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Understanding How Work Habits influence Student Performance

Authors:

Seth Copen Goldstein,

Cameron DashtiAuthors Info & Claims

ITiCSE '19: Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education

Pages 154 - 160

https://doi.org/10.1145/3304221.3319757

Published: 02 July 2019 Publication History

Abstract

Understanding the relationship between a student's broader work habits and their performance, particularly on open-ended programming assignments, is key towards being able to guide students towards success. In spite of this, most evidence of student behavior and its relationship to performance is anecdotal. The advent of large-scale courses which use online tools for delivering course content, monitoring the programming environment, and providing automatic feedback as well as grading now makes it possible to dive into the data and develop data-driven methods for understanding how a student's approach to an assignment---from their first exposure to the description of the problem to their final submission of their completed assignment---influences their final performance.

This study is a first look at a subset of the data collected from a project-based, online, upper-level course on cloud computing. We examine three raw data streams which include information on the time students spend on reading the project write-up, the timing, grades, and number of submissions they make, and the cloud resources used (both time and cost) in solving the assignment. Using these and several synthetic metrics we were surprised to find that there are few behaviors that are highly correlated to student success. Instead, we find that there is a strong correlation between students who continually apply consistent behaviors over the course of the semester to good final performance in the course. From this we use LASSO to create a predictor (adjusted R2=.48) of final performance based on two kinds of consistency measures across 15 metrics.

References

[1]

Hercy NH Cheng, Zhi Liu, Jianwen Sun, Sanya Liu, and Zongkai Yang. 2017. Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs. Interactive Learning Environments 25, 2 (2017), 176--188.

[2]

Damian S Damianov, Lori Kupczynski, Pablo Calafiore, Ekaterina P Damianova, Gökçe Soydemir, and Edgar Gonzalez. 2009. Time spent online and student performance in online business courses: A multinomial logit analysis. Journal of Economics and Finance Education 8, 2 (2009), 11--22.

[3]

Greg C Elvers, Donald J Polzella, and Ken Graetz. 2003. Procrastination in online courses: Performance and attitudinal differences. Teaching of Psychology 30, 2 (2003), 159--162.

[4]

Richard M Felder, Linda K Silverman, et al. 1988. Learning and teaching styles in engineering education. Engineering education 78, 7 (1988), 674--681.

[5]

Rebecca Ferguson and Doug Clow. 2015. Consistent Commitment: Patterns of Engagement across Time in Massive Open Online Courses (MOOCs). Journal of Learning Analytics 2, 3 (2015), 55--80.

[6]

Kyung Ryung Kim and Eun Hee Seo. 2015. The relationship between procrastination and academic performance: A meta-analysis. Personality and Individual Differences 82 (2015), 26--33.

[7]

John S Kinnebrew, James R Segedy, and Gautam Biswas. 2014. Analyzing the temporal evolution of students behaviors in open-ended learning environments. Metacognition and learning 9, 2 (2014), 187--215.

[8]

Alice Y Kolb and David A Kolb. 2005. Learning styles and learning spaces: Enhancing experiential learning in higher education. Academy of management learning & education 4, 2 (2005), 193--212.

[9]

Vitomir Kovanovic, Sreko Joksimovi, Dragan Gaevi James Owers, Anne- Marie Scott, and Amy Woodgate. 2016. Profiling MOOC course returners: how does student behavior change between two course enrollments?. In Proceedings of the Third (2016) ACM Conference on Learning@ Scale. ACM, New York, NY, USA, 269--272.

Digital Library

[10]

Chris Masui, Jan Broeckmans, Sarah Doumen, Anne Groenen, and Geert Molenberghs. 2014. Do diligent students perform better? Complex relations between student and course characteristics, study time, and academic performance in higher education. Studies in Higher Education 39, 4 (2014), 621--643.

[11]

James W Michaels and Terance D Miethe. 1989. Academic effort and college grades. Social Forces 68, 1 (1989), 309--319.

[12]

Nicolas Michinov, Sophie Brunot, Olivier Le Bohec, Jacques Juhel, and Marine Delaval. 2011. Procrastination, participation, and performance in online learning environments. Computers & Education 56, 1 (2011), 243--252.

Digital Library

[13]

Shirin Mojarad, Alfred Essa, Shahin Mojarad, and Ryan S Baker. 2018. Data-driven learner profiling based on clustering student behaviors: learning consistency, pace and effort. In International Conference on Intelligent Tutoring Systems. Springer, New York, NY, USA, 130--139.

Digital Library

[14]

Jihyun Park, Kameryn Denaro, Fernando Rodriguez, Padhraic Smyth, and Mark Warschauer. 2017. Detecting changes in student behavior from clickstream data. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference. ACM, New York, NY, USA, 21--30.

Digital Library

[15]

Hilde Patron and Salvador Lopez. 2011. Student effort, consistency, and online performance. Journal of Educators Online 8, 2 (2011), n2.

[16]

E Ashby Plant, K Anders Ericsson, Len Hill, and Kia Asberg. 2005. Why study time does not predict grade point average across college students: Implications of deliberate practice for academic performance. Contemporary Educational Psychology 30, 1 (2005), 96--116.

[17]

David Romer. 1993. Do students go to class? Should they? Journal of Economic Perspectives 7, 3 (1993), 167--174.

[18]

Howard Schuman, Edward Walsh, Camille Olson, and Barbara Etheridge. 1985. Effort and reward: The assumption that college grades are affected by quantity of study. Social Forces 63, 4 (1985), 945--966.

[19]

Daniel T Seaton, Yoav Bergner, Isaac Chuang, Piotr Mitros, and David E Pritchard. 2014. Who does what in a massive open online course? Commun. ACM 57, 4 (2014), 58--65.

Digital Library

[20]

Kuang-Cheng Tseng and Shan-Ying Chu. 2010. Traditional versus online courses, efforts, and learning performance. The Journal of Human Resource and Adult Learning 6, 1 (2010), 115.

[21]

MM Van den Hurk, HAP Wolfhagen, DHJM Dolmans, and CPM Van der Vleuten. 1998. The relation between time spent on individual study and academic achievement in a problem-based curriculum. Advances in Health Sciences Education 3, 1 (1998), 43--49.

[22]

Wei-KangWong. 2008. How much time-inconsistency is there and does it matter? Evidence on self-awareness, size, and effects. Journal of Economic Behavior & Organization 68, 3--4 (2008), 645--656.

Cited By

Chen HWard PLaakso MMonga MSimon Sheard J(2023)The Value of Time Extensions in Identifying Students AbilitiesProceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 110.1145/3587102.3588847(512-518)Online publication date: 29-Jun-2023
https://dl.acm.org/doi/10.1145/3587102.3588847
Lorås MSindre GTrætteberg HAalberg T(2021)Study Behavior in Computing Education—A Systematic Literature ReviewACM Transactions on Computing Education10.1145/346912922:1(1-40)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3469129
Nguyen HLim MMoore SNyberg ESakr MStamper JScheffel MDowell NJoksimovic SSiemens G(2021)Exploring Metrics for the Analysis of Code Submissions in an Introductory Data Science CourseLAK21: 11th International Learning Analytics and Knowledge Conference10.1145/3448139.3448209(632-638)Online publication date: 12-Apr-2021
https://dl.acm.org/doi/10.1145/3448139.3448209
Show More Cited By

Index Terms

Understanding How Work Habits influence Student Performance
1. Applied computing
  1. Education
2. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Adult education
      2. Computing education programs
        Computer science education

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cover image ACM Conferences

ITiCSE '19: Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education

July 2019

583 pages

ISBN:9781450368957

DOI:10.1145/3304221

General Chairs:
Bruce Scharlau
University of Aberdeen, UK
,
Roger McDermott
Robert Gordon University, UK
,
Program Chairs:
Arnold Pears
KTH Royal Institute of Technology, Sweden
,
Mihaela Sabin
University of New Hampshire, USA

Copyright © 2019 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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Published: 02 July 2019

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AWS
Microsoft
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ITiCSE '19

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SIGCSE

ITiCSE '19: Innovation and Technology in Computer Science Education

July 15 - 17, 2019

Aberdeen, Scotland Uk

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Overall Acceptance Rate 552 of 1,613 submissions, 34%

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Cited By

Chen HWard PLaakso MMonga MSimon Sheard J(2023)The Value of Time Extensions in Identifying Students AbilitiesProceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 110.1145/3587102.3588847(512-518)Online publication date: 29-Jun-2023
https://dl.acm.org/doi/10.1145/3587102.3588847
Lorås MSindre GTrætteberg HAalberg T(2021)Study Behavior in Computing Education—A Systematic Literature ReviewACM Transactions on Computing Education10.1145/346912922:1(1-40)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3469129
Nguyen HLim MMoore SNyberg ESakr MStamper JScheffel MDowell NJoksimovic SSiemens G(2021)Exploring Metrics for the Analysis of Code Submissions in an Introductory Data Science CourseLAK21: 11th International Learning Analytics and Knowledge Conference10.1145/3448139.3448209(632-638)Online publication date: 12-Apr-2021
https://dl.acm.org/doi/10.1145/3448139.3448209
An MZhang HSavelka JZhu SBogart CSakr MSchulte CBecker BDivitini MBarendsen E(2021)Are Working Habits Different Between Well-Performing and at-Risk Students in Online Project-Based Courses?Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 110.1145/3430665.3456320(324-330)Online publication date: 26-Jun-2021
https://dl.acm.org/doi/10.1145/3430665.3456320
Lorås MAalberg TSchulte CBecker BDivitini MBarendsen E(2021)Characteristics of the Student-Driven Learning Environment in Computing Education: A Case Study on the Interaction between Educational Design and Study BehaviorProceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 110.1145/3430665.3456310(11-17)Online publication date: 26-Jun-2021
https://dl.acm.org/doi/10.1145/3430665.3456310
Lorås MAalberg T(2020)First Year Computing Study Behavior: Effects of Educational Design2020 IEEE Frontiers in Education Conference (FIE)10.1109/FIE44824.2020.9274285(1-9)Online publication date: 21-Oct-2020
https://dl.acm.org/doi/10.1109/FIE44824.2020.9274285
Bellettini CLonati VMonga MMorpurgo A(2020)Behind the Shoulders of Bebras Teams: Analyzing How They Interact with the Platform to Solve TasksComputer Supported Education10.1007/978-3-030-58459-7_10(191-210)Online publication date: 10-Nov-2020
https://doi.org/10.1007/978-3-030-58459-7_10
Cabrera-Loayza MCadme EElizalde RPiedra N(2020)Learning Analytics as a Tool to Support TeachingApplied Technologies10.1007/978-3-030-42531-9_33(415-425)Online publication date: 3-Mar-2020
https://doi.org/10.1007/978-3-030-42531-9_33

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