Exploring middle school students' interests in computationally intensive science careers

The Cisci instrument validation and intervention

Dados Bibliográficos

ID21392860
AutoresArif Rachmatullah (0000-0003-0993-0477, Center for Education Research & Innovation (CERI), SRI International Menlo Park California USA, autor correspondente), Eric Wiebe (0000-0002-5920-5225, North Carolina State University), Eric N Wiebe (Department of STEM Education, College of Education North Carolina State University Raleigh North Carolina USA)
Ano2023
Volume107
Fascículo2
Páginas333-367
Data de publicação2023-03-01
Open AccessSim
TipoARTICLE
PeriódicoScience Education (JOURNAL)
Identificadores do periódicoISSN: 0036-8326 • E-ISSN: 1098-237X
EditoraWiley (PUBLISHER • GB)
DOI10.1002/sce.21771
OpenAlexW4298204280
IdiomaEN
Citações recebidas4
Referências citadas74

Computationally intensive science (CIS)‐related disciplines and careers are predicted to be among the cutting‐edge fields and high‐demand jobs. Such disciplines and careers demand expertise in both traditional scientific knowledge and computer science (CS)‐related understanding and skills. Preparing educational systems for developing interest and abilities for such career paths require a new set of research tools. In this study, we developed and validated a multidimensional instrument called computationally intensive science career interests (CISCI) to measure middle school students' interests in CIS careers. We also explored several predictors of students' interests in such careers and examined the impact of a 4‐day online synchronous scientific computational modeling activity on students' career interests. A total of 934 Indonesian middle school students (aged 11–14) participated in this study. A combination of the classical test theory and item response theory approaches was used to validate the CISCI instrument. Multiple linear regression tests were run to identify the predictors of students' career interests, and paired‐sample t ‐tests were used to examine a scientific computational modeling activity's impact on students' career interests. The results revealed that CISCI was a psychometrically valid and reliable instrument to measure students' career interests. We also found that science and CS attitudes, computational thinking, and prior experience in CS‐related activities were significant predictors of students' career interests. In addition, computational modeling activity significantly influenced the frequency of students discussing such career paths with their parents. This study underscores the importance of engaging students in CS‐integrated science learning activities to help develop their interests in CIS jobs

Computational Thinking · Intervention (counseling) · Mathematics education · Medical education · Relevance (law) · Science education · Set (abstract data type) · Test (biology) · Career Development and Diversity · Computer Science · Innovative Teaching and Learning Methods · Medicine · Psychology · Teaching and Learning Programming

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