Exploratory Analysis in Learning Analytics
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Abstract
This article summarizes the methods, observations, challenges and implications for exploratory analysis drawn from two learning analytics research projects. The cases include an analysis of a games-based virtual performance assessment and an analysis of data from 52,000 students over a 5-year period at a large Australian university. The complex datasets were analyzed and iteratively modeled with a variety of computationally intensive methods to provide the most effective outcomes for learning assessment, performance management and learner tracking. The article presents the research contexts, the tools and methods used in the exploratory phases of analysis, the major findings and the implications for learning analytics research methods.
Keywords
Learning analytics Computationally intensive mixed methods research Game-based learning Virtual performance assessmentReferences
- Baker, R. S. J. (2010). Data mining for education. International Encyclopedia of Education, 3, 112–118. doi: 10.4018/978-1-59140-557-3.ch075.CrossRefGoogle Scholar
- Behrens, J., Mislevy, R., Dicerbo, K., & Levy, R. (2011). Evidence centered design for learning and assessment in the digital world. In M. Mayrath, J. Clarke-Midura, D. Robinson, & G. Schraw (Eds.), Technology-based assessments for 21st century skills (pp. 13–54). Charlotte, NC: Information Age Publishers.Google Scholar
- Bollen, J., Van de Sompel, H., Hagberg, A., Bettencourt, L., Chute, R., Rodriguez, M. A., & Balakireva, L. (2009). Clickstream data yields high-resolution maps of science. PLoS One, 4, e4803. doi: 10.1371/journal.pone.0004803.CrossRefGoogle Scholar
- Campanharo, A., Sirer, M., Malmgren, R., Ramos, F., & Amaral, L. (2011). Duality between time series and networks. PLoS One, 6(8), e23378. doi: 10.1371/journal.pone.0023378.CrossRefGoogle Scholar
- Choi, Y., Rupp, A., Gushta, M., & Sweet, S. (2010). Modeling learning trajectories with epistemic network analysis: An investigation of a novel analytic method for learning progressions in epistemic games. In National council on measurement in education (pp. 1–39).Google Scholar
- Christensen, R., Tyler-Wood, T., Knezek, G., & Gibson, D. (2011). SimSchool: An online dynamic simulator for enhancing teacher preparation. International Journal of Learning Technology, 6(2), 201–220.CrossRefGoogle Scholar
- Clarke, J., & Dede, C. (2010). Assessment, technology, and change. Journal of Research in Teacher Education, 42(3), 309–328.Google Scholar
- Clarke-Midura, J., Code, J., Dede, C., Mayrath, M., & Zap, N. (2012). Thinking outside the bubble: Virtual performance assessments for measuring complex learning. Technology-based assessments for 21st Century skills: Theoretical and practical implications from modern research (pp. 125–148). Charlotte, NC: Information Age Publishers.Google Scholar
- Clarke-Midura, J., Mayrath, M., & Dede, C. (2010). Measuring inquiry: New methods, promises and challenges. Library, 2, 89–92.Google Scholar
- Connolly, T. M., Boyle, E. A., MacArthur, E., Hainey, T., & Boyle, J. M. (2012). A systematic literature review of empirical evidence on computer games and serious games. Computers and Education,. doi: 10.1016/j.compedu.2012.03.004.Google Scholar
- Creswell, J. (2003). Research design: Qualitative, quantitative and mixed methods approaches. Thosand Oaks, CA: Sage Publications.Google Scholar
- De Freitas, S. (2014). Education in computer generated environments. London, New York: Routledge.Google Scholar
- De Freitas, S., Gibson, D., Du Plessis, C., Halloran, P., Williams, E., Ambrose, M., Dunwell, I., Arnab, S. (2014). Foundations of dynamic learning analytics: Using university student data to increase retention. British Journal of Educational Technology. doi: 10.1111/bjet.12212.
- Deloitte. (2010). Student retention analytics in the Curtin business school. WA: Bentley.Google Scholar
- Dempster, A., Laird, N., & Rubin, D. (1977). Maximum liklihood from incomplete data via the EM algorithm. Journal of the Royal Stasticial Society Series B (Methodological), 39(1), 1–38.Google Scholar
- Dunleavy, M., Dede, C., & Mitchell, R. (2008). Affordances and limitations of immersive participatory augmented reality simulations for teaching and learning. Journal of Science Education and Technology, 18(1), 7–22. doi: 10.1007/s10956-008-9119-1.CrossRefGoogle Scholar
- Ertmer, P. A., Richardson, J. C., Belland, B., Camin, D., Connolly, P., Coulthard, G., et al. (2007). Using peer feedback to enhance the quality of student online postings…. Journal of Computer-Mediated Communication, 12(2), 1–15.CrossRefGoogle Scholar
- Eseryel, D., Ifenthaler, D., & Ge, X. (2013). Validation study of a method for assessing complex ill-structured problem solving by using causal representations. Educational Technology Research and Development, 61, 443–463. doi: 10.1007/s11423-013-9297-2.CrossRefGoogle Scholar
- Gibson, D. (2012). Game changers for transforming learning environments. In F. Miller (Ed.), Transforming learning environments: Strategies to shape the next generation (advances in educational administration) (Vol. 16, pp. 215–235). Bradford: Emerald Group Publishing Ltd. doi: 10.1108/S1479-3660(2012)0000016014.Google Scholar
- Gibson, D., & Clarke-Midura, J. (2013). Some psychometric and design implications of game-based learning analytics. In D. Ifenthaler, J. Spector, P. Isaias, & D. Sampson (Eds.), E-learning systems, environments and approaches: Theory and implementation. London: Springer.Google Scholar
- Gibson, D., & Jakl, P. (2013). Data challenges of leveraging a simulation to assess learning. West Lake Village, CA. http://www.curveshift.com/images/Gibson_Jakl_data_challenges.pdf
- Gonzalez-Sanchez, J., Chavez-Echeagaray, M. E., Lin, L., Baydogan, M., Christopherson, R., Gibson, D. et al. (2013). Affect recognition in learning scenarios: Matching facial- and BCI-based values. In 2013 IEEE 13th international conference on advanced learning technologies (pp. 70–71). doi: 10.1109/ICALT.2013.26.
- Hall, M., National, H., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: An update. SIGKDD Explorations, 11, 10–18. doi: 10.1145/1656274.1656278.CrossRefGoogle Scholar
- Han, J., Cheng, H., Xin, D., & Yan, X. (2007). Frequent pattern mining: Current status and future directions. Data Mining and Knowledge Discovery, 15(1), 55–86. doi: 10.1007/s10618-006-0059-1.CrossRefGoogle Scholar
- Hasler, B. S., Tuchman, P., & Friedman, D. (2013). Virtual research assistants: Replacing human interviewers by automated avatars in virtual worlds. Computers in Human Behavior, 29, 1608–1616. doi: 10.1016/j.chb.2013.01.004.CrossRefGoogle Scholar
- Hegland, M. (2005). The Apriori algorithm: A tutorial. Institute for Mathematical Sciences Preprint Series. http://www2.ims.nus.edu.sg/preprints/2005-29.pdf
- IBM. (2015). Big data. http://www-01.ibm.com/software/au/data/bigdata/
- Jordan, S. (2009). Assessment for learning: pushing the boundaries of computer-based assessment. Research in Higher Education, 3(1), 11–19.Google Scholar
- Kline, P. (1998). The new psychometrics: Science, psychology, and measurement. London: Routledge.Google Scholar
- Lenhard, W., Baier, H., Hoffmann, J., & Schneider, W. (2007). Automatic scoring of constructed-response items with latent semantic analysis. Diagnostica, 53(3), 155–165. http://apps.isiknowledge.com.libproxy.unm.edu/full_record.do?product=WOS&colname=WOS&search_mode=RelatedRecords&qid=25&SID=2AhKOADF4oMFGj8O5P9&page=3&doc=30
- Mislevy, R. (2011). Evidence-centered design for simulation-based assessment. Los Angeles, CA: The National Center for Research on Evaluation, Standards, and Student Testing.Google Scholar
- Morgenthaler, S. (2009). Exploratory data analysis. Wiley Interdisciplinary Reviews: Computational Statistics,. doi: 10.1002/wics.2.Google Scholar
- Morris, T. W. (2002). Conversational agents for game-like virtual environments. In AAAI 2002 spring symposium on artificial intelligence and interactive entertainment (pp. 82–86). http://www.google.com/url?sa=t&ct=res&cd=1&url=http://www.qrg.northwestern.edu/aigames.org/papers2002/TMorris02.pdf&ei=crZZR9yjNozssgKpgPHlBg&usg=AFQjCNHT8F92vjehn8upv4EK5JlzWP3Hrg&sig2=yBAN659b8qSvJ-IXns3pBQ
- Nair, R., Tambe, M., Marsella, S., & Raines, T. (2004). Automated assistants for analyzing team behaviors. Autonomous Agents and Multi-Agent Systems, 8, 69–111. doi: 10.1023/B:AGNT.0000009411.79208.f4.CrossRefGoogle Scholar
- Olsen, P. (2007). Staying the course: Retention and attrition in Australian universities findings (pp. 1–16). Sydney. http://www.spre.com.au/download/AUIDFRetentionResultsFindings.pdf
- Quellmalz, E., Timms, M., Buckley, B., Davenport, J., Loveland, M., & Silberglitt, M. (2012). 21st Century dynamic assessment. In M. Mayrath, J. Clarke-Midura, & D. Robinson (Eds.), Technology-based assessments for 21st century skills: Theoretical and practical implications from modern research (pp. 55–90). Charlotte, NC: Information Age Publishers.Google Scholar
- Rissanen, M. J., Kume, N., Kuroda, Y., Kuroda, T., Yoshimura, K., & Yoshihara, H. (2008). Asynchronous teaching of psychomotor skills through VR annotations: Evaluation in digital rectal examination. Studies in Health Technology and Informatics, 132, 411–416.Google Scholar
- Rupp, A., Gushta, M., Mislevy, R., & Shaffer, D. (2010). Evidence-centered design of epistemic games: Measurement principles for complex learning environments. Journal of Technology, Learning, and Assessment, 8(4), 1–45.Google Scholar
- Sabourin, J., Mott, B., & Lester, J. (2011). Computational models of affect and empathy for pedagogical virtual agents. In Standards in Emotion Modeling. Leiden, NL: Lorentz Center International Center for workshops in the Sciences. http://www.lorentzcenter.nl/lc/web/2011/464/presentations/Sabourin.pdf
- Schmidt, M., & Lipson, H. (2009). Symbolic regression of implicit equations. Genetic Programming Theory and Practice, 7(Chap 5), 73–85.Google Scholar
- Shum, S. B., & Ferguson, R. (2012). Social learning analytics. Educational Technology and Society, 15, 3–26.Google Scholar
- Siemens, G. (2012). Learning analytics: Envisioning a research discipline and a domain of practice. In 2nd international conference on learning analytics and knowledge (pp. 4–8). doi: 10.1145/2330601.2330605.
- Sporns, O. (2011). Networks of the brain. Cambridge, MA: MIT Press.Google Scholar
- Stevens, R., Johnson, D., & Soller, A. (2005). Probabilities and predictions: Modeling the development of scientific problem-solving skills. Cell Biology Education, 4(1), 42–57. doi: 10.1187/cbe.04-03-0036.CrossRefGoogle Scholar
- Tashakkori, A., & Teddlie. (2003). Handbook of mixed methods in social and behavioral research. SAGE. http://books.google.com/books?id=F8BFOM8DCKoC&pgis=1
- Tukey, J. W. (1977). Exploratory data analysis. Analysis (Vol. 2, p. 688). doi: 10.1007/978-1-4419-7976-6.
- Turkay, S., & Tirthali, D. (2010). Youth leadership development in virtual worlds: A case study. Procedia Social and Behavioral Sciences, 2(2), 3175–3179. doi: 10.1016/j.sbspro.2010.03.485.CrossRefGoogle Scholar
- Van Der Pol, J., Van Den Berg, B., Admiraal, W., & Simons, P. (2008). The nature, reception, and use of online peer feedback in higher education. Computers and Education, 51(4), 1804–1817. doi: 10.1016/j.compedu.2008.06.001.CrossRefGoogle Scholar
- Webb, M. (2010). Beginning teacher education and collaborative formative e-assessment. Assessment and Evaluation in Higher Education, 35(5), 597–618.CrossRefGoogle Scholar
- Witten, F., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques (p. 524).Google Scholar