Video-based modelling examples and self-explanation prompts for teaching a complex problem-solving strategy to learners with different levels of prior knowledge

Hochschulschrift

Abstract: Learning from video-based modelling examples, as compared to learning by problem solving, is effective because it frees up working memory capacities. However, learners need to engage in generative learning activities such as self-explanation to use these freed-up capacities for learning. Such self-explanations can be elicited by prompts. Self-explanations prompts are usually directed backwards, that is, towards just studied solution steps (i.e., retrospective prompts). Forward-directed prompts, that is, towards a next step (i.e., anticipatory prompts) are presumably more demanding but – for higher prior knowledge learners – potentially also more beneficial for learning. In addition, self-explanation prompts are sometimes used to prompt learners to compare example cases. Such example comparisons, however, are difficult to implement for video-based modelling examples, as learners cannot watch two videos simultaneously. Instead, it might be useful to ask learners not to compare video examples directly but to ask them to compare non-transient representations of problem-solving processes that have been illustrated in the video-based modelling examples. Such comparative self-explanation prompts might be more demanding than sequentially studying and self-explaining example cases (or representations thereof) but – for higher prior knowledge learners – potentially also more beneficial for learning.

This dissertation includes three manuscripts describing two studies investigating the use of video-based modelling examples and retrospective versus anticipatory or sequential versus comparative representation-based self-explanation prompts for teaching a complex problem-solving strategy (i.e., the diagnosis of car malfunctions). Overall, results indicate that video-based modelling examples are useful for teaching problem-solving strategies in ill-structured domains. Furthermore, results indicate that anticipatory and comparative self-explanation prompts are more suitable for stronger learners. More research, especially on the exact relationships between the use of worked or modelling examples, cognitive load and learning outcomes, is needed

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch
• Anmerkungen: Universität Freiburg, Dissertation, 2023

Klassifikation und Themen

• Schlagwort: Problemlösen

Beteiligte, Orts- und Zeitangaben

• Ereignis: Veröffentlichung
• (wo): Freiburg
• (wer): Universität
• (wann): 2023
• Urheber: Meier, Julius Moritz
• Beteiligte Personen und Organisationen: Renkl, Alexander ; Glogger-Frey, Inga ; Albert-Ludwigs-Universität Freiburg. Pädagogische Psychologie und Entwicklungspsychologie ; Albert-Ludwigs-Universität Freiburg. Wirtschafts- und Verhaltenswissenschaftliche Fakultät

Weitere Informationen

• DOI: 10.6094/UNIFR/240217
• URN: urn:nbn:de:bsz:25-freidok-2402177
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 14.08.2025, 10:44 MESZ