2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT)

The Relationship between Self-regulation and Flow Experience in Online Learning: A Case Study of Global Competition on Design for Future Education Qingqing Wana, Mengyu Liua, BoJun Gaoa, TingWen Changb, Ronghuai Huanga,b a Faculty of Education, Beijing Normal University, Beijing, China b Smart Learning Institute, Beijing Normal University, Beijing, China {wanqingqing; 201921010200; gaobojun}@mail.bnu.edu.cn; {tingwenchang; huangrh}@bnu.edu.cn

Abstract—Low learning engagement and high dropout rates learning that highlights students' dominant role, self-regulated have been common problems in online learning. From the learning not only benefits students a lot in formal school perspective of learners' emotional experience, this paper education but also serves as the premise and foundation for investigated the self-regulation and flow experience of online lifelong learning and development [9]. learners through questionnaires to explore the relationship Self-regulation is malleable and affected by environmental between them. The data were collected from the students in stimuli [10]. Scholars at home and abroad have conducted three countries of China, Tunisia and Serbia. They have extensive studies on how to promote self-regulated learning. participated in a three-week online course for Global For example, Peng and Cotterall proposed that self-regulated Competition on Design for Future Education (GCD4FE). The learning could be promoted through curriculum syllabus results showed that the goal setting and the task strategies of Chinese students were significantly higher than that of students setting and teaching content design [11-12] while Hua and in Tunisia and there was a positive impact of self-regulation on Wang advocated special strategy training [13-14]. flow of three countries. Consequently, some strategies can be The ability to regulate one’s own learning is essential for taken to improve students' self-regulation in online learning, so success in online courses [15]. Self-regulated learning is as to promote positive emotional experience to increase online indeed a process that may influenced by students' personal learning engagement and reduce dropout rate. learning style and external environment. B. Flow Experience Keywords-online learning; self-regulation; flow experience; Csikszentmihalyi initially presented the concept of flow in 1960 [4]. Through a study of a few hundred experts, artists, I. INTRODUCTION athletes, musicians, chess masters, and surgeons, With the development of information technology, online Csikszentmihalyi discovered that they are almost engrossed in learning has become a kind of learning method covering their work, ignoring the passage of time and the surrounding lifelong education. The number of people who study online is environment, fully involved in the context with deep so large; however, the number who make it to the end is very concentration. Csikszentmihalyi described eight conditions of less [1]. Positive learning emotions contribute to the flow in 1997[16]: 1) a clear goal, 2) feedback, 3) challenges emergence of positive learning behaviors [2], which is an match skills, 4) concentration and focus, 5) control, 6) loss of important variable to promote online learning engagement and self-consciousness, 7) transformation of time, and 8) the reduce the dropout rate of online learning [3]. activity becomes autotelic (that is, perceived as worth doing Flow is a positive learning emotional experience, which for its own sake). Novak and Hoffman grouped eight can help learners immerse themselves in learning, forget the components above according to whether they specify passage of time, and pursue the value of learning itself [4]. antecedent conditions of flow (1, 2, and 3), its characteristics Studies have shown that flow can help online learners to (4 and 5), or the consequences of the experience (6, 7, and 8) improve their online learning engagement and enhance [17]. In the subsequent empirical research, some scholars continuance intention of online learning [5]. As the online proved that these three antecedent conditions have an learning environment is characterized by autonomy, self- important influence on learners’ ability to get flow experience regulation becomes a critical factor for success [6]. When a and achieve better learning performance [18-19]. student is studying online, he needs to determine the learning Flow is a kind of positive emotional experience, and it is objectives and arrange the learning schedule by himself [7]ˈ generated to enable learners to be highly engaged in the at this time, his learning behavior is mainly controlled by process of learning. In flow, the learner is engrossed, can filter himself. Therefore, this paper intends to explore the and ignore the perception and thoughts irrelevant to the relationship between self-regulation and students' flow learning task, and focus on the value of the learning task itself. experience in online learning to get it clearer in increasing online learning engagement and reducing dropout rate. III. METHODOLOGY

II. LITERATURE REVIEW A. Research Design This study was intended to the relationship between self- A. Self-regulation regulation and flow experience in online learning. The online Self-regulation is derived from self-regulated learning learning course was serviced for Global Competition on which generally means that individuals are responsible for Design for Future Education (GCD4FE), providing students their own learning activities and behaviors [8]. As a form of with some knowledge foundation needed to complete the

2161-377X/20/$31.00 ©2020 IEEE 365 DOI 10.1109/ICALT49669.2020.00116 competition tasks. This online learning course consists of China 4.32±0.56 three parts: 1) Educational Technology; 2) Design and Learning; 3) Mini Projects. After completing the online course, 1 Tunisia 3.81±0.87 .082 5.919 .004** we invited students to fill out a questionnaire about self- Serbia 4.10±0.42 regulation and flow experience. China 4.37±0.56 B. Participants 2 Tunisia 3.84±0.96 .013* 5.639 .005** A total of 107 students participated in the three-week Serbia 4.03±0.48 online learning (34 males and 74 females), which included China 4.08±0.64 undergraduate and postgraduate students, as well as a small number of junior college students. There were 18 participants 3 Tunisia 3.87±0.78 .161 5.581 .005** from Serbia, 47 from Tunisia, and 42 from China. Serbia 3.43±0.51 C. Instruments China 4.27±0.66 Online Self-regulated Learning Questionnaire (OSLQ) 4 Tunisia 3.77±0.99 .008** 5.125 .008** was used in this paper to measuring self-regulation in the Serbia 3.72±0.55 online learning environment which includes six dimensions China 4.26±0.69 [6]: goal setting (5 items, =.95), environment structuring (4 items, =.92), task strategies (4 items, =.93), time 5 Tunisia 4.02±0.89 .139 1.168 .315 management (3 items, =.87), help seeking (4 items, =.96) Serbia 4.24±0.56 and self-evaluation (4 items, =.94). Flow experience China 4.21±0.61 questionnaire in online learning from Pearce et al. [20] was 6 Tunisia 3.89±0.92 .105 1.950 .147 used in this paper to measure students' flow experience during online learning, which includes 8 items. Serbia 3.94±0.77 China 4.33±0.54 IV. DATA ANALYSIS AND RESULTS 8 Tunisia 3.62±0.95 .000** 10.276 .000** A. one-way ANOVA Serbia 3.99±0.43 In order to conduct a better survey on Chinese, Tunisian, **. There was a significant correlation at the level of.01 (bilateral); and Serbian students, this paper examine the reliability on the *. There was a significant correlation at the level of.05 (bilateral). translation of the original questionnaires. The Cronbach’s B. Correlation Analysis alpha coefficient of the Chinese and English questionnaires is shown in the following TABLE I. From the Cronbach’s alpha In order to explore the relationship between self-regulation coefficient, the questionnaire data before and after translation and flow experience in online learning, this paper analyzes the can be used for subsequent analysis. correlation between the six dimensions of self-regulation and flow experience, as shown in TABLE III. TABLE I. INTERNAL CONSISTENCY COEFFICIENT TABLE III. CORRELATION ANALYSIS 1 2 3 4 5 6 7 8 Chinese .86 .79 .74 .72 .87 .82 .95 .92 1 2 3 4 5 6 English .84 .82 .63 .78 .78 .89 .95 .91 ** 1= Goal Setting; 2= Environment Structuring; 3= Task Strategies; 4= Time Management; 5= China .540 Help Seeking; 6= Self-Evaluation; 7= Self-regulation; 8= Flow experience (the same below). 澳 2 Tunisia .865**

** Before the one-way ANOVA, the homogeneity test of Serbia .734 variance was carried out. The results showed that the China .451** .357* homogeneity of variance of the goal setting, the task strategies, 3 Tunisia .890** .761** the help seeking and the self-evaluation could be used for Serbia .335 .381 subsequent one-way ANOVA. TABLE II. shows the ** * ** significance test of difference of China, Tunisia, and Serbia in China .477 .381 .565 self-regulation and flow experience. Among them, there was 4 Tunisia .826** .802** .830** an inter-group difference in goal setting (p<.05) and task Serbia .799** .822** .620** strategies (p<.05), and the difference was found to be mainly China .465** .177 .466** .472** between China and Tunisia after post-test. There was no inter- ** ** ** ** group difference in the help seeking and the self-evaluation. 5 Tunisia .738 .716 .770 .804 Serbia .794** .880** .380 .726** TABLE II. ONE-WAY ANOVA China .458** .391** .623** .748** .699** homogeneity inter-group 6 Tunisia .789** .803** .834** .821** .888** Country ± s test of F difference * ** * ** variance (Sig.) (Sig.) Serbia .566 .748 .348 .535 .751

366 China .530** .291 .461** .196 .134 .273 ACKNOWLEDGMENT 8 Tunisia .859** .774** .889** .770** .682** .798** This paper is a research result of “Department of science and technology of Guangxi Zhuang Autonomous Region: Serbia .914** .772** .496* .859** .740** .639** Construction and application of innovative design method **. There was a significant correlation at the level of.01 (bilateral); system of "Internet +" education service industry” (Project no. *. There was a significant correlation at the level of.05 (bilateral). AB18126068).

REFERENCES V. CONCLUSIONS AND DISCUSSIONS [1] M. Kang, B. Y. T. Liew, et al, Learning presence as a predictor of Based on the one-way ANOVA and correlation analysis, achievement and satisfaction in online learning environments. the following conclusions and discussions can be drawn: International Journal on E-Learning, 2014, 13(1), pp.193-208. The goal setting and the task strategies of Chinese students [2] G. C. Marchand, and A. P. Gutierrez, The role of emotion in the learning process: Comparisons between online and face-to-face were significantly higher than those of Tunisian students. learning settings, Internet and Higher Education,2012,15(3),pp.150. However, the difference between Tunisian and Serbian [3] J. Gao, Impact of online academic emotion on learning engagement, students in both self-regulation and flow have no significant. Open Education Research, 2016, 22(02), pp.89-95. The reason may be that the amount of data collected is so [4] M. Csikszentmihalyi, Flow: The Psychology of Optimal Experience, small that the differences are not significant. HarperCollins, New York, 1990. The goal setting and the task strategies in self-regulated [5] W. Wang, R. H. Shi, and X. N. Li, Factors influencing the continuance online learning of Chinese students are significantly intention of online learning from the perspective of flow theory, correlated with the flow experience. As a result, to improve Distance Education in China, 2017(05), pp.17-23+79. flow experience of Chinese students in online learning, the [6] L. Barnard, W. Y. Lan, Y. M. To, V. O. Paton, and S. L. Lai, Measuring intervention can be conducted mainly from two aspects: the self-regulation in online and blended learning environments, The Internet and Higher Education, 2009, 12(1), pp.1-6. goal setting and the task strategies. The goal itself has the [7] Y. Qian, Factors affecting the continued use of online learning user incentive function, it can turn student's need into motivation, behavior, Journal of Modern Information, 2015(3), pp.50-56. and then students can take some strategies to achieve the goal. [8] H. Holec, Autonomy in Foreign Language Learning, Oxford: In this process, students could adjust and modify so as to Pergamon Press, 1981. achieve their own needs. In online learning, task strategies can [9] H. Zhao, L. Chen, Q. H. Zheng, and S. S. Zhang, The Study on the be acquired through educational guidance [21], and need to be Teaching Model of Self-Regulated Learning Ability for Adult Learners, programmed and internalized through a lot of practice. China Educational Technology, 2014(06), pp.37-41+48. Teachers can set up some training courses on task strategy in [10] D. Schunk, and B. J. Zimmerman, Self-regulation of learning and daily teaching and provide a lot of practical opportunities for performance, Hillsdale, NJ: Erlbaum, 1994. students to practice their task strategy and skill in real world. [11] J. D. Peng, A study on learner autonomy in college English teaching, The self-regulation of Tunisian and Serbian students is Foreign Language World, 2002,(3), pp.15-19. significantly correlated with the flow experience, which [12] S. Cotterall, Promoting learner autonomy through the curriculum, ELT means that in order to improve the flow experience of Journal, 2000, 54(2), pp.109-117. Tunisian and Serbian students in online learning, various [13] W. F. Hua, Study on the strategy training of foreign language learners, Foreign Language World, 2002, (3), pp.2-7. learning behaviors of students in self-regulated learning [14] D. Q. Wang, The cultivation of college English self-regulated learning should be paid more attention. The acquisition of self- ability, Foreign Language World, 2002, (5), pp.17-23. regulation is a process of internalizing external learning skills [15] Q. J. Li, R. Baker, and M. Warschauer, Using clickstream data to into one's own ability, which goes through four stages of measure, understand, and support self-regulated learning in online observation, imitation, self-control and self-regulation [22]. courses, The Internet and Higher Education, 2020, pp.45. When studying online, students can look for a good role model [16] M. Csikszentmihalyi, Finding Flow: The Psychology of Engagement to develop their own long-term learning style and skill by with Everyday Life, Basic Books, New York, 1997. observing and imitating role model’s good learning habits and [17] T. Novak, D. Hoffman, et al, Measuring the Customer Experience in behaviors. At the same time, teachers should also pay Online Environments, Marketing Science, 2000, 19(1), pp.22-42. attention to set a good example for students to guide them to [18] K. Kiili, S. de Freitas, et al, The Design Principles for Flow Experience form correct learning attitude and efficient learning method. in Educational Games, Procedia Computer Science, 2012, pp.78-91. The limitation of this paper lies in the small amount of data. [19] Y. G. Wang, T. Zhang, W. Li, and B. Y. Huang, Elements of Educational Game Designing Framework Based on Flow Theory, To better explore the relationship between self-regulation and Distance Education Journal, 2014,32(03), pp.97-104. flow experience in online learning, the results obtained by [20] J. M. Pearce, M. Ainley, and S. Howard, The ebb and flow of online constructing the structural equation model are more reliable learning, Computers in Human Behavior, 2005, 21(5), pp.745-771. and convincing. What’s more, the students participating in the [21] D. Y. Zhang, English learning strategies and self-regulated learning, questionnaire survey include undergraduate students, foreign language teaching,2005,(01), pp.49-55. graduate students, and a small number of junior college [22] D. Schunk, and B. J. Zimmerman, Modeling and self-efficacy students. This paper does not distinguish the participants with influences on children's development of self-regulation. In J. Juvonen, different academic degrees, but only focuses on the research and K. Wentzel(ed), Social motivation: understanding children's results under different countries. school adjustment. Cambridge University Press. 1997.

367