Abstract
AbstractGuided inquiry learning is an effective method for learning about scientific concepts. The present study investigated the effects of combining video modeling (VM) examples and metacognitive prompts on university students’ (N = 127) scientific reasoning and self-regulation during inquiry learning. We compared the effects of watching VM examples combined with prompts (VMP) to watching VM examples only, and to unguided inquiry (control) in a training and a transfer task. Dependent variables were scientific reasoning ability, hypothesis and argumentation quality, and scientific reasoning and self-regulation processes. Participants in the VMP and VM conditions had higher hypothesis and argumentation quality in the training task and higher hypothesis quality in the transfer task compared to the control group. There was no added benefit of the prompts. Screen captures and think aloud protocols during the two tasks served to obtain insights into students’ scientific reasoning and self-regulation processes. Epistemic network analysis (ENA) and process mining were used to model the co-occurrence and sequences of these processes. The ENA identified stronger co-occurrences between scientific reasoning and self-regulation processes in the two VM conditions compared to the control condition. Process mining revealed that in the VM conditions these processes occurred in unique sequences and that self-regulation processes had many self-loops. Our findings show that video modeling examples are a promising instructional method for supporting inquiry learning on both the process and the learning outcomes level.
Highlights
Improving scientific reasoning and argumentation is a central aim of science education (Engelmann et al, 2016; OECD, 2013)
In contrast to previous research focused on task selection and self-assessment skills (Kostons et al, 2012; Raaijmakers et al, 2018a, 2018b), we investigated the effectiveness of video modeling examples for training and transfer of other self-regulation skills—planning, monitoring, and control
Product Level Contrast 1 showed that the VM examples combined with prompts (VMP) and video modeling (VM) conditions had higher hypothesis quality, t(124) = 2.60, p = 0.010, d = 0.49, and higher argumentation quality, t(124) = 2.75, p = 0.007, d = 0.52, than the control group, see Fig. 5
Summary
Improving scientific reasoning and argumentation is a central aim of science education (Engelmann et al, 2016; OECD, 2013). The metacognitive prompts aimed to further ensure the use of self-regulation processes by prompting students to monitor their scientific reasoning activities during inquiry. To our knowledge, this is the first study to develop an intervention aimed at simultaneously fostering scientific reasoning and self-regulation processes in an integrated way and test its effectiveness both at the process and product level (i.e., hypothesis and argumentation quality). Scientific reasoning and argumentation are defined as a set of eight epistemic activities, applicable across scientific domains (extending beyond the natural sciences, see Renkl, 2018 for a similar discussion)—problem identification, questioning, hypothesis generation, construction and redesign of artefacts, evidence generation, evidence evaluation, drawing conclusions, and communicating and scrutinizing (Fischer et al, 2014; Hetmanek et al, 2018). Scientific reasoning and argumentation can be improved with instruction and practice (Osborne et al, 2004), for example, using inquiry learning
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