Nicholas Diana
Michael Eagle
Shuchi Grover
ABSTRACT
Many introductory programming environments generate a large amount of log data, but making insights from these data accessible to instructors remains a challenge. This research demonstrates that student outcomes can be accurately predicted from student program states at various time points throughout the course, and integrates the resulting predictive models into an instructor dashboard. The effectiveness of the dashboard is evaluated by measuring how well the dashboard analytics correctly suggest that the instructor help students classified as most in need. Finally, we describe a method of matching low-performing students with high-performing peer tutors, and show that the inclusion of peer tutors not only increases the amount of help given, but the consistency of help availability as well.
- G. Csardi and T. Nepusz. The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5):1--9, 2006.Google Scholar
- M. Eagle, D. Hicks, B. Peddycord, III, and T. Barnes. Exploring networks of problem-solving interactions. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, LAK '15, 21--30, New York, NY, USA, 2015. ACM. Google ScholarDigital Library
- M. Eagle, M. W. Johnson, T. Barnes, and A. K. Boyce. Exploring player behavior with visual analytics. In FDG, 2013.Google Scholar
- D. Fossati, B. Di Eugenio, S. Ohlsson, C. W. Brown, L. Chen, and D. G. Cosejo. I learn from you, you learn from me: How to make ilist learn from students. In AIED, 491--498, 2009. Google ScholarDigital Library
- J. M. Furner and A. Gonzalez-DeHass. How do students' mastery and performance goals relate to math anxiety. Eurasia Journal of Mathematics, Science & Technology Education, 7(4):227--242, 2011.Google Scholar
- A. Hicks, B. Peddycord III, and T. Barnes. Building games to learn from their players: Generating hints in a serious game. In Intelligent Tutoring Systems, 312--317. Springer, 2014.Google Scholar
- M. C. Jadud. A first look at novice compilation behaviour using bluej. Computer Science Education, 15(1):25--40, 2005.Google ScholarCross Ref
- W. Jin, T. Barnes, J. Stamper, M. J. Eagle, M. W. Johnson, and L. Lehmann. Program representation for automatic hint generation for a data-driven novice programming tutor. In Intelligent Tutoring Systems, 304--309. Springer, 2012. Google ScholarDigital Library
- K. R. Koedinger, J. R. Anderson, W. H. Hadley, M. A. Mark, et al. Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education (IJAIED), 8:30--43, 1997.Google Scholar
- M. Lovett, O. Meyer, and C. Thille. Jime-the open learning initiative: Measuring the effectiveness of the oli statistics course in accelerating student learning. Journal of Interactive Media in Education, 2008(1):1--18, 2008.Google ScholarCross Ref
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825--2830, 2011. Google ScholarDigital Library
- K. Rivers and K. R. Koedinger. Automatic generation of programming feedback: A data-driven approach. In The First Workshop on AI-supported Education for Computer Science (AIEDCS 2013), pages 50--59, 2013.Google Scholar
- A. M. Ryan, L. Hicks, and C. Midgley. Social goals, academic goals, and avoiding seeking help in the classroom. The Journal of Early Adolescence, 17(2):152--171, 1997.Google ScholarCross Ref
- J. Spacco, D. Fossati, J. Stamper, and K. Rivers. Towards improving programming habits to create better computer science course outcomes. In Proceedings of the 18th ACM conference on Innovation and technology in computer science education, 243--248. ACM, 2013. Google ScholarDigital Library
- J. Stamper, M. Eagle, T. Barnes, and M. Croy. Experimental evaluation of automatic hint generation for a logic tutor. International Journal of Artificial Intelligence in Education (IJAIED), 22(1):3--18, 2013. Google ScholarDigital Library
- K. Verbert, E. Duval, J. Klerkx, S. Govaerts, and J. L. Santos. Learning analytics dashboard applications. American Behavioral Scientist, 57(10):1500--1509, 2013.Google ScholarCross Ref
- K. Verbert, S. Govaerts, E. Duval, J. L. Santos, F. Van Assche, G. Parra, and J. Klerkx. Learning dashboards: an overview and future research opportunities. Personal and Ubiquitous Computing, 18(6):1499--1514, 2014. Google ScholarDigital Library
- L. Werner, J. Denner, and S. Campe. The Fairy Performance Assessment : Measuring Computational Thinking in Middle School. Proceedings of the 43rd ACM Technical Symposium on Computer Science Education - SIGCSE '12, 215--220, 2012. Google ScholarDigital Library
Index Terms
- An instructor dashboard for real-time analytics in interactive programming assignments
Recommendations
Real-time learning analytics for C programming language courses
LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge ConferenceMany universities choose the C programming language (C) as the first one they teach their students, early on in their program. However, students often consider programming courses difficult, and these courses often have among the highest dropout rates ...
Designing a learning analytics dashboard for twitter-facilitated teaching
L@S '18: Proceedings of the Fifth Annual ACM Conference on Learning at ScaleSocial media sites are increasingly being adopted to support teaching practice in higher education. Learning Analytics (LA) dashboards can be used to reveal how students engage with course material and others in the class. However, research on the best ...
Investigating the Impact of a Real-time, Multimodal Student Engagement Analytics Technology in Authentic Classrooms
CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing SystemsWe developed a real-time, multimodal Student Engagement Analytics Technology so that teachers can provide just-in-time personalized support to students who risk disengagement. To investigate the impact of the technology, we ran an exploratory semester-...
Comments