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1. 8th Educational Data Mining in Computer Science Education (CSEDM) Workshop

   https://doi.org/10.5281/zenodo.12730035  

   Shi, Yang; Brusilovsky, Peter; Akram, Bita; Price, Thomas W; Leinonen, Juho; Koedinger, Kenneth R; Lan, Andrew (July 2024, Proceedings of 17th International Conference on Educational Data Mining (EDM 2024), International Educational Data Mining Society)

   Benjamin, Paaßen; Carrie, Demmans Epp (Ed.)

   There is a growing community of researchers at the intersection of data mining, AI, and computing education research. The objective of the CSEDM workshop is to facilitate a discussion among this research community, with a focus on how data mining can be uniquely applied in computing education research. For example, what new techniques are needed to analyze program code and CS log data? How do results from CS education inform our analysis of this data? The workshop is meant to be an interdisciplinary event at the intersection of EDM and Computing Education Research. Researchers, faculty, and students are encouraged to share their AI- and data-driven approaches, methodologies, and experiences where data transforms how students learn Computer Science (CS) skills. This full-day hybrid workshop will feature paper presentations and discussions to promote collaboration. 

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2. 9th Educational Data Mining in Computer Science Education (CSEDM) Workshop

   https://doi.org/10.5281/zenodo.15870308  

   Akram, Bita; Shi, Yang; Brusilovsky, Peter; Price, Thomas; Koedinger, Ken; Carvalho, Paulo; Zhang, Shan; Lan, Andrew; Leinonen, Juho (July 2025, Proceedings of 18th International Conference on Educational Data Mining (EDM 2025), International Educational Data Mining Society)

   Mills, Caitlin; Alexandron, Giora; Taibi, Davide; Lo_Bosco, Giosuè; Paquette, Luc (Ed.)

   There is a growing community of researchers at the intersection- tion of data mining, AI, and computing education research. The objective of the CSEDM workshop is to facilitate a dis- Discussion among this research community, with a focus on how data mining can be uniquely applied in computing ed- ucation research. For example, what new techniques are needed to analyze program code and CS log data? How do results from CS education inform our analysis of this data? The workshop is meant to be an interdisciplinary event at the intersection of EDM and Computing Education Research. Researchers, faculty, and students are encouraged to share their AI- and data-driven approaches, methodological- gies, and experiences where data transforms how students learn Computer Science (CS) skills. This full-day workshop will feature paper presentations and discussions to promote collaboration. 

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3. Integrating Computer Science in Science Classrooms: Learning Computational Thinking and Expanding Perceptions of Computer Science

   Greenwald, Eric; Krakowski, Ari (April 2021, NARST 2021 ANNUAL INTERNATIONAL CONFERENCE)

   null (Ed.)

   We will present emerging findings from an ongoing study of instruction at the intersection of science and computer science for middle school science classrooms. This paper focuses on student knowledge and dispositional outcomes in relation to a 2 week/10-lesson learning sequence. Instruction aims to broaden participation in STEM pathways through a virtual simulated internship in which students inhabit the role of interns working to develop a restoration plan to improve the health of coral reef populations. Through this collaborative work, students construct understanding of biotic and abiotic interactions within the reef and develop a computational model of the ecosystem. Analysis of pre/post surveys for n=381 students revealed that students who participated in the 2 week/10 lesson integrated computational thinking in science learning sequence demonstrated significant learning gains on an external measure of CT (0.522***; effect size=0.32). Drawing on scales from the Activation Lab suite of measures, pre/post surveys revealed increased competency beliefs about computer programming (mean difference =1.13***; effect size=1.01), and increased value assigned to STEM (0.78***; effect size=0.945). We also discuss the design of the instructional sequence and the theoretical framework for its development. 

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4. Stereotype threat in computer science

   Lynn, A. (January 2020, American Psychological Association Annual Convention)
5. Integrating Computer Science in Science: Considerations for Scale

   Krakowski, A; Greenwald, E; Duke, J; Comstock, M; Roman, N (June 2020, PROCEEDINGS OF THE INTERNATIONAL CONFERENCE OF THE LEARNING SCIENCES 2020)

   Facility with foundational practices in computer science (CS) is increasingly recognized as critical for the 21st century workforce. Developing this capacity and broadening participation in CS disciplines will require learning experiences that can engage a larger and more diverse student population (Margolis et al., 2008). One promising approach involves including CS concepts and practices in required subjects like science. Yet, research on the scalability of educational innovations consistently demonstrates that their successful uptake in formal classrooms depends on teachers’ perceived alignment of the innovations with their goals and expectations for student learning, as well as with the specific needs of their school context and culture (Blumenfeld et al., 2000; Penuel et al., 2007; Bernstein et al., 2016). Research is nascent, however, about how exactly to achieve this alignment and thereby position integrated instructional models for uptake at scale. To contribute to this understanding, we are developing and studying two units for core middle school science classrooms, known as Coding Science Internships. The units are designed to support broader participation in CS, with a particular emphasis on females, by expanding students’ perception of the nature and value of coding. CS and science learning are integrated through a simulated internship model, in which students, as interns, apply science knowledge and use computer programming as a tool to address real-world problems. In one unit, students gain first-hand experience with sequences, loops, and conditionals as they program and debug an interactive scientific model of a coral reef ecosystem under threat. The second unit engages students in learning concepts related to data analysis and visualization, abstraction, and modularity as they code data visualizations using real EPA air quality data. A core goal for both units is to provide students experience with some of the increasingly prevalent ways that computer science is integrated into the work of scientists. 

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