Measuring scientific reasoning – a review of test instruments

Opitz, Ansgar; Heene, Moritz; Fischer, Frank

doi:10.1080/13803611.2017.1338586

Cited by 74 publications

(96 citation statements)

References 61 publications

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“…The latter assume that scientific reasoning is best conceptualized as a set of multiple interrelated components (Fischer et al, 2014). A recent review of assessment instruments has shown that researchers disagree in their conceptualizations concerning the structure of scientific reasoning Opitz, Heene, & Fischer, 2017). However, there seems to be a slight consensus that, due to its high complexity, scientific reasoning is best conceptualized as being multi-faceted (Opitz et al, 2017).…”

Section: Scientific Reasoningmentioning

confidence: 99%

“…A recent review of assessment instruments has shown that researchers disagree in their conceptualizations concerning the structure of scientific reasoning Opitz, Heene, & Fischer, 2017). However, there seems to be a slight consensus that, due to its high complexity, scientific reasoning is best conceptualized as being multi-faceted (Opitz et al, 2017). D. Kuhn (1989), among others, takes a developmental perspective and views scientific reasoning as a unitary yet broad process of coordinating theory and evidence.…”

Section: Scientific Reasoningmentioning

confidence: 99%

“…Due to the different nature of the databases, a search including all search fields was undertaken in PsycInfo, while in Scopus search fields were limited to include title, abstract, and keywords. Search terms were based on a recent literature review that was undertaken on the topic of assessment instruments for scientific reasoning (Opitz et al, 2017). References from this review were also taken into account.…”

Section: Literature Searchmentioning

confidence: 99%

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The Psychometric Modeling of Scientific Reasoning: a Review and Recommendations for Future Avenues

Edelsbrunner

Dablander

2018

Educ Psychol Rev

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Psychometric modeling has become a frequently used statistical tool in research on scientific reasoning. We review psychometric modeling practices in this field, including model choice, model testing, and researchers' inferences based on their psychometric practices. A review of 11 empirical research studies reveals that the predominant psychometric approach is Rasch modeling with a focus on itemfit statistics, applied in a way strongly similar to practices in national and international large-scale educational assessment programs. This approach is common in the educational assessment community and rooted in subtle philosophical views on measurement. However, we find that based on this approach, researchers tend to draw interpretations that are not within the inferential domain of this specific approach, and not in accordance with the related practices and inferential purposes. In some of the reviewed articles, researchers put emphasis on item infit statistics for dimensionality assessment. Item infit statistics, however, cannot be regarded as a valid indicator of the dimensionality of scientific reasoning. Using simulations as illustration, we argue that this practice is limited in delivering psychological insights; in fact, various recent inferences about the structure, cognitive basis, and correlates of scientific reasoning might be unwarranted. In order to harness its full potential, we make suggestions towards adjusting psychometric modeling practices to the psychological and educational questions at hand.

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Section: Scientific Reasoningmentioning

confidence: 99%

Section: Scientific Reasoningmentioning

confidence: 99%

Section: Literature Searchmentioning

confidence: 99%

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The Psychometric Modeling of Scientific Reasoning: a Review and Recommendations for Future Avenues

Edelsbrunner

Dablander

2018

Educ Psychol Rev

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“…Studies that correlate the students' learning and scientific reasoning point to a greater success in context-based queries through several gain indicators in teachinglearning processes [Acar 2014]. Therefore, the scientific reasoning capability may be determined as an important factor for the students' performance, learning, and mental development promotion [Piraksa et al 2014] [Opitz et al 2017].…”

Section: Cognitive Development and Scientific Reasoningmentioning

confidence: 99%

Cognition Developing of Computer Higher Education Students Through Gamification in the Algorithm Teaching-Learning Process

Nogueira¹,

Campos²,

Ferreira³

2018

Anais Do Workshop Sobre Educação Em Computação (WEI)

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The scientific logical reasoning became an important skill in the students' cognitive development in algorithm teaching-learning processes, stimulating their reasoning and creativity. From this perspective, gamification has been adopted as a mediating tool in this process. Studies report that the inclusion of gamification in algorithm teaching-learning processes stimulates the students to develop new skills, making the knowledge more efficient. Therefore, this paper's purpose is to measure and understand the cognitive development and the experiences lived by students at the addition of gamification in algorithm teaching, evaluating the scientific logical knowledge acquired by them. Consequently, 44 computer higher education students were selected. They were divided into two groups: students that used the Gamification-Mediated Algorithm Teaching Method and those who participated in the traditional teaching method. To evaluate the cognitive development between these two groups, the Scientific Logical Reasoning Test was applied. The results showed that a significant number of students that used the Gamification-Mediated Algorithm Teaching Method reached the transitory intermediary and transitory scientific knowledge levels, with greater right answer rates. We also noticed that both genders gave more right answers using the gamification-mediated algorithm teaching method.

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“…One of them is a furnace used for the heating process of iron before it is formed and during gilding. Knowledge and skills about making hoes both in the process of cutting the material until finishing obtained from generation to generation [6][7][8][9][10]. One form of local wisdom that can be applied in the learning of physics is the process of making hoes in the blacksmith industry, especially in the process of burning or heating iron [11].…”

Section: Introductionmentioning

confidence: 99%

Development of Learning Device based Ethnoscience for Heat Material in Kabupaten Tegal

2018

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The purpose of this research is produce learning set equipment on heat material of ethnoscience based in Tegal district, The results showed that the feasibility of syllabus-based ethnoscience include into the category is very good because it has a percentage of 96,43%. The feasibility learning device of an ethnoscience-based into a very good category because it has a percentage of 91,37%. Ethnoscience-based learning tool able to improve student learning outcomes from the average value of 42,82 to 80,06 and the gains value 0,65. While the questionnaire of student's learning interest after the application of physics learning device on ethnoscience based heat material in Kabupaten tegal get the average value 3,01 with the high criteria.

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Measuring scientific reasoning – a review of test instruments

Cited by 74 publications

References 61 publications

The Psychometric Modeling of Scientific Reasoning: a Review and Recommendations for Future Avenues

The Psychometric Modeling of Scientific Reasoning: a Review and Recommendations for Future Avenues

Cognition Developing of Computer Higher Education Students Through Gamification in the Algorithm Teaching-Learning Process

Development of Learning Device based Ethnoscience for Heat Material in Kabupaten Tegal

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