Academic Performance and Behavioral Patterns

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students

The influence of social support on academic performance: The mediating role of cognitive engagement

Academic performance, which measures a student’s success in learning, is influenced by various factors. One of the most important determinants of academic performance is social support. Parents, teachers, and friends all play a critical role in providing the necessary support that affects students’ cognitive engagement. Therefore, this study aimed to determine the relationship between social support and academic performance, which could be directly or indirectly mediated by cognitive engagement. The subjects used were 228 high school students in 11th grade, aged 15-18. Data were measured using Academic Performance Rating Scale (APRS), Child and Adolescent Social Support Scale (CASS), and Students Engagement Scale (SES) with Cronbach’s alpha values of 0.84, 0.93, and 0.88, respectively. The mediation analysis was conducted using the PROCESS model 4 developed by Hayes. The results showed that the relationship between social support and academic performance occurred through the mediation of cognitive engagement (� = 0.16), with no direct correlation (� = 0.12, p = 0.350). Specifically, more excellent social support was associated with increased cognitive engagement (� = 0.50, p = 0.000), leading to improved academic performance (� = 0.32, p = 0.005). Therefore, it was confirmed that social support is essential to students’ cognitive engagement and academic performance

The Relationships between High School Subjects in terms of School Satisfaction and Academic Performance in Mexican Adolescents

Adolescents’ academic performance and the way it is related to their subjective wellbeing are issues of great interest across educational systems. The purpose of this study was to ascertain how satisfaction with high school subjects can predict school satisfaction and academic performance in Mexican students. The sample consisted of 457 high school students in the Baja California and Nuevo León states in Mexico (247 boys, 210 girls); their mean age being 14.10 (SD = 0.84). We used a questionnaire featuring a subject satisfaction scale, an intrinsic school satisfaction scale, and one related to academic grades. We used descriptive analyses, correlations, and structural regression models. In terms of results, the high satisfaction and academic performance levels in physical education, Spanish and English are worth highlighting. Geography and history are the most relevant predictors of academic grades, while Spanish predicts school satisfaction and physical education predicts boredom. In conclusion, satisfaction with mathematics, Spanish, and English are strong predictors of satisfaction (SATF), and the latter in turn predicts Mexican high school students’ academic performance

Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques

In recent years, there has been an increase in the number of applicants seeking admission into architecture programmes. As expected, prior academic performance (also referred to as pre-enrolment requirement) is a major factor considered during the process of selecting applicants. In the present study, machine learning models were used to predict academic success of architecture students based on information provided in prior academic performance. Two modeling techniques, namely K-nearest neighbour (k-NN) and linear discriminant analysis were applied in the study. It was found that K-nearest neighbour (k-NN) outperforms the linear discriminant analysis model in terms of accuracy. In addition, grades obtained in mathematics (at ordinary level examinations) had a significant impact on the academic success of undergraduate architecture students. This paper makes a modest contribution to the ongoing discussion on the relationship between prior academic performance and academic success of undergraduate students by evaluating this proposition. One of the issues that emerges from these findings is that prior academic performance can be used as a predictor of academic success in undergraduate architecture programmes. Overall, the developed k-NN model can serve as a valuable tool during the process of selecting new intakes into undergraduate architecture programmes in Nigeria

The Effect of UNH Undergraduate Student Exercise on Academic Achievement

A number of existing studies focus on the effect exercise and dietary habits have on social relationships; however, few studies examine the relationship between exercise and academic performance on college students. In this study, surveys were administered to 202 students at the University of New Hampshire. Although the data presented no statistically significant findings to prove a correlation between exercise and academic performance, students who never exercised were shown to be more likely to do poorly rather than excel academically. Future research should consist of a larger sample using a random sampling method for better reliability and validity in determining a relationship between student exercise and academic performance

Fasting During Pregnancy and Children's Academic Performance

We consider the effects of daytime fasting by pregnant women during the lunar month of Ramadan on their children's test scores at age seven. Using English register data, we find that scores are .05 to .08 standard deviations lower for Pakistani and Bangladeshi students exposed to Ramadan in early pregnancy. These estimates are downward biased to the extent that Ramadan is not universally observed. We conclude that the effects of prenatal investments on test scores are comparable to many conventional educational interventions but are likely to be more cost effective and less subject to "fade out".

Predicting Academic Performance

This paper discussed advantages and disadvantages associated with the use of "admission tests" as predictors of performance in undergraduate studies programs. The paper analyzes performance of economics and business administration students. This performance is linked to admission tests results. The paper also analyzes aspects of performance related to (i) differential progress through time, and (ii) differences in the extent to which students have "areas of interest/ability". The paper concludes that admission tests are a usefull tool even when predictions derived from them are far from perfect.

Correlation Between Student Collaboration Network Centrality and Academic Performance

We compute nodal centrality measures on the collaboration networks of students enrolled in three upper-division physics courses, usually taken sequentially, at the Colorado School of Mines. These are complex networks in which links between students indicate assistance with homework. The courses included in the study are intermediate Classical Mechanics, introductory Quantum Mechanics, and intermediate Electromagnetism. By correlating these nodal centrality measures with students' scores on homework and exams, we find four centrality measures that correlate significantly with students' homework scores in all three courses: in-strength, out-strength, closeness centrality, and harmonic centrality. These correlations suggest that students who not only collaborate often, but also collaborate significantly with many different people tend to achieve higher grades. Centrality measures between simultaneous collaboration networks (analytical vs. numerical homework collaboration) composed of the same students also correlate with each other, suggesting that students' collaboration strategies remain relatively stable when presented with homework assignments targeting different skills. Additionally, we correlate centrality measures between collaboration networks from different courses and find that the four centrality measures with the strongest relationship to students' homework scores are also the most stable measures across networks involving different courses. Correlations of centrality measures with exam scores were generally smaller than the correlations with homework scores, though this finding varied across courses.Comment: 10 pages, 4 figures, submitted to Phys. Rev. PE