How Does Self-Regulation Affect Computer-Programming Achievement in a Blended Context?

Yıl 2015, Cilt: 6 Sayı: 1, 19 - 37, 01.03.2015

Harun Cigdem

Öz

This study focuses on learners’ self-regulation which is one of the essential skills for student achievement in blended courses. Research on learners’ self-regulation skills in blended learning environments has gained popularity in recent years however only a few studies investigating the correlation between self-regulation skills and student achievement in blended learning environments exist. Self-regulation is related to self-efficacy, anxiety, interactivity, satisfaction with and usefulness of the system. Self-regulated learners are more likely to accomplish at online learning. In this study, a total of 267 military vocational college students were taught computer programming during a 15-week-long semester in a blended learning context, which involved using both face to face teaching and online learning through MOODLE over intranet. Participants were the graduates of vocational high schools and the students at the departments of Computer Technologies and Electronics & Communication Technologies and were all male. Liaw and Huang’s Self-Regulation Scale with six subscales was used to collect the data during the last two weeks of instruction. Regression analyses were conducted to analyze the data. The results revealed that self-regulation was affected by perceived anxiety, interactivity in the online learning environment, and perceived self-efficacy. Learners’ academic achievement has been affected only by perceived self-efficacy

Anahtar Kelimeler

Blended learning, Computer programming course, Course achievement, Interactivity, Self-regulation, Perceived self-efficacy

Kaynakça

• Adams, D., Nelson, R., & Todd, P. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 16(2), 227-247.
• Akkoyunlu, B. & Soylu, M. Y. (2008). A Study of Student's Perceptions in a Blended Learning Environment Based on Different Learning Styles. Educational Technology & Society, 11(1), 183-193.
• Allen, I. E., Seaman, J., & Garrett, R. (2007). Blending in: The extent and promise of blended learning education in the United States. Retrieved on 3 November 2014 from http://sloanconsortium.org/sites/default/files/Blending_In.pdf
• Altun, A., Gülbahar, Y., & Madran, O. (2008). Use of a Content Management System for Blended Learning: Perceptions of Pre-Service Teachers. Turkish Online Journal of Distance Education- TOJDE, 9(4),138-153.
• Artino, A.R., Jr. (2007). Self-regulated learning in online education: A review of the empirical literature. International.Journal of Instructional Technology and Distance Learning, 4(6), 3- 18.
• Artino, A.R. (2008). Motivational beliefs and perceptions of instructional quality: Predicting satisfaction with online training. Journal of Computer Assisted Learning, 24, 260-270
• Artino, A.R. (2009). Think, feel, act: motivational and emotional influences on military students’ online academic success. J Comput High Educ, 21, 146-166.
• Askar, P. & Davenport, D (2009). An investigation of factors related to self-efficacy for Java programming among engineering students. Turkish Online Journal of Education Technology 8(1), 26-32.
• Azevedo, R. & Cromley, J.G. (2004). Does training on self-regulated learning facilitate students’ learning with hypermedia? Journal of Educational Psychology, 96 (3), 523-535.
• Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W.H. Freeman.
• Bell, P.D. & Akroyd, D. (2006). Can factors related to self-regulated learning predict learning achievement in undergraduate asynchronous Web-based courses? International Journal of Instructional Technology and Distance Learning, 3(10), 5-16.
• Bliuc, A.M., Ellis, R. A., Goodyear, P., & Piggott, L. (2011). A blended learning approach to teaching foreign policy: Student experiences of learning through face-to-face and online discussion and their relationship to academic performance. Computers in Education, 56, 856-864.
• Cavanagh, T.B. (2011). The blended learning toolkit: Improving student performance and retention. Educause Review, 34(4). Retrieved on 3 November 2014 from http://www.educause.edu/ero/article/blended-learning-toolkit-improving-student- performance-and-retention
• Chang, M.M. (2007). Enhancing web-based language learning through self-monitoring. Journal of Computer-Assisted Learning, 23, 187-196
• Chen, S.W., Stocker, J., Wang, R. H., Chung, Y. C., & Chen, M. F. (2009). Evaluation of self- regulatory online learning in a blended course for post-registration nursing students in Taiwan. Nurse Education Today, 29 (2009), 704-709.
• Chua, S.L., Chen, D., & Wong, A.F.L. (1999). Computer anxiety and its correlates: A meta-analysis. Computers in Human Behavior, 15, 609-623.
• Cigdem, H. & Topcu, A. (2013). Students’ perception of e-learning in the technical vocational school. Science Journal of Turkish Military Academy, 23(2), 1-19.
• Cigdem, H. & Yildirim, O.G. (2014). Predictors of C# programming language self efficacy among vocational college students. International Journal on New Trends in Education and Their Implications, 5(3), 145-153.
• Dabbagh, N. & Kitsantas, A. (2004). Supporting self-regulation in student-centered web-based learning environments. International Journal on E-Learning, 3(1), 40-47.
• Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340
• Davis, F.D., Bagozzi, R.P. & Warshaw, P.R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982-1003.
• Derus, S.R. MD. & Ali, A.Z.M. (2014). Integration of visualization techniques and active learning strategy in learning computer programming: a proposed framework. International Journal on New Trends in Education and Their Implications, 5(1), 93-103.
• Delen, E., Liew, J., & Willson, V. (2014). Effects of interactivity and instructional scaffolding on learning: Selfregulation in online video-based environments. Computers & Education, 78, 312-320.
• DeTure, M. (2004). Cognitive style and self-efficacy: Predicting student success in online distance education. American Journal of Distance Education, 18(1), 21-38.
• Ergul, H. (2004). Relationship between student characteristics and academic achievement in distance education and application on students of Anadolu University. Turkish Online Journal of Distance Education-TOJDE, 5(2), 81-90.
• Jain, P.J. (2011). Interactions among online learners: A quantitative interdisciplinrary study. Education, 131(3), 538-544.
• Johnson, R.D., Hornik, S., & Salas, E. (2008). An empirical examination of factors contributing to the creation of successful e-learning environments. International Journal of Human- Computer Studies, 66 (5), 356-369.
• Joo, Y.J., Bong, M., & Choi, H. J. (2000). Self-efficacy for self-regulated learning, academic self- efficacy, and Internet self-efficacy in Web-based instruction. Educational Technology Research and Development, 48, 5-17.
• Joo, Y.J., Lim, K.Y., & Kim, S.M. (2012). A Model for Predicting Learning Flow and Achievement in Corporate e-Learning. Educational Technology & Society, 15 (1), 313-325.
• Khan, B.H. (1997). Web-based instruction. Englewood Cliffs, NJ: Educational Technology Publications.
• Kitsantas, A., & Dabbagh, N. (2004). Promoting self-regulation in distributed learning environments with web-based pedagogical tools: An exploratory study. Journal on Excellence in College Teaching, 15 (1&2), 119-142.
• Kramarski, B. & Gutman, M. (2006). How can self-regulated learning be supported in mathematical e-learning environments? Journal of Computer Assisted Learning, 22, 24-33.
• Lee, T.H., Shen, P.D., & Tsai, C.W. (2010). Enhance students’ computing skills via webmediated self-regulated learning with feedback in blended environment. International Journal of Technology and Human Interaction, 6(1), 15-32.
• Lee, Y.C. (2006). An empirical investigation into factors influencing the acceptance of an e-learning system. Online Information Review,30(5), 517-541.
• Liao, Y.K.C. (2007). Effects of computer-assisted instruction on students’ achievement in Taiwan: A meta-analysis. Computers & Education 48, 216-233.
• Liaw, S.S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: a case study of the Blackboard system. Computers & Education, 51(2), 864-873.
• Liaw, S.S. & Huang, H.M. (2007). Developing a collaborative e-learning system based on users’ perceptions. Lecture Notes in Computer Science, 4402, 751-759.
• Liaw, S.S. & Huang, H.M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14-24.
• Linden, T. & Lederman, R. (2011). Creating visualizations from multimedia building blocks: A simple approach to teaching programming concepts. Information Systems Educators Conference. Wilmington, North Carolina, USA.
• Linnenbrink, E.A. & Pintrich, P.R. (2002). Motivation as an enabler for academic success. The School Psychology Review 31(3), 313-327.
• Lo, C.C. (2010). How student satisfaction factors affect perceived learning. Journal of the Scholarship of Teaching and Learning, 10(1), 47-54.
• Lynch, R. & Dembo, M. (2004). Online learning in a blended learning context. International Review of Research in Open and Distance Learning, 5(2), Retrieved 25 February2014 from http://www.irrodl.org/index.php/irrodl/article/view/189/271
• Madorin, S. & Iwasiw, C. (1999), The effects of computer-assisted instruction on the self-efficacy of baccalaureate nursing students. The Journal of nursing education, 38(6), 282-95.
• Melton, B., Graf, J., & Chopak-Foss, J. (2009). Achievement and satisfaction in blended learning versus traditional general health course designs. International Journal for the Scholarship of Teaching and Learning, 3(1), 26.
• Michailidou, A. & Economides, A. (2003). E-learn: Towards a collaborative educational virtual environment. Journal of Information Technology Education, 2, 131-152.
• Miltiadou, M. & Savenye, W.C. (2003). Applying social cognitive constructs of motivation to enhance student success in online distance education. AACE Journal, 11(1), 78-95.
• Moore, M.G. & Kearsley, G. (1996). Distance education: A systems view. Belmonth, CA: Wadsworth.
• Motiwalla, L.F. (2007). Mobile learning: a framework and evaluation. Computers & Education, 49(3), 581-596.
• Ndubisi, N.O. (2004). Factors influencing E-learning adoption intention: examining the determinant structure of the decomposed theory of planned behaviour constructs. Paper presented at the HERDSA International Conference (pp. 252-62).
• Osguthorpe, R.T., & Graham, C.R. (2003). Blended learning systems: Definitions and directions. Quarterly Review of Distance Education, 4(3), 227-234.
• Owston, R., York, D., & Murtha, S. (2013). Student perceptions and achievement in a university blended learning strategic initiative. Internet and Higher Education, 18, 38-46.
• Paechter, M., Maier, B., & Macher, D. (2010). Students' expectations of and experiences in e- learning: Their relation to learning achievements and course satisfaction. Computers & Education, 54(1), 222-229.
• Pintrich, P.R. (2000). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95, 667-686.
• Puzziferro, M. (2008). Online technologies self-efficacy and self-regulated learning as predictors of final grade and satisfaction in college-level online courses. American Journal of Distance Education, 22(2), 72-89.
• Saadé, R.G. & Kira, D. (2006). The emotional state of technology acceptance. Issues in Informing Science & Information Technology, 3, 529-40.
• Sahin, I. & Shelley, M. (2008).Considering students’ perceptions: The distance education student satisfaction model. Educational Technology & Society, 11(3), 216-223.
• Schunk, D.H. & Zimmerman, B. J. (Eds.). (1998). Self-regulated learning: From teaching to self- reflective practice. New York: The Guilford Press.
• Sharma, S., Dick, G., Chin, W.W., & Land, L. (2007). Self-regulation and e-learning. In Proceedings of the Fifteenth European Conference on Information System (pp. 383-394). St. Gallen: University of St. Gallen.
• Simsek, A. (2011). The relationship between computer anxiety and computer self-efficacy. Contemporary Educational Technology, 2(3), 177-187.
• Smyth, S., Houghton, C., Cooney, A., & Casey, D. (2012). Students' experiences of blended learning across a range of postgraduate programmes. Nurse Education Today, 32, 464-468
• Sumak, B., Hericko, M., & Pusnik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27(6), 2067-2077
• Sun, P.C., Tsai, R.J., Finger, G., Chen, Y.Y., & Yeh, D. (2008). What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50, 1183-1202.
• Tabak, F. & Nguyen, N.T. (2013). Technology acceptance and performance in online learning environments: Impact of self-regulation. MERLOT Journal of Online Learning and Teaching, 9(1), 116-130.
• Ting, K. & Chao, M. (2013). The application of self-regulated strategies to blended learning. English Language Teaching, 6(7), 26-32.
• Tsai, C. C. (2009). Conceptions of learning versus conceptions of web-based learning: The differences revealed by college students. Computers & Education, 53, 1092-1103.
• Uysal, M.P. (2014). Improving first computer programming experiences: The case of adapting a web-supported and well-structured problem-solving method to a traditional course. Contemporary Educational Technology, 5(3), 198-217
• Vaughan, N. (2007). Perspectives on blended learning in higher education. International Journal on E-Learning, 6 (1) ,. 81-94.
• Vighnarajah, Wong, S.L., & Kamariah, A.B. (2009). Qualitative findings of students’ perception on practice of self-regulated strategies in online community discussion. Computers & Education, 53, 94-103.
• Wang, A.T. & Newlin, M.H. (2002). Online lectures: Benefits for the virtual classroom. THE Journal, 29, 17-22.
• Wang, C., Shannon, D., & Ross, M. (2013). Students' Characteristics, Self-Regulated Learning, Technology, Self-Efficacy, and Course Outcomes in Online Learning. Distance Education, 34(3), 302-323.
• Whipp, J.L. & Chiarelli, S. (2004). Self-regulation in a Web-based course: A case study. Educational Technology Research and Development, 52(4), 5-22.
• Wiedenbeck, S., LaBelle, D., & Kain, V.N.R. (2004). Factors affecting course outcomes in introductory programming. Proceedings of the Sixteenth Annual Workshop of the Psychology of Programming Interest Group (PPIG ’04).
• Wilson, B.C. & Shrock, S. (2001). Contributing to success in an introductory computer science course: A study of twelve factors. INROADS of SIGCSE, 33, 184-188.
• Yukselturk, E. & Bulut, S. (2007). Predictors for student Success in an online course. Educational Technology & Society, 10(2), 71-83.
• Yukselturk, E. & Yildirim, Z. (2008). Investigation of interaction, online support, course structure and flexibility as the contributing factors to students' satisfaction in an online certificate program. Educational Technology & Society, 11(4), 51-65.
• Zimmerman, B. J. (1986). Development of self-regulated learning: Which are the key subprocesses? Contemporary Educational Psychology, 76, 307-313.
• Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329-339.
• Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64-70.
• Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45 (1), 166-183
• Correspondence: Harun Cigdem, Instructor, Turkish Land Forces Non-Commissioned Officer
• Vocational College, Balikesir, Turkey