Assessing quantitative modelling practices, metamodelling, and capability confidence of biology undergraduate students

Dauer, Joseph T.; Mayes, Robert L.; Rittschof, Kent A.; Gallant, Bryon Jason

doi:10.1080/09500693.2021.1928325

Cited by 8 publications

(5 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Such limited understanding hinders a model‐based engagement in scientific reasoning and inquiry in the classroom (Khan, 2011; Schwarz & Gwekwerere, 2007). Thus, it seems apparent that preservice science teachers' modeling practice lacks complexity and skill (Dauer et al, 2021; Göhner & Krell, 2022) and their modeling products leave room for improvement (Ammoneit et al, 2023; Göhner et al, 2022; Mulder et al, 2011). A comprehensive understanding of modeling competence can help systematically improve teachers' modeling competence (Chiu & Lin, 2019).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Why most definitions of modeling competence in science education fall short: Analyzing the relevance of volition for modeling

Ammoneit,

Göhner,

Bielik

et al. 2023

Science Education

View full text Add to dashboard Cite

Definitions of modeling competence in science education do not yet include noncognitive factors. However, noncognitive factors are central to competence and might thus substantially improve our understanding of modeling competence. In this article, we analyze volition during preservice science teachers' engagement with a black‐box modeling task and its relation to established aspects of modeling competence: metamodeling knowledge, modeling process, and modeling product. A cluster analysis of the occurrence of volition categories resulted in three clusters of volitional behavior. The clusters describe three different volition types: one action‐oriented type applying a self‐regulative strategy and two state‐oriented types applying self‐controlling strategies. Correlation analyses between clusters, volition categories and modeling process variables indicate benefits of the self‐regulative strategy.

show abstract

Section: Theoretical Backgroundmentioning

confidence: 99%

Why most definitions of modeling competence in science education fall short: Analyzing the relevance of volition for modeling

Ammoneit,

Göhner,

Bielik

et al. 2023

Science Education

View full text Add to dashboard Cite

show abstract

“…Upaya tersebut bisa dilakukan mulai dari tingkat fakultas dan jurusan, misalnya menggunakan Ujian Penalaran Kuantitatif Ilmu Biologi (BioSQuaRE) (Stanhope et al, 2017). Tenaga pendidik juga bisa terus melakukan upaya peningkatan literasi numerasi pada mahasiswa misalnya dengan permodelan data kuantitatif pada proses pembelajaran, sehingga mahasiswa merasa percaya diri dalam melakukan interpretasi dan representasi data numerasi (Dauer et al, 2021).…”

Section: Analisis Perbedaan Kemampuan Interpretasi Data Numerasi Anta...unclassified

Analisis Perbedaan Kemampuan Interpretasi Data Numerasi Mahasiswa Pendidikan Biologi di Kalimantan Selatan Berdasarkan Masa Studi yang Telah Ditempuh

Arsyad

2023

J-Pendidikan Biologi

View full text Add to dashboard Cite

Numeracy literacy is important for biology education students as pre-service teachers. Numeracy literacy ability can support students in conducting analyses related to quantitative data. Meanwhile, for biology teachers, numeracy literacy ability can support the process of analysis and evaluation of learning that is closely related to quantitative data. One indicator of numeracy literacy is the ability to interpret numerical data, which can support 21st-century learning competencies. This study aims to analyze differences in the ability to interpret numerical data for biology education students in South Kalimantan based on the study period that has been taken. This type of research is quantitative comparative research. The sample subjects for this study were 245 students of the Biology Education study program in South Kalimantan. The instrument for measuring the ability to interpret numeracy data was collected using a test instrument referring to AACU (2009). Data were analyzed using the ANOVA test and continued with Duncan's test to analyze differences between groups of students based on the study period that has been taken. The results showed significant differences in the ability to interpret numerical data for biology education students based on the study period that has been taken.

show abstract

“…It generally pays attention to understanding models and modelling knowledge and the mastery of modelling practical experience. In other words, science education not only focuses on the positive role of MM as a tool to achieve teaching objectives but also on understanding MM itself as one of the objectives [ 18 , 19 ]. It is also necessary for teachers to master additional pedagogical knowledge about modelling [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Research hotspots and development trends of model and modelling education research: Bibliometric analysis based on CiteSpace

Jin,

Jian

2024

Heliyon

View full text Add to dashboard Cite

Assessing quantitative modelling practices, metamodelling, and capability confidence of biology undergraduate students

Cited by 8 publications

References 49 publications

Why most definitions of modeling competence in science education fall short: Analyzing the relevance of volition for modeling

Why most definitions of modeling competence in science education fall short: Analyzing the relevance of volition for modeling

Analisis Perbedaan Kemampuan Interpretasi Data Numerasi Mahasiswa Pendidikan Biologi di Kalimantan Selatan Berdasarkan Masa Studi yang Telah Ditempuh

Research hotspots and development trends of model and modelling education research: Bibliometric analysis based on CiteSpace

Contact Info

Product

Resources

About