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Digital Scaffolding and Active Learning Strategies for Self-Regulated Learning in STEM Higher Education: A Systematic Review: Comparison
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Please note this is a comparison between Version 2 by Perry Fu and Version 3 by Perry Fu.

This systematic review examines recent research on self-regulated learning (SRL) in STEM disciplines in higher education, with particular emphasis on the pedagogical and technological factors that support its development. Following the PRISMA 2020 guidelines, 26 indexed and peer-reviewed studies published between 2022 and 2025 in Web of Science and Scopus were analyzed. Thematic and bibliometric analyses were conducted to identify the main research trends, theoretical frameworks, and forms of pedagogical and technological mediation. The findings suggest an asymmetrical pattern within the reviewed corpus. Although active learning and digital technologies, including learning analytics, simulations, and AI-supported tools, are increasingly used in STEM education, the evidence does not consistently demonstrate a transfer from externally scaffolded regulation to sustained learner autonomy. Cognitive-constructivist and problem-based learning frameworks were more frequently represented within the reviewed corpus, whereas co-regulated and socially shared regulation frameworks were less frequently addressed. The findings further suggest that technological sophistication alone does not consistently support SRL; rather, its contribution appears to be closely associated with the pedagogical functions assigned to digital tools and their integration into learning activities. Finally, the review identifies tensions between externally guided regulation and sustained learner autonomy, as well as concerns related to unequal access, participation, and support in STEM education.

  • active learning
  • artificial intelligence
  • educational technology
  • higher education
  • learning analytics
  • STEM
Self-regulated learning has been associated with positive academic outcomes in science, technology, engineering, and mathematics (STEM) education, where students frequently engage in complex cognitive tasks. Students must be able to demonstrate the application of knowledge in different contexts and solve complex problems. Although self-regulated learning has been addressed to some degree within higher education, significant differences still exist with respect to foundational cognitive processes, metacognitive processes, and motivational processes for students working on their own [1].
In STEM education, SRL is commonly conceptualized as involving cognitive, metacognitive, motivational, and behavioral processes that enable students to plan, monitor, and evaluate their learning. For this reason, many researchers are engaged in studying active learning techniques (problem-based learning, flipped classrooms) in relation to student engagement and self-regulated learning. The results of several studies indicate that when active learning techniques are paired with the use of digital technologies, and when the learning environment incorporates blended-learning methodologies, student engagement and self-regulated learning are enhanced through the establishment of progress-monitoring devices, receipt of timely feedback, and use of learning analytics as tools for assessment [2][3].
While there is an increasing volume of scientific literature on self-regulated learning (SRL) in STEM education, the field continues to exhibit considerable theoretical and methodological fragmentation. On the one hand, there is a significant body of research examining elements of SRL in isolation from each other. On the other hand, there are other studies that focus exclusively on approaches to pedagogy or technological tools as transversal elements. This fragmentation may hinder the development of integrative frameworks for explaining how these elements interact within complex and dynamic STEM environments [4][5][6]. Furthermore, individual approaches to self-regulated learning (SRL) remain largely disconnected from social frameworks, despite the inherently collaborative nature of STEM disciplines [7]. Specifically, the literature frequently conflates or overlooks the distinction between Co-Regulated Learning (CoRL)—where regulatory processes are temporarily supported or guided by peers or instructors—and Socially Shared Regulation of Learning (SSRL)—where a group functions as a single collective entity to jointly plan, monitor, and regulate their shared cognitive processes [7]. The limited integration of these distinct social dimensions may constrain understanding of how students regulate learning in team-based environments.
From a methodological perspective, the majority of studies use cross-sectional designs or short-term interventions to measure SRL, limiting the understanding of SRL as a developmental process that exists within a larger social context. As evidenced in the field of learning analytics and educational data mining, researchers have identified the existence of divergent patterns of self-regulation and behavioral patterns [4][8]. However, many of these researchers have also encountered challenges when attempting to develop models of cumulative self-regulation across multiple time frames [5][6].
The use of digital technologies, including artificial intelligence, within STEM education has increased in recent years, yet empirical evidence regarding their role in self-regulated learning (SRL) remains limited. Some studies report positive outcomes associated with the use of artificial intelligence and technological tools in STEM education, particularly in supporting monitoring and feedback processes [9][10], while other studies identify limitations related to cognitive dependency, unequal access, and the lack of explicit learning embedded within well-defined pedagogical frameworks [11][12].
In line with these findings, the objective of this study is to analyze how digital technologies and pedagogical scaffolding support self-regulated learning in STEM higher education. Therefore, this research project is guided by the following research questions: (RQ1) What are the recent publication, disciplinary, geographical, and methodological characteristics of SRL research in STEM higher education? (RQ2) Which SRL dimensions and regulatory/theoretical approaches are represented in the corpus? (RQ3) How are active learning, digital scaffolding, and technological mediation associated with reported SRL-related outcomes? (RQ4) What gaps remain regarding external regulation, CoRL, SSRL, and sustained learner autonomy? This review aims to bring greater integration and structure to SRL research, laying the groundwork for future studies and contributing to the design of evidence-based pedagogical interventions.

References

  1. Hodijah, S.R.N.; Rahmat, A.; Priyandoko, D.; Wahyudin, W. Cognitive Process of Pre-service Science Teachers Using Problem-based Learning on Nervous System Concepts. J. Penelit. Pembelajaran IPA 2023, 9, 256–270.
  2. Ravi, M. Digital Simulation-Based Learning for Reaction Engineering: Activity Design and Student Metacognition. Comput. Appl. Eng. Educ. 2025, 33, e70038.
  3. Awi, A.; Upu, H.; Rusli, R.; Ahmar, A.S.; Musa, H. The role of scaffolding in flipped classroom: Improving learning outcomes through Weblog-based and Google Drive-based assignments. Cogent Educ. 2025, 12, 2543115.
  4. Abouelenein, Y.A.M.; Selim, S.A.S.; Aldosemani, T.I. Impact of an adaptive environment based on learning analytics on pre-service science teacher behavior and self-regulation. Smart Learn. Environ. 2025, 12, 8.
  5. López-Pernas, S.; Conde, M.A.; Saqr, M. Three shades of self-regulation with unique complex dynamics, drivers and targets for intervention. Br. J. Educ. Technol. 2025; in press.
  6. Nuankaew, P. Self-Regulated Learning Model in Educational Data Mining. Int. J. Emerg. Technol. Learn. 2022, 17, 4–27.
  7. Khuder, B.; Negretti, R. Collaborative writing regulation: A comparative case study of co-regulation and socially shared regulation in higher education. High. Educ. 2025, 91, 677–699.
  8. Hou, C.; Kuhlmann, S.L.; Greene, J.A.; Bernacki, M.L.; Hogan, K.A. Process mining measures students’ help-seeking transitions when completing assignments in an online learning and assessment platform. Metacognition Learn. 2025, 20, 40.
  9. Huda, N.; Anwar, K.; Kurniawan, W. ChatGPT Scaffolding in Supporting Metacognition for Limit Concepts in Guided Inquiry Mathematics Learning. J. Inf. Technol. Educ. Res. 2025, 24, 38.
  10. Jin, F.J.-Y.; Maheshi, B.; Lai, W.; Li, Y.; Gasevic, D.; Chen, G.; Charwat, N.; Chan, P.W.K.; Martinez-Maldonado, R.; Gašević, D.; et al. Students’ Perceptions of Generative AI-Powered Learning Analytics in the Feedback Process: A Feedback Literacy Perspective. J. Learn. Anal. 2025, 12, 152–168.
  11. Abueva, N.; Zhussupova, A.; Conservatory, K.K.N.; Massakova, S.; Buzelo, A.; Abuyev, O. Analyzing the formation of metacompetence in students using EdTech in the learning process: An economic perspective. Econ. Ann.-XXI 2024, 208, 27–39.
  12. Borchers, C.; Fleischer, H.; Schanze, S.; Scheiter, K.; Aleven, V. High scaffolding of an unfamiliar strategy improves conceptual learning but reduces enjoyment compared to low scaffolding and strategy freedom. Comput. Educ. 2025, 236, 105364.
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