research-article

Small but Powerful: A Learning Study to Address Secondary Students’ Conceptions of Everyday Computing Technology

Authors:

Michael T. Rücker,

Wouter R. van Joolingen,

Niels PinkwartAuthors Info & Claims

ACM Transactions on Computing Education (TOCE), Volume 20, Issue 2

Article No.: 11, Pages 1 - 27

https://doi.org/10.1145/3377880

Published: 06 February 2020 Publication History

Abstract

Enabling students to recognize and evaluate the ubiquitous impact of computing technology on society is an internationally proclaimed goal of a K-12 computing education. To that end, students need to actually engage with their computing knowledge in concrete everyday situations. From the perspectives of learning transfer and variation theory, we conducted three iterations of a classroom intervention and qualitatively analyzed students’ learning processes. As a result, we propose a model of four so-called critical aspects of everyday computing technology in that context. We present various classroom situations and learning experiences in relation to those aspects, and discuss what seems to have enabled or prevented meaningful learning. In particular, we found that several students had difficulties in conceiving of computing technology as simultaneously economical and powerful, thus limiting its potential ubiquity. We discuss our findings in the context of contemporary theories of learning transfer and argue that they suggest specific issues that may seriously inhibit students to appropriately engage with their computing knowledge in the context of everyday technologies.

References

[1]

Sanne Akkerman, Wilfried Admiraal, Mieke Brekelmans, and Heinze Oost. 2008. Auditing quality of research in social sciences. Quality 8 Quantity 42, 2 (April 2008), 257--274.

[2]

Louis Alfieri, Timothy J. Nokes-Malach, and Christian D. Schunn. 2013. Learning through case comparisons: A meta-analytic review. Educational Psychologist 48, 2 (2013), 87--113.

[3]

J. Bohn, V. Coroamǎ, M. Langheinrich, F. Mattern, and M. Rohs. 2005. Social, economic, and ethical implications of ambient intelligence and ubiquitous computing. In Ambient Intelligence. Springer-Verlag, Berlin, Germany, 5--29.

[4]

David E. Brown. 1992. Using examples and analogies to remediate misconceptions in physics: Factors influencing conceptual change. Journal of Research in Science Teaching 29, 1 (Jan. 1992), 17--34.

[5]

David E. Brown and John Clement. 1987. Overcoming misconceptions in mechanics: A comparison of two example-based teaching strategies. Paper Presented at Annual Meeting of the American Educational Research Association. https://eric.ed.gov/?id=ED283712.

[6]

David E. Brown and John Clement. 1989. Overcoming misconceptions via analogical reasoning: Abstract transfer versus explanatory model construction. Instructional Science 18, 4 (1989), 237--261.

[7]

Kathy Charmaz. 2014. Constructing Grounded Theory (2nd ed.). Sage, Los Angeles, CA.

[8]

Louis Cohen, Lawrence Manion, and Keith Morrison. 2007. Research Methods in Education (6th ed.). Routledge, London, England.

[9]

Paul Curzon, Peter W. McOwan, James Donohue, Seymour Wright, and William Marsh. 2018. Teaching of concepts. In Computer Science Education: Perspectives on Teaching and Learning in School, S. Sentance, E. Barendsen, and C. Schulte (Eds.). Bloomsbury Publishing, London, England, 91--108.

[10]

Samuel B. Day and Robert L. Goldstone. 2012. The import of knowledge export: Connecting findings and theories of transfer of learning. Educational Psychologist 47, 3 (2012), 153--176.

[11]

Peter Dudley. 2014. Lesson Study: A Handbook. Lesson Study UK. Available at http://repositorio.minedu.gob.pe/handle/123456789/5017

[12]

Michael Eisenberg, Ann Eisenberg, Leah Buechley, and Nwanua Elumeze. 2006. Invisibility considered harmful: Revisiting traditional principles of ubiquitous computing in the context of education. In Proceedings of the 2006 4th IEEE International Workshop on Wireless, Mobile, and Ubiquitous Technology in Education (WMTE’06). IEEE, Los Alamitos, CA, 103--110.

[13]

Aidan Feeney and Evan Heit. 2011. Properties of the diversity effect in category-based inductive reasoning. Thinking 8 Reasoning 17, 2 (May 2011), 156--181.

[14]

Michael Friedewald and Oliver Raabe. 2011. Ubiquitous computing: An overview of technology impacts. Telematics and Informatics 28, 2 (May 2011), 55--65.

Digital Library

[15]

Gesellschaft für Informatik e.V. 2008. Grundsätze und Standards für die Informatik in der Schule: Bildungsstandards Informatik für die Sekundarstufe I. Beilage zu Log In 150/151. Available at https://www.informatikstandards.de/docs/bildungsstandards_2008.pdf.

[16]

Barney G. Glaser. 1978. Theoretical Sensitivity: Advances in the Methodology of Grounded Theory (4th reprint ed.). Sociology Press, Mill Valley, CA.

[17]

Shuchi Grover and Roy D. Pea. 2013. Computational thinking in K-12. Educational Researcher 42, 1 (2013), 38--43.

[18]

Shuchi Grover, Daisy Rutstein, and Eric Snow. 2016. “What is a computer?” What do secondary school students think? In Proceedings of the 47th ACM Technical Symposium on Computing Science Education (SIGCSE’16). ACM, New York, NY, 564--569.

Digital Library

[19]

Jian-Peng Guo, Ling-Yan Yang, and Yi Ding. 2014. Effects of example variability and prior knowledge in how students learn to solve equations. European Journal of Psychology of Education 29, 1 (March 2014), 21--42.

[20]

Peter Hubwieser, Michail N. Giannakos, Marc Berges, Torsten Brinda, Ira Diethelm, Johannes Magenheim, Yogendra Pal, Jana Jackova, and Egle Jasute. 2015. A global snapshot of computer science education in K-12 schools. In Proceedings of the 2015 ITiCSE on Working Group Reports (ITICSE-WGR’15). ACM, New York, NY, 65--83.

Digital Library

[21]

IBM Research. 2018. Crypto-anchors and Blockchain. Retrieved January 21, 2020 from https://www.research.ibm.com/5-in-5/crypto-anchors-and-blockchain/

[22]

Anne Jacot, Isabel Raemdonck, and Mariane Frenay. 2015. A review of motivational constructs in learning and training transfer. Zeitschrift für Erziehungswissenschaft 18, S1 (March 2015), 201--219.

[23]

K-12 Computer Science Framework Steering Committee. 2016. K-12 Computer Science Framework. Technical Report. ACM, New York, NY. https://k12cs.org/wp-content/uploads/2016/09/K%E2%80%9312-Computer-Science-Framework.pdf.

[24]

Landesinstitut für Schule und Medien Berlin-Brandenburg. 2015. Informatik Wahlpflichtfach: Jahrgangsstufe 7-10. Retrieved January 21, 2020 from http://bildungsserver.berlin-brandenburg.de/fileadmin/bbb/unterricht/rahmenlehrplaene/Rahmenlehrplanprojekt/amtliche_Fassung/Teil_C_Informatik_2015_11_10_WEB.pdf.

[25]

Diane Larsen-Freeman. 2013. Transfer of learning transformed. Language Learning 63, s1 (March 2013), 107--129.

[26]

Yvonna S. Lincoln and Egon G. Guba. 1982. Establishing dependability and confirmability in naturalistic inquiry through an audit. Paper Presented at the Annual Meeting of the American Educational Research Association. https://eric.ed.gov/?id=ED216019.

[27]

Yvonna S. Lincoln and Egon G. Guba. 1985. Naturalistic Inquiry. Sage, Newbury Park, CA.

[28]

Mun Ling Lo. 2012. Variation Theory and the Improvement of Teaching and Learning. Ph.D. Dissertation. Göteborgs Universitet, Acta Universitatis Gothoburgensis.

[29]

Joanne Lobato. 2003. How design experiments can inform a rethinking of transfer and vice versa. Educational Researcher 32, 1 (Jan. 2003), 17--20.

[30]

Joanne Lobato. 2006. Alternative perspectives on the transfer of learning: History, issues, and challenges for future research. Journal of the Learning Sciences 15, 4 (Oct. 2006), 431--449.

[31]

Joanne Lobato. 2012. The actor-oriented transfer perspective and its contributions to educational research and practice. Educational Psychologist 47, 3 (July 2012), 232--247.

[32]

Joanne Lobato, Bohdan Rhodehamel, and Charles Hohensee. 2012. “Noticing” as an alternative transfer of learning process. Journal of the Learning Sciences 21, 3 (July 2012), 433--482.

[33]

Ference Marton. 2006. Sameness and difference in transfer. Journal of the Learning Sciences 15, 4 (Oct. 2006), 499--535.

[34]

Ference Marton. 2015. Necessary Conditions of Learning. Routledge, New York, NY.

[35]

Ference Marton, Wai Ming Cheung, and Stephanie W. Y. Chan. 2019. The object of learning in action research and learning study. Educational Action Research 27, 4 (Jan. 2019), 1--15.

[36]

Susan McKenney and Thomas C. Reeves. 2012. Conducting Educational Design Research. Routledge, London, England.

[37]

Pekka Mertala. 2019. Young children’s conceptions of computers, code, and the Internet. International Journal of Child-Computer Interaction 19 (March 2019), 56--66.

[38]

Pekka Mertala. 2019. Young children’s perceptions of ubiquitous computing and the Internet of Things. British Journal of Educational Technology. Epub ahead of print.

[39]

Kathrin Müller and Carsten Schulte. 2018. Are children perceiving robots as supporting or replacing humans? In Proceedings of the 13th Workshop in Primary and Secondary Computing Education (WiPSCE’18). ACM, New York, NY, 1--4.

Digital Library

[40]

Aki Murata. 2011. Introduction: Conceptual overview of lesson study. In Lesson Study Research and Practice in Mathematics Education, L. C. Hart, A. S. Alston, and A. Murata (Eds.). Springer Netherlands, Dordrecht, 1--12.

[41]

Daniel N. Osherson, Edward E. Smith, Ormond Wilkie, Alejandro López, and Eldar Shafir. 1990. Category-based induction. Psychological Review 97, 2 (1990), 185--200.

[42]

Ming Fai Pang and Wing Wah Ki. 2016. Revisiting the idea of “critical aspects.” Scandinavian Journal of Educational Research 60, 3 (May 2016), 323--336.

[43]

David N. Perkins and Gavriel Salomon. 2012. Knowledge to go: A motivational and dispositional view of transfer. Educational Psychologist 47, 3 (July 2012), 248--258.

[44]

Alexander Renkl, Heinz Mandl, and Hans Gruber. 1996. Inert knowledge: Analyses and remedies. Educational Psychologist 31, 2 (March 1996), 115--121.

[45]

Michael T. Rücker and Niels Pinkwart. 2016. Review and discussion of children’s conceptions of computers. Journal of Science Education and Technology 25, 2 (April 2016), 274--283.

[46]

Michael T. Rücker and Niels Pinkwart. 2018. The things that belong: A grounded theory study of student categorizations of complex technical artifacts. International Journal of Technology and Design Education 28, 3 (Sept. 2018), 701--720.

[47]

Michael T. Rücker and Niels Pinkwart. 2019. “How else should it work?” A grounded theory of pre-college students’ understanding of computing devices. ACM Transactions on Computing Education 19, 1 (2019), 1--23.

Digital Library

[48]

Carsten Schulte, Sue Sentance, and Erik Barendsen. 2018. Computer science, interaction and the world. In Computer Science Education: Perspectives on Teaching and Learning in School, S. Sentance, E. Barendsen, and C. Schulte (Eds.). Bloomsbury Publishing, London, England, 57--71.

[49]

Andreas Schwill. 1997. Computer science education based on fundamental ideas. In Information Technology, D. Passey and B. Samways (Eds.). Springer, Boston, MA, 285--291.

[50]

Máximo Trench and Ricardo A. Minervino. 2015. The role of surface similarity in analogical retrieval: Bridging the gap between the naturalistic and the experimental traditions. Cognitive Science 39, 6 (Aug. 2015), 1292--1319.

[51]

Jan van den Akker. 2010. Building bridges: How research may improve curriculum policies and classroom practices. In Beyond Lisbon 2010: Perspectives from Research and Development for Education Policy in Europe, S. M. Stoney (Ed.). CIDREE, Sint-Katelijne-Waver, Belgium, 175--195. https://research.utwente.nl/en/publications/building-bridges-how-research-may-improve-curriculum-policies-and

[52]

Joseph F. Wagner. 2006. Transfer in pieces. Cognition and Instruction 24, 1 (March 2006), 1--71.

[53]

Joseph F. Wagner. 2010. A transfer-in-pieces consideration of the perception of structure in the transfer of learning. Journal of the Learning Sciences 19, 4 (Oct. 2010), 443--479.

[54]

Mark Weiser. 1999. The computer for the 21st century. ACM SIGMOBILE Mobile Computing and Communications Review 3, 3 (July 1999), 3--11.

Digital Library

Cited By

Höper LSchulte CMühling A(2024)Learning an Explanatory Model of Data-Driven Technologies can Lead to Empowered Behavior: A Mixed-Methods Study in K-12 Computing EducationProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671118(326-342)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3632620.3671118
Malmi LSheard JKinnunen PSimon Sinclair J(2022)Development and Use of Domain-specific Learning Theories, Models, and Instruments in Computing EducationACM Transactions on Computing Education10.1145/353022123:1(1-48)Online publication date: 29-Dec-2022
https://dl.acm.org/doi/10.1145/3530221

Index Terms

Small but Powerful: A Learning Study to Address Secondary Students’ Conceptions of Everyday Computing Technology
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. K-12 education

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cover image ACM Transactions on Computing Education

ACM Transactions on Computing Education Volume 20, Issue 2

June 2020

174 pages

EISSN:1946-6226

DOI:10.1145/3382496

Editor:
Chris Hundhausen
Washington State University, USA

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Copyright © 2020 ACM.

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Publication History

Published: 06 February 2020

Accepted: 01 January 2020

Revised: 01 August 2019

Received: 01 February 2019

Published in TOCE Volume 20, Issue 2

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Cited By

Höper LSchulte CMühling A(2024)Learning an Explanatory Model of Data-Driven Technologies can Lead to Empowered Behavior: A Mixed-Methods Study in K-12 Computing EducationProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671118(326-342)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3632620.3671118
Malmi LSheard JKinnunen PSimon Sinclair J(2022)Development and Use of Domain-specific Learning Theories, Models, and Instruments in Computing EducationACM Transactions on Computing Education10.1145/353022123:1(1-48)Online publication date: 29-Dec-2022
https://dl.acm.org/doi/10.1145/3530221

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