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1. A Qualitative Examination of Undergraduate CS Students’ Self-Regulated Learning

   Bacher, John; Hollander, Christina; Berro, Michael; Choi, Julia; Price, Thomas; Greene, Jeffrey; Bernacki, Matthew (July 2026, Association for Computing Machinery)

   Self-regulated learning (SRL) is an instrumental skill for success in learning computer science (CS) and software engineering. SRL is an active process where students engage in cycles of planning, strategy-use, monitoring and control, and adaptation to accomplish goals. There have been calls to investigate whether SRL theory and measurement need to be made more specific to the CS education context and to integrate SRL theory when interpreting observations of student behavior in computing education research. In this work we examine the self-regulatory behavior of CS students in a 200-level CS course with an emphasis on software engineering. We performed a think-aloud study with thirty-three students working on their programming projects. Researchers then coded the think aloud data using a theory-driven codebook aligning with Winne and Hadwin’s COPES model of SRL. Using the coded data we performed a thematic analysis of two codes to better understand how students monitor their understanding of the task and set goals. Our results revealed a more complex picture than prior work of how students monitor their understanding of the task and set subgoals. We discuss the implications of our results for CS instruction and intervention designs to promote more elective SRL and learning. 

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2. Modeling temporal self-regulatory processes in STEM learning of engineering design

   Zheng, Juan; Pan, Zilong; Li, Shan; Xie, Charles (October 2024, Educational technology society)

   Self-regulation is crucial for student success in scientific inquiry and engineering design. However, it remains unclear how students dynamically engage in self-regulated learning (SRL) processes to achieve high performance. In this study, we investigated the temporal nature of self-regulation during engineering design by leveraging computer trace data from 101 high school students who designed an energy-plus house in a simulated learning environment. Using sequential mining, we found that high-performing students were more engaged in the Observation, Analysis, and Evaluation phases of SRL than low-performing students. Additionally, high-performing students demonstrated consecutive sequential patterns between Observation and Analysis, Reformation and Evaluation, and Analysis and Evaluation behaviors. These findings provide insights into students’ SRL processes and the design of scaffoldings. 

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3. Subtopic-specific heterogeneity in computer-based learning behaviors

   https://doi.org/10.1186/s40594-024-00519-x  

   Lee, HaeJin; Bosch, Nigel (December 2024, International Journal of STEM Education)

   Self-regulated learning (SRL) strategies can be domain specific. However, it remains unclear whether this specificity extends to different subtopics within a single subject domain. In this study, we collected data from 210 college students engaged in a computer-based learning environment to examine the heterogeneous manifestations of learning behaviors across four distinct subtopics in introductory statistics. Further, we explore how the time spent engaging in metacognitive strategies correlated with learning gain in those subtopics. ResultsBy employing two different analytical approaches that combine data-driven learning analytics (i.e., sequential pattern mining in this case), and theory-informed methods (i.e., coherence analysis), we discovered significant variability in the frequency of learning patterns that are potentially associated with SRL-relevant strategies across four subtopics. In a subtopic related to calculations, engagement in coherent quizzes (i.e., a type of metacognitive strategy) was found to be significantly less related to learning gains compared to other subtopics. Additionally, we found that students with different levels of prior knowledge and learning gains demonstrated varying degrees of engagement in learning patterns in an SRL context. ConclusionThe findings imply that the use—and the effectiveness—of learning patterns that are potentially associated with SRL-relevant strategies varies not only across contexts and domains, but even across different subtopics within a single subject. This underscores the importance of personalized, context-aware SRL training interventions in computer-based learning environments, which could significantly enhance learning outcomes by addressing the heterogeneous relationships between SRL activities and outcomes. Further, we suggest theoretical implications of subtopic-specific heterogeneity within the context of various SRL models. Understanding SRL heterogeneity enhances these theories, offering more nuanced insights into learners’ metacognitive strategies across different subtopics. 

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4. The effectiveness of an integrated STEM curriculum unit on middle school students' life science learning

   https://doi.org/10.1002/tea.21756  

   Anwar, S.; Menekse, M.; Guzey, S.; Bryan, L. (September 2022, Journal of research in science teaching the official journal of the National Association for Research in Science Teaching)

   Troy Sadler & Felicia Moore Mensah (Ed.)

   Recent calls for reform in K-12 science education and the National Academy of Engineering's Grand Challenges for Engineering in the 21st Century emphasize improving science teaching, students' engagement, and learning. In this study, we designed and implemented a curriculum unit for sixth-grade students (i = 1305). The curriculum unit integrated science and engineering content and practices to teach ecology, water pollution, and engineering design. We investigated the designed integrated STEM unit's effectiveness in students' science learning outcomes on pre-, post-, and delayed post-assessments. We collected pre-and post-assessment data of students' science learning outcomes for both the baseline group (taught via existing district-adopted curriculum) and an intervention group (taught with integrated life science and engineering curriculum). We used a quasi-experimental research design and examined differences between baseline and intervention groups. We used ANCOVA to explore differences in students' learning in baseline and intervention groups. Furthermore, for students in the intervention group, we conducted repeated-measures ANOVA to investigate knowledge retention. Our analyses also accounted for students' gender and People of Color (POC) status. We conducted multiple regression analyses to explore the relationship between students' gender, POC status, and their learning outcomes. The results indicated that the intervention group students performed significantly better than the students in the baseline group. The repeated measures ANOVA showed that students in the intervention group retained science knowledge after 8 weeks of instruction. Finally, the regression analysis for the baseline group showed that gender and POC status were not significant predictors of their post-assessment scores. However, POC status was a significant predictor of post-assessment scores and knowledge retention for the intervention group. Overall, this study provides valuable findings on how an integrated STEM curriculum designed with engineering design and practices improves students' science learning outcomes. 

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5. BOARD # 352: DRK-12 Examining Changes in Elementary Teachers’ Engineering Self-Efficacy Across a Year-Long Professional Learning Program

   https://doi.org/10.18260/1-2--55722  

   Summers, Ryan; Hammack, Rebekah; Robinson, Julie; Lee, Min Jung; Boz, Tugba; Inouye, Martha; Macias, Meghan; Iveland, Ashley (June 2025, ASEE Conferences)

   The ambitious vision outlined in A Framework for K-12 Science Education, which is delineated in the Next Generation Science Standards, calls for advancing engineering. Nationwide data shows that teachers report comparatively low self-efficacy for engineering, compared to other subjects (e.g., math), and elementary teachers generally report the lowest levels. Prior research shows that teachers’ self-efficacy impacts their inclusion of engineering in classrooms, and self-efficacy may be influenced by content and pedagogical knowledge among other variables. Professional learning (PL), a strategy for strengthening teachers’ knowledge, can also foster self-efficacy. This project recruited elementary teachers from four states (CA, MT, ND, and WY). Teachers received intense summer PL for five days and stayed connected through PL activities during the 2023-2024 school year. To accommodate rural teachers from multiple states, all PL activities and research were completed virtually. The online PL modeled shifts called for by NGSS and offered guidance for teachers as they introduced engineering practices into their classrooms. Likert scale surveys were administered at three time points—before and immediately after the summer PL with a delayed post-PL survey at the school year’s end. Measures captured teachers’ engineering self-efficacy and outcome expectancy, plus an array of background characteristics. Analysis explores data collected from participants who responded to all three surveys (n=111). We found improvement in teacher self-efficacy. The full paper will include descriptive and inferential statistics to investigate associations between teachers’ engineering self-efficacy and background variables (e.g., prior PL experience, years of teaching experience) and characteristics (e.g., geographic location, grade level). 

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