successful educational practices and their pedagogical implications.
  2. Pedagogical Evaluation Studies:
    • Studies that provide empirical evidence of the effectiveness of educational interventions;
    • Evaluations of existing CS curricula and recommendations for improvement;
    • Analysis of student engagement and learning outcomes in CS courses;
    • Rigorous qualitative and/or quantitative studies that provide new insights into CS education;
    • Replication studies that validate or challenge existing research findings;
    • Studies that report null or negative findings and their implications for CS education;
    • Critical analyses of why certain interventions may not have succeeded as expected;
    • Lessons learned from unsuccessful educational experiments and how they inform future research.
  3. Advancements in CS Teaching and Learning:
    • Research that advances the theoretical understanding of how students learn CS concepts;
    • Identification and analysis of common misconceptions in CS education;
    • Applications of generative AI in education.
  4. Diversity, Inclusion and Equity:
    • Examination of the impact of diverse learning environments on CS education;
    • Inclusive teaching practices that address the needs of underrepresented groups in CS.

Dr. Mohammed Farghally
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computer science education
  • learning analytics
  • educational data mining
  • computer science ethics

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Published Papers (1 paper)

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Research

14 pages, 549 KiB  
Article
Detecting Credit-Seeking Behavior with Programmed Instruction Framesets in a Formal Languages Course
by Yusuf Elnady, Mohammed Farghally, Mostafa Mohammed and Clifford A. Shaffer
Educ. Sci. 2025, 15(4), 439; https://doi.org/10.3390/educsci15040439 - 31 Mar 2025
Viewed by 101
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Perspectives on Computer Science Education)
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