Abstract
Collaborative learning by teaching (CLBT) is a pedagogical approach that combines collaborative learning and learning by teaching pedagogy, which can be applied to university classrooms with the support of information and communication technology (ICT). This pedagogy not only emphasizes the independent learning of group members, but also emphasizes the process of collaborative learning and cooperative teaching among group members. For current Chinese college students, even with the support of online learning resources and ICT, CLBT is a relatively difficult task for group members, which needs to be combined with some regulatory strategies. Socially shared learning regulation (SSRL) has attracted widespread attention from educators and researchers as an effective collaborative learning regulation strategy, but so far, there is few studies on the effects of SSRL in CLBT environment, especially on psychological variables. This study explored the effects of SSRL on the computational thinking, learning motivation, engagement, and academic achievement of university students in CLBT by conducting a semester-long quasi-experiment in a data mining course with 72 third-year undergraduates at a Chinese public university. The experimental class adopted SSRL in CLBT with 41 students (33 males, eight females), while the control class only implemented CLBT with 31 students (26 males, five females). The independent sample t-test results showed that the students in the experimental class significantly improved their intrinsic motivation, engagement (Deep processing strategy and affective engagement), and computational thinking (Algorithmic thinking, critical thinking and problem solving) than those in the control class. In addition, the student’s academic achievements in the experimental class were significantly higher than the students in the control class. This study enriches the practical cases of educators and researchers and makes recommendations for future research, such as combining project-based learning approaches with CLBT or investigating the relationship among these psychological variables and academic performance.

Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data availability
The data collected and used for this study is available at:
References
Anthonysamy, L., Koo, A. C., & Hew, S. H. (2020). Self-regulated learning strategies and non-academic outcomes in higher education blended learning environments: a one decade review. Education and Information Technologies, 25(5), 3677–3704. https://doi.org/10.1007/s10639-020-10134-2.
Assaf, L. C., Ralfe, L., & Steinbach, B. (2016). South african teachers learning to become writers and writing teachers: a study of generative learning. Teaching and Teacher Education, 56, 173–184. https://doi.org/10.1016/j.tate.2016.02.011.
Bargh, J. A., & Schul, Y. (1980). On the cognitive benefits of teaching. Journal of Educational Psychology, 72(5), 593–604.
Baars, M., & Wijnia, L. (2018). The relation between task-specific motivational profiles and training of self-regulated learning skills. Learning and Individual Differences, 64, 125–137. https://doi.org/10.1016/j.lindif.2018.05.007.
Basogain, X., Olabe, M., Olabe, J. C., & Rico, M. J. (2018). Computational thinking in pre-university blended learning classrooms. Computers in Human Behavior, 80, 412–419. https://doi.org/10.1016/j.chb.2017.04.058.
Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: a systematic review. The Internet and Higher Education, 27, 1–13. https://doi.org/10.1016/j.iheduc.2015.04.007.
Chong, W. H., Liem, G. A. D., Huan, V. S., Kit, P. L., & Ang, R. P. (2018). Student perceptions of self-efficacy and teacher support for learning in fostering youth competencies: roles of affective and cognitive engagement. Journal of Adolescence, 68, 1–11. https://doi.org/10.1016/j.adolescence.2018.07.002.
Cortázar, C., Nussbaum, M., Harcha, J., Alvares, D., López, F., Goñi, J., & Cabezas, V. (2021). Promoting critical thinking in an online, project-based course. Computers in Human Behavior, 119(5), 106705. https://doi.org/10.1016/j.chb.2021.106705.
Davari Torshizi, M., & Bahraman, M. (2019). I explain, therefore I learn: improving students’ assessment literacy and deep learning by teaching. Studies in Educational Evaluation, 61, 66–73. https://doi.org/10.1016/j.stueduc.2019.03.002.
Duchatelet, D., & Donche, V. (2019). Fostering self-efficacy and self-regulation in higher education: a matter of autonomy support or academic motivation? Higher Education Research & Development, 38(4), 733–747. https://doi.org/10.1080/07294360.2019.1581143.
Fiorella, L., & Mayer, R. E. (2013). The relative benefits of learning by teaching and teaching expectancy. Contemporary Educational Psychology, 38(4), 281–288. https://doi.org/10.1016/j.cedpsych.2013.06.001.
Fiorella, L., & Mayer, R. E. (2014). Role of expectations and explanations in learning by teaching. Contemporary Educational Psychology, 39(2), 75–85. https://doi.org/10.1016/j.cedpsych.2014.01.001.
Greene, B. A., & Miller, R. B. (1996). Influences on achievement: goals, perceived ability, and cognitive engagement. Contemporary Educational Psychology, 21(2), 181–192. https://doi.org/10.1006/ceps.1996.0015.
Hsu, T. C., Chang, S. C., & Hung, Y. T. (2018). How to learn and how to teach computational thinking: suggestions based on a review of the literature. Computers & Education, 126, 296–310. https://doi.org/10.1016/j.compedu.2018.07.004.
Hwang, G. J., Wang, S. Y., & Lai, C. L. (2021). Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics. Computers & Education, 160(6), 104031. https://doi.org/10.1016/j.compedu.2020.104031.
Isohätälä, J., Järvenoja, H., & Järvelä, S. (2017). Socially shared regulation of learning and participation in social interaction in collaborative learning. International Journal of Educational Research, 81, 11–24. https://doi.org/10.1016/j.ijer.2016.10.006.
Jamet, F., Masson, O., Jacquet, B., Stilgenbauer, J. L., & Baratgin, J. (2018). Learning by teaching with humanoid robot: a new powerful experimental tool to improve children’s learning ability. Journal of Robotics, 2018(2), 1–11. https://doi.org/10.1155/2018/4578762.
Järvenoja, H., Järvelä, S., & Malmberg, J. (2020). Supporting groups’ emotion and motivation regulation during collaborative learning. Learning and Instruction, 70. https://doi.org/10.1016/j.learninstruc.2017.11.004.
Järvelä, S., Malmberg, J., & Koivuniemi, M. (2016). Recognizing socially shared regulation by using the temporal sequences of online chat and logs in CSCL. Learning and Instruction, 42, 1–11.
Kirschner, F., Paas, F., & Kirschner, P. A. (2011). Superiority of collaborative learning with complex tasks: a research note on an alternative affective explanation. Computers in Human Behavior, 27(1), 53–57. https://doi.org/10.1016/j.chb.2010.05.012.
Korkmaz, Ö., Çakir, R., & Özden, M. Y. (2017). A validity and reliability study of the computational thinking scales (CTS). Computers in Human Behavior, 72, 558–569. https://doi.org/10.1016/j.chb.2017.01.005.
Lai, Y., Saab, N., & Admiraal, W. (2022). Learning strategies in self-directed Language Learning using Mobile Technology in Higher Education: a systematic scoping review. Education and Information Technologies, 76(4), 851. https://doi.org/10.1007/s10639-022-10945-5.
Lee, A., O’Donnell, A. M., & Rogat, T. K. (2015). Exploration of the cognitive regulatory sub-processes employed by groups characterized by socially shared and other-regulation in a CSCL context. Computers in Human Behavior, 52, 617–627. https://doi.org/10.1016/j.chb.2014.11.072.
Li, J., Liu, J., Yuan, R., & Shadiev, R. (2022). The influence of Socially Shared Regulation on Computational thinking performance in Cooperative Learning. Educational Technology & Society, 25(1), 48–60.
Li, K., & Keller, J. M. (2018). Use of the ARCS model in education: a literature review. Computers & Education, 122, 54–62. https://doi.org/10.1016/j.compedu.2018.03.019.
Lobczowski, N. G., Allen, E. M., Firetto, C. M., Greene, J. A., & Murphy, P. K. (2020). An exploration of social regulation of learning during scientific argumentation discourse. Contemporary Educational Psychology, 63(2), 101925. https://doi.org/10.1016/j.cedpsych.2020.101925.
Malmberg, J., Järvelä, S., & Järvenoja, H. (2017). Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning. Contemporary Educational Psychology, 49, 160–174. https://doi.org/10.1016/j.cedpsych.2017.01.009.
Manwaring, K. C., Larsen, R., Graham, C. R., Henrie, C. R., & Halverson, L. R. (2017). Investigating student engagement in blended learning settings using experience sampling and structural equation modeling. The Internet and Higher Education, 35, 21–33. https://doi.org/10.1016/j.iheduc.2017.06.002.
Marcelino, M. J., Pessoa, T., Vieira, C., Salvador, T., & Mendes, A. J. (2018). Learning computational thinking and scratch at distance. Computers in Human Behavior, 80, 470–477. https://doi.org/10.1016/j.chb.2017.09.025.
Miller, R. B., Greene, B. A., Montalvo, G. P., Ravindran, B., & Nichols, J. D. (1996). Engagement in academic work: the role of learning goals, future consequences, pleasing others, and perceived ability. Contemporary Educational Psychology, 21(4), 388–422. https://doi.org/10.1006/ceps.1996.0028.
Nguyen, H., Lim, K. Y., Wu, L. L., Fischer, C., & Warschauer, M. (2021). “We’re looking good”: Social exchange and regulation temporality in collaborative design. Learning and Instruction, 74(1), 101443. https://doi.org/10.1016/j.learninstruc.2021.101443.
Okita, S. Y., Turkay, S., Kim, M., & Murai, Y. (2013). Learning by teaching with virtual peers and the effects of technological design choices on learning. Computers & Education, 63, 176–196. https://doi.org/10.1016/j.compedu.2012.12.005.
Paas, F., & Sweller, J. (2012). An evolutionary upgrade of cognitive load theory: using the human motor system and collaboration to support the learning of complex cognitive tasks. Educational Psychology Review, 24(1), 27–45. https://doi.org/10.1007/s10648-011-9179-2.
Park, S., & Kim, C. (2015). Boosting learning-by-teaching in virtual tutoring. Computers & Education, 82, 129–140. https://doi.org/10.1016/j.compedu.2014.11.006.
Pilegard, C., & Fiorella, L. (2016). Helping students help themselves: generative learning strategies improve middle school students’ self-regulation in a cognitive tutor. Computers in Human Behavior, 65, 121–126. https://doi.org/10.1016/j.chb.2016.08.020.
Pintrich, P. R., Smith, D. A. F., & Garcia, T. (1993). Reliability and predictive validity of the motivated strategies for learning questionnaire (MSLQ). Educational and Psychological Measurement, 53(3), 801–813.
Rashid, T., & Asghar, H. M. (2016). Technology use, self-directed learning, student engagement and academic performance: examining the interrelations. Computers in Human Behavior, 63, 604–612. https://doi.org/10.1016/j.chb.2016.05.084.
Rosário, P., Núñez, J. C., Trigo, L., Guimarães, C., Fernández, E., Cerezo, R., & Figueiredo, M. (2015). Transcultural analysis of the effectiveness of a program to promote self-regulated learning in Mozambique, Chile, Portugal, and Spain. Higher Education Research & Development, 34(1), 173–187. https://doi.org/10.1080/07294360.2014.935932.
Sinatra, G. M., Heddy, B. C., & Lombardi, D. (2015). The challenges of defining and measuring student engagement. Educational Psychologist, 50(1), 1–13.
Smit, K., de Brabander, C. J., Boekaerts, M., & Martens, R. L. (2017). The self-regulation of motivation: motivational strategies as mediator between motivational beliefs and engagement for learning. International Journal of Educational Research, 82, 124–134. https://doi.org/10.1016/j.ijer.2017.01.006.
Splichal, J. M., Oshima, J., & Oshima, R. (2018). Regulation of collaboration in project-based learning mediated by CSCL scripting reflection. Computers & Education, 125, 132–145. https://doi.org/10.1016/j.compedu.2018.06.003.
Su, C. H. (2016). The effects of students’ learning anxiety and motivation on the learning achievement in the activity theory based gamified learning environment. EURASIA Journal of Mathematics Science and Technology Education, 13(5), 1229–1258. https://doi.org/10.12973/eurasia.2017.00669a.
Tsai, C. W. (2013). An effective online teaching method: the combination of collaborative learning with initiation and self-regulation learning with feedback. Behaviour & Information Technology, 32(7), 712–723. https://doi.org/10.1080/0144929X.2012.667441.
Tsai, C. W. (2015). The effect of online co-regulated learning in the implementation of team-based learning on improving students’ involvement. Higher Education Research & Development, 34(6), 1270–1280. https://doi.org/10.1080/07294360.2015.1024631.
Tsai, C. W., Shen, P. D., Chiang, I. C., Chen, W. Y., & Chen, Y. F. (2018). Exploring the effects of web-mediated socially-shared regulation of learning and experience-based learning on improving students’ learning. Interactive Learning Environments, 26(6), 815–826. https://doi.org/10.1080/10494820.2017.1415940.
Tsai, M. C., Shen, P. D., Chen, W. Y., Hsu, L. C., & Tsai, C. W. (2020). Exploring the effects of web-mediated activity-based learning and meaningful learning on improving students’ learning effects, learning engagement, and academic motivation. Universal Access in the Information Society, 19(4), 783–798.
Van Alten, D. C. D., Phielix, C., Janssen, J., & Kester, L. (2021). Secondary students’ online self-regulated learning during flipped learning: a latent profile analysis. Computers in Human Behavior, 118(1), 106676. https://doi.org/10.1016/j.chb.2020.106676.
Wang, M. T., Fredricks, J. A., Ye, F., Hofkens, T. L., & Linn, J. S. (2016). The math and science engagement scales: scale development, validation, and psychometric properties. Learning and Instruction, 43, 16–26. https://doi.org/10.1016/j.learninstruc.2016.01.008.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.
Xie, K., Heddy, B. C., & Greene, B. A. (2019). Affordances of using mobile technology to support experience-sampling method in examining college students’ engagement. Computers & Education, 128, 183–198. https://doi.org/10.1016/j.compedu.2018.09.020.
Xiong, W., Yang, J., & Shen, W. (2022). Higher education reform in China: a comprehensive review of policymaking, implementation, and outcomes since 1978. China Economic Review, 72, 101752. https://doi.org/10.1016/j.chieco.2022.101752.
Zheng, J., Xing, W., & Zhu, G. (2019). Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment. Computers & Education, 136, 34–48. https://doi.org/10.1016/j.compedu.2019.03.005.
Zheng, L., Zhong, L., Niu, J., Long, M., & Zhao, J. (2021). Effects of personalized intervention on collaborative knowledge building, Group Performance, Socially Shared Metacognitive Regulation, and cognitive load in computer-supported collaborative learning. Educational Technology & Society, 24(3), 174–193.
Zhou, X., Chen, L. H., & Chen, C. L. (2019). Collaborative learning by teaching: a pedagogy between learner-centered and learner-driven. Sustainability, 11(4), 1174. https://doi.org/10.3390/su11041174.
Acknowledgements
The authors would like to express appreciation for the financial support of high-level talent research start-up project of Xiamen University of Technology [No. YKJ19005R].
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of interest
None.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Personal Information.
Class Name: Gender: Age:
Question Items are as follows. 5-point Likert scale is adopted with the range of “1 totally disagree”, “2 disagree”, “3 not sure”, “4 agree” and “5 totally agree”.
-
1.
I like the people who are realistic and neutral.
-
2.
I believe that I can solve most of the problems I face if I have sufficient amount of time and if I show effort.
-
3.
I have a belief that I can solve the problems possible to occur when I encounter with a new situation.
-
4.
I trust that I can apply the plan while making it to solve a problem of mine.
-
5.
Dreaming causes my most important projects to come to light.
-
6.
I trust my intuitions and feelings of “trueness” and “wrongness” when I approach the solution of a problem.
-
7.
When I encounter with a problem, I stop before proceeding to another subject and think over that problem.
-
8.
I think that I have a special interest in the mathematical processes.
-
9.
I think that I learn better the instructions made with the help of mathematical symbols and concepts.
-
10.
I believe that I can easily catch the relation between the Fig.
-
11.
I can mathematically express the solution ways of the problems I face in the daily life.
-
12.
I can digitize a mathematical problem expressed verbally.
-
13.
I like experiencing cooperative learning together with my group friends.
-
14.
In the cooperative learning, I think that I attain/will attain more successful results because I am working in a group.
-
15.
I like solving problems related to group project together with my friends in cooperative learning.
-
16.
More ideas occur in cooperative learning.
-
17.
I am good at preparing regular plans regarding the solution of the complex problems.
-
18.
It is fun to try to solve the complex problems.
-
19.
I am willing to learn challenging things.
-
20.
I am proud of being able to think with a great precision.
-
21.
I make use of a systematic method while comparing the options at my hand and while reaching a decision.
-
22.
I have no problem in the demonstration of the solution of a problem in my mind.
-
23.
I have no problem in the issue of where and how I should use the variables such as X and Y in the solution of a problem.
-
24.
I can apply the solution ways I plan respectively and gradually.
-
25.
I can develop my own ideas in the environment of cooperative learning.
-
26.
I like to learn something together with my group friends in cooperative learning.
-
27.
In a class like this, I prefer course material that really challenges me so I can learn new things.
-
28.
In a class like this, I prefer course material that arouses my curiosity, even if it is difficult to learn.
-
29.
The most satisfying thing for me in this course is trying to understand the content as thoroughly as possible.
-
30.
When I have the opportunity in this class, I choose course assignments that I can learn from even if they don’t guarantee a good grade.
-
31.
Getting a good grade in this class is the most satisfying thing for me right now.
-
32.
The most important thing for me right now is improving my overall average score, so my main concern in this class is getting a good grade.
-
33.
If I can, I want to get better grades in this class than most of the other students.
-
34.
I want to do well in this class because it is important to show my ability to my family, friends, employer, or others.
-
35.
When studying, I try to combine different pieces of information from course material in new ways.
-
36.
I draw pictures or diagrams to help me solve some problems.
-
37.
I work several examples of the same type of problem when studying mathematics so I can understand the problems better.
-
38.
I work practice problems to check my understanding of new concepts or rules.
-
39.
I examine example problems that have already been worked to help me figure out how to do similar problems on my own.
-
40.
I classify problems into categories before I begin to work them.
-
41.
When I work a problem, I analyze it to see if there is more than one way to get the right.
-
42.
When I study for tests, I review my class notes and look at solved problems.
-
43.
When I study for tests, I use solved problems in my notes or in the book to help me memorize the steps involved.
-
44.
I find reviewing previously solved problems to be a good way to study for a test.
-
45.
Studying this material is worthwhile to me.
-
46.
I like what we are learning in this class.
-
47.
I am studying now because my achievement plays a role in reaching my future goals.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhou, X., Tsai, CW. The Effects of Socially Shared Regulation of Learning on the Computational Thinking, Motivation, and Engagement in Collaborative Learning by Teaching. Educ Inf Technol 28, 8135–8152 (2023). https://doi.org/10.1007/s10639-022-11527-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-022-11527-1