Using heuristic worked examples to promote inquiry-based learning

Mulder, Yvonne G.; Lazonder, Adrianus W.; Jong, Ton de

doi:10.1016/j.learninstruc.2013.08.001

Cited by 43 publications

(44 citation statements)

References 33 publications

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“…Kirschner et al (2006) recommend the use of worked examples as effective methods for guided learning. However, only a few studies have analyzed the effect of examplebased learning on a special form of problem-solving, inquirybased learning (Mulder et al, 2014;Kant et al, 2017). The present study investigates the need for video modeling examples (combining features of modeling examples and worked examples, Leahy and Sweller, 2011) prior to participation in two different levels of inquiry involving less (guided inquiry) or more guidance (structured inquiry).…”

Section: Introductionmentioning

confidence: 99%

The Long-Term Benefit of Video Modeling Examples for Guided Inquiry

Kaiser

Mayer

2019

Front. Educ.

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Inquiry-based learning can be considered a critical component of science education in which students can assess their understanding of scientific concepts and scientific reasoning skills while actively constructing new knowledge through different types of activity levels (Klahr and Dunbar, 1988; Bell et al., 2005; Hmelo-Silver et al., 2007; Mayer, 2007). However, engaging in inquiry activities can be cognitively demanding for students, especially those with low prior knowledge of scientific reasoning skills (reasoning ability). Learning new information when preexisting schemata are absent entails more interacting elements and thus imposes a high working memory load, resulting in lower long-term learning effects (Paas and van Merriënboer, 1994; Kirschner et al., 2006). Borrowing knowledge from others via video modeling examples before carrying out an inquiry task provides learners with more working memory capacity to focus on problem-solving strategies and construct useful cognitive schemata for solving subsequent (virtual) inquiry tasks (Kant et al., 2017). The goal of the present study (N = 174 6/7th graders) is to investigate the benefits of combining example-based learning with physical, hands-on investigations in inquiry-based learning for acquiring scientific reasoning skills. The study followed a 2 (video modeling example vs. no example) × 2 (guided vs. structured inquiry) × 2 (retention interval: immediate vs. delayed) mixed-factorial design. In addition, the students' need for cognition (Preckel, 2014), cognitive abilities (Heller and Perleth, 2000) (intrinsic, extraneous, and germane) cognitive load (Cierniak et al., 2009) and performance success were measured. Although the results of an intermediate test after the first manipulation were higher among students who watched a video modeling example (d = 0.97), combining video modeling examples with inquiry was not found to benefit performance success. Furthermore, regardless of manipulation, all students achieved equal results on an assessment immediately following the inquiry task. Only in the long run did a video modeling example prove to be advantageous for guided inquiry (η p 2 = 0.023). A video modeling example turned out to be a crucial prerequisite for the long-term effectiveness of guided inquiry because it helped create stable problem-solving schemata; however, the long-term retention of structured inquiry did not rely on a video modeling example.

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Section: Introductionmentioning

confidence: 99%

The Long-Term Benefit of Video Modeling Examples for Guided Inquiry

Kaiser

Mayer

2019

Front. Educ.

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“…Nevertheless, the basic advantages seen for partial models illustrate that scaffolds based on the example‐based learning approach can be successfully applied in learning by modelling. A previous, less successful attempt used heuristic worked‐out examples to try to enhance students’ performance success and learning outcomes (Mulder et al, ). Although students who received the worked‐out examples created better models than their unsupported counterparts, the overall quality of their models was still rather low and the expected improvement in learning outcomes failed to occur.…”

Section: Discussionmentioning

confidence: 99%

Scaffolding learning by modelling: The effects of partially worked‐out models

et al. 2015

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Creating executable computer models is a potentially powerful approach to science learning. Learning by modelling is also challenging because students can easily get overwhelmed by the inherent complexities of the task. This study investigated whether offering partially worked-out models can facilitate students' modelling practices and promote learning. Partially worked-out models were expected to aid model construction by revealing the overall structure of the model, and thus enabling student to create better models and learn from the experience. This assumption was tested in high school biology classes where students modelled the human glucose-insulin regulatory system. Students either received support in the form of a partial model that outlined the basic structure of the glucose-insulin system (PM condition; n ¼ 26), an extended partial model that also contained a set of variables students could use to complete the model (PMþ condition; n ¼ 21), or no support (control condition; n ¼ 23). Results showed a significant knowledge increase from pretest to posttest in all conditions. Consistent with expectations, knowledge gains were higher in the two partial model conditions than in the control condition. Students in both partial model conditions also ran their model more often to check its accuracy, and eventually built better models than students from the control condition. Comparison between the PM and PMþ conditions showed that more extensive support further increased knowledge acquisition, model quality, and model testing activities. Based on these findings, it was concluded that partial solutions can support learning by modelling, and that offering both a structure of a model and a list of variables yields the best results. © 2015 Wiley Periodicals, Inc. J Res Sci Teach 53: 502-523, 2016 Keywords: learning by modelling; completion problems; worked-out examples Dutch summary (Nederlandse samenvatting): Modelleren is een veelbelovende manier voor het leren van natuurwetenschappelijke principes en verschijnselen. Leren door te modelleren is echter niet eenvoudig: het maken van een werkend computermodel blijkt voor veel leerlingen een struikelblok te zijn. Het aanbieden van een gedeeltelijk uitgewerkt model zou deze problemen kunnen doen verminderen. In een gedeeltelijk uitgewerkt model wordt de algemene modelstructuur gegeven, wat de leerlingen kan helpen om een beter model te maken en de inhoud ervan beter te begrijpen. Deze hypotheses zijn onderzocht tijdens een biologiepracticum waarin leerlingen uit de bovenbouw van het VWO een computermodel van het glucoseinsuline regulatieproces moesten maken. Een deel van de leerlingen had hierbij de beschikking over een gedeeltelijk uitgewerkt model (PM conditie; n ¼ 26). De tweede groep kreeg hetzelfde uitgewerkte model plus een lijst met variabelen die zij aan het model konden toevoegen (PMþ conditie; n ¼ 21), terwijl de

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“…Some consider students’ ideas to be stable, persistent misconceptions that are highly resistant to change and should be thought of as resources for cognitive development as opposed to ideas to be replaced (Enderle, Smith, & Southerland, ; Evans and Lane, ; Smith, diSessa, & Roschelle, ). In this case, instruction should address students’ ideas directly, thus allowing them to weigh alternative ideas for new insight (Chi, Bassok, Lewis, Reimann, & Glaser, ; Mulder, Lazonder, & De Jong, ; Osborne & Patterson, ; Renkl, ; Sandoval, ; Sinatra, Brem, & Evans, ). However, even with opportunities to consider alternative ideas, students may instead develop hybrid frameworks of normative and non‐normative ideas rather than completely rejecting non‐normative ideas (Evans & Lane, ).…”

Section: Alternative Explanations Of Evolutionmentioning

confidence: 99%

Enhancing student explanations of evolution: Comparing elaborating and competing theory prompts

et al. 2016

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In this study we explore how two different prompt types within an online computer-based inquiry learning environment enhance 392 7th grade students' explanations of evolution with 3 teachers. In the elaborating prompt condition, students are prompted to write explanations that support the accepted theory of evolution. In the competing prompt condition, students are prompted to write explanations that differentiate two views of evolution associated with Darwin and Lamarck. Data sources included a pretest and posttest, an embedded item, observations, logged teacher guidance, and teacher interviews. Findings show similar pretest to posttest gains in students' understanding of evolution for both conditions, but this pattern was not uniform across all three teachers.For one teacher, students who received competing theory prompts produced significantly higher gains than those who received elaborating theory prompts. A closer look at embedded student work reveals a higher degree of teacher participation (i.e., grading and guidance) than for the other teachers. Our findings illustrate how helping students distinguish between competing scientific claims can support learning in an inquiry unit, but may require a higher degree of teacher participation and reinforcement. We discuss the implications of these findings for enhancing students' scientific explanations.

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Using heuristic worked examples to promote inquiry-based learning

Cited by 43 publications

References 33 publications

The Long-Term Benefit of Video Modeling Examples for Guided Inquiry

The Long-Term Benefit of Video Modeling Examples for Guided Inquiry

Scaffolding learning by modelling: The effects of partially worked‐out models

Enhancing student explanations of evolution: Comparing elaborating and competing theory prompts

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