The Worked-Example Effect and a Mastery Approach Goal Orientation
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThank you for the opportunity to review the manuscript "The worked-example effect and a mastery approach goal orientation" submitted to Educational Science! The MS reports an experimental study in which learners were randomly assigned to a worked-example or problem-solving group. In contrast to previous studies in this area, this study focuses on investigating the moderating role of the motivational construct of goal orientation.
Overall, I am positive about MS and should be of interest to the readers of this journal. In the same breath, I would like to point out a few points that could further strengthen MS. I hope you will find them helpful.
1) I am generally fine with the literature review. However, a more nuanced view of CLT would strengthen the MS. In this context, germane cognitive load (GCL) is no longer considered an independent source of cognitive load. ICL and ECL are thought to contribute additively to total cognitive load. GCL (or germane processing) refers to the allocation of cognitive resources to learning-relevant processes.
Paas, F., & van Merriënboer, J. J. (2020). Cognitive-load theory: Methods to manage working memory load in the learning of complex tasks. Current Directions in Psychological Science, 29(4), 394-398.
Greenberg, K., & Zheng, R. (2023). Revisiting the debate on germane cognitive load versus germane resources. Journal of Cognitive Psychology, 35(3), 295-314.
2) Could you please provide the reader with some information about the participating students (e.g., gender distribution, subject orientation of the high school)?
3) How did you determine the sample size (e.g., a priori power analysis)?
4) Why didn't you just calculate the average of the three MAGO items? Were they summed?
5) Based on the MAGO score, participants were classified as low or high, taking into account a median split. There are some methodological problems with this approach. I think it would be useful to include this in the study limitations.
Some problems with the median split: artificial dichotomization (division into two groups) of the interval-scaled third variable can lead to a loss of information and thus also to a loss of power; learners with a medium MAGO level are assigned to one of the two groups (low or high MAGO) in the third variable, so that potential differences between people with a medium MAGO and those with a low or high MAGO cannot be detected.
6) Why didn't you measure cognitive load after each task? This would better account for the dynamic nature of the construct.
7) Did you test your assumptions before each analysis (i.e., homogeneity of variance, normal distribution)?
8) Line 306/307: There is no marginally significant effect, either an effect is significant or not significant (Olsson-Collentine et al., 2019; https://journals.sagepub.com/doi/full/10.1177/0956797619830326).
9) By considering the 95% confidence intervals of Cohen's d, you could determine whether the increase from pre- to post-test was greater in the worked-example or problem-solving group.
10) Another limitation of the study is the measurement of cognitive load with a single item. A differentiated measurement of the different types could yield interesting results (e.g. effects on ICL and ECL). This is especially important as you mentioned the division of cognitive load into ICL and ECL types. For this purpose, future studies could rely on recent multidimensional cognitive load questionnaires.
Klepsch, M., Schmitz, F., & Seufert, T. (2017). Development and validation of two instruments to measure intrinsic, extrinsic, and germane cognitive load. Frontiers in Psychology, 8, 1997.
Krieglstein, F., Beege, M., Rey, G. D., Sanchez-Stockhammer, C., & Schneider, S. (2023). Development and validation of a theory-based questionnaire to measure different types of cognitive load. Educational Psychology Review, 35(1), 9.
Comments on the Quality of English Language
Please check the comma placement in the MS again.
Author Response
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Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsComments for author File: Comments.pdf
Author Response
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Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsI would like to thank the authors for the thorough revision of the manuscript and the consideration of my comments. I think the manuscript has improved during the revision (especially from a statistical point of view with the regression models) and is now a nice contribution to the research on worked examples. Congratulations!
One minor point: On page 8, where you report the regression analyses, the SE values seem to be very high (or do you mean SD here?). Could you please check this?
