Effects of worked examples, example-problem, and problem-example pairs on novices’ learning

Gog, Tamara van; Kester, Liesbeth; Paas, Fred

doi:10.1016/j.cedpsych.2010.10.004

Cited by 216 publications

(243 citation statements)

References 41 publications

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“…On the other hand, an "in press" study, one that occurred more-or-less concurrently to ours (yet after our hypothesis was formulated), casts doubt on the notion that an alternating condition is better than all examples, at least with respect to regular, non-tutored problem solving. In this study all worked examples were as good as alternating examples/problem solving, with both conditions better than all problems, in terms of both lower cognitive load during learning and higher learning outcomes [11]. Thus, it is clear that the outcome of our study, which will now be described, was not obvious.…”

Section: Introductionmentioning

confidence: 57%

When Is It Best to Learn with All Worked Examples?

McLaren

Isotani

2011

Lecture Notes in Computer Science

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Abstract.Worked examples have repeatedly demonstrated learning benefits in a range of studies, particularly with low prior knowledge students and when the examples are presented in alternating fashion with problems to solve. Recently, worked examples alternating with intelligently-tutored problems have been shown to provide at least as much learning benefit to students as all tutored problems, with the advantage of taking significantly less learning time (i.e., more efficiency) than all tutored problems. Given prior findings, together with the prevailing belief that students should be prompted to actively solve problems after studying examples, rarely have all worked examples been tried as a learning intervention. To test the conventional wisdom, as well as to explore an understudied approach, a study was conducted with 145 high school students in the domain of chemistry to compare alternating worked examples / tutored problems, all tutored problems, and all worked examples. It was hypothesized that the alternating condition would lead to better results (i.e., better learning and/or learning efficiency) than either all examples or all tutored problems. However, the hypothesis was not confirmed: While all three conditions learned roughly the same amount, the all worked examples condition took significantly less time and was a more efficient learning treatment than either alternating examples/tutored problems or all tutored problems. This paper posits an explanation for why this (seemingly) surprising result was found.

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Section: Introductionmentioning

confidence: 57%

When Is It Best to Learn with All Worked Examples?

McLaren

Isotani

2011

Lecture Notes in Computer Science

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“…Problem-example pairs allow for noting deficiencies in students' own performance, which may increase their attention to those aspects during example study, but in the study of Van Gog et al (2010), problem-example pairs were less effective for learning than example-problem pairs and examples only and did not lead to better learning than problem solving only. A possible explanation is that students are very often unable to diagnose their own performance deficiencies (for a review, see Bjork 1999).…”

Section: How Should Examples Be Delivered To Optimize Their Effectivementioning

confidence: 99%

Example-Based Learning: Integrating Cognitive and Social-Cognitive Research Perspectives

2010

Self Cite

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Example-based learning has been studied from different perspectives. Cognitive research has mainly focused on worked examples, which typically provide students with a written worked-out didactical solution to a problem to study. Social-cognitive research has mostly focused on modeling examples, which provide students the opportunity to observe an adult or a peer model performing the task. The model can behave didactically or naturally, and the observation can take place face to face, on video, as a screen recording of the model's computer screen, or as an animation. This article reviews the contributions of the research on both types of example-based learning on questions such as why examplebased learning is effective, for what kinds of tasks and learners it is effective, and how examples should be designed and delivered to students to optimize learning. This will show both the commonalities and the differences in research on example-based learning conducted from both perspectives and might inspire the identification of new research questions. Bandura's (1977Bandura's ( , 1986) social learning theory. Both argue that it would be Educ Psychol Rev (2010) 22:155-174 Keywords

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“…Worked examples that provide all problem solving steps worked out (solved) for the learner are a widely studied instructional design strategy for reducing cognitive demands in instruction on problem solving (Atkinson, Derry, Renkl, & Wortham, 2000;Biesinger & Crippen, 2010). The effects of different sequences (schedules) of worked examples and practice problems, that are to be solved by the learner, have been examined in several studies (e.g., Leppink, Paas, van Gog, van der Vleuten, & van Merriënboer, 2014; Van Gog, Kester, & Paas, 2011). Transitioning from worked examples to practice problems has generally been found to better foster learning for novices than transitioning from practice problems to worked examples.…”

Section: Brief Overview Of Cognitive Load Theorymentioning

confidence: 99%

“…Transitioning from worked examples to practice problems has generally been found to better foster learning for novices than transitioning from practice problems to worked examples. Akin to comparing the transitioning from either worked examples to practice problems with transitioning from practice problems to worked examples (Leppink, et al, 2014;Van Gog, et al, 2011), we compare the transitioning from either feedback for each practice problem step to feedback for an entire practice problem with transitioning from feedback for an entire practice problem to feedback after each problem step in the present study.…”

Section: Brief Overview Of Cognitive Load Theorymentioning

confidence: 99%

Transitional feedback schedules during computer-based problem-solving practice

Johnson

Reisslein

2015

Computers & Education

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Feedback has a strong influence on effective learning from computer-based instruction. Prior research on feedback in computer-based instruction has mainly focused on static feedback schedules that employ the same feedback schedule through an instructional session. This study examined transitional feedback schedules in computer-based multimedia instruction on procedural problem-solving in electrical circuit analysis. Specifically, we compared two transitional feedback schedules: the TFS-P schedule switched from initial feedback after each problem step to feedback after a complete problem at later learning states; the TFP-S schedule transitioned from feedback after a complete problem to feedback after each problem step. As control conditions, we also considered two static feedback schedules, namely providing feedback after each practice problem-solving step (SFS) or providing feedback after attempting a complete multi-step practice problem (SFP). Results indicate that the static stepwise (SFS) and transitional stepwise to problem (TFS-P) feedback produce higher problem solving near-transfer post-test performance than static problem (SFP) and transitional problem to step (TFP-S) feedback. Also, TFS-P resulted in higher ratings of program liking and feedback helpfulness than TFP-S. Overall, the study results indicate benefits of maintaining high feedback frequency (SFS) and reducing feedback frequency (TFS-P) compared to low feedback frequency (SFP) or increasing feedback frequency (TFP-S) as novice learners acquire engineering problem solving skills.

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Effects of worked examples, example-problem, and problem-example pairs on novices’ learning

Cited by 216 publications

References 41 publications

When Is It Best to Learn with All Worked Examples?

When Is It Best to Learn with All Worked Examples?

Example-Based Learning: Integrating Cognitive and Social-Cognitive Research Perspectives

Transitional feedback schedules during computer-based problem-solving practice

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