Relating teenagers’ science interest network characteristics to later science course enrolment
An analysis of Australian Pisa 2006 and Longitudinal Surveys of Australian Youth data
Bibliographic Data
| ID | 21293844 |
|---|---|
| Authors | Maien S M Sachisthal (0000-0002-9833-0723, Free University Amsterdam, the Netherlands, corresponding author), Bjørn Jansen (0000-0001-9262-933X, University of Amsterdam), Brenda R J Jansen (University of Amsterdam, The Netherlands), Jonas Dalege (0000-0002-1844-0528, Santa Fe Institute), Maartje E J Raijmakers (0000-0003-1843-6462, Free University Amsterdam, the Netherlands) |
| Year | 2020 |
| Volume | 64 |
| Issue | 3 |
| Pages | 264-281 |
| Publication date | 2020-11-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Australian Journal of Education (JOURNAL) |
| Journal identifiers | ISSN: 0004-9441 • E-ISSN: 2050-5884 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/0004944120957477 |
| OpenAlex | W3090332251 |
| Language | EN |
| Citations received | 1 |
| References cited | 42 |
Recently, students’ interest in science has been conceptualized as a network model: the science interest network model (SINM) in which affective, behavioural and cognitive components interact together; building on science interest being a dynamic relational construct. In the current study, we combine the Australian Programme for International Student Assessment (PISA) 2006 and Longitudinal Surveys of Australian Youth 2008 data (N = 4758) to investigate relationships between the network characteristics of Year 10 students with their decision to enrol in a science course in Year 12. Specifically, we identified indicators central to the SINM and tested whether they predicted chemistry, physics and biology course enrolment. Students’ intentions to pursue a science-related study or career (future intentions in science) and their enjoyment of science (science enjoyment) were the most central indicators for all three science courses. Centrality was strongly related to course enrolment ( r = .36–.74), lending support to the validity of network theory in the context of science interest, as central indicators may play an influential role within the network
Centrality · Complex network · Construct (python library) · Context (archaeology) · Geography · Longitudinal study · Mathematics education · Network science · Science education · Computer Science · Education, Achievement, and Giftedness · Mathematics · Mental Health Research Topics · Psychological and Temporal Perspectives Research · Psychology
Modern Applied Statistics with S
Promoting Interest and Performance in High School Science Classes
Revisiting the Conceptualization, Measurement, and Generation of Interest
MissMech
The Concept of Validity.
Research on Interest in Science
Can network analysis transform psychopathology?
Toward a formalized account of attitudes
Academic Self-Concept, Interest, Grades, and Standardized Test Scores
Pipeline persistence
Sparse inverse covariance estimation with the graphical lasso
A Model of Factors Contributing to STEM Learning and Career Orientation
Structural and dynamic aspects of interest development
Graph drawing by force‐directed placement
Estimating psychological networks and their accuracy
Qgraph
Network Analysis
Regression Shrinkage and Selection Via the Lasso
The Four-Phase Model of Interest Development
The Gaussian Graphical Model in Cross-Sectional and Time-Series Data
Bridge Centrality
Centrality in social networks conceptual clarification
Node centrality in weighted networks
Coursework selection
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |