Time for Change: Why Learning Analytics Needs Temporal Analysis
DOI:
https://doi.org/10.18608/jla.2017.43.2Abstract
Learning is a process that occurs over time: We build understanding, change perspectives, and develop skills over the course of extended experiences. As a field, learning analytics aims to generate understanding of, and support for, such processes of learning. Indeed, a core characteristic of learning analytics is the generation of high-resolution temporal data about various types of actions. Thus, we might expect study of the temporal nature of learning to be central in learning analytics research and applications. However, temporality has typically been underexplored in both basic and applied learning research. As Reimann (2009) notes, although “researchers have privileged access to process data, the theoretical constructs and methods employed in research practice frequently neglect to make full use of information relating to time and order” (p. 239). Typical approaches to analysis often aggregate across data due to a collection of conceptual, methodological, and operational challenges. As described below, insightful temporal analysis requires (1) conceptualising the temporal nature of learning constructs, (2) translating these theoretical propositions into specific methodological approaches for the capture and analysis of temporal data, and (3) practical methods for capturing temporal data features and using analyses to impact learning contexts. There is a pressing need to address these challenges if we are to realize the exciting possibilities for temporal learning analytics.
References
Akhras, F. N., & Self, J. A. (1999). Modeling the process, not the product, of learning. In S. P. Lajoie P. & S. J. Derry (Eds.), Computers as cognitive tools (Vol. 2, pp. 3–28). Hillsdale, N.J.,: Lawrence Erlbaum Associates.
Barbera, E., Gros, B., & Kirschner, P. (2014). Paradox of time in research on educational technology. Time & Society. https://doi.org/10.1177/0961463X14522178
Biswas, G., Jeong, H., Kinnebrew, J. S., Sulcer, B., & Roscoe, R. (2010). Measuring self-regulated learning skills through social interactions in a teachable agent environment. Research and Practice in Technology Enhanced Learning, 5(02), 123-152. https://doi.org/10.1142/s1793206810000839
Blikstein, P. (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 (pp. 110-116). Edmonton, AB: ACM. https://doi.org/10.1145/2090116.2090132
Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological bulletin, 132(3), 354. https://doi.org/10.1037/0033-2909.132.3.354
Chen, B., Resendes, M., Chai, C. S., & Hong, H.-Y. (2017). Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse. Interactive Learning Environments, 25(2), 162–175. https://doi.org/10.1080/10494820.2016.1276081
Chen, B., Wise, A. F., Knight, S., & Cheng, B. H. (2016). It’s About Time: Putting Temporal Analytics into Practice: The 5th International Workshop on Temporality in Learning Data (pp. 488–489). In Proceedings of at the 6th ACM Learning Analytics and Knowledge Conference, Edinburgh, UK: ACM. https://doi.org/10.1145/2883851.2883865
Cheng, B. H., Molenaar, I., Chiu, M. M., Svihla, V., Wise, A., Peters, V., & Zourou, K. (2010). It's about time: purpose, methods, and challenges of temporal analyses of multiple data streams. In Proceedings of the 9th International Conference of the Learning Sciences-Volume 2 (pp. 501-502). International Society of the Learning Sciences. https://dl.acm.org/citation.cfm?id=1854770
Dyke, G., Kumar, R., Ai, H., & Rosé, C. P. (2012). Challenging assumptions: Using sliding window visualizations to reveal time-based irregularities in CSCL processes. Proceedings of the International Conference of the Learning Sciences (pp. 363-370). Sydney, Australia: ICLS. https://pdfs.semanticscholar.org/c88d/1081e9414c23046cacda127528832f0728b8.pdf
Faraone, S. V., & Dorfman, D. D. (1987). Lag sequential analysis: Robust statistical methods. Psychological Bulletin, 101(2), 312–323. https://doi.org/10.1037/0033-2909.101.2.312
Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71. https://doi.org/10.1007/s11528-014-0822-x
Gunawardena, C. N., Lowe, C. A., & Anderson, T. (1997). Analysis of a global online debate and the development of an interaction analysis model for examining social construction of knowledge in computer conferencing. Journal of educational computing research, 17(4), 397-431. https://doi.org/10.2190/7mqv-x9uj-c7q3-nrag
Halatchliyski, I., Hecking, T., Goehnert, T., & Hoppe, H. U. (2014). Analyzing the main paths of knowledge evolution and contributor roles in an open learning community. Journal of Learning Analytics, 1(2), 72-93. https://doi.org/10.18608/jla.2014.12.5
Haythornthwaite, C., & Gruzd, A. (2012). Exploring patterns and configurations in networked learning texts. In 45th Hawaii International Conference on System Science (HICSS), (pp. 3358-3367). Maui, HI: IEEE. https://doi.org/10.1109/hicss.2012.268
Kapur, M., Voiklis, J., & Kinzer, C. K. (2008). Sensitivities to early exchange in synchronous computer-supported collaborative learning (CSCL) groups. Computers & Education, 51(1), 54-66. https://doi.org/10.3115/1599600.1599663
Knight, S., Wise, A. F., Chen, B., & Cheng, B. H. (2015). It’s About Time: 4th International Workshop on Temporal Analyses of Learning Data (pp. 388–389). In the Proceedings of the 5th International Learning Analytics & Knowledge Conference (LAK15): Scaling Up: Big Data to Big Impact, Poughkeepsie, NY, USA: ACM. https://doi.org/10.1145/2723576.2723638
Knight, S., Wise, A. F., Ochoa, X., & Hershkovitz, A. (2017). Learning Analytics: Looking to the Future. Journal of Learning Analytics, 4(2), 1-5. https://doi.org/10.18608/jla.2017.42.1
Littleton, K. (1999). Productivity through interaction. In K. Littleton & P. Light (Eds.), Learning with computers: Analysing productive interaction (pp. 179–194). London, UK: Routledge.
Mercer, N. (2008). The seeds of time: why classroom dialogue needs a temporal analysis. Journal of the Learning Sciences, 17(1), 33–59. https://doi.org/10.1080/10508400701793182
Mercer, N., & Littleton, K. (2007). Dialogue and the Development of Children’s Thinking: A Sociocultural Approach (New edition). Oxon: Routledge.
Molenaar, I., & Järvelä, S. (2014). Sequential and temporal characteristics of self and socially regulated learning. Metacognition and Learning, 9(2), 75-85. https://doi.org/10.1007/s11409-014-9114-2
Putnam, L. L. (1983). Small Group Work Climates A Lag-Sequential Analysis of Group Interaction. Small Group Research, 14(4), 465–494. https://doi.org/10.1177/104649648301400405
Reimann, P. (2009). Time is precious: Variable-and event-centred approaches to process analysis in CSCL research. International Journal of Computer-Supported Collaborative Learning, 4(3), 239–257. https://doi.org/10.1007/s11412-009-9070-z
Schneider, B., & Pea, R. (2013). Real-time mutual gaze perception enhances collaborative learning and collaboration quality. International Journal of Computer-supported collaborative learning, 8(4), 375-397. https://doi.org/10.1007/s11412-013-9181-4
Wise, A. F., & Chiu, M. M. (2011). Analyzing temporal patterns of knowledge construction in a role-based online discussion. International Journal of Computer-Supported Collaborative Learning, 6(3), 445–470. https://doi.org/10.1007/s11412-011-9120-1
Wise, A. F., Perera, N., Hsiao, Y. , Speer, J., & Marbouti, F. (2012). Microanalytic case studies of individual participation patterns in an asynchronous online discussion in an undergraduate blended course. The Internet and Higher Education, 15(2), 108-117.
Wise, A. F. & Shaffer, D. W. (2015). Why theory matters more than ever in the age of big data. Journal of Learning Analytics (Special Section on Learning Analytics and Learning Theory), 2(2), 5-13. https://doi.org/10.18608/jla.2015.22.2
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