Year 2020, Volume 11 , Issue 2, Pages 386 - 401 2020-08-31

Üniversite Öğrencilerinin Ödev Gönderme Davranışları ile Ders Başarıları Arasındaki İlişkinin Süreç Madenciliği Analizi ile İncelenmesi
Analyzing the Relationship between Student’s Assignment Submission Behaviors and Course Achievement through Process Mining Analysis

Gökhan AKÇAPINAR [1] , Mehmet KOKOÇ [2]


Bu çalışmada üniversite öğrencilerinin ödev gönderme davranışları ile ders başarıları arasındaki ilişkinin incelenmesi amaçlanmıştır. Bu amaçla, bir devlet üniversitesinde İşletim Sistemleri ve Uygulamaları dersine kayıtlı 75 öğrencinin Moodle öğrenme yönetim sistemi üzerinden dersin dördüncü haftasında verilen bir ödevi gönderme davranışları analiz edilmiştir. Aynı zamanda farklı ödev gönderme davranışı sergileyen öğrenciler dönem sonu notları açısından da analiz edilmiştir. Analiz aşamasında, öğrencilerin ödev gönderirken izledikleri adımlar sıralı olarak belirlenmiş ve kümeleme analizi yardımı ile benzer örüntü sergileyen öğrenciler gruplara ayrılmıştır. Aynı zamanda süreç madenciliği analizi kullanılarak farklı gruplardaki öğrencilerin ödev gönderme süreçleri detaylı olarak analiz edilmiştir. Yapılan analizler, öğrencilerin ödev gönderme davranışlarına göre üç farklı gruba ayrılabileceğini göstermiştir. Ders başarısı açısından bakıldığında ise ödevi gönderen öğrencilerin önemli bir bölümünün dersten başarılı olduğu gözlemlenirken, ödev gönderiminde bulunmayan öğrencilerin önemli bir bölümünün dersten başarısız olduğu görülmüştür. Elde edilen bulgular, dersten başarısız olma ihtimali yüksek olan öğrencilerin erken haftalarda belirlenmesinde ve bu öğrencilere yönelik olası müdahalelerin tasarlanmasında yol gösterici olacaktır.


In this study, it is aimed to analyze the relationship between student’s assignment submission behaviors and course achievement. For this purpose, the behaviors of 75 students’ who enrolled in the Operating Systems and Applications course at a public university, submission an assignment through the Moodle learning management system given in the fourth week of the course is analyzed. Students who exhibit different assignment submission behaviors are also analyzed in terms of end-of-term grades. During analyzing, the steps followed by the students while submitting their assignments are determined respectively, and students who display a similar pattern are divided into groups by means of cluster analysis. Moreover, using process mining analysis assignment submission processes of students in different groups are analyzed in detail. The analysis shows that students can be divided into three different groups based on their assignment submission behaviors. In terms of course achievement, it is observed that a significant portion of the students who submitted the assignments are successful in the course, while a significant portion of the students who did not submit the assignment failed. The findings will be guideway in determining the students who are likely to fail the course in the early weeks and in designing possible interferences for these students.
  • Akçapınar, G., Altun, A., & Aşkar, P. (2019). Using learning analytics to develop early-warning system for at-risk students. International Journal of Educational Technology in Higher Education, 16(1), 40.
  • Bayrak, F. (2018). Üniversite öğrencilerinin karma öğrenme ortamındaki akademik erteleme davranışları. Ege Eğitim Dergisi, 19(2), 470-487.
  • Broadbent, J. (2017). Comparing online and blended learner's self-regulated learning strategies and academic performance. The Internet and Higher Education, 33, 24–32.
  • Cerezo, R., Sánchez-Santillán, M., Paule-Ruiz, M. P., & Núñez, J. C. (2016). Students' LMS interaction patterns and their relationship with achievement: A case study in higher education. Computers & Education, 96, 42-54.
  • Cerezo, R., Esteban, M., Sánchez-Santillán, M., & Núñez, J. C. (2017). Procrastinating behavior in computer-based learning environments to predict performance: A case study in Moodle. Frontiers in Psychology, 8, 1403. doi: 10.3389/fpsyg.2017.01403
  • Conijn, R., Snijders, C., Kleingeld, A., & Matzat, U. (2017). Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS. IEEE Transactions on Learning Technologies, 10(1), 17–29. doi:10.1109/tlt.2016.2616312
  • Dabbagh, N., & Kitsantas, A. (2005). Using web-based pedagogical tools as scaffolds for self-regulated learning. Instructional Science, 33(5-6), 513-540.
  • Gabadinho, A., Ritschard, G., Müller, N. S., & Studer, M. (2011). Analyzing and visualizing state sequences in R with TraMineR. Journal of Statistical Software, 40(4), 1-37.
  • Graham, C. R. (2006). Blended learning systems: Definition, current trends, and future directions. In C. J. Bonk, & C. R. Graham (Eds.), The handbook of blended learning: Global perspectives, local designs (pp. 3e21). San Francisco: Pfeiffer. http://doi.org/10.2307/4022859.
  • Huang, S., & Fang, N. (2013). Predicting student academic performance in an engineering dynamics course: A Comparison of four types of predictive mathematical models. Computers & Education, 61, 133-145.
  • Jo, II-Hyun, Park, Y., Kim, J., & Song, J. (2014). Analysis of online behavior and prediction of learning performance in blended learning environments. Educational Technology International, 15(2), 71-88.
  • Kokoç, M., & Altun, A. (2019). Building a learning experience: What do learners' online interaction data imply? In D. G. Sampson, D. Ifenthaler, J. M. Spector, P. Isaias, & S. Sergis (Eds.), Learning technologies for transforming teaching, learning and assessment at large scale (pp.55-70). New York, NY: Springer.
  • Kovanović, V., Gašević, D., Dawson, S., Joksimović, S., Baker, R. S., & Hatala, M. (2015, March). Penetrating the black box of time-on-task estimation. In Proceedings of the Fifth International Conference on Learning Analytics and Knowledge (pp. 184-193). New York, NY: ACM.
  • Lim, J. M. (2016). The relationship between successful completion and sequential movement in self-paced distance courses. International Review of Research in Open and Distributed Learning, 17(1), 159-179.
  • Lu, O. H. T., Huang, A. Y. Q., Lin, A. J. Q., Ogata, H., & Yang, S. J. H. (2018). Applying learning analytics for the early prediction of students’ academic performance in blended learning. Educational Technology & Society, 21(2), 220–232.
  • Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & education, 54(2), 588-599.
  • Michinov, N., Brunot, S., Le Bohec, O., Juhel, J., & Delaval, M. (2011). Procrastination, participation, and performance in online learning environments. Computers & Education, 56, 243–252.
  • Paule-Ruiz, M. P., Riestra-Gonzalez, M., Sánchez-Santillan, M., & Pérez-Pérez, J. R. (2015). The Procrastination related indicators in e-learning platforms. Journal of Universal Computer Science, 21(1), 7–22.
  • Porter, W. W., Graham, C. R., Spring, K. A., & Welch, K. R. (2014). Blended learning in higher education: Institutional adoption and implementation. Computers & Education, 75, 185–195.
  • Prasad, P. W. C., Maag, A., Redestowicz, M., & Hoe, L. S. (2018b). Unfamiliar technology: Reaction of international students to blended learning. Computers & Education, 122, 92–103.
  • R Core Team. (2017). R: A language and environment for statistical computing: R Foundation for Statistical Computing. Retrieved October 28, 2019 from https://www.R-project.org/
  • Rasheed, R. A., Kamsin, A., & Abdullah, N. A. (2020). Challenges in the online component of blended learning: A systematic review. Computers & Education, 144, 103701.
  • Smyth, S., Houghton, C., Cooney, A., & Casey, D. (2012). Students' experiences of blended learning across a range of postgraduate programmes. Nurse Education Today, 32(4), 464-468.
  • You, J. W. (2015). Examining the effect of academic procrastination on achievement using LMS data in e-Learning. Educational Technology & Society, 18(3), 64-74.
  • You, J.W. (2016). Identifying significant indicators using LMS data to predict course achievement in online learning. Internet and Higher Education, 29, 23-30.
  • Zacharis, N. Z. (2015). A multivariate approach to predicting student outcomes in web-enabled blended learning courses. The Internet and Higher Education, 27, 44-53.
Primary Language en
Subjects Education and Educational Research
Journal Section Research Articles
Authors

Author: Gökhan AKÇAPINAR
Institution: HACETTEPE ÜNİVERSİTESİ
Country: Turkey


Author: Mehmet KOKOÇ (Primary Author)
Institution: Trabzon Üniversitesi
Country: Turkey


Dates

Publication Date : August 31, 2020

Bibtex @research article { turkbilmat711683, journal = {Turkish Journal of Computer and Mathematics Education (TURCOMAT)}, issn = {}, eissn = {1309-4653}, address = {}, publisher = {Türkbilmat Eğitim Hizmetleri}, year = {2020}, volume = {11}, pages = {386 - 401}, doi = {10.16949/turkbilmat.711683}, title = {Analyzing the Relationship between Student’s Assignment Submission Behaviors and Course Achievement through Process Mining Analysis}, key = {cite}, author = {Akçapınar, Gökhan and Kokoç, Mehmet} }
APA Akçapınar, G , Kokoç, M . (2020). Analyzing the Relationship between Student’s Assignment Submission Behaviors and Course Achievement through Process Mining Analysis . Turkish Journal of Computer and Mathematics Education (TURCOMAT) , 11 (2) , 386-401 . DOI: 10.16949/turkbilmat.711683
MLA Akçapınar, G , Kokoç, M . "Analyzing the Relationship between Student’s Assignment Submission Behaviors and Course Achievement through Process Mining Analysis" . Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11 (2020 ): 386-401 <https://dergipark.org.tr/en/pub/turkbilmat/issue/56636/711683>
Chicago Akçapınar, G , Kokoç, M . "Analyzing the Relationship between Student’s Assignment Submission Behaviors and Course Achievement through Process Mining Analysis". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11 (2020 ): 386-401
RIS TY - JOUR T1 - Analyzing the Relationship between Student’s Assignment Submission Behaviors and Course Achievement through Process Mining Analysis AU - Gökhan Akçapınar , Mehmet Kokoç Y1 - 2020 PY - 2020 N1 - doi: 10.16949/turkbilmat.711683 DO - 10.16949/turkbilmat.711683 T2 - Turkish Journal of Computer and Mathematics Education (TURCOMAT) JF - Journal JO - JOR SP - 386 EP - 401 VL - 11 IS - 2 SN - -1309-4653 M3 - doi: 10.16949/turkbilmat.711683 UR - https://doi.org/10.16949/turkbilmat.711683 Y2 - 2020 ER -
EndNote %0 Türk Bilgisayar ve Matematik Eğitimi Dergisi Analyzing the Relationship between Student’s Assignment Submission Behaviors and Course Achievement through Process Mining Analysis %A Gökhan Akçapınar , Mehmet Kokoç %T Analyzing the Relationship between Student’s Assignment Submission Behaviors and Course Achievement through Process Mining Analysis %D 2020 %J Turkish Journal of Computer and Mathematics Education (TURCOMAT) %P -1309-4653 %V 11 %N 2 %R doi: 10.16949/turkbilmat.711683 %U 10.16949/turkbilmat.711683
ISNAD Akçapınar, Gökhan , Kokoç, Mehmet . "Analyzing the Relationship between Student’s Assignment Submission Behaviors and Course Achievement through Process Mining Analysis". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11 / 2 (August 2020): 386-401 . https://doi.org/10.16949/turkbilmat.711683
AMA Akçapınar G , Kokoç M . Analyzing the Relationship between Student’s Assignment Submission Behaviors and Course Achievement through Process Mining Analysis. Turkish Journal of Computer and Mathematics Education (TURCOMAT). 2020; 11(2): 386-401.
Vancouver Akçapınar G , Kokoç M . Analyzing the Relationship between Student’s Assignment Submission Behaviors and Course Achievement through Process Mining Analysis. Turkish Journal of Computer and Mathematics Education (TURCOMAT). 2020; 11(2): 386-401.