Critical Issues in Designing and Implementing Temporal Analytics
Keywords:Temporality, time, temporal learning analytics, impact
The importance of temporality in learning has been long established, but it is only recently that serious attention has begun to be paid to the precise identification, measurement, and analysis of the temporal features of learning. From 2009 to 2016, a series of temporality workshops explored temporal concepts and data types, analysis methods for exploiting temporal data, techniques for visualizing temporal information, and practical considerations for the use of temporal analyses in particular contexts of learning. Following from these efforts, this two-part Special Section serves to consolidate research working to progress conceptual, technical and practical tools for temporal analyses of learning data. In addition, in this second and final editorial, we aim to make four contributions to the ongoing dialogue around temporal learning analytics to help us move towards a clearer mapping of the research space. First, the editorial presents an overview of the five papers in Part 2 of the Special Section on Temporal Analyses, highlighting the dimensions of data types, learning constructs, analysis approaches, and potential impact. Second, it draws on the fluid relationship between ‘analyzed time’ and ‘experienced time’ to highlight the need for caution and criticality in the purposes temporal analyses are mobilized to serve. Third, it offers a guide for future work in this area by outlining important questions that all temporal analyses should intentionally address. Finally, it proposes next steps learning analytics researchers and practitioners can take collectively to advance work on the use of temporal analyses to support learning
Baker, R. S. J. d., D’Mello, S. K., Rodrigo, M. M. T., & Graesser, A. C. (2010). Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive-affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68(4), 223–241. https://doi.org/10.1016/j.ijhcs.2009.12.003
Buckingham Shum, S. (2016). Algorithmic accountability for learning analytics. Presented at the Institute of Education & London Knowledge Lab, University College London. Retrieved from http://simon.buckinghamshum.net/2016/03/algorithmic-accountability-for-learning-analytics/
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). Putting temporal analytics into practice. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK ’16 (pp. 488–489). New York, New York, USA: ACM Press. https://doi.org/10.1145/2883851.2883865
Denham, S. A. (2006). Social-Emotional Competence as Support for School Readiness: What Is It and How Do We Assess It? Early Education and Development, 17(1), 57–89. https://doi.org/10.1207/s15566935eed1701_4
D’Mello, S., & Graesser, A. (2011). The half-life of cognitive-affective states during complex learning. Cognition & Emotion, 25(7), 1299–1308. https://doi.org/10.1080/02699931.2011.613668
Epp, C. D., Phirangee, K., & Hewitt, J. (2017). Talk with Me: Student Pronoun Use as an Indicator of Discourse Health. Journal of Learning Analytics, 4(3), 47–75. https://doi.org/10.18608/jla.2017.43.4
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
Kim, J., Guo, P. J., Cai, C. J., Li, S.-W. (daniel), Gajos, K. Z., & Miller, R. C. (2014). Data-driven Interaction Techniques for Improving Navigation of Educational Videos. In Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology (pp. 563–572). New York, NY, USA: ACM. https://doi.org/10.1145/2642918.2647389
Knight, S., Buckingham Shum, S., & Littleton, K. (2014). Epistemology, assessment, pedagogy: where learning meets analytics in the middle space. Journal of Learning Analytics, 1(2), 23–47. http://doi.org/10.18608/jla.2014.12.3
Knight, S., Wise, A. F., & Chen, B. (2017). Time for change: Why learning analytics needs temporal analysis. Journal of Learning Analytics, 4(3), 7–17. https://doi.org/10.18608/jla.2017.43.2
Lee, A. V. Y., & Tan, S. C. (2017). Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis. Journal of Learning Analytics, 4(3), 76–101. https://doi.org/10.18608/jla.2017.43.5
Ma, L., Matsuzawa, Y., & Scardamalia, M. (2016). Rotating leadership and collective responsibility in a grade 4 Knowledge Building classroom. International Journal of Organisational Design and Engineering, 4(1-2), 54–84. https://doi.org/10.1504/IJODE.2016.080159
Malmberg, J., Järvelä, S., & Järvenoja, H. (2017/4). Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning. Contemporary Educational Psychology, 49, 160–174. https://doi.org/10.1016/j.cedpsych.2017.01.009
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
Mumford, L. (1934). Technics and civilization (pp. 145–215). London, UK: Routledge & Kegan Paul Ltd.
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
Sannino, A., Daniels, H., & Gutiérrez, K. D. (2009). Activity theory between historical engagement and future-making practice. In A. Sannino, H. Daniels, & K. D. Gutiérrez (Eds.), Learning and expanding with activity theory (pp. 1–15). Cambridge: Cambridge University Press.
Slattery, P. (1995). A Postmodern Vision of Time and Learning: A Response to the National Education Commission Report Prisoners of Time. Harvard Educational Review, 65(4), 612–634. https://doi.org/10.17763/haer.65.4.0908t56382151541
Sperling, R. A., Howard, B. C., Staley, R., & DuBois, N. (2004). Metacognition and Self-Regulated Learning Constructs. Educational Research and Evaluation: An International Journal on Theory and Practice, 10(2), 117–139. https://doi.org/10.1076/edre.10.2.117.27905
Winne, P. H. (2014). Issues in researching self-regulated learning as patterns of events. Metacognition and Learning, 9(2), 229–237. https://doi.org/10.1007/s11409-014-9113-3
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., & Padmanabhan, P. (2009). Developing structural-temporal visualizations of asynchronous threaded online learning conversations. In Proceedings of E-Learn World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 3253-3261). Vancouver, BC: Association for the Advancement of Computing in Education (AACE).
Wise, A. F., & Shaffer, D. W. (2015). Why theory matters more than ever in the age of big data. Journal of Learning Analytics, 2(2), 5–13. https://doi.org/10.18608/jla.2015.22.2
Wise, A., Zhao, Y., & Hausknecht, S. (2014). Learning analytics for online discussions: Embedded and extracted approaches. Journal of Learning Analytics, 1(2), 48-71. http://doi.org/10.18608/jla.2014.12.4
How to Cite
Copyright (c) 2018 Journal of Learning Analytics
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) license that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).