Modeling of student academic achievement in engineering education using cognitive and non-cognitive factors
<h3>Purpose</h3><p dir="ltr">The retention and success of engineering undergraduates are increasing concern for higher-education institutions. The study of success determinants are initial steps in any remedial initiative targeted to enhance student success and prevent an...
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2019
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| _version_ | 1864513525990293504 |
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| author | Bothaina A. Al-Sheeb (18069379) |
| author2 | A.M. Hamouda (18069382) Galal M. Abdella (14779087) |
| author2_role | author author |
| author_facet | Bothaina A. Al-Sheeb (18069379) A.M. Hamouda (18069382) Galal M. Abdella (14779087) |
| author_role | author |
| dc.creator.none.fl_str_mv | Bothaina A. Al-Sheeb (18069379) A.M. Hamouda (18069382) Galal M. Abdella (14779087) |
| dc.date.none.fl_str_mv | 2019-04-05T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1108/jarhe-10-2017-0120 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Modeling_of_student_academic_achievement_in_engineering_education_using_cognitive_and_non-cognitive_factors/25304278 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Other engineering Psychology Cognitive and computational psychology Retention Prediction Academic achievement Engineering students |
| dc.title.none.fl_str_mv | Modeling of student academic achievement in engineering education using cognitive and non-cognitive factors |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Purpose</h3><p dir="ltr">The retention and success of engineering undergraduates are increasing concern for higher-education institutions. The study of success determinants are initial steps in any remedial initiative targeted to enhance student success and prevent any immature withdrawals. This study provides a comprehensive approach toward the prediction of student academic performance through the lens of the knowledge, attitudes and behavioral skills (KAB) model. The purpose of this paper is to aim to improve the modeling accuracy of students’ performance by introducing two methodologies based on variable selection and dimensionality reduction.</p><h3>Design/methodology/approach</h3><p dir="ltr">The performance of the proposed methodologies was evaluated using a real data set of ten critical-to-success factors on both attitude and skill-related behaviors of 320 first-year students. The study used two models. In the first model, exploratory factor analysis is used. The second model uses regression model selection. Ridge regression is used as a second step in each model. The efficiency of each model is discussed in the Results section of this paper.</p><h3>Findings</h3><p dir="ltr">The two methods were powerful in providing small mean-squared errors and hence, in improving the prediction of student performance. The results show that the quality of both methods is sensitive to the size of the reduced model and to the magnitude of the penalization parameter.</p><h3>Research limitations/implications</h3><p dir="ltr">First, the survey could have been conducted in two parts; students needed more time than expected to complete it. Second, if the study is to be carried out for second-year students, grades of general engineering courses can be included in the model for better estimation of students’ grade point averages. Third, the study only applies to first-year and second-year students because factors covered are those that are essential for students’ survival through the first few years of study.</p><h3>Practical implications</h3><p dir="ltr">The study proposes that vulnerable students could be identified as early as possible in the academic year. These students could be encouraged to engage more in their learning process. Carrying out such measurement at the beginning of the college year can provide professional and college administration with valuable insight on students perception of their own skills and attitudes toward engineering.</p><h3>Originality/value</h3><p dir="ltr">This study employs the KAB model as a comprehensive approach to the study of success predictors. The implementation of two new methodologies to improve the prediction accuracy of student success.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Applied Research in Higher Education<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1108/jarhe-10-2017-0120" target="_blank">https://dx.doi.org/10.1108/jarhe-10-2017-0120</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_64c81e6026ad55f182f4e5d6aeb66d6a |
| identifier_str_mv | 10.1108/jarhe-10-2017-0120 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25304278 |
| publishDate | 2019 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Modeling of student academic achievement in engineering education using cognitive and non-cognitive factorsBothaina A. Al-Sheeb (18069379)A.M. Hamouda (18069382)Galal M. Abdella (14779087)EngineeringOther engineeringPsychologyCognitive and computational psychologyRetentionPredictionAcademic achievementEngineering students<h3>Purpose</h3><p dir="ltr">The retention and success of engineering undergraduates are increasing concern for higher-education institutions. The study of success determinants are initial steps in any remedial initiative targeted to enhance student success and prevent any immature withdrawals. This study provides a comprehensive approach toward the prediction of student academic performance through the lens of the knowledge, attitudes and behavioral skills (KAB) model. The purpose of this paper is to aim to improve the modeling accuracy of students’ performance by introducing two methodologies based on variable selection and dimensionality reduction.</p><h3>Design/methodology/approach</h3><p dir="ltr">The performance of the proposed methodologies was evaluated using a real data set of ten critical-to-success factors on both attitude and skill-related behaviors of 320 first-year students. The study used two models. In the first model, exploratory factor analysis is used. The second model uses regression model selection. Ridge regression is used as a second step in each model. The efficiency of each model is discussed in the Results section of this paper.</p><h3>Findings</h3><p dir="ltr">The two methods were powerful in providing small mean-squared errors and hence, in improving the prediction of student performance. The results show that the quality of both methods is sensitive to the size of the reduced model and to the magnitude of the penalization parameter.</p><h3>Research limitations/implications</h3><p dir="ltr">First, the survey could have been conducted in two parts; students needed more time than expected to complete it. Second, if the study is to be carried out for second-year students, grades of general engineering courses can be included in the model for better estimation of students’ grade point averages. Third, the study only applies to first-year and second-year students because factors covered are those that are essential for students’ survival through the first few years of study.</p><h3>Practical implications</h3><p dir="ltr">The study proposes that vulnerable students could be identified as early as possible in the academic year. These students could be encouraged to engage more in their learning process. Carrying out such measurement at the beginning of the college year can provide professional and college administration with valuable insight on students perception of their own skills and attitudes toward engineering.</p><h3>Originality/value</h3><p dir="ltr">This study employs the KAB model as a comprehensive approach to the study of success predictors. The implementation of two new methodologies to improve the prediction accuracy of student success.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Applied Research in Higher Education<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1108/jarhe-10-2017-0120" target="_blank">https://dx.doi.org/10.1108/jarhe-10-2017-0120</a></p>2019-04-05T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1108/jarhe-10-2017-0120https://figshare.com/articles/journal_contribution/Modeling_of_student_academic_achievement_in_engineering_education_using_cognitive_and_non-cognitive_factors/25304278CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/253042782019-04-05T03:00:00Z |
| spellingShingle | Modeling of student academic achievement in engineering education using cognitive and non-cognitive factors Bothaina A. Al-Sheeb (18069379) Engineering Other engineering Psychology Cognitive and computational psychology Retention Prediction Academic achievement Engineering students |
| status_str | publishedVersion |
| title | Modeling of student academic achievement in engineering education using cognitive and non-cognitive factors |
| title_full | Modeling of student academic achievement in engineering education using cognitive and non-cognitive factors |
| title_fullStr | Modeling of student academic achievement in engineering education using cognitive and non-cognitive factors |
| title_full_unstemmed | Modeling of student academic achievement in engineering education using cognitive and non-cognitive factors |
| title_short | Modeling of student academic achievement in engineering education using cognitive and non-cognitive factors |
| title_sort | Modeling of student academic achievement in engineering education using cognitive and non-cognitive factors |
| topic | Engineering Other engineering Psychology Cognitive and computational psychology Retention Prediction Academic achievement Engineering students |