Advances in temporal analysis in learning and instruction

Main Article Content

Inge Molenaar


This paper focuses on a trend to analyse temporal characteristics of constructs important to learning and instruction. Different researchers have indicated that we should pay more attention to time in our research to enhance explanatory power en increase validity. Constructs formerly viewed as personal traits, such as self-regulated learning and motivation, are now conceptualized as a series of events that unfold over time. This raises new questions with regard to the temporal characteristics of these constructs and their dynamic interplay with learner and context characteristics. Even though the value of analyzing temporal characteristics slowly becomes evident a number of challenges need to be tackled in order to make progress in de field of learning and instruction. First, we need to be aware of the paradigm shift that temporal analysis entails. Second, a common understanding of different dimensions of time and the position of temporal characteristics therein  can facilitate our time related research dialogue. Third, a better understanding how to answer time related questions with appropriate methodological approaches needs to emerge. Fourth, researching temporal characteristics entails segmenting time which procedures are needed for.  Fifth, temporal data are mostly collected at the micro level, whereas most theory is defined at a macro level; consequently we need to bridge these differences in the granularity used between collecting, coding and theorizing to enhance meaning making.  Finally, so far most examples of time related research are explorative or comparative studies, the next step is to move toward confirmative studies which constitutes the “Holy Grail” of temporal analysis.

Article Details

How to Cite
Molenaar, I. (2014). Advances in temporal analysis in learning and instruction. Frontline Learning Research, 2(4), 15-24.
Author Biography

Inge Molenaar, Radboud University

Dr. Inge Molenaar is assistant professor at the Radboud University Nijmegen, Behavioural Science Institute. Her main interests are technology and innovation in education working towards differentiated and adaptive instruction that facilitate students’ optimal talent development. Temporal analysis offer a powerful way to make new steps forward in this field.  Her research focuses on the development of intelligent tutors (Atgentive) for adaptive scaffolding of metacognition and self-regulated learning. She also is the initiator and CEO of the e-learning application Ontdeknet. The development of the Dutch national educational repository for elementary and secondary education (Wikiwijs) made her an expert on educational metadata standards and the adaptive assignment of learning content. She received several national and international awards and grants for her work.


Akhras, F. N., & Self, J. A. (2000). Modeling the process, not the product, of learning. In S. P. Lajoie, Computers as cognitive tools, volume two: No more walls (pp. 3-28). Mahwah, NJ: Lawrence Erlbaum Associates.
Bannert, M. (2006). Effects of reflection prompts when learning with hypermedia. Journal of Educational Computing Research, 4, 359-375.
Bannert, M., Reimann, P. & Sonnenberg, C. (2014). Process Mining Techniques for Analysing Patterns and Strategies in Students' Self-Regulated Learning. Metacognition and learning, Vol 9, pp.
Bloome, D., Beierle, M. Grigorenko, M. & Goldman, S. (2009). Learning over time: uses of intercontextuality, collective memories, and classroom chronotopes in the construction of learning opportunities in a ninth-grade language arts classroom. Language and Education, 23(4), pp. 313-334.
Chiu, M. M., & Khoo, L. (2005). A new method for analyzing sequential processes: Dynamic multi-level analysis. Small Group Research, 36, 600-631.
Chiu, M. M. (2008). Flowing toward correct contributions during groups' mathematics problem solving: A statistical discourse analysis. Journal of the Learning Sciences, 17 (3), 415 - 463.
Goldstein, H. (1995). Multilevel statistical models. Sydney: Edward Arnold.
Günther, C., & Van der Aalst, W. (2007). Fuzzy Mining: Adaptive process simplification based on multi-perspective metrics. In G. Alonso, P. Dadam & M. Rosemann (Eds.), International Conference on Business Process Management (BPM 2007) (pp. 328-343). Berlin: Springer.
Greene, J. A. & Azevedo, R. (2010). The Measurement of Learners’ Self-Regulated Cognitive and Metacognitive Processes while Using Computer-based Learning Environments. Educational psychologist, 45, 203 – 209
Hadwin, A.F., & Järvelä, S. (2011). Introduction to a special issue on social aspects of self-regulated learning: Where social and self meet in the strategic regulation of learning. Teachers College Record, 113(2), 235-239
Hollenstein, T. (2013).State Space Grids: Depicting Dynamics Across Development.New York: Springer.
Iiskala. T., Vauras, M., Lehtinen, E., & Salonen, P. (2011). Socially Shared Metacognition within Primary School Pupil Dyads’ Collaborative Processes. Learning and Instruction, 21, 379-393.
Järvelä, S. & Hadwin, A. (2013). New Frontiers: Regulating learning in CSCL. Educational Psychologist, 48(1), 25-39.
Lemke, J.L. (2000). Across the Scales of Time: Artifacts, Activities, and Meanings in Ecosocial Systems. Mind, Culture and activity, 7(4), 273–290.
Kapur, M., Voiklis, J., & Kinzer, C. (2008). Sensitivities to early exchange in synchronous computer-supported collaborative learning (CSCL) groups. Computers and Education, 51, 54-66.
Kapur, M. (2011). Temporality matters: Advancing a method for analyzing problem-solving processes in a computer-supported collaborative environment. International Journal of Computer-Supported Collaborative Learning (ijCSCL), 6,(1), 39-56.
Kennedy, P. (2008). A guide to econometrics. Cambridge: Blackwell.
Kinnebrew, J. S., Segedy J.R. & Biswas, G. (2014). Analyzing the Temporal Evolution of Students' Behaviors in Open-Ended Learning Environments. Metacognition and learning, Vol 9, pp.
Kuvalja, M., Verma, M. & Whitebread, D. (2014). Patterns of co-occurring non-verbal behavior and self-directed speech; a comparison of three methodological approaches. Metacognition and learning, Vol 9, pp.
Malmberg, J., Järvelä, S. & Kirchner, P. (2014). Elementary school students’ strategic learning: does task-type matter? Metacognition and learning, Vol 9, pp.
Mayer, R.E. (2008). Learning and Instruction. Pearson; New Jersey.
Mercer, N. (2008) The seeds of time: why classroom dialogue needs a temporal analysis. Journal of the Learning Sciences, 17, 1, 33-59.
Molenaar, I., Chiu, M. M., van Boxtel, C. & Sleegers, P. J.C. (2011). Scaffolding of small groups’ metacognitive activities with an avatar. International Journal of Computer-Supported Collaborative Learning, 6, 601-624.
Molenaar, I., van Boxtel, C.A.M & Sleegers, P.J.C. (2011). Metacognitive Scaffolding in an Innovative Learning Arrangement. Instructional Science, vol 39(6), 785-803
Molenaar, I & Chiu M.M. (2014). Dissecting sequences of regulation and cognition: statistical discourse analysis of primary school children’s collaborative learning. Metacognition and learning, Vol 9, pp.
Reimann, P. (2009). Time is precious: Variable- and event-centred approaches to process analysis in CSCL research, International Journal of Computer-supported Collaborative Learning, 3, 239-257.
Schegloff, E. A., 2007. Sequence Organization in Interaction: A Primer in Conversation Analysis. Cambridge: Cambridge University Press.
Schmitz, B. (2006). Advantages of studying processes in educational research. Learning and Instruction. 16, 433-449.
Schoor, C. & Bannert, M. (2012). Exploring Regulatory Processes during a Computer-Supported Collaborative Learning Task Using Process Mining. Computers in Human Behavior. 28(4), 1321-1331.
Suthers. D., Teplovs, C., de Laat, M., Oshima, J., & Zeini, S. (2011). Connecting levels of learning in networked communities. Workshop conducted at the 9th International Conference on Computer Supported Collaborative Learning, July 9, 2011, Hong Kong.
Veenman, M.V.J. (2011). Learning to Self-Monitor and Self-Regulate. In R. Mayer,& P. Alexander (Eds.), Handbook of Research on Learning and Instruction. New York: Routledge.
Weinberger, A., & Fischer, F. (2006). A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers & Education, 46, 71-95.
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.
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.
Zimmerman, B. J. (2002). Becoming a Self-Regulated Learner: an Overview. Theory into Practice, 42(2), 64-70.