Scientific Reasoning Across Different Domains

Glaser, Robert; Schauble, Leona; Raghavan, Kalyani; Zeitz, Colleen M.

doi:10.1007/978-3-642-77228-3_16

Cited by 66 publications

(46 citation statements)

References 13 publications

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“…Models may also differ considerably in complexity, and range from very simple straightforward models, e.g., simple Mendelian genetics (Brant, Hooper, & Sugrue, 1991) to very complex models, e.g., the medical simulation HUMAN (Coleman & Randall, 1986) in which 200 variables and parameters can be changed. Also, specific characteristics like the place of variables in the model, or the distance between theoretical and operational variables characterize the conceptual model (Glaser, Schauble, Raghavan, & Zeitz, 1992). In scientific discovery learning the main task of the learner is to infer the characteristics of the model underlying the simulation.…”

Section: Simulations Of Conceptual Domainsmentioning

confidence: 99%

Scientific Discovery Learning with Computer Simulations of Conceptual Domains

Jong

Joolingen

1998

Review of Educational Research

255

353

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Scientific discovery learning is a highly self-directed and constructivistic form of learning. A computer simulation is a type of computer-based environment that is very suited for discovery learning, the main task of the learner being to infer, through experimentation, characteristics of the model underlying the simulation. In this article we give a review of the observed effectiveness and efficiency of discovery learning in simulation environments together with problems that learners may encounter in discovery learning, and we discuss how simulations may be combined with instructional support in order to overcome these problems.

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Section: Simulations Of Conceptual Domainsmentioning

confidence: 99%

Scientific Discovery Learning with Computer Simulations of Conceptual Domains

Jong

Joolingen

1998

Review of Educational Research

255

353

View full text Add to dashboard Cite

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“…Several studies attempting to look across different learning domains have yielded inconclusive results. Some studies indicate that metacognitive skills are generalizable across learning domains and subject areas (Schraw et al 1995, Schraw and nietfeld 1998, Veenman and Verheij 2003, Veenman et al 2004) while other studies suggest that different subjects and different types of tasks require different types of metacognitive activities (Glaser et al 1992, Kelemen et al 2000.…”

Section: Research Context and Questionsmentioning

confidence: 99%

“…Hennessey 1991, Blank 2000, is only one among many metacognitive learning strategies that have been developed for K-12 and postsecondary classroom use (Table 1; nichols et al 1997, Hogan 1999, Thomas and McRobbie 2001, D'Avanzo 2003b, Larkin 2006, Wall et al 2009). Glaser et al (1992) and Kelemen et al (2000) found that different subjects and different types of tasks require different types of metacognitive activities. The intervention we used was developed in an ecology classroom.…”

Section: Resilience Thinking Abilitymentioning

confidence: 99%

Metacognitive learning in the ecology classroom: A tool for preparing problem solvers in a time of rapid change?

et al. 2016

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Abstract. Building communities that are resilient and adaptive to climate change requires the development of education strategies that train community members in higher order thinking skills that can be used to solve complex environmental problems. This study provides an empirical test of hypotheses within social-ecological systems resilience theory that have suggested metacognitive learning strategies could increase resilience thinking skills such as scenarios thinking, systems thinking, and the ability to interpret and apply ecological data in complex problem solving. During a 6-week long ecology unit with 108 seventh-grade students, we taught half the students using standard inquiry teaching methods and the other half using the same method, with the addition of a daily metacognitive learning intervention. We investigated the short-term (after six weeks of intervention) and long-term (1 yr after the intervention ended) effects of the intervention on student's metacognitive ability and resilience thinking skills. Over the long term, we found a modest increase in the metacognitive ability of students who received the daily metacognitive journaling exercise. Interview data suggest that the structured metacognitive practice did most to improve the resilience thinking level of students who had low resilience thinking ability prior to the intervention period. However, the interaction between pre-treatment ability level and the treatment group was not detected in the written assessment data. These data suggest that the metacognitive learning intervention we used can benefit metacognitive ability over the long term, but has limited transferability to resilience thinking skills for most students. We suggest additional instructional practices for implementing metacognitive teaching approaches that could enhance the generalizability of their benefits across resilience thinking skills and student's ability levels.

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“…A suite of three computer-based coaching systems for discovery learning developed at LRDC, University of Pittsburgh, are based on critics. These systems each address a different domain: Smithtown -microeconomics (Raghaven, Schultz, Glaser & Schauble), Voltaville -direct current electricity (Glaser, Raghavan & Schauble, 1988), and Refract -geometrical optics (Reimann, Raghaven, GlaserI988). These discovery environments are designed to build scientific inquiry skills.…”

Section: Short Descriptions Of Criticsmentioning

confidence: 99%