Session Information
Paper Session
Contribution
For more than two decades, the concept of self-regulation and self-regulated learning has received a great deal of attention in the scientific discourse of educational science, due to its proposed positive relationship with students' learning outcomes (Boekaerts, Pintrich & Zeidner, 2000; Zimmerman, 2000; Zimmerman & Schunk, 2011). To date, several different definitions and models of self-regulation and self-regulated learning have been proposed (Panadero, 2017), leading to some theoretical fragmentation and confusion. Nevertheless, most definitions and models consider self-regulated learning as a cyclical process consisting of three phases: 1) the preparatory phase, 2) the performance phase (i.e., the phase of actual task performance), and 3) the reflective or the appraisal phase. Within each of these three general phases, students then engage in a range of cognitive, affective and metacognitive processes (Panadero, 2017; Wong et al., 2019; Zimmerman & Schunk, 2011).
Since the existing research suggests that students engaging in self-regulated learning are able to efficiently manage their own learning and perform better on learning tasks, leading to their academic success (Boekaerts, Pintrich & Zeidner, 2000; McInerney et al., 2012), researchers have focused on different support mechanisms or scaffolds in order to help students engage in effective self-regulation during learning. The need for external support for students' ability to regulate their own learning seems to be particularly important in the context of online learning and learning from digital media, due to the higher demands on the students' autonomy and their competence to navigate themselves in complex multimedia learning materials (Wong et al., 2019).
One of the proposed mechanisms to support self-regulated learning that has received increased research attention, especially in recent years, is metacognitive prompting. Prompts in general can be seen as a temporary support mechanism or scaffold for students in order to assist them in the use of an appropriate learning strategy (Bannert, 2009). Metacognitive prompts, in contrast to cognitive prompts, focus on engaging students in higher-level learning strategies such as goal setting, monitoring, reflection, etc. A considerable amount of available studies provide evidence for the effectiveness of metacognitive prompts in improving students' learning outcomes (Azevedo et al., 2011; Devolder, van Braak, & Tondeur, 2012; Guo, 2022; Manlove, Lazonder, & de Jong, 2009).
Although current research suggests that metacognitive prompts can stimulate the use of higher-order learning strategies and thereby improve learning outcomes, it still remains unclear whether and to what extent metacognitive prompts improve students' learning outcomes, and how this relationship changes in the context of learning from multimedia learning materials. The aim of this paper is therefore to present the results of an experiment focusing on the use of metacognitive prompts in the context of multimedia learning. The aim of the experiment was to investigate the effects of metacognitive prompts on students' learning outcomes and whether these effects varied according to the type of learning material (i.e., text vs. multimedia).
Method
The paper presents the results of an experiment conducted in the form of a laboratory-controlled experiment with experimental and control conditions and task randomisation. The experiment used a 2x2 within-between subjects factorial design to assess the performance of more than 100 participants. Two balanced groups of participants represent between-subjects cases, where the presence of metacognitive prompts was manipulated as the main independent variable (i.e., the first factor). At the same time, all subjects were exposed to two different types of learning materials represented by plain text and multimedia learning content respectively, which corresponds to the second independent variable (i.e., the second factor). The individual tasks were randomised to prevent serial position effects in within-subjects cases. Participants were assigned randomly into each group (between-subjects cases) in order to avoid the possible transfer of the effect of metacognitive prompts to non-prompted tasks.
Expected Outcomes
The paper will provide a description of the experiment focusing on the use of metacognitive prompts in the context of multimedia learning and it will describe and explain its main findings. In particular, the attention will be given to 1) a thorough description of the methodology of the experiment conducted, including a description of the stimuli, setting, and procedure of the experiment, 2) answers to the main research questions dealing with the effects of metacognitive prompts on students' learning outcomes and the possible effects of the type of learning material, and 3) implications of the results for further research and the use of metacognitive prompts in practice.
References
Azevedo, R., Cromley, J. G., Moos, D. C., Greene, J. A., & Winters, F. I. (2011). Adaptive content and process scaffolding: A key to facilitating students' self-regulated learning with hypermedia. Psychology Science, 53(1), 106. Bannert, M. (2009). Promoting self-regulated learning through prompts. Journal of Pedagogical Psychology, 23(2), 139–145. Boekaerts, M., Pintrich, P. R., and Zeidner, M. (2000). Handbook of Self-Regulation. San Diego, CA: Academic Press. Devolder, A., van Braak, J., & Tondeur, J. (2012). Supporting self-regulated learning in computer-based learning environments: Systematic review of effects of scaffolding in the domain of science education. Journal of Computer Assisted Learning, 28(6), 557–573. Guo, J. (2022). Using metacognitive prompts to enhance self-regulated learning and learning outcomes: A meta-analysis of experimental studies in computer-based learning environments. Journal of Computer Assisted Learning, 38(3), 11–832. Manlove, S., Lazonder, A. W., & de Jong, T. (2009). Trends and issues of regulative support use during inquiry learning: Patterns from three studies. Computers in Human Behavior, 25(4), 795–803. McInerney, D. M., Cheng, R. W., Mok, M. M. C., & Lam, A. K. H. (2012). Academic self-concept and learning strategies: Direction of effect on student academic achievement. Journal of Advanced Academics, 23(3), 249–269. Panadero, E. (2017). A Review of Self-regulated Learning: Six Models and Four Directions for Research. Frontiers in Psychology, 8(422), 1–28. Wong, J., Baars, M., Davis, D., Van Der Zee, T., Houben, G.-J., & Paas, F. (2019). Supporting Self-Regulated Learning in Online Learning Environments and MOOCs: A Systematic Review. International Journal of Human–Computer Interaction, 35(4–5), 356–373. Zimmerman, B. J. (2000). Attaining self-regulation: a social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of Self-Regulation (pp. 13–40). Academic Press. Zimmerman, B. J., & Schunk, D. H. (2011). Handbook of Self-Regulation of Learning and Performance. New York, NY: Routledge.
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