Cheng-Yu Chung
ABSTRACT
A student's short-term study behavior may not necessary infer his/her long-term behavior. It is very common to see a student changes study strategy throughout a semester and adapts to learning condition. For example, a student may work very hard before the first exam but gradually reducing the effort due to several possible reasons, e.g., being overwhelmed by various course work or discouraged by increasing complexity in the subject. Consistency or differences of one student's behavior is more likely to be discovered by multiple granularity of learning analytics. In this study, we investigate students' study persistence on a self-assessment platform and explore how such a behavioral pattern is related to the performance in exams. A probabilistic mixture model trained by response streams of log data is applied to cluster students' behavior into persistence patterns, which are further categorized into "micro" (short-term) and "macro" (long-term) patterns according to the span of time being modeled. We found four types of micro persistence patterns and several macro patterns in the analysis and analyzed their relations with exam performances. The result suggests that the consistency of persistence patterns can be an important factor driving student's overall performance in the semester, and students achieving higher exam scores show relatively persistent behavior compared to students receiving lower scores.
- Marzieh Ahmadzadeh, Dave Elliman, and Colin Higgins. 2005. An analysis of patterns of debugging among novice computer science students . ACM SIGCSE Bulletin , Vol. 37, 3 (9 2005), 84. https://doi.org/10.1145/1151954.1067472Google Scholar
Digital Library
- Amjad Altadmri and Neil C C Brown. 2015. 37 Million Compilations: Investigating Novice Programming Mistakes in Large-Scale Student Data. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (SIGCSE '15). ACM, New York, NY, USA, 522--527. https://doi.org/10.1145/2676723.2677258Google ScholarDigital Library
- Mohammed Alzaid, Devanshi Trivedi, and I-Han Hsiao. 2017. The effects of bite-size distributed practices for programming novices. In 2017 IEEE Frontiers in Education Conference (FIE). IEEE, 1--9. https://doi.org/10.1109/FIE.2017.8190593Google ScholarCross Ref
- Susan Bergin, Ronan Reilly, and Desmond Traynor. 2005. Examining the role of self-regulated learning on introductory programming performance. In Proceedings of the 2005 international workshop on Computing education research - ICER '05. ACM Press, New York, New York, USA, 81--86. https://doi.org/10.1145/1089786.1089794Google ScholarDigital Library
- Paulo Blikstein, Marcelo Worsley, Chris Piech, Mehran Sahami, Steven Cooper, and Daphne Koller. 2014. Programming pluralism: Using learning analytics to detect patterns in the learning of computer programming . Journal of the Learning Sciences , Vol. 23, 4 (2014), 561--599.Google ScholarCross Ref
- Frank N. Dempster. 1988. The spacing effect: A case study in the failure to apply the results of psychological research. American Psychologist , Vol. 43, 8 (1988), 627--634. https://doi.org/10.1037/0003-066X.43.8.627Google ScholarCross Ref
- Anthony Estey, Hieke Keuning, and Yvonne Coady. 2017. Automatically Classifying Students in Need of Support by Detecting Changes in Programming Behaviour. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education - SIGCSE '17 . ACM Press, New York, New York, USA, 189--194. https://doi.org/10.1145/3017680.3017790Google ScholarDigital Library
- Anneli Etel"a pelto. 1993. Metacognition and the Expertise of Computer Program Comprehension . Scandinavian Journal of Educational Research , Vol. 37, 3 (1 1993), 243--254. https://doi.org/10.1080/0031383930370305Google Scholar
- Katrina Falkner, Rebecca Vivian, and Nickolas J.G. Falkner. 2014. Identifying computer science self-regulated learning strategies. In Proceedings of the 2014 conference on Innovation & technology in computer science education - ITiCSE '14. ACM Press, New York, New York, USA, 291--296. https://doi.org/10.1145/2591708.2591715Google Scholar
- Sue Fitzgerald, Gary Lewandowski, Renée McCauley, Laurie Murphy, Beth Simon, Lynda Thomas, and Carol Zander. 2008. Debugging: finding, fixing and flailing, a multi-institutional study of novice debuggers . Computer Science Education , Vol. 18, 2 (2008), 93--116. https://doi.org/10.1080/08993400802114508Google ScholarCross Ref
- I-Han Hsiao, Po-Kai Huang, and Hannah Murphy. 2017. Uncovering reviewing and reflecting behaviors from paper-based formal assessment. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference on - LAK '17. ACM Press, New York, New York, USA, 319--328. https://doi.org/10.1145/3027385.3027415Google ScholarDigital Library
- I-H Hsiao, Sergey Sosnovsky, and Peter Brusilovsky. 2010. Guiding students to the right questions: adaptive navigation support in an E-Learning system for Java programming . Journal of Computer Assisted Learning , Vol. 26, 4 (2010), 270--283.Google ScholarCross Ref
- Kalle Ilves, Juho Leinonen, and Arto Hellas. 2018. Supporting Self-Regulated Learning with Visualizations in Online Learning Environments. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education - SIGCSE '18. ACM Press, New York, New York, USA, 257--262. https://doi.org/10.1145/3159450.3159509Google ScholarDigital Library
- Avi Kaplan and Martin L. Maehr. 2007. The Contributions and Prospects of Goal Orientation Theory . Educational Psychology Review , Vol. 19, 2 (4 2007), 141--184. https://doi.org/10.1007/s10648-006--9012--5Google ScholarCross Ref
- Hassan Khosravi and Kendra M.L. Cooper. 2017. Using Learning Analytics to Investigate Patterns of Performance and Engagement in Large Classes. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education - SIGCSE '17 . ACM Press, New York, New York, USA, 309--314. https://doi.org/10.1145/3017680.3017711Google Scholar
- Dongho Kim, Meehyun Yoon, Il Hyun Jo, and Robert Maribe Branch. 2018. Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women's university in South Korea . Computers and Education , Vol. 127, September 2016 (2018), 233--251. https://doi.org/10.1016/j.compedu.2018.08.023Google ScholarCross Ref
- René F. Kizilcec, Mar Pé rez-Sanagustí n, and Jorge J. Maldonado. 2017. Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses . Computers & Education , Vol. 104 (1 2017), 18--33. https://doi.org/10.1016/j.compedu.2016.10.001Google Scholar
- Dastyni Loksa, Andrew J Ko, Will Jernigan, Alannah Oleson, Christopher J Mendez, and Margaret M Burnett. 2016. Programming, Problem Solving, and Self-Awareness: Effects of Explicit Guidance. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems - CHI '16 . ACM Press, New York, New York, USA, 1449--1461. https://doi.org/10.1145/2858036.2858252Google ScholarDigital Library
- Yihan Lu and I-Han Hsiao. 2017. Personalized Information Seeking Assistant (PiSA): from programming information seeking to learning . Information Retrieval Journal , Vol. 20, 5 (2017), 433--455.Google ScholarDigital Library
- Jihyun Park, Renzhe Yu, Fernando Rodriguez, Rachel Baker, Padhraic Smyth, and Mark Warschauer. 2018. Understanding Student Procrastination via Mixture Models. In Educational Data Mining . International Educational Data Mining Society, Buffalo, NY, 187--197. http://educationaldatamining.org/EDM2018/proceedings/Google Scholar
- Paul R Pintrich, David A F Smith, Teresa Garcia, and Wilbert J Mckeachie. 1993. Reliability and Predictive Validity of the Motivated Strategies for Learning Questionnaire (Mslq) . Educational and Psychological Measurement , Vol. 53, 3 (9 1993), 801--813. https://doi.org/10.1177/0013164493053003024Google Scholar
- Arthur E. Poropat. 2009. A meta-analysis of the five-factor model of personality and academic performance. Psychological Bulletin , Vol. 135, 2 (2009), 322--338. https://doi.org/10.1037/a0014996Google ScholarCross Ref
- Jaime Spacco, Paul Denny, Brad Richards, David Babcock, David Hovemeyer, James Moscola, and Robert Duvall. 2015. Analyzing Student Work Patterns Using Programming Exercise Data. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education - SIGCSE '15 . ACM Press, New York, New York, USA, 18--23. https://doi.org/10.1145/2676723.2677297Google ScholarDigital Library
- Sabrina Trapmann, Benedikt Hell, Jan-Oliver W Hirn, and Heinz Schuler. 2007. Meta-analysis of the relationship between the Big Five and academic success at university . Zeitschrift fü r Psychologie/Journal of Psychology , Vol. 215, 2 (2007), 132--151.Google ScholarCross Ref
- C.E. Weinstein, D.R. Palmer, and A.C. Schulte. 1987. The Learning and Study Strategies Inventory . Clearwater, FL: H & H Publishing , Vol. 30, 1 (1987), 115--126. http://www.amazon.com/dp/0131723154Google Scholar
- Philip H Winne, John C Nesbit, Vive Kumar, Allyson F Hadwin, Susanne P Lajoie, Roger Azevedo, and Nancy E Perry. 2006. Supporting self-regulated learning with gStudy software: The Learning Kit Project . Technology Instruction Cognition and Learning , Vol. 3, 1/2 (2006), 105.Google Scholar
- Barry J Zimmerman. 2008. Investigating Self-Regulation and Motivation: Historical Background, Methodological Developments, and Future Prospects . American Educational Research Journal , Vol. 45, 1 (3 2008), 166--183. https://doi.org/10.3102/0002831207312909Google ScholarCross Ref
Index Terms
- Investigating Patterns of Study Persistence on Self-Assessment Platform of Programming Problem-Solving
Recommendations
Examining change in students’ self-regulated learning patterns after a formative assessment using process mining techniques
AbstractThe growing popularity of e-learning platforms, such as learning management systems, has foregrounded the role of self-regulated learning (SRL) in student success. In many e-learning environments, students typically complete learning assignments ...
Highlights- Students engage in a loosely sequenced recursive self-regulated learning cycle.
- Students actively adjust self-regulated learning patterns in response to a formative assessment.
- There are group differences in the adjustment of self-...
Comments