The canonical correlation between online self-regulation collectivity and secondary school student's cognitive flexibility components in virtual training circulation in coronavirus prevalence

Khatib Zanjani, N.; Mohammadi, N.; Paahoo, A.

doi:10.22061/tej.2022.8380.2664

Document Type : Original Research Paper

Authors

¹ Department of Educational Science, Payame Noor University, Tehran, Iran

² Department of Educational Psychology, Islamic Azad University, Ardabil, Iran

³ Department of Distance Education Planning, Payame Noor University, Tehran, Iran

https://doi.org/10.22061/tej.2022.8380.2664

Abstract

Background and Objectives: Canonical correlation deals with two series of variables, each of which can be given a theoretical meaning. The purpose of this study was Canonical analysis of online self-regulatory collectivity (online interaction) and components of cognitive flexibility during cyber teaching (during Corona virus pandemics) among students of junior high school.
Methods: The statistical society included all male and female junior high school students in Ardabil numerated as 43098 (23239 boys and 19859 girls) in the academic year 2020-2021 so that 381 ones (205 boys and 176 girls) were selected via proportional stratified sampling method. For collecting the required data, online self-regulation questionnaire (OSRQ) and the content of cognitive flexibility Inventory (CFI) were used. The collected data was analyzed using Pearson Product-moment correlation and canonical correlation analysis in SPSS 26.00.
Findings: The obtained results pointed to a significant relationship between the predictor and criterion variable components (p<0.01). Also, the canonical correlation identified that the components of online self-regulation collectivity including learner-content interaction, learner-Instructor interaction and learner-learner interaction can predict changes of components of cognitive flexibility collectivity including alternatives and control subscales among junior high school students (p<0.01).
Conclusion: The results of the study identified that the more the students interact in online teaching and in cyber space with content, as well as the teacher and the other students, the more power of cognitive complexity they would have to deal with difficulties in problems in learning and teaching issues. Using cognitive strategies could assist the students in accessing vague teaching concepts and enable them to access higher levels of learning. At the present time, virtual training provides the students with ample opportunities to make some changes in themselves in terms of motivational aspects, skills, and operational learning steps for reaching teaching purposes. The learner-content interaction is a channel that reconciles the cognitive structure and the mental content of the learner with their macro-cognitive, motivational, and behavioral aspects. These changes in cognition can empower the learners in self-regulation and turn them from passive and strict receivers into active and flexible learners. Accordingly, this perception of ability and internal power assists the learner to reach the belief of self-controllability in solving problems and facing with challenging circumstances.

Keywords

20.1001.1.20080441.1401.16.4.10.8

Main Subjects

Electronic learning- virtual

COPYRIGHTS
©2022 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.

References

[1] Kashdan TB, Rottenberg J. Psychological flexibility as a fundamental aspect of health. Clinical psychology review. 2010; 30(7): 865-878.

[2] Dajani DR, et al. Measuring cognitive flexibility with the flexible item selection task: From fMRI adaptation to individual connectome mapping. Journal of cognitive neuroscience. 2020; 32(6): 1026-1045.

[3] Rahayuningsih S, Sirajuddin S, Nasrun N. Cognitive flexibility: exploring students’ problem-solving in elementary school mathematics learning. JRAMathEdu (Journal of Research and Advances in Mathematics Education). 2020; 6(1): 59-70.

[4] Algharaibeh SAS. Cognitive flexibility as a predictor of subjective vitality among university students. Cypriot Journal of Educational Sciences. 2020; 15(5): 923-936.

[5] Dennis JP, Vander Wal JS. The cognitive flexibility inventory: Instrument development and estimates of reliability and validity. Cognitive therapy and research. 2010. 34(3): 241-253.

[6] Tuncer N, COMPARING THE EXECUTIVE FUNCTION SKILLS OF TURKISH AND REFUGEE PRESCHOOL CHILDREN: FLEXIBLE ITEM SELECTION TASK (FIST)/OKUL ÖNCESİ DÖNEMDEKİ TÜRK VE MÜLTECİ ÇOCUKLARIN YÜRÜTÜCÜ İŞLEV BECERİLERİNİN KARŞILAŞTIRILMASI: NESNE SEÇİMİNDE ESNEKLİK GÖREVİ (NSEG). European Journal of Education Studies. 2021; 8(1(.

[7] Deliyianni E, et al. Representational flexibility and problem-solving ability in fraction and decimal number addition: A structural model. International Journal of Science and Mathematics Education. 2016; 14(2): 397-417.

[8] Cox KS. The effects of second-language study on the cognitive flexibility of freshman university students. London: The Ohio State University ProQuest Dissertations Publishing; 1980.

[9] Yucel, Ã.z., D. Karahoca, and A. Karahoca, The effects of problem based learning on cognitive flexibility, self-regulation skills and studentsâ€™ achievements. Global Journal of Information Technology: Emerging Technologies. 2016; 6(1): 86-93.

[10] Demirtaş AS. ÜNİVERSİTE ÖĞRENCİLERİNİN STRESLE BAŞA ÇIKMA STRATEJİLERİ: BİLİŞSEL ESNEKLİK VE ÖZ-DÜZENLEMENİN ROLÜ. Social Sciences. 2019; 14(3): 447-464.

[11] Walwanis MM, Ponto SJ. Clarifying Cognitive Flexibility from a Self-regulatory Perspective. 21stInternational Conference on Human-Computer Interaction: 2019 July 26-31. Springer.

[12] Zimmerman BJ, Pons MM. Development of a structured interview for assessing student use of self-regulated learning strategies. American educational research journal. 1986; 23(4): 614-628.

[13] Esen BK, Özcan HD, Sezgin M. HIGH SCHOOL STUDENTS’COGNITIVE FLEXIBILITY IS PREDICTED BY SELF-EFFICACY AND ACHIEVEMENT. European Journal of Education Studies. 2017.

[14] Yildiz M, Eldeleklioglu J. The Relationship between Decision-Making and Intolerance to Uncertainty, Cognitive Flexibility and Happiness. Eurasian Journal of Educational Research. 2021; 91: 39-60.

[15] Pintrich PR. The role of goal orientation in self-regulated learning. in Boekaerts M, Pintrich RP, Zeidner M. (eds) Handbook of self-regulation. Elsevier; 2000.. p. 451-502.

[16] Kara M, Kukul V, Çakır R. Self-regulation in three types of online interaction: How does it predict online pre-service teachers’ perceived learning and satisfaction?. The Asia-Pacific Education Researcher. 2021; 30(1): 1-10.

[17] Asikainen H, Hailikari T, Mattsson M. The interplay between academic emotions, psychological flexibility and self-regulation as predictors of academic achievement. Journal of further and Higher Education. 2018; 42(4): 439-453.

[18] Al Fadda H. The Relationship between Self-Regulations and Online Learning in an ESL Blended Learning Context. English Language Teaching. 2019; 12(6): 87-93.

[19] Shih M, Liang JC, Tsai CC. Exploring the role of university students’ online self-regulated learning in the flipped classroom: a structural equation model. Interactive Learning Environments. 2019; 27(8): 1192-1206.

[20] Bertiz Y, Karoglu AK. Distance Education Students' Cognitive Flexibility Levels and Distance Education Motivations. International journal of research in education and science. 2020; 6(4)): 638-648.

[21] Jeffords JR, et al. Investigating the Relationship Between University Students’ Psychological Flexibility and College Self-Efficacy. Journal of College Student Retention: Research, Theory & Practice. 2018; 22(2): 351-372.

[22] Lafreniere B, Gutwin C, Cockburn A. Investigating the post-training persistence of expert interaction techniques. ACM Transactions on Computer-Human Interaction (TOCHI). 2017; 24(4): 1-46.

[23] Eisenring MAA, Margana M. The Importance of Teacher – Students Interaction in Communicative Language Teaching (CLT). Journal of Linguistics. 2019; 4(1): 46-54.

[24] BERG GVD. Context matters: Student experiences of interaction in open distance learning. Turkish Online Journal of Distance Education. 2020; 21(4): 223-236.

[25] Moore MG, Editorial: Three Types of Interaction. The American Journal of Distance Education. 1989; 3(2): 1-7.

[26] Cho MH, Cho Y Self-regulation in three types of online interaction: A scale development. Distance Education. 2017; 38(1): 70-83.

[27] Yuan Y, Cun X. Exploring the Interaction Factors Effecting the Student Retention in Online Courses. 3^rd International Conference on Contemporary Education, Social Sciences and Humanities (ICCESSH 2018):2018 April25-27: Moscow, Russia.

[28] Abrami PC, et al. Interaction in distance education and online learning: Using evidence and theory to improve practice. In: Moller L, Huett BJ. (eds). The next generation of distance education; Springer; 2012. p. 49-69.

[29] Sherry A, Henson RK. Conducting and interpreting canonical correlation analysis in personality research: A user-friendly primer. Journal of personality assessment. 2005; 84(1): 37-48.

[30] Tabachnick BG, Fidell LS, Ullman JB. Using multivariate statistics. Boston: Pearson; 2007.