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Investigating Students' Usage of Self-regulation of Learning Scaffoldings in a Computer-based Programming Learning Environment

Published: 07 March 2024 Publication History

Abstract

Learning programming is a multifaceted process shaped by diverse factors, including effectively utilizing self-regulation of learning (SRL) skills. However, students frequently require a certain level of support to effectively engage in SRL, prompting a question regarding the optimal means of providing such assistance. The current literature is limited in identifying which types of support are most effective, and it's also noted that some students do not benefit from its usage. The underlying causes remain underexplored, and a deeper understanding of this phenomenon can offer insights into improving the design of regulatory support. This research is situated within this scope and was guided by the following research questions: RQ1) How does the use of self-regulation of learning support differ among students with high and low-performance levels? RQ2) How do students use and evaluate the self-regulation of learning support? Fifty-four students enrolled in an online introductory programming course participated in this investigation. The findings indicate that higher-performance students made more extensive use of the provided regulatory support, implying the potential utilization of this resource as a learning aid. Simultaneously, it's noted that some students either avoided or utilized this support to a lesser extent, underscoring that the mere provision of resources is insufficient for their effective usage. A qualitative analysis revealed some factors that might have influenced this behavior.

References

[1]
Saleh Alhazbi. 2014. Using e-journaling to improve self-regulated learning in introductory computer programming course. In 2014 IEEE Global Engineering Education Conference. IEEE, 352--356.
[2]
Kai Arakawa, Qiang Hao, Tyler Greer, Lu Ding, Christopher D. Hundhausen, and Abigayle Peterson. [n.,d.]. In Situ Identification of Student Self-Regulated Learning Struggles in Programming Assignments., bibinfonumpages467--473 pages.
[3]
Roger Azevedo and Allyson F. Hadwin. 2005. Scaffolding Self-regulated Learning and Metacognition textendash Implications for the Design of Computer-based Scaffolds. Instructional Science, Vol. 33, 5--6 (2005), 367--379.
[4]
Francc ois Bouchet, Roger Azevedo, John S Kinnebrew, and Gautam Biswas. 2012. Identifying Students' Characteristic Learning Behaviors in an Intelligent Tutoring System Fostering Self-Regulated Learning. International Educational Data Mining Society (2012).
[5]
Hugo Castellanos, Felipe Restrepo-Calle, Fabio A González, and Jhon Jairo Ram'irez Echeverry. 2017. Understanding the relationships between self-regulated learning and students source code in a computer programming course. In 2017 IEEE Frontiers in Education Conference (FIE). IEEE, 1--9.
[6]
Jose M. Cortina. 1993. What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, Vol. 78, 1 (1993), 98--104.
[7]
Mayela Coto, Sonia Mora, Beatriz Grass, and Juan Murillo-Morera. 2021. Emotions and programming learning: systematic mapping. Computer Science Education (2021), 1--36.
[8]
Marcus Credé and L Alison Phillips. 2011. A meta-analytic review of the Motivated Strategies for Learning Questionnaire. Learning and individual differences, Vol. 21, 4 (2011), 337--346.
[9]
Paul Denny, James Prather, Brett A Becker, Zachary Albrecht, Dastyni Loksa, and Raymond Pettit. 2019. A Closer Look at Metacognitive Scaffolding: Solving Test Cases Before Programming. In Proceedings of the 19th Koli Calling International Conference on Computing Education Research. 1--10.
[10]
Katrina Falkner, Claudia Szabo, Rebecca Vivian, and Nickolas Falkner. 2015. Evolution of software development strategies. In 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, Vol. 2. IEEE, 243--252.
[11]
Rita Garcia, Katrina Falkner, and Rebecca Vivian. 2018. Systematic literature review: Self-Regulated Learning strategies using e-learning tools for Computer Science. Computers & Education, Vol. 123 (2018), 150--163.
[12]
J Greene, D Moos, and Roger Azevedo. 2011. Self-regulation of learning with computer-based learning environments. New directions for teaching and learning. Publicado en l'inea en Wiley Online Library. https://doi. org/10.1002/tl, Vol. 449 (2011).
[13]
Jitka Jakevs ová and Karla Hrbávc ková. 2014. The Czech adaptation of motivated strategies for learning questionnaire (MSLQ). Asian Social Science (2014).
[14]
Check-Yee Law, John Grundy, Andrew Cain, Rajesh Vasa, and Alex Cummaudo. 2017. User perceptions of using an open learner model visualisation tool for facilitating self-regulated learning. In Proceedings of the Nineteenth Australasian Computing Education Conference. 55--64.
[15]
Oenardi Lawanto, Harry B Santoso, Kevin N Lawanto, and Wade Goodridge. 2017. Self-regulated learning skills and online activities between higher and lower performers on a web-intensive undergraduate engineering course. Journal of Educators Online, Vol. 11, 3 (2017), n3.
[16]
Chiu-Pin Lin and Su-Jian Yang. 2021. Multiple Scaffolds Used to Support Self-Regulated Learning in Elementary Mathematics Classrooms. International Journal of Online Pedagogy and Course Design (IJOPCD), Vol. 11, 4 (2021), 1--19.
[17]
Dastyni Loksa, Andrew J Ko, Will Jernigan, Alannah Oleson, Christopher J Mendez, and Margaret M Burnett. 2016. Programming, problem solving, and self-awareness: Effects of explicit guidance. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 1449--1461.
[18]
Dastyni Loksa, Lauren Margulieux, Brett A Becker, Michelle Craig, Paul Denny, Raymond Pettit, and James Prather. 2022. Metacognition and Self-Regulation in Programming Education: Theories and Exemplars of Use. ACM Transactions on Computing Education (2022).
[19]
Dastyni Loksa, Benjamin Xie, Harrison Kwik, and Amy J Ko. 2020. Investigating Novices' In Situ Reflections on Their Programming Process. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education. 149--155.
[20]
Seren Mabley, Esther Ventura-Medina, and Anthony Anderson. 2020. "I'm lost'--a qualitative analysis of student teams' strategies during their first experience in problem-based learning. European Journal of Engineering Education, Vol. 45, 3 (2020), 329--348.
[21]
Anabil Munshi, Ramkumar Rajendran, Jaclyn Ocumpaugh, Allison Moore, and Gautam Biswas. 2018. Studying the interactions between components of self regulated learning in open ended learning environments. International Society of the Learning Sciences, Inc.[ISLS].
[22]
Ernesto Panadero. 2017. A review of self-regulated learning: Six models and four directions for research. Frontiers in psychology, Vol. 8 (2017), 422.
[23]
Ernesto Panadero and Sanna J"arvel"a. 2015. Socially Shared Regulation of Learning: A Review. European Psychologist, Vol. 20, 3 (July 2015), 190--203.
[24]
Paul R Pintrich and Elisabeth V De Groot. 1990. Motivational and self-regulated learning components of classroom academic performance. Journal of educational psychology, Vol. 82, 1 (1990), 33.
[25]
James Prather, Brett A Becker, Michelle Craig, Paul Denny, Dastyni Loksa, and Lauren Margulieux. 2020. What Do We Think We Think We Are Doing? Metacognition and Self-Regulation in Programming. In Proceedings of the 2020 ACM Conference on International Computing Education Research. 2--13.
[26]
Yizhou Qian and James Lehman. 2017. Students' misconceptions and other difficulties in introductory programming: A literature review. ACM Transactions on Computing Education, Vol. 18, 1 (2017), 1--24.
[27]
Shilpi Rao and Vive Kumar. 2008. A Theory-Centric Real-Time Assessment of Programming. In 2008 Eighth IEEE International Conference on Advanced Learning Technologies. IEEE.
[28]
Anthony V. Robins. [n.,d.]. Novice Programmers and Introductory Programming., bibinfonumpages327--376 pages.
[29]
Dale H. Schunk. 2005. Self-Regulated Learning: The Educational Legacy of Paul R. Pintrich. Educational Psychologist, Vol. 40, 2 (2005), 85--94.
[30]
Beat A Schwendimann, Gabriel Kappeler, Laetitia Mauroux, and Jean-Luc Gurtner. 2018. What makes an online learning journal powerful for VET? Distinguishing productive usage patterns and effective learning strategies. Empirical Research in Vocational Education and Training, Vol. 10, 1 (2018), 1--20.
[31]
Jeremie Seanosky, Isabelle Guillot, David Boulanger, Rébecca Guillot, Claudia Guillot, Vivekanandan Kumar, Shawn N Fraser, Nahla Aljojo, and Asmaa Munshi. 2017. Real-time visual feedback: a study in coding analytics. In 2017 IEEE 17th International Conference on Advanced Learning Technologies. IEEE, 264--266.
[32]
Leonardo Silva, Anabela Gomes, and António José Mendes. 2023 a. Exploring the Impact of Self-Regulation of Learning Support on Programming Performance and Code Development. In 2023 IEEE Frontiers in Education Conference (FIE) Proceedings. IEEE.
[33]
Leonardo Silva, António Mendes, Anabela Gomes, and Gabriel Fortes. 2023 b. Fostering regulation of learning processes among programming students using computationalscaffolding. International Journal of Computer-Supported Collaborative Learning (2023), 1--34.
[34]
Leonardo Silva, António Mendes, Anabela Gomes, and Gabriel Fortes. 2024. What Learning Strategies are Used by Programming Students? A Qualitative Study Grounded on the Self-regulation of Learning Theory. Transactions on Computing Education (2024).
[35]
Leonardo Silva, António José Mendes, and Anabela Gomes. 2020. Computer-supported collaborative learning in programming education: A systematic literature review. In 2020 IEEE Global Engineering Education Conference. IEEE, 1086--1095.
[36]
Leonardo Silva, António José Mendes, Anabela Gomes, and Gabriel Fortes Cavalcanti de Macêdo. 2021a. Regulation of Learning Interventions in Programming Education: A Systematic Literature Review and Guideline Proposition. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education. 647--653.
[37]
Leonardo Silva, António José Mendes, Anabela Gomes, Gabriel Fortes Cavalcanti de Macêdo, Cahn Lam, and Calana Chan. 2021b. Exploring the Association Between Self-Regulation of Learning and Programming Learning: A Systematic Literature Review and Multinational Investigation. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education. IEEE.
[38]
Leonardo Silva, António José Mendes, Anabela Gomes, Chan Tong Lam, Calana Chan, and Gabriel Fortes. 2022. Reliability and Predictive Validity of the Self-regulation Programming Strategies Questionnaire. In 2022 International Symposium on Computers in Education (SIIE). IEEE, 1--6.
[39]
Donggil Song, Hyeonmi Hong, and Eun Young Oh. 2021. Applying computational analysis of novice learners' computer programming patterns to reveal self-regulated learning, computational thinking, and learning performance. Computers in Human Behavior, Vol. 120 (2021), 106746.
[40]
Ana Cláudia de Souza, Neusa Maria Costa Alexandre, and Edinêis de Brito Guirardello. 2017. Psychometric properties in instruments evaluation of reliability and validity. Epidemiologia e servicos de saude, Vol. 26 (2017), 649--659.
[41]
Errol Thompson, Andrew Luxton-Reilly, Jacqueline L Whalley, Minjie Hu, and Phil Robbins. 2008. Bloom's taxonomy for CS assessment. In Proceedings of the tenth conference on Australasian computing education-Volume 78. 155--161.
[42]
Estela Aparecida Oliveira Vieira, Aleph Campos da SILVEIRA, and Ronei Ximenes Martins. 2019. Heuristic evaluation on usability of educational games: A systematic review. Informatics in Education, Vol. 18, 2 (2019), 427--442.
[43]
Simone Volet and Chris Lund. 1994. Metacognitive instruction in introductory computer programming: A better explanatory construct for performance than traditional factors. Journal of educational computing research, Vol. 10, 4 (1994), 297--328.
[44]
Philip H. Winne. 2017. Cognition and Metacognition within Self-Regulated Learning., bibinfonumpages36--48 pages.
[45]
Lanqin Zheng. 2016. The effectiveness of self-regulated learning scaffolds on academic performance in computer-based learning environments: A meta-analysis. Asia Pacific Education Review, Vol. 17, 2 (2016), 187--202.
[46]
Barry J Zimmerman and Dale H Schunk. 2011. Handbook of self-regulation of learning and performance. Routledge/Taylor & Francis Group.

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  1. Investigating Students' Usage of Self-regulation of Learning Scaffoldings in a Computer-based Programming Learning Environment

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cover image ACM Conferences
SIGCSE 2024: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1
March 2024
1583 pages
ISBN:9798400704239
DOI:10.1145/3626252
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 07 March 2024

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  1. programming education
  2. scaffolding
  3. self-regulation of learning

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