ASSESSING AND CLASSIFICATION OF ACADEMIC EFFICIENCY IN ENGINEERING TEACHING PROGRAMS
This research uses a three-phase method to evaluate and forecast the academic efficiency of engineering programs. In the first phase, university profiles are created through cluster analysis. In the second phase, the academic efficiency of these profiles is evaluated through Data Envelopment Analysi...
- Autores:
-
De La Hoz, Enrique
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad del Atlántico
- Repositorio:
- Repositorio Uniatlantico
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniatlantico.edu.co:20.500.12834/880
- Acceso en línea:
- https://hdl.handle.net/20.500.12834/880
- Palabra clave:
- Efficiency, higher education, machine learning, predictive evaluation
- Rights
- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
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ASSESSING AND CLASSIFICATION OF ACADEMIC EFFICIENCY IN ENGINEERING TEACHING PROGRAMS |
| title |
ASSESSING AND CLASSIFICATION OF ACADEMIC EFFICIENCY IN ENGINEERING TEACHING PROGRAMS |
| spellingShingle |
ASSESSING AND CLASSIFICATION OF ACADEMIC EFFICIENCY IN ENGINEERING TEACHING PROGRAMS Efficiency, higher education, machine learning, predictive evaluation |
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ASSESSING AND CLASSIFICATION OF ACADEMIC EFFICIENCY IN ENGINEERING TEACHING PROGRAMS |
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ASSESSING AND CLASSIFICATION OF ACADEMIC EFFICIENCY IN ENGINEERING TEACHING PROGRAMS |
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ASSESSING AND CLASSIFICATION OF ACADEMIC EFFICIENCY IN ENGINEERING TEACHING PROGRAMS |
| title_full_unstemmed |
ASSESSING AND CLASSIFICATION OF ACADEMIC EFFICIENCY IN ENGINEERING TEACHING PROGRAMS |
| title_sort |
ASSESSING AND CLASSIFICATION OF ACADEMIC EFFICIENCY IN ENGINEERING TEACHING PROGRAMS |
| dc.creator.fl_str_mv |
De La Hoz, Enrique |
| dc.contributor.author.none.fl_str_mv |
De La Hoz, Enrique |
| dc.contributor.other.none.fl_str_mv |
Zuluaga, Rohemi Mendoza, Adel |
| dc.subject.keywords.spa.fl_str_mv |
Efficiency, higher education, machine learning, predictive evaluation |
| topic |
Efficiency, higher education, machine learning, predictive evaluation |
| description |
This research uses a three-phase method to evaluate and forecast the academic efficiency of engineering programs. In the first phase, university profiles are created through cluster analysis. In the second phase, the academic efficiency of these profiles is evaluated through Data Envelopment Analysis. Finally, a machine learning model is trained and validated to forecast the categories of academic efficiency. The study population corresponds to 256 university engineering programs in Colombia and the data correspond to the national examination of the quality of education in Colombia in 2018. In the results, two university profiles were identified with efficiency levels of 92.3% and 97.3%, respectively. The Random Forest model presents an Area under ROC value of 95.8% in the prediction of the efficiency profiles. The proposed structure evaluates and predicts university programs’ academic efficiency, evaluating the efficiency between institutions with similar characteristics, avoiding a negative bias toward those institutions that host students with low educational levels. |
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2020 |
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2020-03-09 |
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2021-03-31 |
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2022-11-15T20:47:27Z |
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2022-11-15T20:47:27Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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https://hdl.handle.net/20.500.12834/880 |
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10.7160/eriesj.2021.140104 |
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Universidad del Atlántico |
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Repositorio Universidad del Atlántico |
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https://hdl.handle.net/20.500.12834/880 |
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10.7160/eriesj.2021.140104 Universidad del Atlántico Repositorio Universidad del Atlántico |
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De La Hoz, Enriquef3b78b7d-c4dd-46b0-b537-10fcb2bab000Zuluaga, RohemiMendoza, Adel2022-11-15T20:47:27Z2022-11-15T20:47:27Z2021-03-312020-03-09https://hdl.handle.net/20.500.12834/88010.7160/eriesj.2021.140104Universidad del AtlánticoRepositorio Universidad del AtlánticoThis research uses a three-phase method to evaluate and forecast the academic efficiency of engineering programs. In the first phase, university profiles are created through cluster analysis. In the second phase, the academic efficiency of these profiles is evaluated through Data Envelopment Analysis. Finally, a machine learning model is trained and validated to forecast the categories of academic efficiency. The study population corresponds to 256 university engineering programs in Colombia and the data correspond to the national examination of the quality of education in Colombia in 2018. In the results, two university profiles were identified with efficiency levels of 92.3% and 97.3%, respectively. The Random Forest model presents an Area under ROC value of 95.8% in the prediction of the efficiency profiles. The proposed structure evaluates and predicts university programs’ academic efficiency, evaluating the efficiency between institutions with similar characteristics, avoiding a negative bias toward those institutions that host students with low educational levels.application/pdfengFull Research PaperASSESSING AND CLASSIFICATION OF ACADEMIC EFFICIENCY IN ENGINEERING TEACHING PROGRAMSPúblico generalEfficiency, higher education, machine learning, predictive evaluationinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1BarranquillaSede Norteinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Agasisti, T., Munda, G. and Hippe, R. 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