Predicting Students' end-of-term Performances using ML Techniques and Environmental Data

Abstract: This study introduces a machine learning-based model for predicting student performance using a comprehensive dataset derived from educational sources, encompassing 15 key features and comprising 62,631 student samples. Our five-layer neural network demonstrated remarkable performance, achieving an accuracy of 89.14% and an average error of 0.000715, underscoring its effectiveness in predicting student outcomes. Crucially, this research identifies pivotal determinants of student success, including factors such as socio-economic background, prior academic history, study habits, and attendance patterns, shedding light on the nuanced dynamics of student performance. The key influential features identified in this study offer valuable insights into the complex factors shaping student achievement. These insights are vital for educators, policymakers, and institutions seeking to enhance educational outcomes and promote equitable access to quality education. This research provides a data-driven foundation for proactive interventions, personalized learning strategies, and support systems, ultimately contributing to improved student performance and academic success. The high accuracy of the predictive model and the feature analysis it provides empower decision-makers in the education sector. This model holds significant potential for applications in student performance monitoring, early intervention, and tailored educational strategies. By adopting a data-driven approach, this work advances the field of educational analytics and contributes to the goal of fostering student success and educational equity

A methodology to predict community college STEM student retention and completion

Numerous government reports point to the multifaceted issues facing the country\u27s capacity to increase the number of STEM majors, while also diversifying the workforce. Community colleges are uniquely positioned as integral partners in the higher education ecosystem. These institutions serve as an access point to opportunity for many students, especially underrepresented minorities and women. Community colleges should serve as a major pathway to students pursuing STEM degrees; however student retention and completion rates are dismally low. Therefore, there is a need to predict STEM student success and provide interventions when factors indicate potential failure. This enables educational institutions to better advise and support students in a more intentional and efficient manner. The objective of this research was to develop a model for predicting success. The methodology uses the Mahalanobis Taguchi System as a novel approach to pattern recognition and gives insight into the ability of MTS to predict outcomes based on student demographic data and academic performance. The method accurately predicts institution-specific risk factors that can be used to better retain STEM students. The research indicates the importance of using community college student data to target this distinctive student population that has demonstrated risk factors outside of the previously reported factors in prior research. This methodology shows promise as a mechanism to close the achievement gap and maximize the power of open-access community college pathways for STEM majors --Abstract, page iv

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

Predicting university performance in psychology: the role of previous performance and discipline-specific knowledge

Recent initiatives to enhance retention and widen participation ensure it is crucial to understand the factors that predict students' performance during their undergraduate degree. The present research used Structural Equation Modeling (SEM) to test three separate models that examined the extent to which British Psychology students' A-level entry qualifications predicted: (1) their performance in years 1-3 of their Psychology degree, and (2) their overall degree performance. Students' overall A-level entry qualifications positively predicted performance during their first year and overall degree performance, but negatively predicted their performance during their third year. Additionally, and more specifically, students' A-level entry qualifications in Psychology positively predicted performance in the first year only. Such findings have implications for admissions tutors, as well as for students who have not studied Psychology before but who are considering applying to do so at university

The efficacy of using data mining techniques in predicting academic performance of architecture students.

In recent years, there has been a tremendous increase in the number of applicants seeking placement in the undergraduate architecture programme. It is important to identify new intakes who possess the capability to succeed during the selection phase of admission at universities. Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during selection process. The present study investigates the efficacy of using data mining techniques to predict academic performance of architecture student based on information contained in prior academic achievement. The input variables, i.e. prior academic achievement, were extracted from students' academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data was divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models. The results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement are good predictors of academic performance of architecture students. Although the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students. The developed SVM model can be used a decision-making tool for selecting new intakes into the architecture program at Nigerian universities

A Multi-Gene Genetic Programming Application for Predicting Students Failure at School

Several efforts to predict student failure rate (SFR) at school accurately still remains a core problem area faced by many in the educational sector. The procedure for forecasting SFR are rigid and most often times require data scaling or conversion into binary form such as is the case of the logistic model which may lead to lose of information and effect size attenuation. Also, the high number of factors, incomplete and unbalanced dataset, and black boxing issues as in Artificial Neural Networks and Fuzzy logic systems exposes the need for more efficient tools. Currently the application of Genetic Programming (GP) holds great promises and has produced tremendous positive results in different sectors. In this regard, this study developed GPSFARPS, a software application to provide a robust solution to the prediction of SFR using an evolutionary algorithm known as multi-gene genetic programming. The approach is validated by feeding a testing data set to the evolved GP models. Result obtained from GPSFARPS simulations show its unique ability to evolve a suitable failure rate expression with a fast convergence at 30 generations from a maximum specified generation of 500. The multi-gene system was also able to minimize the evolved model expression and accurately predict student failure rate using a subset of the original expressionComment: 14 pages, 9 figures, Journal paper. arXiv admin note: text overlap with arXiv:1403.0623 by other author

The Relationship Between Prior Experiences in Mathematics and Pharmacy School Success

Objective. To assess students’ pre-pharmacy math experiences, confidence in math ability, and relationship between experiences, confidence, and grades in math-based pharmacy courses. Methods. A cross-sectional survey of first year to third year pharmacy students was conducted. Students reported type of pre-pharmacy math courses taken, when they were taken [high school (HS) vs. college] and year of HS and college graduation. Students rated their confidence in math ability using the previously validated 11-item Fogerty Math Confidence Scale (Cronbach alpha=0.92). Math grade point average (GPA), Pharmacy College Admission Test quantitative (PCAT quant) scores, and grades (calculations and kinetics) were obtained from transcripts and school records. Spearman correlation and multivariate linear regression were used to compare math experiences, confidence, and grades. Results. There were 198 students who reported taking math courses 7.1 years since HS graduation and 2.9 years since their last schooling prior to pharmacy school. Students who took math courses with more time since HS/last schooling had lower calculations and kinetics grades. Students reporting having taken more HS math courses had better calculations grades. Students with higher math GPA, and PCAT quant scores also had higher calculations and kinetics grades. Greater confidence in math ability was associated with higher calculations grades. In multivariate regressions, PCAT quant scores and years since HS independently predicted calculations grades, and PCAT quant scores independently predicted kinetics grades. Conclusion. The number of pre-pharmacy math courses and time elapsed since they were taken are important factors to consider when predicting a pharmacy student’s success in math-based pharmacy school courses

The Evaluation of Rhode Island Public High School Teachers: The Impact on Students

In 2012, the state of Rhode Island began the full implementation of a high-stakes teacher evaluation system. Its purpose is to increase teacher accountability and to improve student performance. However, a significant amount of literature casts doubt about the effectiveness and validity of teacher evaluation. This paper utilizes statistical methods including regression and decision trees in order to determine whether or not there is a relationship between teacher evaluation in Rhode Island and student performance, using RI Department of Education Data for each school from 2008-2015. Furthermore, this presentation investigates other factors that affect schools, to see if changes in student performance can be explained by factors other than the teacher evaluation system, such as discipline, the student-teacher ratio, and student demographics