Prompted by the sudden shift to remote instruction in March 2020 brought on by the COVID-19 pandemic, teachers explored online resources to support their students learning from home. We report on how twelve teachers identified and creatively leveraged open educational resources (OERs) and practices to facilitate self-directed science learning. Based on interviews and logged data, we illustrate how teachers’ use of OER starkly differed from the typical uses of technology for transmitting information or increasing productivity. These experiences provide insights into ways teachers and professional developers can take advantage of OER to promote self-directed learning when in-person instruction resumes.

A previous study found that task shifting and fluid intelligence, but not working memory capacity (WMC) and prior knowledge, influenced the worked example effect (Schwaighofer, Bühner, & Fischer, 2016). To increase confidence in these findings, we report a preregistered extended replication study of Schwaighofer et al.’s investigation. University students (N = 231, Mage = 22.40 [SD = 4.33], 87% women) solved statistical problems with textbook materials presented on a laptop in one of four conditions in a 2 × 2 factorial between-subjects design. We compared worked examples versus problem-solving (replication) and with versus without time pressure (extension). Time pressure was added to test whether learners in the original study were able to offload WMC demands, which would explain why the WMC moderation was not found. Results showed that the advantage of worked examples over problem-solving decreased with increasing prior knowledge, suggesting that problem-solving becomes eventually more effective than worked example study. Similarly, the benefit of worked examples over problem-solving decreased with increasing shifting ability of a learner. However, contingencies on WMC or fluid intelligence were not detected. Our extension analysis indicated that the worked example effect was also not contingent on WMC even when learners were under time pressure. These findings underline the important role that task shifting might play in scaffolded learning environments and suggest that trading in the focus on WMC for a broader perspective on cognitive architecture provides novel explanations for instructional effectiveness. Our study further highlights the importance of more customized instructional support.

With the widespread adoption of the Next Generation Science Standards (NGSS), science teachers and online learning environments face the challenge of evaluating students' integration of different dimensions of science learning. Recent advances in representation learning in natural language processing have proven effective across many natural language processing tasks, but a rigorous evaluation of the relative merits of these methods for scoring complex constructed response formative assessments has not previously been carried out. We present a detailed empirical investigation of feature-based, recurrent neural network, and pre-trained transformer models on scoring content in real-world formative assessment data. We demonstrate that recent neural methods can rival or exceed the performance of feature-based methods. We also provide evidence that different classes of neural models take advantage of different learning cues, and pre-trained transformer models may be more robust to spurious, dataset-specific learning cues, better reflecting scoring rubrics.

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Extensive research has established that successful learning from an example is conditional on an important learning activity: self-explanation. Moreover, a model for learning from examples suggests that self-explanation quality mediates effects of examples on learning outcomes (Atkinson et al. in Rev Educ Res 70:181–214, 2000). We investigated self-explanation quality as mediator in a worked examples—problem-solving paradigm. We developed a coding scheme to assess self-explanation quality in the context of ill-defined statistics problems and analyzed self-explanation data of a study by Schwaighofer et al. (J Educ Psychol 108: 982–1000, 2016). Schwaighofer et al. (J Educ Psychol 108: 982–1000, 2016) investigated whether the worked example effect depends on prior knowledge, working memory capacity, shifting ability, and fluid intelligence. In our study, we included these variables to jointly explore mediating and moderating factors when individuals learn with worked examples versus through problem-solving. Seventy-four university students (mean age = 23.83, SD = 5.78) completed an open item pretest, self-explained while either studying worked examples or solving problems, and then completed a post-test. We used conditional process analysis to test whether the effect of worked examples on learning gains is mediated by self-explanation quality and whether any effect in the mediation model depends on the suggested moderators. We reproduced the interaction effects reported by Schwaighofer et al. (J Educ Psychol 108: 982–1000, 2016) but did not detect a mediation effect. This might indicate that worked examples are directly effective because they convey a solution strategy, which might be particularly important when learning to solve problems that have no algorithmic solution procedure.