參考文獻 |
Arakawa, K., Hao, Q., Greer, T., Ding, L., Hundhausen, C. D., & Peterson, A. (2021). In Situ Identification of Student Self-Regulated Learning Struggles in Programming Assignments. Proceedings of the 52nd ACM Technical Symposium on Computer Science Education,
Araya, I., Beas, V., Stamulis, K., & Allende-Cid, H. (2022). Predicting student performance in computing courses based on programming behavior. Computer Applications in Engineering Education. https://doi.org/10.1002/cae.22519
Arthur, D., & Vassilvitskii, S. (2006). k-means++: The advantages of careful seeding.
Auvinen, T. (2015). Harmful study habits in online learning environments with automatic assessment. 2015 International Conference on Learning and Teaching in Computing and Engineering,
Bai, Y., Chen, L., Yin, G., Mao, X., Deng, Y., Wang, T., Lu, Y., & Wang, H. (2017). Quantitative analysis of learning data in a programming course. International Conference on Database Systems for Advanced Applications,
Bey, A., & Champagnat, R. (2021). An Exploratory Study to Identify Learners′ Programming Behavior Interactions. 2021 International Conference on Advanced Learning Technologies (ICALT),
Bey, A., Pérez-Sanagustín, M., & Broisin, J. (2019). Unsupervised automatic detection of learners’ programming behavior. European Conference on Technology Enhanced Learning,
Brame, C. J., & Biel, R. (2015). Test-enhanced learning: the potential for testing to promote greater learning in undergraduate science courses. CBE—Life Sciences Education, 14(2), es4.
Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of educational research, 65(3), 245-281.
Campbell, J., Horton, D., & Craig, M. (2016). Factors for success in online CS1. Proceedings of the 2016 acm conference on innovation and technology in computer science education,
Carter, A. S., Hundhausen, C. D., & Adesope, O. (2015). The normalized programming state model: Predicting student performance in computing courses based on programming behavior. Proceedings of the Eleventh Annual International Conference on International Computing Education Research,
Carter, A. S., Hundhausen, C. D., & Adesope, O. (2017). Blending measures of programming and social behavior into predictive models of student achievement in early computing courses. ACM Transactions on Computing Education (TOCE), 17(3), 1-20.
Castellanos, H., Restrepo-Calle, F., González, F. A., & Echeverry, J. J. R. (2017). Understanding the relationships between self-regulated learning and students source code in a computer programming course. 2017 IEEE Frontiers in Education Conference (FIE),
Chen, H.-M., Nguyen, B.-A., Yan, Y.-X., & Dow, C.-R. (2020). Analysis of learning behavior in an automated programming assessment environment: A code quality perspective. IEEE Access, 8, 167341-167354.
Chung, C.-Y., Paredes, Y. V. M., Alzaid, M., Papakannu, K. R., & Hsiao, I.-H. (2020). A Longitudinal Study on Student Persistence in Programming Self-assessments. CSEDM@ EDM,
Cohen, J. (1988). Statistical power analysis Jbr the behavioral. Sciences. Hillsdale (NJ): Lawrence Erlbaum Associates, 18-74.
Echeverry, J. J. R., Rosales-Castro, L. F., Restrepo-Calle, F., & González, F. A. (2018). Self-regulated learning in a computer programming course. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 13(2), 75-83.
Estey, A., Keuning, H., & Coady, Y. (2017). Automatically classifying students in need of support by detecting changes in programming behaviour. Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education,
Fu, X., Shimada, A., Ogata, H., Taniguchi, Y., & Suehiro, D. (2017). Real-time learning analytics for C programming language courses. Proceedings of the seventh international learning analytics & knowledge conference,
Fuchs, D., Mock, D., Morgan, P. L., & Young, C. L. (2003). Responsiveness‐to‐intervention: Definitions, evidence, and implications for the learning disabilities construct. Learning Disabilities Research & Practice, 18(3), 157-171.
Gao, G., Marwan, S., & Price, T. W. (2021). Early performance prediction using interpretable patterns in programming process data. Proceedings of the 52nd ACM Technical Symposium on Computer Science Education,
González-Pienda, J. A., Fernández, E., Bernardo, A., Núñez, J. C., & Rosário, P. (2014). Assessment of a self-regulated learning intervention. The Spanish Journal of Psychology, 17.
Hawlitschek, A., Köppen, V., Dietrich, A., & Zug, S. (2019). Drop-out in programming courses–prediction and prevention. Journal of Applied Research in Higher Education.
Hsiao, I.-H., Huang, P.-K., & Murphy, H. (2017). Uncovering reviewing and reflecting behaviors from paper-based formal assessment. Proceedings of the seventh international learning analytics & knowledge conference,
Huang, T.-C., Shu, Y., Chang, S.-H., Huang, Y.-Z., Lee, S.-L., Huang, Y.-M., & Liu, C.-H. (2014). Developing a self-regulated oriented online programming teaching and learning system. 2014 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE),
Kato, T., Kambayashi, Y., Terawaki, Y., & Kodama, Y. (2017). Analysis of students’ behaviors in programming exercises using deep learning. International Conference on Smart Education and Smart E-Learning,
Kiran, E. L., & Moudgalya, K. M. (2015). Evaluation of programming competency using student error patterns. 2015 International Conference on Learning and Teaching in Computing and Engineering,
Kurnia, A., Lim, A., & Cheang, B. (2001). Online judge. Computers & Education, 36(4), 299-315.
López-Pernas, S., Saqr, M., & Viberg, O. (2021). Putting it all together: Combining learning analytics methods and data sources to understand students’ approaches to learning programming. Sustainability, 13(9), 4825.
Law, C.-Y., Grundy, J., Cain, A., Vasa, R., & Cummaudo, A. (2017). User perceptions of using an open learner model visualisation tool for facilitating self-regulated learning. Proceedings of the Nineteenth Australasian Computing Education Conference,
Lishinski, A., Yadav, A., Good, J., & Enbody, R. (2016). Learning to program: Gender differences and interactive effects of students′ motivation, goals, and self-efficacy on performance. Proceedings of the 2016 ACM Conference on International Computing Education Research,
Lu, O. H., Huang, A. Y., Huang, J. C., Huang, C. S., & Yang, S. J. (2016). Early-Stage Engagement: Applying Big Data Analytics on Collaborative Learning Environment for Measuring Learners′ Engagement Rate. 2016 International Conference on Educational Innovation through Technology (EITT),
Luo, J., & Wang, T. (2020). Analyzing Students′ Behavior in Blended Learning Environment for Programming Education. Proceedings of the 2020 The 2nd World Symposium on Software Engineering,
Luxton-Reilly, A., Albluwi, I., Becker, B. A., Giannakos, M., Kumar, A. N., Ott, L., Paterson, J., Scott, M. J., Sheard, J., & Szabo, C. (2018). Introductory programming: a systematic literature review. Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education,
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the fifth Berkeley symposium on mathematical statistics and probability,
Magno, C. (2011). Validating the academic self-regulated learning scale with the motivated strategies for learning questionnaire (MSLQ) and learning and study strategies inventory (LASSI). The International Journal of Educational and Psychological Assessment, 7(2).
McKay, T., Miller, K., & Tritz, J. (2012). What to do with actionable intelligence: E2Coach as an intervention engine. Proceedings of the 2nd international conference on learning analytics and knowledge,
Morshed Fahid, F., Tian, X., Emerson, A., B. Wiggins, J., Bounajim, D., Smith, A., Wiebe, E., Mott, B., Elizabeth Boyer, K., & Lester, J. (2021). Progression Trajectory-Based Student Modeling for Novice Block-Based Programming. Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization,
Ott, C., Robins, A., Haden, P., & Shephard, K. (2015). Illustrating performance indicators and course characteristics to support students’ self-regulated learning in CS1. Computer Science Education, 25(2), 174-198.
Öztürk, M. (2021). The effect of self-regulated programming learning on undergraduate students’ academic performance and motivation. Interactive Technology and Smart Education.
Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in psychology, 8, 422.
Paredes, Y. V., & Hsiao, I.-H. (2021). WebPGA: An Educational Technology That Supports Learning by Reviewing Paper-Based Programming Assessments. Information, 12(11), 450.
Pedrosa, D., Cravino, J., Morgado, L., & Barreira, C. (2017). Self-regulated learning in higher education: strategies adopted by computer programming students when supported by the SimProgramming approach. Production, 27.
Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In Handbook of self-regulation (pp. 451-502). Elsevier.
Pintrich, P. R., Smith, D. A., Garcia, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and psychological measurement, 53(3), 801-813.
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20, 53-65.
Silva, L. (2021). Fostering Programming Students Regulation of Learning Using a Computer-Based Learning Environment. 2021 International Symposium on Computers in Education (SIIE),
Song, D., Hong, H., & Oh, E. Y. (2021). Applying computational analysis of novice learners′ computer programming patterns to reveal self-regulated learning, computational thinking, and learning performance. Computers in Human Behavior, 120, 106746.
Wasik, S., Antczak, M., Badura, J., Laskowski, A., & Sternal, T. (2018). A survey on online judge systems and their applications. ACM Computing Surveys (CSUR), 51(1), 1-34.
Watson, C., Li, F. W., & Godwin, J. L. (2013). Predicting performance in an introductory programming course by logging and analyzing student programming behavior. 2013 IEEE 13th international conference on advanced learning technologies,
Wong, B. T.-m., & Li, K. C. (2020). A review of learning analytics intervention in higher education (2011–2018). Journal of Computers in Education, 7(1), 7-28.
Yang, S. J. (2021). Guest Editorial: Precision Education-A New Challenge for AI in Education. Journal of Educational Technology & Society, 24(1).
Zhao, X., Zhang, J., Li, W., Kahn, K., Lu, Y., & Winters, N. (2021). Learners’ non-cognitive skills and behavioral patterns of programming: A sequential analysis. 2021 International Conference on Advanced Learning Technologies (ICALT),
Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of educational psychology, 81(3), 329.
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In Handbook of self-regulation (pp. 13-39). Elsevier.
Zinovieva, I., Artemchuk, V., Iatsyshyn, A. V., Popov, O., Kovach, V., Iatsyshyn, A. V., Romanenko, Y., & Radchenko, O. (2021). The use of online coding platforms as additional distance tools in programming education. Journal of Physics: Conference Series, |