Can prompts improve self-explaining an online video lecture? Yes, but do not disturb!

In recent years, COVID-19 policy measures massively affected university teaching. Seeking an effective and viable way to transform their lecture material into asynchronous online settings, many lecturers relied on prerecorded video lectures. Whereas researchers in fact recommend implementing prompts to ensure students process those video lectures sufficiently, open questions about the types of prompts and role of students' engagement remain. We thus conducted an online field experiment with teacher students at a German university (N = 124; 73 female, 49 male). According to the randomly assigned experimental conditions, the online video lecture on topic Cognitive Apprenticeship was supplemented by (A) notes prompts (n = 31), (B) principle-based self-explanation prompts (n = 36), (C) elaboration-based self-explanation prompts (n = 29), and (D) both principle- and elaboration-based self-explanation prompts (n = 28). We found that the lecture fostered learning outcomes about its content regardless of the type of prompt. The type of prompt did induce different types of self-explanations, but had no significant effect on learning outcomes. What indeed positively and significantly affected learning outcomes were the students' self-explanation quality and their persistence (i.e., actual participation in a delayed posttest). Finally, the self-reported number of perceived interruptions negatively affected learning outcomes. Our findings thus provide ecologically valid empirical support for how fruitful it is for students to engage themselves in self-explaining and to avoid interruptions when learning from asynchronous online video lectures.

Testing Is More Desirable When It Is Adaptive and Still Desirable When Compared to Note-Taking.

Testing is a well-established desirable difficulty. Yet there are still some open issues regarding the benefits of testing that need to be addressed. First, the possibility to increase its benefits by adapting the sequence of test questions to the learners' level of knowledge has scarcely been explored. In view of theories that emphasize the benefits of adapting learning tasks to learner knowledge, it is reasonable to assume that the common practice of providing all learners with the same test questions is not optimal. Second, it is an open question as to whether the testing effect prevails if stronger control conditions than the typical restudy condition are used. We addressed these issues in an experiment with N = 200 university students who were randomly assigned to (a) adaptive testing, (b) non-adaptive testing, or note-taking (c) without or (d) with focus guidance. In an initial study phase, all participants watched an e-lecture. Afterward, they processed its content according to their assigned conditions. One week later, all learners took a posttest. As main results, we found that adaptive testing yielded higher learning outcomes than non-adaptive testing. These benefits were mediated by the adaptive learners' higher testing performance and lower perceived cognitive demand during testing. Furthermore, we found that both testing groups outperformed the note-taking groups. Jointly, our results show that the benefits of testing can be enhanced by adapting the sequence of test questions to learners' knowledge and that testing can be more effective than note-taking.

Learning from instructional explanations: effects of prompts based on the active-constructive-interactive framework.

Although instructional explanations are commonly provided when learners are introduced to new content, they often fail because they are not integrated into effective learning activities. The recently introduced active-constructive-interactive framework posits an effectiveness hierarchy in which interactive learning activities are at the top; these are then followed by constructive and active learning activities, respectively. Against this background, we combined instructional explanations with different types of prompts that were designed to elicit these learning activities and tested the central predictions of the active-constructive-interactive framework. In Experiment 1, N = 83 students were randomly assigned to one of four combinations of instructional explanations and prompts. To test the active < constructive learning hypothesis, the learners received either (1) complete explanations and engaging prompts designed to elicit active activities or (2) explanations that were reduced by inferences and inference prompts designed to engage learners in constructing the withheld information. Furthermore, in order to explore how interactive learning activities can be elicited, we gave the learners who had difficulties in constructing the prompted inferences adapted remedial explanations with either (3) unspecific engaging prompts or (4) revision prompts. In support of the active < constructive learning hypothesis, we found that the learners who received reduced explanations and inference prompts outperformed the learners who received complete explanations and engaging prompts. Moreover, revision prompts were more effective in eliciting interactive learning activities than engaging prompts. In Experiment 2, N = 40 students were randomly assigned to either (1) a reduced explanations and inference prompts or (2) a reduced explanations and inference prompts plus adapted remedial explanations and revision prompts condition. In support of the constructive < interactive learning hypothesis, the learners who received adapted remedial explanations and revision prompts as add-ons to reduced explanations and inference prompts acquired more conceptual knowledge.