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Abstract Capturing evidence for dynamic changes in self‐regulated learning (SRL) behaviours resulting from interventions is challenging for researchers. In the current study, we identified students who were likely to do poorly in a biology course and those who were likely to do well. Then, we randomly assigned a portion of the students predicted to perform poorly to a science of learning to learn intervention where they were taught SRL study strategies. Learning outcome and log data (257 K events) were collected fromn = 226 students. We used a complex systems framework to model the differences in SRL including the amount, interrelatedness, density and regularity of engagement captured in digital trace data (ie, logs). Differences were compared between students who were predicted to (1) perform poorly (control,n = 48), (2) perform poorly and received intervention (treatment,n = 95) and (3) perform well (not flagged,n = 83). Results indicated that the regularity of students' engagement was predictive of course grade, and that the intervention group exhibited increased regularity in engagement over the control group immediately after the intervention and maintained that increase over the course of the semester. We discuss the implications of these findings in relation to the future of artificial intelligence and potential uses for monitoring student learning in online environments. Practitioner notesWhat is already known about this topicSelf‐regulated learning (SRL) knowledge and skills are strong predictors of postsecondary STEM student success.SRL is a dynamic, temporal process that leads to purposeful student engagement.Methods and metrics for measuring dynamic SRL behaviours in learning contexts are needed.What this paper addsA Markov process for measuring dynamic SRL processes using log data.Evidence that dynamic, interaction‐dominant aspects of SRL predict student achievement.Evidence that SRL processes can be meaningfully impacted through educational intervention.Implications for theory and practiceComplexity approaches inform theory and measurement of dynamic SRL processes.Static representations of dynamic SRL processes are promising learning analytics metrics.Engineered features of LMS usage are valuable contributions to AI models.
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Get the GIST: Exploring Self-regulation and Cognitive Strategy Use in Geographic Information Science & Technology (GIST) in a Hybrid Learning Program for Autistic Students
This study highlights the application of self-regulated learning and cognitive strategy use research in the Connecting Students with Autism to Geographic Information Science & Technology (CSA-GIST) study, an NSF-funded project. This study investigated the program’s impact on autistic high school students’ self-regulated learning (SRL) and cognitive strategy use during STEM instruction. Though the findings were not significant regarding the impact of the program on students SRL and cognitive strategy use at the midpoint of the program, increased interest in math, science, and technology were reported for each cohort during year one. These findings provide implications for adaptations to this workforce development program to increase students’ SRL and cognitive strategy use for GIST and STEM careers among autistic students. Implications for research and practice are discussed.
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- Award ID(s):
- 2048937
- PAR ID:
- 10610140
- Publisher / Repository:
- Association for the Advancement of Computing in Education
- Date Published:
- ISBN:
- 978-1-939797-76-6
- Format(s):
- Medium: X
- Location:
- Waynesville, NC USA
- Sponsoring Org:
- National Science Foundation
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Abstract Current research underscores that there are only a few evidence-based programs that teach STEM (science, technology, engineering, and mathematics) as part of their curriculum, especially for autistic students. Even fewer programs focus on engineering and design learning. Hence, we developed an informal afterschool maker program to develop autistic and non-autistic students’ interests in engineering to understand their experiences learning STEM concepts and values while applying the engineering mindset to develop projects. This qualitative study aimed to explore and understand students’ experiences participating in STEM activities in the maker club. We interviewed twenty-six students (seventeen autistic and nine non-autistic), nine teachers, and thirteen parents representing diverse cultural and socio-economic backgrounds across three public middle schools in a large urban metropolitan city between 2018 and 2019. Our thematic analysis yielded four themes:(1) active participation in STEM; (2) curiosity about STEM topics, concepts, and practices, (3) capacity-building to engage in STEM learning; and 4) understanding of the importance of STEM education in daily life.The results of this study enabled us to understand that students were deeply engaged with the content and curriculum of our program, expanded their knowledge base about scientific concepts, used engineering-specific scientific terminologies, and engaged with the engineering design process to conceptualize, test, improvise, and problem-solve. Furthermore, this afterschool engineering education program created a safe, nurturing, and stimulating environment for students to build engineering readiness skills.more » « less
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Research on self-regulated learning (SRL) in engineering design is growing. While SRL is an effective way of learning, however, not all learners can regulate themselves successfully. There is a lack of research regarding how student characteristics, such as science knowledge and design knowledge, interact with SRL. Adapting the SRL theory in the field of engineering design, this study proposes a research model to examine the mediation and causal relationships among science knowledge, design knowledge, and SRL activities (i.e. observation, formulation, reformulation, analysis, evaluation). Partial least squares modeling was utilized to examine how the science and design knowledge of 108 ninth-grade participants interacted with their SRL activities in the process of performing an engineering task. Results reveal that prior science and design knowledge positively predict SRL activities. They also show that reformulation and analysis are the two SRL activities that can lead to an improvement in post science and design knowledge, but excessive observation can hinder post design knowledge. These results have important implications for the construction of learning environments to support SRL based on students’ prior knowledge levels.more » « less
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IntroductionSelf-regulated learning (SRL), or learners’ ability to monitor and change their own cognitive, affective, metacognitive, and motivational processes, encompasses several operations that should be deployed during learning including Searching, Monitoring, Assembling, Rehearsing, and Translating (SMART). Scaffolds are needed within GBLEs to both increase learning outcomes and promote the accurate and efficient use of SRL SMART operations. This study aims to examine how restricted agency (i.e., control over one’s actions) can be used to scaffold learners’ SMART operations as they learn about microbiology with Crystal Island, a game-based learning environment. MethodsUndergraduate students (N = 94) were randomly assigned to one of two conditions: (1) Full Agency, where participants were able to make their own decisions about which actions they could take; and (2) Partial Agency, where participants were required to follow a pre-defined path that dictated the order in which buildings were visited, restricting one’s control. As participants played Crystal Island, participants’ multimodal data (i.e., log files, eye tracking) were collected to identify instances where participants deployed SMART operations. ResultsResults from this study support restricted agency as a successful scaffold of both learning outcomes and SRL SMART operations, where learners who were scaffolded demonstrated more efficient and accurate use of SMART operations. DiscussionThis study provides implications for future scaffolds to better support SRL SMART operations during learning and discussions for future directions for future studies scaffolding SRL during game-based learning.more » « less
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