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Cited by 555

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Interest as a Predictor of Academic Achievement: A Meta-Analysis of Research

·2014·The Role of interest in Learning and Development

Abstract

The explanation and prediction of academic achievement is an important area of research in educational psychology. The prevalence of research efforts in this area reflects the fact that many decisions reached in the modem educational system are based upon predictions of school success. Such decisions include choosing the optimal time for entering school, selecting the appropriate type of school or academic track, being accepted at a certain college or university, or the choice of a particular field of study. Because these decisions can greatly influence the life of a young person, a period of careful consideration often precedes the final decision. Parents and students often seek counseling. Institutions such as schools and colleges have developed special entrance tests, and base their recommendations or decisions upon systematic diagnostic data. The estimation of a candidate’s probability of future academic success is a central aspect of the decision-making process, regardless of whether the process involves personal decisions or institutional decisions (Cronbach & Gleser, 1965; Jungermann, 1976; Krapp, 1979; Lee, 1971).

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Metadata

Venue
The Role of interest in Learning and Development
Published
2014-02-04
Pages
197-226
OpenAlex
W2784409721
Cited by
555
References
1
FWCI
5.66
Type
journal article
Language
en
Open access
closed

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Interest as a Predictor of Academic Achievement: A Meta-Analysis of Research. (2014). The Role of interest in Learning and Development. https://doi.org/10.4324/9781315807430-18

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.