Computer simulations and clear observations do not guarantee conceptual understanding

Renken, Maggie; Nuñez, Narina

doi:10.1016/j.learninstruc.2012.08.006

Cited by 64 publications

(50 citation statements)

References 36 publications

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“…However, research on students' learning in lab-work settings, including computer-supported lab settings, have shown that linking the practical lab work to a conceptual understanding of the underlying scientific principles and procedures is challenging for students (van Joolingen et al 2007;Lunetta et al 2007;Renken and Nunez 2013). Studies focusing on teacher support in computer-supported settings have shown that such support is pivotal (Gillen et al 2008;Strømme and Furberg 2015).…”

Section: Discussionmentioning

confidence: 99%

“…For instance, recurrent findings are that students struggle with testing hypotheses, connecting procedural skills with conceptual knowledge and transferring acquired conceptual understandings or inquiry skills from one setting to another (van Joolingen et al 2007;Renken and Nunez 2013). Furthermore, studies focusing on computer-supported help-seeking across various knowledge domains have indicated that students often do not take full advantage of support tools aimed at supporting their conceptual reflection (Aleven et al 2003;Clarebout and Elen 2006;Furberg 2009).…”

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confidence: 99%

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Teacher support in computer-supported lab work: bridging the gap between lab experiments and students’ conceptual understanding

Furberg

2016

Intern. J. Comput.-Support. Collab. Learn

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This paper reports on a study of teacher support in a setting where students engaged with computer-supported collaborative learning (CSCL) in science. The empirical basis is an intervention study where secondary school students and their teacher performed a lab experiment in genetics supported by a digital learning environment. The analytical focus is on student-teacher interactions taking place in help-seeking settings during group-based activities where students analysed and reported their findings from the lab experiment. A combination of quantitative methods in the form of frequency counts of students' help requests and detailed micro-analyses of student-teacher interactions are used. The findings are that the majority of challenges faced by students concerned conceptually oriented issues and procedural challenges in the sense of how to practically solve the assignments provided to them in the digital learning environment. Most importantly, the analyses of student-teacher interactions provide insight into the considerable amount of support that is needed from the teacher to bridge the conceptual gap between the lab experiment and the students' understanding of the underlying scientific principles and procedures. The findings are discussed according to possible implications for the design of digital support tools and instruction.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Teacher support in computer-supported lab work: bridging the gap between lab experiments and students’ conceptual understanding

Furberg

2016

Intern. J. Comput.-Support. Collab. Learn

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“…These results suggest that outcomes for learning and problem solving may differ when using the body and the concrete world to explore the problem space. Education is rapidly advancing toward the use of computercentered learning; coupling bodies to a dynamic and modifiable world during learning, problem solving, and decision making poses important pedagogical questions (Kirsh, 1997;Klahr, Triona, & Williams, 2007;Moreno & Mayer, 2007;Renken & Nunez, 2013). Interactivity is now often couched in terms of interaction with a computer interface, knowledge and skills learnt from that mode of interactivity need to be assessed against interaction with the lived in physical world.…”

Section: Discussionmentioning

confidence: 99%

Learning and interactivity in solving a transformation problem

Guthrie

Vallée-Tourangeau

et al. 2015

Mem Cogn

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Outside the psychologist's laboratory, thinking proceeds on the basis of a great deal of interaction with artefacts that are recruited to augment problem-solving skills. The role of interactivity in problem solving was investigated using a river-crossing problem. In Experiment 1A, participants completed the same problem twice, once in a low interactivity condition, and once in a high interactivity condition (with order counterbalanced across participants). Learning, as gauged in terms of latency to completion, was much more pronounced when the high interactivity condition was experienced second. When participants first completed the task in the high interactivity condition, transfer to the low interactivity condition during the second attempt was limited; Experiment 1B replicated this pattern of results. Participants thus showed greater facility to transfer their experience of completing the problem from a low to a high interactivity condition. Experiment 2 was designed to determine the amount of learning in a low and high interactivity condition; in this experiment participants completed the problem twice, but level of interactivity was manipulated between subjects. Learning was evident in both the low and high interactivity groups, but latency per move was significantly faster in the high interactivity group, in both presentations. So-called problem isomorphs instantiated in different task ecologies draw upon different skills and abilities; a distributed cognition analysis may provide a fruitful perspective on learning and transfer.

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“…While this difference might be expected to impact student learning, previous research does not support the idea that there is an inherent advantage of interaction with either physical or virtual materials. Many studies in K-12 STEM education have compared physical and virtual activities that teach the same concepts, and most have found no difference in learning (Chen et al 2014;Klahr et al 2007;Lazonder and Ehrenhard 2014;Marshall et al 2010;Renken and Nunez 2013). Studies at the undergraduate level (mostly in STEM fields outside of biology) generally compare lab activities using physical equipment to activities with virtual lab equipment and have found similar trends.…”

Section: Physical Vs Virtual Learning Activitiesmentioning

confidence: 99%

Testing the effectiveness of two natural selection simulations in the context of a large-enrollment undergraduate laboratory class

2017

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Background: Simulations can be an active and engaging way for students to learn about natural selection, and many have been developed, including both physical and virtual simulations. In this study we assessed the student experience of, and learning from, two natural selection simulations, one physical and one virtual, in a large enrollment introductory biology lab course. We assigned students to treatments (the physical or virtual simulation activity) by section and assessed their understanding of natural selection using a multiple-choice pre-/post-test and short-answer responses on a post-lab assignment. We assessed student experience of the activities through structured observations and an affective survey.Results: Students in both treatments showed increased understanding of natural selection after completing the simulation activity, but there were no differences between treatments in learning gains on the pre-/post-test, or in the prevalence of concepts and misconceptions in written answers. On a survey of self-reported enjoyment they rated the physical activity significantly higher than the virtual activity. In classroom observations of student behavior, we found significant differences in the distribution of behaviors between treatments, including a higher frequency of offtask behavior during the physical activity. Conclusions:Our results suggest that both simulations are valuable active learning tools to aid students' understanding of natural selection, so decisions about which simulation to use in a given class, and how to best implement it, can be motivated by contextual factors.

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Computer simulations and clear observations do not guarantee conceptual understanding

Cited by 64 publications

References 36 publications

Teacher support in computer-supported lab work: bridging the gap between lab experiments and students’ conceptual understanding

Teacher support in computer-supported lab work: bridging the gap between lab experiments and students’ conceptual understanding

Learning and interactivity in solving a transformation problem

Testing the effectiveness of two natural selection simulations in the context of a large-enrollment undergraduate laboratory class

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