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An instructor dashboard for real-time analytics in interactive programming assignments

Authors:

Nicholas Diana,

Marie Bienkowski,

Satabdi BasuAuthors Info & Claims

LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference

Pages 272 - 279

https://doi.org/10.1145/3027385.3027441

Published: 13 March 2017 Publication History

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.

References

[1]

G. Csardi and T. Nepusz. The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5):1--9, 2006.

[2]

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.

Digital Library

[3]

M. Eagle, M. W. Johnson, T. Barnes, and A. K. Boyce. Exploring player behavior with visual analytics. In FDG, 2013.

[4]

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.

Digital Library

[5]

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.

[6]

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.

[7]

M. C. Jadud. A first look at novice compilation behaviour using bluej. Computer Science Education, 15(1):25--40, 2005.

[8]

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.

Digital Library

[9]

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.

[10]

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.

[11]

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.

Digital Library

[12]

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.

[13]

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.

[14]

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.

Digital Library

[15]

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.

Digital Library

[16]

K. Verbert, E. Duval, J. Klerkx, S. Govaerts, and J. L. Santos. Learning analytics dashboard applications. American Behavioral Scientist, 57(10):1500--1509, 2013.

[17]

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.

Digital Library

[18]

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.

Digital Library

Cited By

George SHuang TRobinson CSchell GShan WZhao ZZhou ZDewan P(2024)Assistant Dashboard Plus – Enhancing an Existing Instructor Dashboard with Difficulty Detection and GPT-based Code ClusteringCompanion Proceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640544.3645231(54-57)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640544.3645231
Orlando SGaudioso Ede la Paz F(2024)Toward Embedding Robotics in Learning Environments With Support to Teachers: The IDEE ExperienceIEEE Transactions on Learning Technologies10.1109/TLT.2023.333988217(874-884)Online publication date: 2024
https://doi.org/10.1109/TLT.2023.3339882
Shabani NBeheshti AFarhood HBower MGarrett MAlinejad-Rokny H(2023)A Rule-Based Approach for Mining Creative Thinking Patterns from Big Educational DataAppliedMath10.3390/appliedmath30100143:1(243-267)Online publication date: 20-Mar-2023
https://doi.org/10.3390/appliedmath3010014
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Index Terms

An instructor dashboard for real-time analytics in interactive programming assignments
1. Applied computing
  1. Education
    1. Interactive learning environments

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Published In

cover image ACM Other conferences

LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference

March 2017

631 pages

ISBN:9781450348706

DOI:10.1145/3027385

General Chairs:
Alyssa Wise
New York University, USA
,
Phillip H. Winne
Simon Fraser University, Canada
,
Grace Lynch
Society for Learning Analytics Research
,
Program Chairs:
Xavier Ochoa
Escuela Superior Politecnica del Litoral, Ecuador
,
Inge Molenaar
Radboud University, The Netherlands
,
Shane Dawson
University of South Australia, Australia
,
Publications Chair:
Marek Hatala
Simon Fraser University, Canada

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 March 2017

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National Science Foundation

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LAK '17

LAK '17: 7th International Learning Analytics and Knowledge Conference

March 13 - 17, 2017

British Columbia, Vancouver, Canada

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LAK '17 Paper Acceptance Rate 36 of 114 submissions, 32%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

George SHuang TRobinson CSchell GShan WZhao ZZhou ZDewan P(2024)Assistant Dashboard Plus – Enhancing an Existing Instructor Dashboard with Difficulty Detection and GPT-based Code ClusteringCompanion Proceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640544.3645231(54-57)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640544.3645231
Orlando SGaudioso Ede la Paz F(2024)Toward Embedding Robotics in Learning Environments With Support to Teachers: The IDEE ExperienceIEEE Transactions on Learning Technologies10.1109/TLT.2023.333988217(874-884)Online publication date: 2024
https://doi.org/10.1109/TLT.2023.3339882
Shabani NBeheshti AFarhood HBower MGarrett MAlinejad-Rokny H(2023)A Rule-Based Approach for Mining Creative Thinking Patterns from Big Educational DataAppliedMath10.3390/appliedmath30100143:1(243-267)Online publication date: 20-Mar-2023
https://doi.org/10.3390/appliedmath3010014
Mirhosseini SHenley AParnin CDoyle MStephenson BDorn BSoh LBattestilli L(2023)What Is Your Biggest Pain Point?Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 110.1145/3545945.3569816(291-297)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3545945.3569816
Ley TTammets KPishtari GChejara PKasepalu RKhalil MSaar MTuvi IVäljataga TWasson B(2023)Towards a partnership of teachers and intelligent learning technology: A systematic literature review of model‐based learning analyticsJournal of Computer Assisted Learning10.1111/jcal.1284439:5(1397-1417)Online publication date: 29-Jun-2023
https://doi.org/10.1111/jcal.12844
Tan BJin HCutumisu M(2023)The applications of machine learning in computational thinking assessments: a scoping reviewComputer Science Education10.1080/08993408.2023.2245687(1-29)Online publication date: 12-Aug-2023
https://doi.org/10.1080/08993408.2023.2245687
Bhattacharya SBiswas UDamkondwar SYadav B(2023)Real-time ICT-based interactive learning analytics to facilitate blended classroomsEducation and Information Technologies10.1007/s10639-023-12327-x29:10(11701-11731)Online publication date: 1-Dec-2023
https://doi.org/10.1007/s10639-023-12327-x
Mai TCrane MBezbradica M(2023)Computergestütztes prädiktives Lernen: Einige neuere Methoden zur Vorhersage des LernerfolgsEducational Data Mining und Learning Analytics10.1007/978-3-658-39607-7_5(169-218)Online publication date: 10-Jun-2023
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Safsouf YMansouri KPoirier F(2023)Enhanced Online Academic Success and Self-Regulation Through Learning Analytics DashboardsTowards a Collaborative Society Through Creative Learning10.1007/978-3-031-43393-1_30(332-342)Online publication date: 28-Sep-2023
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Aguirre Reid SKammer FSchüller DSiepermann MWölfer J(2023)Know the Knowledge of Your Students: A Flexible Analytics Tool for Student ExercisesDesign Science Research for a New Society: Society 5.010.1007/978-3-031-32808-4_21(329-344)Online publication date: 19-May-2023
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