About this paper

Appears in:
Pages: 4071-4078
Publication year: 2019
ISBN: 978-84-09-08619-1
ISSN: 2340-1079
doi: 10.21125/inted.2019.1020

Conference name: 13th International Technology, Education and Development Conference
Dates: 11-13 March, 2019
Location: Valencia, Spain

IDENTIFYING FACTORS FOR ACADEMIC MISCONDUCT USING LOGISTIC REGRESSION

B. Santos1, J. Gonçalves2, P.G. Almeida3

1University of Beira Interior, CERIS-CESUR (PORTUGAL)
2University of Beira Interior, CITTA (PORTUGAL)
3University of Beira Interior, GEOBIOTEC (PORTUGAL)
A preliminary statistical approach to the academic conduct of a group of students at the University of Beira Interior (Portugal) was performed using data from voluntary participated surveys. Inquiries were made before and after attending a training on the meaning and types of plagiarism, citation rules for academic writing and bibliography preparation.

The adopted methodology includes the development and application of a stated preferences survey and the statistical analysis of the collected data. A descriptive analysis was performed, and logistic regression models were developed for the data collected before the training to quantify the student’s behaviors and establish potential associations relevant to the characterization and future monitoring of academic misconducts.

The study found that 90% of the students surveyed had never attended a lecture on plagiarism and academic behavior, about 50% of the total were aware of a case of plagiarism committed by colleagues. Results revealed that 80% of the students had committed fraud at least once in an exam or academic work and 83% of those were never detected. The logistic regression revealed that students aged 18 to 20 and employee students have a statistically significant effect on the Logit of the probability of committing fraud at an exam or academic work. The results suggest that academic misconduct among students is widespread and that measures must be taken to reduce their prevalence and to ensure a merit-based education system.
@InProceedings{SANTOS2019IDE,
author = {Santos, B. and Gonçalves, J. and Almeida, P.G.},
title = {IDENTIFYING FACTORS FOR ACADEMIC MISCONDUCT USING LOGISTIC REGRESSION},
series = {13th International Technology, Education and Development Conference},
booktitle = {INTED2019 Proceedings},
isbn = {978-84-09-08619-1},
issn = {2340-1079},
doi = {10.21125/inted.2019.1020},
url = {http://dx.doi.org/10.21125/inted.2019.1020},
publisher = {IATED},
location = {Valencia, Spain},
month = {11-13 March, 2019},
year = {2019},
pages = {4071-4078}}
TY - CONF
AU - B. Santos AU - J. Gonçalves AU - P.G. Almeida
TI - IDENTIFYING FACTORS FOR ACADEMIC MISCONDUCT USING LOGISTIC REGRESSION
SN - 978-84-09-08619-1/2340-1079
DO - 10.21125/inted.2019.1020
PY - 2019
Y1 - 11-13 March, 2019
CI - Valencia, Spain
JO - 13th International Technology, Education and Development Conference
JA - INTED2019 Proceedings
SP - 4071
EP - 4078
ER -
B. Santos, J. Gonçalves, P.G. Almeida (2019) IDENTIFYING FACTORS FOR ACADEMIC MISCONDUCT USING LOGISTIC REGRESSION, INTED2019 Proceedings, pp. 4071-4078.
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