‘SECOND DEGREE ENGLISH-MAJOR’ ATSAI GON UNIVERSITY: AN ANALYSIS OFRECENT STUDENTS’ PERFORMANCE
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Abstract
This study aims to analyze the self-assessment data of students par-
ticipating in the ‘Second-Degree English-Major’ (SDEM) program at Sai
Gon University (SGU). Using data from a recent survey conducted in Oc-
tober 2023 with a graduating batch of 55 students, this study examines
students’ perceptions of their improvements in listening, reading, writ-
ing, and speaking skills. Data from 37 students (though for some skills
it was 38 as well as 39) with complete information were analyzed using
the logistic regression model as well as the bootstrap method (to assess
the variations in the estimates of the model parameters) to determine
whether the level of improvement depended on their background factors
(such as - gender, age and the type of job held). The results show no sig-
nificant impact of background factors on the students’ self-assessment,
suggesting that improvements were evenly distributed across different
student groups (identified by the background factors). The subjective
nature of the self-assessment data is recognized as a potential source of
bias which needs to be addressed in future studies.
ticipating in the ‘Second-Degree English-Major’ (SDEM) program at Sai
Gon University (SGU). Using data from a recent survey conducted in Oc-
tober 2023 with a graduating batch of 55 students, this study examines
students’ perceptions of their improvements in listening, reading, writ-
ing, and speaking skills. Data from 37 students (though for some skills
it was 38 as well as 39) with complete information were analyzed using
the logistic regression model as well as the bootstrap method (to assess
the variations in the estimates of the model parameters) to determine
whether the level of improvement depended on their background factors
(such as - gender, age and the type of job held). The results show no sig-
nificant impact of background factors on the students’ self-assessment,
suggesting that improvements were evenly distributed across different
student groups (identified by the background factors). The subjective
nature of the self-assessment data is recognized as a potential source of
bias which needs to be addressed in future studies.
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