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Cited by 306

The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.

A Longitudinal Study of Engineering Student Performance and Retention

Felder, R.M., Baker-Ward, L., Dietz, E.J., Mohr, P.H.·2005·Proceedings. Twenty-Second Annual conference Frontiers in Education
DOI 10.1109/fie.1992.683370
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Abstract

A profile of 123 students enrolled in an introductory chemical engineering course has been assembled. The information collected includes data on family and educational backgrounds, profiles on the Myers-Briggs Type Indicator and the Learning and Study Strategies Inventory, and responses to a questionnaire regarding attitudes and expectations. Student performance in the introductory course was correlated with the assessment data. The results suggest several significant predictors of success or failure in the introductory course, and by extension, in the chemical engineering curriculum.

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Registry

Crossref · 10.1109/fie.1992.683370

Cites

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Metadata

Authors
Felder, R. M., Baker-Ward, L., Dietz, E. J., & Mohr, P. H.
Venue
Proceedings. Twenty-Second Annual conference Frontiers in Education
Publisher
IEEE
Published
2005-08-24
Pages
122-124
ISSN
0190-5848 (ISSN-L)
DOI
10.1109/fie.1992.683370
OpenAlex
W2115195613
Cited by
306
References
18
FWCI
29.69
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: A Longitudinal Study of Engineering Student Performance and Retention. https://app.cochat.ai/papers/10.1109/fie.1992.683370
The paper
Felder, R. M., Baker-Ward, L., Dietz, E. J., & Mohr, P. H. (2005). A Longitudinal Study of Engineering Student Performance and Retention. Proceedings. Twenty-Second Annual conference Frontiers in Education. https://doi.org/10.1109/fie.1992.683370

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.