A periodic table of computing education learning theories

Claudia Szabo, Nickolas Falkner, Andrew Petersen, Heather Bort, Cornelia Connolly, Kathryn Cunningham, Peter Donaldson, Arto Hellas, James Robinson, Judy Sheard

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

Abstract

Computing education research is built on the use of suitable methods within appropriate theoretical frameworks to provide guidance and solutions for our discipline, in a way that is rigorous and repeatable. However, the scale of theory covered extends well beyond the CS discipline and includes educational theory, behavioural psychology, statistics, economics, and game theory, among others. A computing education researcher’s journey towards appropriate and discipline relevant theory can be challenging and, when a researcher has learned one area of theory, it can be easy to return to familiar theory, as it may not be clear what the next step could be. The periodic table is a visual arrangement of the elements to group like with like, providing insight into how families of elements will react. Could we do the same with learning theories located in the domain of computer science education, and would it be useful? The working group will identify and survey existing literature on relationships between key areas of theory in computing education, identify ways of organising these research areas to show how knowledge of one could assist another, and produce initial graphical representations of theory and their relationship groupings to assist researchers in understanding how computing theory is currently used in the discipline and what theories might become of interest.

Original languageEnglish
Title of host publicationProceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education
EditorsArnold Pears, Mihaela Sabin
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages269-270
Number of pages2
ISBN (Electronic)9781450363013, 9781450368957
DOIs
Publication statusPublished - 2019
EventAnnual Conference on Innovation and Technology in Computer Science Education 2019 - Aberdeen, United Kingdom
Duration: 12 Jul 201917 Jul 2019
Conference number: 24th
https://iticse.acm.org/

Conference

ConferenceAnnual Conference on Innovation and Technology in Computer Science Education 2019
Abbreviated titleITiCSE 2019
CountryUnited Kingdom
CityAberdeen
Period12/07/1917/07/19
Internet address

Keywords

  • Computing education
  • Epistemology
  • Learning theory

Cite this

Szabo, C., Falkner, N., Petersen, A., Bort, H., Connolly, C., Cunningham, K., ... Sheard, J. (2019). A periodic table of computing education learning theories. In A. Pears, & M. Sabin (Eds.), Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education (pp. 269-270). New York NY USA: Association for Computing Machinery (ACM). https://doi.org/10.1145/3304221.3325534
Szabo, Claudia ; Falkner, Nickolas ; Petersen, Andrew ; Bort, Heather ; Connolly, Cornelia ; Cunningham, Kathryn ; Donaldson, Peter ; Hellas, Arto ; Robinson, James ; Sheard, Judy. / A periodic table of computing education learning theories. Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education. editor / Arnold Pears ; Mihaela Sabin. New York NY USA : Association for Computing Machinery (ACM), 2019. pp. 269-270
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Szabo, C, Falkner, N, Petersen, A, Bort, H, Connolly, C, Cunningham, K, Donaldson, P, Hellas, A, Robinson, J & Sheard, J 2019, A periodic table of computing education learning theories. in A Pears & M Sabin (eds), Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education. Association for Computing Machinery (ACM), New York NY USA, pp. 269-270, Annual Conference on Innovation and Technology in Computer Science Education 2019, Aberdeen, United Kingdom, 12/07/19. https://doi.org/10.1145/3304221.3325534

A periodic table of computing education learning theories. / Szabo, Claudia; Falkner, Nickolas; Petersen, Andrew; Bort, Heather; Connolly, Cornelia; Cunningham, Kathryn; Donaldson, Peter; Hellas, Arto; Robinson, James; Sheard, Judy.

Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education. ed. / Arnold Pears; Mihaela Sabin. New York NY USA : Association for Computing Machinery (ACM), 2019. p. 269-270.

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

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AU - Robinson, James

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AB - Computing education research is built on the use of suitable methods within appropriate theoretical frameworks to provide guidance and solutions for our discipline, in a way that is rigorous and repeatable. However, the scale of theory covered extends well beyond the CS discipline and includes educational theory, behavioural psychology, statistics, economics, and game theory, among others. A computing education researcher’s journey towards appropriate and discipline relevant theory can be challenging and, when a researcher has learned one area of theory, it can be easy to return to familiar theory, as it may not be clear what the next step could be. The periodic table is a visual arrangement of the elements to group like with like, providing insight into how families of elements will react. Could we do the same with learning theories located in the domain of computer science education, and would it be useful? The working group will identify and survey existing literature on relationships between key areas of theory in computing education, identify ways of organising these research areas to show how knowledge of one could assist another, and produce initial graphical representations of theory and their relationship groupings to assist researchers in understanding how computing theory is currently used in the discipline and what theories might become of interest.

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Szabo C, Falkner N, Petersen A, Bort H, Connolly C, Cunningham K et al. A periodic table of computing education learning theories. In Pears A, Sabin M, editors, Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education. New York NY USA: Association for Computing Machinery (ACM). 2019. p. 269-270 https://doi.org/10.1145/3304221.3325534