Volume 14 · Number 3 ·
Pages 272–283
Studying Conceptual Change in Classrooms: Using Association Rule Mining to Detect Changes in Students’ Explanations of the Effects of Urban Planning and Social Policy
Log in to download the full text for free
Abstract
Context: Conceptual developments in our understanding of knowledge are merging with machine-learning methods for making sense of data. This creates new, and interesting ways in which we can document and analyse knowledge, and conceptual change. Problem: Currently, the study of conceptual change is often limited to small sample sizes because of the laborious nature of existing, purely qualitative approaches. Method: We present Association Rule Mining to better measure and understand changes in students’ thinking at the classroom level, based on data collected while implementing a constructionist learning activity in a US college classroom. Association Rule Mining is used on a set of qualitatively coded student responses. We then look at changes in the association rules between students’ responses before and after a learning activity to better understand students’ conceptual change at the classroom level. Results: We find that students converge on a more complete and accurate set of causal claims in their post-responses. Finding these changes would have been difficult or impossible without Association Rule Mining, or a similar approach. This suggests that Association Rule Mining is a potentially fruitful approach to analysing conceptual change at the classroom level. Implications: Adding Association Rule Mining to the arsenal of computational qualitative methods will let us study student data of larger sizes than previously. Constructivist content: Association Rule Mining is agnostic with regard to the ontology of its data. This makes Association Rule Mining a particularly suitable analysis method when taking a constructivist view of learning
Key words: Qualitative research, computational methods, knowledge analysis, education, conceptual change, complex systems thinking, learning sciences, constructionism.
Citation
Hjorth A. & Wilensky U. (2019) Studying conceptual change in classrooms: Using association rule mining to detect changes in students’ explanations of the effects of urban planning and social policy. Constructivist Foundations 14(3): 272–283. https://constructivist.info/14/3/272
Export article citation data: Plain Text · BibTex · EndNote · Reference Manager (RIS)
Similar articles
References
Adams P. C. (1998) Teaching and learning with SimCity 2000. The Journal of Geography 97(2): 47–55. ▸︎ Google︎ Scholar
Agrawal R., Imieliński T. & Swami A. (1993) Mining association rules between sets of items in large databases. ACM SIGMOD Record 22(2): 207–216. ▸︎ Google︎ Scholar
Aldowah H., Al-Samarraie H. & Fauzy W. M. (2019) Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics 37: 13–49. ▸︎ Google︎ Scholar
Baker R. S. & Yacef K. (2009) The state of educational data mining in 2009: A review and future visions. JEDM 1(1): 3–17. ▸︎ Google︎ Scholar
Barth-Cohen L. A. & Wittmann M. C. (2017) Aligning coordination class theory with a new context: Applying a theory of individual learning to group learning. Science Education 101(2): 333–363. ▸︎ Google︎ Scholar
Bereitschaft B. (2016) Gods of the city? Reflecting on city building games as an early introduction to urban systems. Journal of Geography 115(2): 51–60. ▸︎ Google︎ Scholar
Berland M., Baker R. S. & Blikstein P. (2014) Educational data mining and learning analytics: Applications to constructionist research. Technology, Knowledge and Learning 19(1–2): 205–220. ▸︎ Google︎ Scholar
Blikstein P. (2011) Using learning analytics to assess students’ behavior in open-ended programming tasks. In: Proceedings of the first international conference on learning analytics and knowledge. ACM, New York: 110–116. ▸︎ Google︎ Scholar
Cherry G., Ioannidou A., Rader C., Brand C. & Repenning A. (1999) Simulations for lifelong learning. In: Spotlight on the future: Procedings of the National Educational Computing Conference (NEC ’99) International Society for Technology in Education, Eugene OR: 43–52. ▸︎ Google︎ Scholar
Devisch O. (2008) Should planners start playing computer games? Arguments from SimCity and Second Life. Planning Theory & Practice 9(2): 209–226. ▸︎ Google︎ Scholar
diSessa A. A. & Sherin B. (1998) What changes in conceptual change? International Journal of Science Education 20(10): 1155–1191. ▸︎ Google︎ Scholar
diSessa A. A. (1993) Toward an epistemology of physics. Cognition and Instruction 10(2–3): 105–225. ▸︎ Google︎ Scholar
diSessa A. A. (2002) Why “conceptual ecology” is a good idea. In: Limón M. & Mason L. (eds.) Reconsidering conceptual change: Issues in theory and practice. Springer, Dordrecht: 28–60. ▸︎ Google︎ Scholar
diSessa A. A., Sherin B. & Levin M. (2015) Knowledge analysis: An introduction. In: diSessa A. A., Levin M. & Brown N. J. S. (eds.) Knowledge and interaction: A synthetic agenda for the learning sciences. ▸︎ Google︎ Scholar
Dorn D. S. (1989) Simulation games: One more tool on the pedagogical shelf. Teaching Sociology 17(1): 1–18. ▸︎ Google︎ Scholar
Epstein J. M. (1999) Agent-based computational models and generative social science. Chapter 1 in: Generative social science: Studies in agent-based computational modeling. Princeton University Press, Princeton NJ: 4–46. ▸︎ Google︎ Scholar
Felsen M., Watson B. & Wilensky U. (2006) Urban complexity + emergence: Procedural modeling of city activity and form. In: Roberts N. & Singley P. (eds.) Surfacing urbanisms: Recent approaches to metropolitan design. Woodbury University, Burbank CA: 261–265. ▸︎ Google︎ Scholar
Frasca G. (2001) Videogames of the oppressed: Videogames as a means for critical thinking and debate. Master’s Thesis, Georgia Institute of Technology. ▸︎ Google︎ Scholar
Frasca G. (2003) The video game theory reader. Routledge, London. ▸︎ Google︎ Scholar
Gaber J. (2007) Simulating planning: SimCity as a pedagogical tool. Journal of Planning Education and Research 27(2): 113–121. ▸︎ Google︎ Scholar
Gee J. P. (2003) What video games have to teach us about learning and literacy. Computers in Entertainment 1(1): 1–4. ▸︎ Google︎ Scholar
Gee J. P. (2005) Learning by design: Good video games as learning machines. E-Learning and Digital Media 2(1): 5–16. ▸︎ Google︎ Scholar
Gee J. P. (2007) Good video games and good learning. First edition. Peter Lang, Frankfurt. ▸︎ Google︎ Scholar
Guo Y., Xing W. & Lee H. (2015) Identifying students’ mechanistic explanations in textual responses to science questions with Association Rule Mining. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW). IEEE Press, Piscataway NJ: 264–268. ▸︎ Google︎ Scholar
Hjorth A. & Krist C. (2016) Unpacking social factors in mechanistic reasoning (or, why a wealthy person is not exactly like a grey squirrel). In: Looi C. K., Polman J. L., Cress U. & Reimann P. (eds.) Transforming learning, empowering learners: The International Conference of the Learning Sciences (ICLS 2016) Volume 2. International Society of the Learning Sciences, Singapore: 894–897. https://repository.isls.org/bitstream/1/337/1/130.pdf
Hjorth A. & Wilensky U. (2014) Re-grow your city: A NetLogo curriculum unit on regional development. In: Polman J. L., Kyza E. A., O’Neill D. K., Tabak I., Penuel W. R., Jurow A. S., O’Connor K., Lee T. & D’Amico L. (eds.) Learning and becoming in practice: The International Conference of the Learning Sciences (ICLS 2014) Volume 3. International Society of the Learning Sciences, Colorado CO: 1553–1554. ▸︎ Google︎ Scholar
Hjorth A. & Wilensky U. (2014) Redesigning your city: A constructionist environment for urban planning education. Informatics in Education 13(2): 197–208. ▸︎ Google︎ Scholar
Kim M. & Shin J. (2016) The pedagogical benefits of SimCity in urban geography education. Journal of Geography 115(2): 39–50. ▸︎ Google︎ Scholar
Kolson K. (1996) The politics of SimCity. PS: Political Science and Politics 29(1): 43–46. ▸︎ Google︎ Scholar
Levrini O. & diSessa A. S. (2008) How students learn from multiple contexts and definitions: Proper time as a coordination class. Physical Review Physics Education Research 4(1): 010107. https://link.aps.org/pdf/10.1103/PhysRevSTPER.4.010107
Maroulis S., Bakshy E., Gomez L. & Wilensky U. (2014) Modeling the Transition to Public School Choice. Journal of Artificial Societies and Social Simulation 17(2): 3. http://jasss.soc.surrey.ac.uk/17/2/3.html
Maroulis S., Guimera R., Petry H., Stringer M. J. Gomez L., Amaral L. & Wilensky U. (2010) Complex systems view of educational policy research. Science 330(6000): 38–39. ▸︎ Google︎ Scholar
Martin T. & Sherin B. (2013) Learning analytics and computational techniques for detecting and evaluating patterns in learning: An introduction to the special issue. Journal of the Learning Sciences 22(4): 511–520. ▸︎ Google︎ Scholar
Miles M. B. & Huberman A. M. (1994) Qualitative data analysis: An expanded sourcebook. Sage, Thousand Oaks CA. ▸︎ Google︎ Scholar
NCSS (2013) The college, career, and civic life (C3) framework for social studies state standards: Guidance for enhancing the rigor of K-12 civics, economics, geography, and history. National Council for the Social Studies, Silver Spring MD. https://www.socialstudies.org/c3
NGSS Lead States (2013) Next Generation Science Standards: For States, By States. Washington DC: The National Academies Press. ▸︎ Google︎ Scholar
Pahl J. (1991) Finally, a good way to teach city government! A review of the computer simulation game “SimCity.” The Social Studies 82(4): 165–166. ▸︎ Google︎ Scholar
Papamitsiou Z. & Economides A. A. (2014) Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Journal of Educational Technology & Society 17(4): 49–64. ▸︎ Google︎ Scholar
Romero C. & Ventura S. (2007) Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications 33(1): 135–146. ▸︎ Google︎ Scholar
Romero C. & Ventura S. (2010) Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 40(6): 601–618. ▸︎ Google︎ Scholar
Shaffer D. W. (2006) Epistemic frames for epistemic games. Computers & Education 46(3): 223–234. ▸︎ Google︎ Scholar
Shaffer D. W. (2006) How computer games help children learn. First edition. Palgrave Macmillan, Basingstoke. ▸︎ Google︎ Scholar
Shaffer D., Squire K., Halverson R. & Gee J. (2005) Video games and the future of learning. Phi Delta Kappan 87(2): 105–111. ▸︎ Google︎ Scholar
Sherin B. (2006) Common sense clarified: The role of intuitive knowledge in physics problem solving. Journal of Research in Science Teaching 43(6): 535–555. ▸︎ Google︎ Scholar
Sherin B. (2013) A computational study of commonsense science: An exploration in the automated analysis of clinical interview data. Journal of the Learning Sciences 22(4): 600–638. ▸︎ Google︎ Scholar
Sherin B., Krakowski M. & Lee V. R. (2012) Some assembly required: How scientific explanations are constructed during clinical interviews. Journal of Research in Science Teaching 49(2): 166–198. ▸︎ Google︎ Scholar
Siemens G. & Baker R. S. J. d. (2012) Learning analytics and educational data mining: Towards communication and collaboration. In: Buckingham Shum S., Gasevic D. & Ferguson R. (eds.) Proceedings of the second international conference on Learning Analytics and Knowledge (LAK ’12) ACM, New York: 252–254 ▸︎ Google︎ Scholar
Squire K. (2003) Video games in education. International Journal of Intelligent Simulations and Gaming 2(1): 49–62. ▸︎ Google︎ Scholar
Starr P. (1994) Seductions of Sim: Policy as a simulation game. American Prospect 17: 19–29. ▸︎ Google︎ Scholar
Turkle S. (1997) Seeing through computers: Education in a culture of simulation. The American Prospect 8(31): 76–82. https://prospect.org/article/seeing-through-computers
Wilensky U. (1999) NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo
Wilensky U. (2001) Modeling nature’s emergent patterns with multi-agent languages. In: Proceedings of the Eurologo 2001 Conference, Linz, Austria: 1–6. ▸︎ Google︎ Scholar
Comments: 0
To stay informed about comments to this publication and post comments yourself, please log in first.