Learning from worked-out examples: A study on individual differences

RENKL, A

doi:10.1016/s0364-0213(99)80017-2

Cited by 679Visual and Cognitive Learning ProcessesInnovative Teaching and Learning MethodsIntelligent Tutoring Systems and Adaptive Learning

Learning from worked-out examples: A study on individual differences

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RENKL, A·1997·Cognitive Science

DOI 10.1016/s0364-0213(99)80017-2

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Abstract

The goal of this study was to Investigate individual differences in learning from worked-out examples with respect to the quality of self-explanations. Restrictions of former studies (e.g., lacking control of time-on-task) were avoided and additional research questions (e.g., reliability and dimensionality of self-explanation characteristics) were addressed. An investigation with 36 university freshmen students of education working in individual sessions was conducted. The domain was probability calculation. Prior knowledge and the quality of self-explanations (protocols of the individuals' thinking aloud) were assessed as predictors of learning. A post-test was employed to measure the learning gains as the dependent variable. The following main results were obtained. Most self-explanation characteristics could be regarded as relatively stable person characteristics. The individual differences in the quality of self-explanations were, however, found to be multidimensional. Most Important, even when controlling for time-on-task (quantitative aspect), learning gains could be substantially predicted by qualitative differences of self-explanation characteristics. In particular, successful learners tended to employ more prlnciplebased explanations, more explication of operator-goal combinations, and more anticipative reasoning. In addition, there were two types of effective learners, labeled anticipative reasoners and principle-based explainers.

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Cognitive load research has tended to focus on rule or schema knowledge, induction or compilation processes, and transfer tests to argue for increased study of examples.
“Cognitive load research has tended to focus on rule or schema knowledge (kinds of KCs), induction or compilation processes (kinds of LEs), and transfer tests (a kind of AE) to argue in favor of instructional approaches such as increased study of examples (Renkl, 1997).”
From The Knowledge‐Learning‐Instruction Framework: Bridging the Science‐Practice Chasm to Enhance Robust Student Learning (2012) · cites this paper as Renkl, 1997
The section on goal-operator combinations discusses induction of abstract schemata from examples, and the Discussion reports that inspecting more examples predicted medium-transfer performance. The paper thus supports the examples, schemata, and transfer elements, but the passages do not characterize what cognitive-load research has generally focused on or discuss compilation processes. Page numbers are unavailable in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Asking learners to explain to themselves aloud what they have understood from a learning task has proved to be an effective instructional method.
“Asking learners to explain to themselves aloud what they have understood from a learning task has proved to be an effective instructional method (Chi et al. 1989;Renkl 1997;Renkl and Atkinson 2002).”
From Interactivity in Video-based Models (2007) · cites this paper as Renkl 1997

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Authors: RENKL, A.
Affiliations: Ludwig-Maximilians-Universität München (DE)
Corresponding author: RENKL, A
ORCID: RENKL, A
Venue: Cognitive Science
Publisher: Wiley
Published: 1997-01
Volume: 21
Issue: 1
Pages: 1-29
ISSN: 0364-0213 (ISSN-L)
DOI: 10.1016/s0364-0213(99)80017-2
OpenAlex: W2084895013
Cited by: 679
References: 0
Citations by year: 2026: 6 · 2025: 11 · 2024: 14 · 2023: 15 · 2022: 20 · 2021: 32 · 2020: 21 · 2019: 60 · 2018: 20 · 2017: 22 · 2016: 27 · 2015: 35 · 2014: 26 · 2013: 40 · 2012: 30
FWCI: 18.41
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: doi.wiley.com/10.1002/tdm_license_1 (tdm, 6817 d)
Locations: Cognitive Science (publishedVersion)
Funders: Deutsche Forschungsgemeinschaft
Topics: Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords: worked examples, self-explanation, individual differences, learning gains, probability calculation, think-aloud, time on task, prior knowledge, mathematics learning

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Learning from worked-out examples: A study on individual differences

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