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With a Little Help From My Friends: An Empirical Study of the Interplay of Students' Social Activities, Programming Activities, and Course Success

Authors:

Adam S. Carter,

Christopher D. HundhausenAuthors Info & Claims

ICER '16: Proceedings of the 2016 ACM Conference on International Computing Education Research

Pages 201 - 209

https://doi.org/10.1145/2960310.2960322

Published: 25 August 2016 Publication History

Abstract

Computing education researchers have become increasingly interested in leveraging log data automatically collected within computer programming environments in order to understand students' learning processes and tailor instruction to student needs. While data on students' programming activities has been positively correlated with their learning outcomes, those data tell only part of the story. Another part of the story lies in students' social activities, which, according to social learning theory, can also be predictive of students' learning outcomes. In order to gain further insight into how computing students' learning processes influence their learning outcomes, we present an empirical study that explores the interplay of students' social activities, programming activities, and course outcomes in an early computing course. By analyzing log data collected through a programming environment augmented with a social networking-style activity stream, we found that answers to questions posed through the activity stream were positively correlated with students' ability to make programming progress, and their eventual success in the course. Based on our findings, we present recommendations for the design of pedagogical environments to support a more social programming process.

References

[1]

Ahadi, A. et al. 2015. Exploring Machine Learning Methods to Automatically Identify Students in Need of Assistance. Proceedings of the Eleventh Annual International Conference on International Computing Education Research (Omaha, NE, USA, 2015).

Digital Library

[2]

Altadmri, A. and Brown, N.C.C. 2015. 37 Million Compilations: Investigating Novice Programming Mistakes in Large-Scale Student Data. Proceedings of the 46th ACM Technical Symposium on Computer Science Education (Kansas City, MO, USA, 2015), 522--527.

Digital Library

[3]

Baker, R.S.J. and Siemens, G. 2014. Educational data mining and learning analytics. The Cambridge Handbook of the Learning Sciences. Cambridge University Press. 253--274.

[4]

Bandura, A. 1997. Self-efficacy: the exercise of control. Worth Publishers.

[5]

Bergin, S. et al. 2005. Examining the role of self-regulated learning on introductory programming performance. Proc. 2005 ACM International Computing Education Research Workshop. ACM Press. 81--86.

Digital Library

[6]

Bransford, J. et al. eds. 1999. How people learn: Brain, mind, experience, and school. National Academy Press.

[7]

Carter, A.S. et al. 2015. The Normalized Programming State Model: Predicting Student Performance in Computing Courses Based on Programming Behavior. Proceedings of the Eleventh Annual Conference on International Computing Education Research (2015).

Digital Library

[8]

Carter, A.S. and Hundhausen, C.D. 2015. The Design of a Programming Environment to Support Greater Social Awareness and Participation in Early Computing Courses. J. Comput. Sci. Coll. 31, 1 (Oct. 2015), 143--153.

Digital Library

[9]

Denny, P. et al. 2011. PeerWise: Exploring Conflicting Efficacy Studies. Proceedings of the Seventh International Workshop on Computing Education Research (Providence, Rhode Island, USA, 2011).

Digital Library

[10]

Hartmann, B. et al. 2010. What would other programmers do: suggesting solutions to error messages. Proc. 28th Conference on Human Factors in Computing Systems. ACM. 1019--1028.

Digital Library

[11]

Hundhausen, C.D. et al. 2011. Online vs. face-to-face pedagogical code reviews: An empirical comparison. Proceedings 2011 SIGCSE Symposium on Computer Science Education. ACM Press. 117--122.

Digital Library

[12]

Hundhausen, C.D. et al. 2015. Supporting programming assignments with activity streams: an empirical study. Proceedings of the 46th ACM Technical Symposium on Computer Science Education (New York, 2015), 320--325.

Digital Library

[13]

Hundhausen, C.D. and Carter, A.S. 2014. Facebook me about your code: An empirical study of the use of activity streams in early computing courses. Journal of Computing Sciences in Colleges. 30, 1 (2014), 151--160.

Digital Library

[14]

Jadud, M.C. 2006. Methods and tools for exploring novice compilation behaviour. Proce. Second International Workshop on Computing Education Research. ACM. 73--84.

Digital Library

[15]

Jadud, M.C. and Dorn, B. 2015. Aggregate Compilation Behavior: Findings and Implications from 27,698 Users. Proceedings of the Eleventh Annual International Conference on International Computing Education Research (Omaha, NE, USA, 2015).

Digital Library

[16]

Jeske, D. et al. 2014. Learner characteristics predict performance and confidence in e-Learning: An analysis of user behavior and self-evaluation. Journal of Interactive Learning Research. 25, 4 (2014), 509--529.

[17]

Lave, J. and Wenger, E. 1991. Situated Learning: Legitimate Peripheral Participation. Cambridge University Press.

[18]

Luxton-Reilly, A. et al. 2012. The Impact of Question Generation Activities on Performance. Proceedings of the 43rd ACM Technical Symposium on Computer Science Education (Raleigh, NC, USA, 2012).

Digital Library

[19]

Ma, W. et al. 2014. Intelligent tutoring systems and learning outcomes: A meta-analytic survey. Journal of Educational Psychology. 106, 2007 (2014), 901--918.

[20]

Mujumdar, D. et al. 2011. Crowdsourcing suggestions to programming problems for dynamic web development languages. CHI '11 Extended Abstracts on Human Factors in Computing Systems (Vancouver, BC, Canada, 2011), 1525--1530.

Digital Library

[21]

Pifarre, M. and Cobos, R. 2010. Promoting metacognitive skills through peer scaffolding in a CSCL environment. Computer-Supported Collaborative Learning. 5, (2010).

[22]

Rosson, M.B. et al. 2011. Orientation of Undergraduates Toward Careers in the Computer and Information Sciences: Gender, Self-Efficacy and Social Support. ACM Transactions on Computing Education. 11, 3 (Oct. 2011), 1--23.

Digital Library

[23]

Schunk, D.H. 2012. Learning theories: An educational perspective. Merrill Prentice Hall.

[24]

Slavin, R.E. 2011. Educational psychology: Theory and practice. Pearson Education.

[25]

U.S. Department of Education, Office of Educational Technology 2012. Enhancing Teaching and Learning through Educational Data Mining and Learning Analytics: An Issue Brief.

[26]

Verbert, K. and Duval, E. 2012. Learning Analytics. Learning and Education. 1, 8 (2012).

[27]

Watson, C. et al. 2014. No tests required: comparing traditional and dynmaic predictors of programming success. Proceedings of the 45th ACM Technical Symposium on Computer Science Education (2014), 469--474.

Digital Library

[28]

Watson, C. et al. 2013. Predicting Performance in an Introductory Programming Course by Logging and Analyzing Student Programming Behavior. Proceedings of the 2013 IEEE 13th International Conference on Advanced Learning Technologies (2013), 319--323.

Digital Library

[29]

Wise, A.F. 2014. Designing Pedagogical Interventions to Support Student Use of Learning Analytics. Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (New York, NY, USA, 2014), 203--211.

Digital Library

Cited By

Cohen AOleson AKo A(2023)Factors Influencing the Social Help-seeking Behavior of Introductory Programming Students in a Competitive University EnvironmentACM Transactions on Computing Education10.1145/363905924:1(1-27)Online publication date: 30-Dec-2023
https://dl.acm.org/doi/10.1145/3639059
Olivares DHundhausen CRay N(2021)Designing IDE Interventions to Promote Social Interaction and Improved Programming Outcomes in Early Computing CoursesACM Transactions on Computing Education10.1145/345316522:1(1-29)Online publication date: 25-Oct-2021
https://dl.acm.org/doi/10.1145/3453165
Lishinski AYadav A(2021)Self-evaluation Interventions: Impact on Self-efficacy and Performance in Introductory ProgrammingACM Transactions on Computing Education10.1145/344737821:3(1-28)Online publication date: 23-Jun-2021
https://dl.acm.org/doi/10.1145/3447378
Show More Cited By

Index Terms

With a Little Help From My Friends: An Empirical Study of the Interplay of Students' Social Activities, Programming Activities, and Course Success

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cover image ACM Conferences

ICER '16: Proceedings of the 2016 ACM Conference on International Computing Education Research

August 2016

310 pages

ISBN:9781450344494

DOI:10.1145/2960310

General Chairs:
Judy Sheard
Monash University, Australia
,
Josh Tenenberg
University of Washington Tacoma, USA
,
Donald Chinn
University of Washington Tacoma, USA
,
Brian Dorn
University of Nebraska at Omaha, USA

Copyright © 2016 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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SIGCSE: ACM Special Interest Group on Computer Science Education

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 August 2016

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NSF

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ICER '16

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SIGCSE

ICER '16: International Computing Education Research Conference

September 8 - 12, 2016

VIC, Melbourne, Australia

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ICER '16 Paper Acceptance Rate 26 of 102 submissions, 25%;

Overall Acceptance Rate 189 of 803 submissions, 24%

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Cited By

Cohen AOleson AKo A(2023)Factors Influencing the Social Help-seeking Behavior of Introductory Programming Students in a Competitive University EnvironmentACM Transactions on Computing Education10.1145/363905924:1(1-27)Online publication date: 30-Dec-2023
https://dl.acm.org/doi/10.1145/3639059
Olivares DHundhausen CRay N(2021)Designing IDE Interventions to Promote Social Interaction and Improved Programming Outcomes in Early Computing CoursesACM Transactions on Computing Education10.1145/345316522:1(1-29)Online publication date: 25-Oct-2021
https://dl.acm.org/doi/10.1145/3453165
Lishinski AYadav A(2021)Self-evaluation Interventions: Impact on Self-efficacy and Performance in Introductory ProgrammingACM Transactions on Computing Education10.1145/344737821:3(1-28)Online publication date: 23-Jun-2021
https://dl.acm.org/doi/10.1145/3447378
Muller SBabes-Vroman MEmenike MNguyen TZhang JSherriff MHeckman SCutter PMonge A(2020)Exploring Novice Programmers' Homework PracticesProceedings of the 51st ACM Technical Symposium on Computer Science Education10.1145/3328778.3366885(333-339)Online publication date: 26-Feb-2020
https://dl.acm.org/doi/10.1145/3328778.3366885
Liao SValstar SThai KAlvarado CZingaro DGriswold WPorter LScharlau BMcDermott RPears ASabin M(2019)Behaviors of Higher and Lower Performing Students in CS1Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education10.1145/3304221.3319740(196-202)Online publication date: 2-Jul-2019
https://dl.acm.org/doi/10.1145/3304221.3319740
Carter AHundhausen COlivares D(2019)Leveraging the Integrated Development Environment for Learning AnalyticsThe Cambridge Handbook of Computing Education Research10.1017/9781108654555.024(679-706)Online publication date: 15-Feb-2019
https://doi.org/10.1017/9781108654555.024
(2019)The Cambridge Handbook of Computing Education Research10.1017/9781108654555Online publication date: 15-Feb-2019
https://doi.org/10.1017/9781108654555
Wong JBaars Mde Koning Bvan der Zee TDavis DKhalil MHouben GPaas F(2019)Educational Theories and Learning Analytics: From Data to KnowledgeUtilizing Learning Analytics to Support Study Success10.1007/978-3-319-64792-0_1(3-25)Online publication date: 18-Jan-2019
https://doi.org/10.1007/978-3-319-64792-0_1
Szabo CFalkner NKnutas ADorodchi MSheard JKorhonen A(2018)Understanding the Effects of Lecturer Intervention on Computer Science Student BehaviourProceedings of the 2017 ITiCSE Conference on Working Group Reports10.1145/3174781.3174787(105-124)Online publication date: 30-Jan-2018
https://dl.acm.org/doi/10.1145/3174781.3174787
Carter AHundhausen CAdesope O(2017)Blending Measures of Programming and Social Behavior into Predictive Models of Student Achievement in Early Computing CoursesACM Transactions on Computing Education10.1145/312025917:3(1-20)Online publication date: 28-Aug-2017
https://dl.acm.org/doi/10.1145/3120259
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