research-article

An Exploration of Cognitive Shifting in Writing Code

Authors:

Petri Ihantola, and

Tommi MikkonenAuthors Info & Claims

CompEd '19: Proceedings of the ACM Conference on Global Computing Education

May 2019

Pages 65 - 71

https://doi.org/10.1145/3300115.3309522

Published: 09 May 2019 Publication History

Abstract

Programming is considered a demanding task that requires focusing on detail at code level. Students learning to program need to learn to think like a programmer, which involves coming up with plans needed to solve problems, and they need to learn to write the code that corresponds to the plans that they have thought of. The use of multiple files creates additional overhead to the process, as part of the code is not visible to the student. If a student does not remember the contents of a particular file, she needs to consciously move from writing code in one file to reading code in another file. This conscious transition of attention from one location to another is known as cognitive shifting. Using key-level data collected from a programming exam, we analyze students' movements within files and between files, and relate these movements with students' performance in the course. Our results indicate that frequently moving from one file to another may lead to worse performance than more focused actions, but no such effect exists when analyzing movements within an individual file.

References

[1]

Rachel F Adler and Raquel Benbunan-Fich. 2012. Juggling on a high wire: Multitasking effects on performance. International Journal of Human-Computer Studies, Vol. 70, 2 (2012), 156--168.

Digital Library

[2]

Alireza Ahadi and Raymond Lister. 2013. Geek genes, prior knowledge, stumbling points and learning edge momentum: parts of the one elephant?. In Proceedings of the ninth annual international ACM conference on International computing education research. ACM, 123--128.

Digital Library

[3]

Erik M Altmann and J Gregory Trafton. 2002. Memory for goals: An activation-based model. Cognitive science, Vol. 26, 1 (2002), 39--83.

[4]

Carole Ames. 1992. Classrooms: Goals, structures, and student motivation. Journal of educational psychology, Vol. 84, 3 (1992), 261.

[5]

Eric M Anderman and Christopher A Wolters. 2006. Goals, Values, and Affect: Influences on Student Motivation. (2006).

[6]

James A Anderson, Janet Colvin, Nancy Tobler, and D Lindsay. 2003. Productivity and multi-screen displays. CIC Report No, Vol. 200351 (2003), 1--113.

[7]

Christoph Aschwanden and Martha Crosby. 2006. Code scanning patterns in program comprehension. In Proc. of the 39th Hawaii int. conf. on system sciences .

[8]

Russell Beale and William Edmondson. 2007. Multiple carets, multiple screens and multi-tasking: new behaviours with multiple computers. In Proceedings of the 21st British HCI Group Annual Conference on People and Computers: HCI... but not as we know it-Volume 1. British Computer Society, 55--64.

Digital Library

[9]

Paulo Blikstein. 2011. Using learning analytics to assess students' behavior in open-ended programming tasks. In Proceedings of the 1st international conference on learning analytics and knowledge. ACM, 110--116.

Digital Library

[10]

Stephanie R Boger-Mehall. 1996. Cognitive flexibility theory: Implications for teaching and teacher education. In Society for Information Technology & Teacher Education International Conference. Association for the Advancement of Computing in Education (AACE), 991--993.

[11]

Jelmer P Borst, Niels A Taatgen, and Hedderik van Rijn. 2015. What makes interruptions disruptive?: A process-model account of the effects of the problem state bottleneck on task interruption and resumption. In Proceedings of the 33rd annual ACM conference on human factors in computing systems. ACM, 2971--2980.

Digital Library

[12]

Teresa Busjahn, Roman Bednarik, Andrew Begel, Martha Crosby, James H Paterson, Carsten Schulte, Bonita Sharif, and Sascha Tamm. 2015. Eye movements in code reading: Relaxing the linear order. In Program Comprehension (ICPC), 2015 IEEE 23rd International Conference on. IEEE, 255--265.

Digital Library

[13]

Guillermo Campitelli and Fernand Gobet. 2011. Deliberate Practice: Necessary But Not Sufficient. Current Directions in Psych. Science, Vol. 20, 5 (2011), 280--285.

[14]

Simon P Davies. 1991. The role of notation and knowledge representation in the determination of programming strategy: a framework for integrating models of programming behavior. Cognitive Science, Vol. 15, 4 (1991), 547--572.

[15]

Kerry Allison Delbridge. 2002. Individual differences in multi-tasking ability: Exploring a nomological network. (2002).

[16]

Paul Denny, Andrew Luxton-Reilly, Michelle Craig, and Andrew Petersen. 2018. Improving Complex Task Performance Using a Sequence of Simple Practice Tasks. In Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE 2018). ACM, 4--9.

Digital Library

[17]

Franccoise Détienne. 1995. Design Strategies and Knowledge in Object-oriented Programming: Effects of Experience. Hum.-Comput. Interact., Vol. 10, 2 (Sept. 1995), 129--169.

Digital Library

[18]

John Dunlosky, Katherine A Rawson, Elizabeth J Marsh, Mitchell J Nathan, and Daniel T Willingham. 2013. Improving students' learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, Vol. 14, 1 (2013), 4--58.

[19]

K Anders Ericsson, Ralf T Krampe, and Clemens Tesch-Römer. 1993. The role of deliberate practice in the acquisition of expert performance. Psychological review, Vol. 100, 3 (1993), 363.

[20]

Kathi Fisler. 2014. The recurring rainfall problem. In Proceedings of the tenth annual conference on International computing education research. ACM, 35--42.

Digital Library

[21]

Ilenia Fronza, Alberto Sillitti, Giancarlo Succi, and Jelena Vlasenko. 2011. Understanding how novices are integrated in a team analysing their tool usage. In Proc. of the 2011 Int. Conference on Software and Systems Process. ACM, 204--207.

Digital Library

[22]

Vanessa E Ghosh and Asaf Gilboa. 2014. What is a memory schema? A historical perspective on current neuroscience literature. Neuropsych., Vol. 53 (2014), 104--114.

[23]

Petri Ihantola, Arto Vihavainen, Alireza Ahadi, Matthew Butler, Jürgen Börstler, Stephen H. Edwards, Essi Isohanni, Ari Korhonen, Andrew Petersen, Kelly Rivers, Miguel Ángel Rubio, Judy Sheard, Bronius Skupas, Jaime Spacco, Claudia Szabo, and Daniel Toll. 2015. Educational Data Mining and Learning Analytics in Programming: Literature Review and Case Studies. In Proc. of the 2015 ITiCSE on Working Group Reports (ITICSE-WGR '15). ACM, New York, NY, USA, 41--63.

Digital Library

[24]

Youn-ah Kang and John Stasko. 2008. Lightweight task/application performance using single versus multiple monitors: a comparative study. In Proceedings of Graphics Interface 2008. Canadian Information Processing Society, 17--24.

Digital Library

[25]

Katja Kevic, Braden M Walters, Timothy R Shaffer, Bonita Sharif, David C Shepherd, and Thomas Fritz. 2015. Tracing software developers' eyes and interactions for change tasks. In Proc. of the 2015 10th Joint Meeting on Foundations of Software Engineering. ACM, 202--213.

Digital Library

[26]

Andrew J Ko, Brad A Myers, Michael J Coblenz, and Htet Htet Aung. 2006. An exploratory study of how developers seek, relate, and collect relevant information during software maintenance tasks. IEEE Transactions on software engineering, Vol. 32, 12 (2006), 971--987.

Digital Library

[27]

M Leinikka, A Vihavainen, J Lukander, and S Pakarinen. 2014. Cognitive flexibility and programming performance. In Proc. of the Psychology of Programming Interest Group Annual Conference 2014. 1--11.

[28]

Juho Leinonen, Leo Leppanen, Petri Ihantola, and Arto Hellas. 2017. Comparison of time metrics in programming. In Proceedings of the 2017 ACM Conference on International Computing Education Research. ACM, 200--208.

Digital Library

[29]

Juho Leinonen, Krista Longi, Arto Klami, and Arto Vihavainen. 2016. Automatic Inference of Programming Performance and Experience from Typing Patterns. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education (SIGCSE '16). ACM, 132--137.

Digital Library

[30]

Raymond Lister, Beth Simon, Errol Thompson, Jacqueline L Whalley, and Christine Prasad. 2006. Not seeing the forest for the trees: novice programmers and the SOLO taxonomy. ACM SIGCSE Bulletin, Vol. 38, 3 (2006), 118--122.

Digital Library

[31]

Andrew Luxton-Reilly, Brett A. Becker, Yingjun Cao, Roger McDermott, Claudio Mirolo, Andreas Mühling, Andrew Petersen, Kate Sanders, Simon, and Jacqueline Whalley. 2017. Developing Assessments to Determine Mastery of Programming Fundamentals. In Proceedings of the 2017 ITiCSE Conference on Working Group Reports (ITiCSE-WGR '17). ACM, New York, NY, USA, 47--69.

Digital Library

[32]

Russell Mosemann and Susan Wiedenbeck. 2001. Navigation and comprehension of programs by novice programmers. In Program Comprehension, 2001. IWPC 2001. Proceedings. 9th International Workshop on. IEEE, 79--88.

Digital Library

[33]

Donald A Norman. 2014. Some observations on mental models. In Mental models. Psychology Press, 15--22.

[34]

David N Perkins, Chris Hancock, Renee Hobbs, Fay Martin, and Rebecca Simmons. 1986. Conditions of learning in novice programmers. Journal of Educational Computing Research, Vol. 2, 1 (1986), 37--55.

[35]

Jean Piaget. 1971. Biology and knowledge: An essay on the relations between organic regulations and cognitive processes. (1971).

[36]

Vennila Ramalingam, Deborah LaBelle, and Susan Wiedenbeck. 2004. Self-efficacy and mental models in learning to program. In ACM SIGCSE Bulletin, Vol. 36. ACM, 171--175.

Digital Library

[37]

Robert S Rist. 1989. Schema creation in programming. Cognitive Science, Vol. 13, 3 (1989), 389--414.

[38]

Otto Seppala, Petri Ihantola, Essi Isohanni, Juha Sorva, and Arto Vihavainen. 2015. Do we know how difficult the rainfall problem is?. In Proceedings of the 15th Koli Calling Conference on Computing Education Research. ACM, 87--96.

Digital Library

[39]

Lee S Shulman et al. 1968. Studies of the Inquiry Process; Inquiry Patterns of Students in Teacher-Training Programs. Final Report. (1968).

[40]

Elliot Soloway. 1986. Learning to program= learning to construct mechanisms and explanations. Commun. ACM, Vol. 29, 9 (1986), 850--858.

Digital Library

[41]

John Sweller. 1988. Cognitive load during problem solving: Effects on learning. Cognitive science, Vol. 12, 2 (1988), 257--285.

[42]

John Sweller and Graham A Cooper. 1985. The use of worked examples as a substitute for problem solving in learning algebra. Cognition and instruction, Vol. 2, 1 (1985), 59--89.

[43]

Shelley E Taylor. 1981. Schematic bases of social information processing. Social cognition (1981), 89--134.

[44]

Alexey Tregubov, Barry Boehm, Natalia Rodchenko, and Jo Ann Lane. 2017. Impact of task switching and work interruptions on software development processes. In Proceedings of the 2017 International Conference on Software and System Process. ACM, 134--138.

Digital Library

[45]

Bogdan Vasilescu, Kelly Blincoe, Qi Xuan, Casey Casalnuovo, Daniela Damian, Premkumar Devanbu, and Vladimir Filkov. 2016. The sky is not the limit: multitasking across github projects. In Software Engineering (ICSE), 2016 IEEE/ACM 38th International Conference on. IEEE, 994--1005.

Digital Library

[46]

Arto Vihavainen, Jonne Airaksinen, and Christopher Watson. 2014. A systematic review of approaches for teaching introductory programming and their influence on success. In Proceedings of the tenth annual conference on International computing education research. ACM, 19--26.

Digital Library

[47]

Arto Vihavainen, Juha Helminen, and Petri Ihantola. 2014. How novices tackle their first lines of code in an ide: Analysis of programming session traces. In Proceedings of the 14th Koli Calling International Conference on Computing Education Research. ACM, 109--116.

Digital Library

[48]

Arto Vihavainen, Matti Luukkainen, and Jaakko Kurhila. 2012. Multi-faceted Support for MOOC in Programming. In Proc. of the 13th Annual Conf. on Information Technology Education (SIGITE '12). ACM, New York, NY, USA, 171--176.

Digital Library

[49]

Arto Vihavainen, Thomas Vikberg, Matti Luukkainen, and Martin Partel. 2013. Scaffolding Students' Learning Using Test My Code. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE '13). ACM, New York, NY, USA, 117--122.

Digital Library

[50]

Leon E Winslow. 1996. Programming pedagogy - a psychological overview. ACM Sigcse Bulletin, Vol. 28, 3 (1996), 17--22.

Digital Library

Cited By

Cowley BHellas AIhantola PLeinonen JSpape MPéraire CSerebrenik A(2022)Seeking flow from fine-grained log dataProceedings of the ACM/IEEE 44th International Conference on Software Engineering: Software Engineering Education and Training10.1145/3510456.3514138(247-253)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510456.3514138
Ultan Cowley BHellas AIhantola PLeinonen JSpape M(2022)Seeking Flow from Fine-Grained Log Data2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)10.1109/ICSE-SEET55299.2022.9794177(247-253)Online publication date: May-2022
https://doi.org/10.1109/ICSE-SEET55299.2022.9794177

Index Terms

An Exploration of Cognitive Shifting in Writing Code
1. Applied computing
  1. Education

Recommendations

CodeProcess Charts: Visualizing the Process of Writing Code
ACE '22: Proceedings of the 24th Australasian Computing Education Conference

Instructors of computer programming courses evaluate student progress on code submissions, exams, and other activities. The evaluation of code submissions is typically a summative assessment that gives very little insight into the process the student ...
Read More
Short Code: An Efficient RAID-6 MDS Code for Optimizing Degraded Reads and Partial Stripe Writes

As reliability requirements are increasingly important in both clusters and data centers, RAID-6, which can tolerate any two concurrent disk failures, has been widely used in modern storage systems. However, most existing RAID-6 codes cannot provide ...
Read More
D-Code: An Efficient RAID-6 Code to Optimize I/O Loads and Read Performance
IPDPS '15: Proceedings of the 2015 IEEE International Parallel and Distributed Processing Symposium

With the reliability requirement increasingly important, RAID-6, which can tolerate any two concurrent disk failures, has been widely used in various storage systems. One class of typical RAID-6 implementations is to use Maximum Distance Separable (MDS) ...
Read More

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CompEd '19: Proceedings of the ACM Conference on Global Computing Education

May 2019

260 pages

ISBN:9781450362597

DOI:10.1145/3300115

General Chairs:
Ming Zhang
Peking University, China
,
Bo Yang
Linyi University, China
,
Program Chairs:
Steve Cooper
University of Nebraska-Lincoln, USA
,
Andrew Luxton-Reilly
University of Auckland, New Zealand

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]

Sponsors

SIGCSE: ACM Special Interest Group on Computer Science Education

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 May 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Badges

Best Paper

Author Tags

Qualifiers

Research-article

Conference

CompEd '19

Sponsor:

SIGCSE

CompEd '19: ACM Global Computing Education Conference 2019

May 9 - 19, 2019

Chengdu,Sichuan, China

Acceptance Rates

CompEd '19 Paper Acceptance Rate 33 of 100 submissions, 33%;

Overall Acceptance Rate 33 of 100 submissions, 33%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
266
Total Downloads

Downloads (Last 12 months)14
Downloads (Last 6 weeks)4

Other Metrics

View Author Metrics

Citations

Cited By

Cowley BHellas AIhantola PLeinonen JSpape MPéraire CSerebrenik A(2022)Seeking flow from fine-grained log dataProceedings of the ACM/IEEE 44th International Conference on Software Engineering: Software Engineering Education and Training10.1145/3510456.3514138(247-253)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510456.3514138
Ultan Cowley BHellas AIhantola PLeinonen JSpape M(2022)Seeking Flow from Fine-Grained Log Data2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)10.1109/ICSE-SEET55299.2022.9794177(247-253)Online publication date: May-2022
https://doi.org/10.1109/ICSE-SEET55299.2022.9794177

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents