Why some Fail and others Succeed: Explaining the Academic Performance of PA Undergraduate Students

Abstract

<jats:title>Abstract</jats:title><jats:p>The current research explores the influence of multiple factors (such as class attendance, previous academic performance, in-class involvement and attention, class schedule, gender and other control variables) on the academic performance of public-administration undergraduate students. The regression models developed based on the academic literature were tested on a sample of 1<jats:sup>st</jats:sup>-year students (N = 115) enrolled in the Public Administration bachelor program of Babeș-Bolyai University (Cluj-Napoca) in order to explain their performance (grade) in a final examination. Since none of the variables included in the model are self-reported (i.e. classic self-administered questionnaires) we have reduced the potential that social desirability bias could influence our results, thus strengthening the reliability and robustness of our findings.</jats:p><jats:p>Our results show that the main factors which can influence students’ academic performance are attendance at seminars and in-class involvement. All other factors which were included in the regression model (age, gender, distance between class site and their home, residence in urban or rural environment, attention and motivation, class schedule and four proxies / measures for previous performance) were either not statistically significant in any of the models or showed an inconsistent / unstable influence on academic performance.</jats:p><jats:p>These results can be of interest not only for Public Administration scholars, but also for university decision makers. As such, starting from the aforementioned findings and the literature, we also propose and discuss specific measures, which can be adopted by faculty-level decision makers in order to help students improve their academic performance and ensure better educational outcomes, especially in regard to the admission criteria currently in place.</jats:p><jats:p><jats:bold>Points for practitioners</jats:bold></jats:p><jats:p>The research investigates, among other potential factors, whether class attendance influences performance regardless of other individual characteristics. In doing so, the research tries to provide an answer to the ongoing debate on the usefulness of compulsory attendance at lectures and seminars in higher education. Furthermore, by observing the influence of previous performance (during high school) on current academic performance, the research can identify more adequate admission criteria, which can be used by university decision makers to ensure a better selection of candidates, thus potentially decreasing dropout rates.</jats:p>

Predictors of University Attrition: Looking for an Equitable and Sustainable Higher Education

“…This finding is consistent with that obtained by other recent studies since it has been shown that the persistence and university dropout of university students depends on a combination of individual, institutional, and economic factors, whose effects on the decision to drop out are mediated by the student's ability to successfully integrate into the academic system [1]. In addition, beyond the situation of vulnerability or social exclusion present in university students, other variables have been detected, such as motivation, which plays a key predictive role in the academic achievement of students [7,68,70,73,143], thus preventing dropout or failure and empowering the students to overcome difficulties, even when starting from disadvantaged social situations.…”

“…Third, in terms of student motivation, it has been shown that there is a clear relationship between this variable and university dropout cross-culturally, obtaining the same clear positive tie between high motivation and a lower probability of university dropout in different countries and diverse cultural settings [65][66][67][68][69][70][71][72][73]. In fact, a correlation has been found between motivation and positive behavior of students, as well as favorable involvement in the learning process and academic achievement [7,46,[74][75][76][77][78][79][80][81], in such a way that the most motivated students show less disruptive and/or challenging behavior, greater commitment to the learning process, and higher probabilities of achieving academic achievement.…”

Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods

“…Research has been carried out in areas such as psychology, where, apart from demographic data, the influence of variables related to interest, motivation, attendance, integration, self-regulation, commitment, participation, anxiety, and communication on academic performance have been considered (17)(18)(19)(20)(21). From the field of statistics, contributions have been made such as those reflected in (21)(22)(23), which apply statistical models that seek to examine the variables involved in university admission (admission and pre-university exams), proposing a model that involves various interrelated variables in an attempt to predict academic performance. Some early research have grouped the variables into economic, demographic, and psychological factors (24,25).…”

“…The fields or areas of knowledge that have studied the multidimensional variable of academic performance are diverse. This has been approached from the field of psychology (17)(18)(19)(55)(56)(57), which has applied tools related to questionnaires on students' perceptions regarding academic performance, followed mainly by statistical tools that have a much more marked focus on demographic data and their influence on the variable of interest (21,22,58,59). Likewise, research related to data science is important, especially studies that use data mining algorithms and ML applied to the field of education.…”