Predictors Of Academic Performance Research Articles

ECSYAPPS – A framework for analyzing the effectiveness of classification techniques for early prediction of students academic performance in education sector

Predicting students' academic performance is of paramount importance to educational institutions. If students' academic performance is predicted course-wise, semester-wise and year-wise then it will be helpful for students to a great extent. In this research article, we generate twenty nine dataset with the help of students' result analysis. Datasets 1 to 9 is the datasets of students' result in first attempt while datasets 10 to 19 represent datasets of students after passing all courses of semesters and datasets 20 to 29 is overall dataset of students' result analysis. To predict the course-wise, semester-wise and year-wise performance of students, we developed a framework titled as ECSYAPPS (Educational CourseSemesterYear-wiseAcademic Performance Prediction System) based on classification techniques and designed algorithm for analyzing students' performance in education sector. This ECSYAPPS predicts the course-wise, semester-wise and year-wise grade of students. Fifteen classification algorithms such as Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVM), Linear Support Vector Classification (LSVC), K-Nearest Neighbors (KNN), Gradient Boosting (GB), Adaptive Boosting (AdaBoost), Bagging, Extreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Linear Discriminant Analysis (LDA), and Stochastic Gradient Descent Classifier (SGDC) are selected and applied on 29 datasets and compared on basis of performance parameters such as Accuracy, Precision, Recall, F1-score and Mean Absolute Error (MAE). These fifteen classification algorithms are under ten classifiers such as Linear Model, Naive Bayes, Tree, Support Vector Machine, Nearest Neighbors, Ensemble, xgboost, lightgbm, Discriminant Analysis and catboost. If the accuracy of two more classification algorithms for a dataset is same then in that case Precision, Recall, F1-score and Mean Absolute Error (MAE) are compared for deciding the best classification algorithm for the dataset. In this way, best classification algorithm is selected for each of 29 datasets. It is found that classifiers xgboost works best for eleven datasets while ensemble techniques Gradient Boosting and AdaBoost work best for two datasets and six datasets respectively among 29 datasets. Other classification algorithms such as Decision Tree, LightGBM, LDA, and KNN are noted to be best classification algorithm for two, four, three, and two datasets respectively. This framework is tested on new eight datasets related to students' result two methods such as K-Fold Cross-validation and Train-Validation-Test method. The results of this framework on new datasets shows that accuracy obtained on test dataset or validation dataset as compared to the accuracy obtained on old dataset is less than 6%. This framework will be helpful for students as well instructor. For students, it will help them to improve the performance of difficult courses from students'point of view in the examination while faculty can use this framework to improve pedagogical practices.

The Effects of Nursing Students' Locus of Control, Motivation, and Learning Strategies on Their Academic Performance.

The academic performance of nursing students significantly impacts their ability to deliver quality patient care. This descriptive correlational study investigated the effects of nursing students' locus of control, motivation, and learning strategies on their academic performance in a sample of 515 students. External locus of control was associated with higher scores in extrinsic goal orientation, task value, elaboration, and academic performance. Locus of control, test anxiety, and organization were identified as predictors of academic performance. The study highlights the need for further research on factors affecting academic performance and effective educational interventions.

Predicting Student Achievement via Machine Learning: Evidence from Turkish Subset of PISA

This study seeks to identify the determinants of academic performance in mathematics, science, and reading among Turkish secondary school students. Using data from the OECD's PISA 2018 survey, which includes several student- and school-level variables as well as test scores, we employed a range of supervised machine learning methods specifically ensemble decision trees to assess their predictive performance. Our results indicate that the boosted regression tree (BRT) method outperforms other methods bagging and random forest regression trees. Notably, the BRT highlights the importance of general secondary education programs over vocational and technical (VAT) education in predicting academic achievement. Moreover, both characteristics specific to student and school environment are demonstrated to be significant predictors of academic performance in all subject areas. These findings contribute to the development of evidence-based educational policies in Turkey.

I think therefore I learn: metacognition is a better predictor of school readiness than executive functions

Previous research suggests that metacognition (the knowledge and skills related to knowledge acquisition) and executive functions (skills needed to plan and execute goals) are possible predictors of academic performance, including math and reading abilities. This study sought to clarify the relationship between school readiness and these abilities. A visual identification task was used to measure preschool children's metacognitive skills, specifically their ability to monitor their confidence on their answers (explicit) and ability to ask for a clue only when necessary (implicit). Response time to answering was also measured to obtain a non-verbal implicit measure of metacognition. Executive functions were measured using the Flanker and Dimensional Change Card Sorting (DCCS) tasks from the NIH toolbox. It was hypothesized that both metacognition and executive functions would predict school readiness and that implicit metacognitive skills would be more highly related to school readiness than explicit skills. A hierarchical linear regression was run with age and sex as control variables, and with executive function and metacognition (implicit and explicit) as predictors. Results indicated that both implicit and explicit metacognition remained significant predictors of school readiness scores beyond age and sex. In addition, we found correlations between explicit metacognition and executive functions and a relationship between response time and explicit metacognitive skill. Results highlight the importance of early metacognitive abilities beyond other cognitive skills and the importance of being able to effectively use metacognitive strategies from a young age. The implications relating to academic abilities are discussed.

Exploring the Efficacy of Machine Learning Techniques for Predicting Students’ Academic Performances

Predicting students' academic success in tertiary institutions in terms of determining accurately whether a student will be a top 10 in his class, an average student or a dropout is a significant issue in higher education. Therefore, to predict the behavior of a learner, many data mining techniques are used, such as clustering, classification, and regression. In this paper, students’ academic performance prediction model and new features are introduced that have a great influence on student’s overall academic achievement. Twenty features were considered from the dataset obtained from about 200 students using questionnaire and information from the institutions database. The Naive Bayes classifier was used and the experiment was carried out in a WEKA implementation workbench. The attributes were first analyzed for relevance, the analysis showed that a student’s first semester grade point average (GPA) has the highest relevance followed by friends’ study affinity, learning facilities, private home lesson, gender and available transport facilities. One hundred and sixty-nine (169) students’ records were used for the analysis, the model recorded 162 instances that were correctly classified which translates to 95.86% accuracy and incorrectly predicted 4.142%. The experiment showed an excellent performance from the Naive Bayes classifier in predicting the dataset correctly Keywords: Educational Data Mining, Students' Performance Management, Tertiary Institutions, Classification, Naive Bayes Classifier. Agwi, C.U. & Akpojaro, J. (2024): Exploring the Efficacy of Machine Learning Techniques for Predicting Students’ Academic Performances. Journal of Advances in Mathematical & Computational Science. Vol. 12, No. 1. Pp 53-66. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N1P6

Academic Performance Prediction System for Polytechnic Students Based on Machine Learning Algorithms

Abstract: Recent years have seen a rise in the use of machine learning techniques for creating predictive models to evaluate academic performance. This academic performance prediction method makes use of student data, including demographics, academic records, and socioeconomic background, to forecast future academic performance outcomes. An academic prediction system for Polytechnic Students based on machine learning model that predicts whether the student pass/ fail in the examination is introduced in this paper. A comparative analysis of eight prediction models and factors affecting the performance of students has been analyzed. Employing a performance prediction system can help teachers promote student achievement, allocate resources more effectively, and incorporate data-driven methods into their instruction.

Data Analysis of Student Academic Performance and Prediction of Student Academic Performance Based on Machine Learning Algorithms

With the development and popularization of education, the quality of education has become one of the key factors in the development of a country. And students' academic performance, as one of the important indicators of education quality, has been attracting much attention. This paper mines and analyzes the data affecting students' academic performance, and also conducts a predictive study of students' academic performance using logistic regression model. In this study, 30 indicators such as gender, age, family size, parental education, parental occupation, family relationship, health, and the number of drinks per this paperek and per month this paperre used as input variables, and students' academic performance was categorized into SUCCESS and FAIL, and the training and test sets this paperre divided according to the ratio of 7:3, and the logistic regression model was used for training and prediction. The results show that the logistic regression model has high prediction accuracy in predicting students' academic performance (whether they fail or not), with an accuracy of 95.8%, precision of 96.7%, recall of 95.1%, and F1 of 95.8%. This indicates that the logistic regression model has high accuracy and reliability in predicting students' academic performance. The results of this study are important for schools and educational organizations. Through the prediction of students' academic performance, schools can identify students' learning problems in time and take targeted measures to help students improve their academic performance. Meanwhile, this study also provides some useful reference information for individual students to help them better understand their learning situation, adjust their learning strategies in time and improve their learning efficiency. In the future, the method can be further explored and improved to enhance the accuracy and reliability of the prediction and to provide better support and assistance for students' learning and development.

Enhancing academic performance prediction with temporal graph networks for massive open online courses

Educational big data significantly impacts education, and Massive Open Online Courses (MOOCs), a crucial learning approach, have evolved to be more intelligent with these technologies. Deep neural networks have significantly advanced the crucial task within MOOCs, predicting student academic performance. However, most deep learning-based methods usually ignore the temporal information and interaction behaviors during the learning activities, which can effectively enhance the model’s predictive accuracy. To tackle this, we formulate the learning processes of e-learning students as dynamic temporal graphs to encode the temporal information and interaction behaviors during their studying. We propose a novel academic performance prediction model (APP-TGN) based on temporal graph neural networks. Specifically, in APP-TGN, a dynamic graph is constructed from online learning activity logs. A temporal graph network with low-high filters learns potential academic performance variations encoded in dynamic graphs. Furthermore, a global sampling module is developed to mitigate the problem of false correlations in deep learning-based models. Finally, multi-head attention is utilized for predicting academic outcomes. Extensive experiments are conducted on a well-known public dataset. The experimental results indicate that APP-TGN significantly surpasses existing methods and demonstrates excellent potential in automated feedback and personalized learning.

Emotional Intelligence in Improving Academic Performance in College Students: A Systematic Review

The present investigation was carried out with the purpose of publicizing how emotional intelligence contributes to academic performance; posing as a research question: What is the impact of emotional intelligence in improving academic performance in university students in the last five years?; Due to this, the methodology used for this purpose was qualitative, basic and a review of articles published in the SCOPUS and Web of Science databases; following a search protocol, collection, extraction, analysis and selection of articles under the eligibility criteria: academic publications with SJR (Scimago Journal Rank) impact indicators and publications in Spanish and English. For the analysis of the review, 79 articles were selected and after filtering, only 10 articles met the inclusion and exclusion criteria as part of the review. The results show that there is a limited number of publications that have specified emotional intelligence as a predictor of academic performance in university students.

Predictor of academic performance: personality traits and catechol-O-methyltransferase polymorphisms

Personality traits and genetics are important factors in determining academic performance. Although there have been studies on the association between genetics and academic performance, there is limited data on the effect of personality traits and the catechol-O-methyltransferase (COMT) rs4680 polymorphism on academic performance, particularly among university students in Malaysia. The objective of this study was to correlate the relationship between personality traits, COMT rs4680 polymorphism, and academic performance among health sciences undergraduate students at a Malaysian university. The research design was a correlational study where 221 students were recruited based on the convenient sampling approach. The demographic and international personality item pool (IPIP) questionnaires were administered online, whereas the COMT rs4680 single nucleotide polymorphism (SNP) was determined using a tetra-primer allele-specific polymerase chain reaction (PCR). From the IPIP results, the conscientiousness trait was positively and significantly correlated with academic performance (r=0.141, p=0.036). Participants with homozygous Met/Met allele of the COMT rs4680 polymorphism tended to perform better in academics (p=0.009) as compared to participants with homozygous Val/Val and heterozygous Val/Met alleles. As a conclusion, students with a higher conscientiousness score and carriers of the homozygous Met/Met allele of COMT rs4680 showed better academic performance.

Bibliometric insights into data mining in education research: A decade in review

This bibliometric study on data mining in education synonymous with big educational data utilizes VOSviewer and Harzing’s Publish and Perish to analyze the metadata of 1,439 journal articles found in Scopus from 2010 to 2022. As bibliometric analyses in this field are lacking, this study aims to provide a comprehensive outlook on the current developments and impact of research in this field. This study employs descriptive and trends analysis, co-authorship analysis, co-citation analysis, co-occurrences of keywords, terms map analysis, and analysis of the impact and performance of publications. It also partially replicates a similar study conducted by Wang et al. (2022), who used the Web of Science (WoS) database. The study is reported in an article entitled ‘Big data and data mining in education: A bibliometrics study from 2010 to 2022’. Results show that data mining in education is a growing research field. There is also a significant difference between the publications in Scopus and WoS. The study found several research areas and topics, such as student academic performance prediction, e-learning, machine learning, and innovative data mining techniques, to be the core basis for collaborating and continuing current research in this field. These results highlight the importance of continuing research on data mining in education, guiding future research in tackling educational challenges.

Student academic performance prediction enhancement using t-SIDSBO and Triple Voter Network

Student academic performance prediction enhancement using t-SIDSBO and Triple Voter Network

Multidimensional determinants of academic performance: Insights from undergraduate students in Moroccan universities

This research investigates the multifaceted determinants of academic performance in a large-scale study of 5,092 undergraduate students across diverse Moroccan universities. We delved into demographic, academic, psychological, societal, and lifestyle factors, providing a comprehensive analysis of potential influencers on academic success. These dimensions were identified as significant predictors of academic performance, as gauged by the students' Grade Point Average (GPA). The findings reveal the intricate and interconnected nature of these factors and their cumulative influence on a student's academic trajectory. Our study emphasizes the necessity for a holistic approach that integrates not only traditional academic strategies, but also psychological support, balanced lifestyle practices, and effective societal engagement. Our research underscores the rich interplay among these variables and offers a profound understanding of their collective impact on academic performance. The derived insights could be instrumental for educators, policymakers, and researchers striving to create strategies that amplify student success. While acknowledging the inherent limitations of the study, we believe the results serve as a pivotal guidepost in the ongoing quest to optimize student academic outcomes and overall well-being.

Factors influencing academic performance and dropout rates in higher education

ABSTRACT The aim of this study was to identify and evaluate the most frequently used research methods and factors influencing academic performance, based on a pool of 95 studies, published after 2012. We considered only peer-reviewed papers containing 78 empirical and 17 meta-analytic studies. Our theoretical background lies in the different approaches of the terms ‘university dropout’ and ‘academic performance’. After the systematic analysis we ascertained the most commonly used methods are Educational Data Mining (EDM) algorithms (decision tree, logistic regression and neural networks) and Structural Equation Modelling (SEM). The strength of the predictive power depends on the dataset, however Support Vector Machines, Multilayer Perceptron, Naïve Bayes algorithm were found to be the most precise in prediction. Regarding factors influencing academic performance we derived our results based on 600,000 university students. Considering the data from meta-analyses and systematic reviews, reaching up to 900 studies, we found grade point average (GPA), obtained credits (ECTS) and gender to be the most consistent and decisive predictors of academic performance. Nevertheless, GPA and ECTS (as output variables) are mediated by student factors (intrinsic motivation, self-regulated learning strategies, self-efficacy, prior education) and throughput factors (work, finances, academic engagement). We had contradictory results on age and family background.

Modified Fuzzy Neural Network Approach for Academic Performance Prediction of Students in Early Childhood Education

Modern education relies heavily on educational technology, which provides students with unique learning opportunities and enhances their ability to learn. For many years now, computers and other technological tools have been an integral part of education. However, compared to other educational levels, the incorporation of educational technology in early childhood education is a more recent trend. It is because of this that materials and procedures tailored to young children must be created, implemented, and studied. The use of artificial intelligence techniques in educational technology resources has resulted in better engagement for students. Early childhood special education students' academic achievement is predicted using a Modified Fuzzy Neural Network (MFNN). Before constructing the classifier, the dataset had to be preprocessed to remove any extraneous information. As a follow-up, this study will put to the test an organized approach to the implementation of customized fuzzy neural networks for the prediction of academic achievement in early childhood settings. Considerations for the analysis of academic achievement in early childhood education are discussed in this article, including recommendations for the implementation of proposed modified fuzzy neural networks. In terms of evaluation metrics such as Precision, recall, accuracy, and the F1 coefficient, the proposed model outperforms conventional machine-learning (ML) techniques.

Bayesian Model for Academic Performance Prediction in Learning Analytics

Bayesian Model for Academic Performance Prediction in Learning Analytics

Prevalence and correlates of generalized anxiety disorder and perceived stress among Sudanese medical students

BackgroundGeneralized Anxiety Disorder (GAD) causes significant disturbance in an individual’s well-being and activity. Whereby, interfering with the dynamic progress in life. Also, anxiety is a product of stress and a major predictor of academic performance. This study aimed to assess the prevalence of Generalized Anxiety Disorder (GAD), measure levels of anxiety and perceived stress, evaluate the academic profile, identify lifestyle characteristics, and explore the relationship between these factors.MethodsIn this cross-sectional study, 340 Sudanese medical students filled out online questionnaires, composed of the sociodemographic and lifestyle characteristics, academic profile, Generalized Anxiety Disorder-2 scale (GAD-2), and Perceived Stress Scale-10 (PSS-10). Descriptive and inferential statistics were applied using Statistical Package for Social Science (SPSS) Version 20.0 for data analysis.ResultsOf 340 medical students, 3.8% of them were diagnosed with GAD, while 29.1% scored ≥ 3 in GAD-2, indicating a possible diagnosis. The study found that 9.7% of the participants used addictive substances, with 42% of them having high GAD-2 scores. Moreover, high anxiety levels were associated with high-stress scores (p-value = 0.000). Also, high GAD-2 scores were significantly associated with students who spent less than 10,000 SDG (18 USD) weekly, spent more time on entertainment using smart devices (p-value = 0.004), and had an unhealthy diet (p-value = 0.004). Low anxiety levels were associated with better sleep quality (p-value = 0.00), satisfaction with religious practices (p-value = 0.00), and increased leisure/hobby time (p-value = 0.018). High-stress levels were observed in females (p-value = 0.035), those with lower academic performance satisfaction levels, and increased hours of smart device usage for entertainment (p-value = 0.001). Reduced stress levels were associated with being ≥ 23 years old, increased leisure/hobby time (p-value = 0.002), satisfaction with religious practices [F(3, 166.6) = 10.8, p-value = 0.00)], and having a healthy diet (p-value = 0.006).ConclusionThe low prevalence of GAD corresponded with previous literature, but 29.1% of medical students had a high probability of having GAD. The study emphasizes on providing accessible mental health services for medical students and interventions addressing modifiable risk factors.

Resilience and self-regulated learning as predictors of student competence gain in times of the COVID 19 pandemic – evidence from a binational sample

Emergency Remote Teaching (ERT) was introduced around the globe during the COVID-19 pandemic to ensure that students could continue with their studies during social distancing. Subsequent studies found considerable individual differences in the adjustment to ERT and identified resilience as a critical factor for coping with the challenges of independent studying. The growing evidence led to calls for resilience training in preparation for emergency situations. Against the backdrop of a three-phase model of self-regulated learning, this study examines self-regulated learning activities as an additional protective resource and a predictor for academic performance in ERT. Results from a survey of resilience, self-regulated learning strategies, and competence gain completed by students from universities in the US and Germany (N = 333) found self-regulated learning strategies to be more predictive of student competence gain than resilience. As a consequence, in addition to fostering resilience, institutions should also include self-regulation strategies in student training and support programs to better prepare students for academic success.

Headteachers' Transformational Leadership and its Influence on Academic Performance: A Case of Public Secondary Schools in Uganda

Leadership style is an important aspect of students' academic performance in schools. Transformational leadership style is associated with students' academic achievement in Uganda's public secondary schools. The aim of this paper was to examine the influence of transformational leadership attributes on academic performance in public secondary schools in Uganda. Specifically, the study investigated the influence of transformational leadership attributes on academic performance at UCE and UACE in public secondary schools. The research was underpinned by the transformational leadership theory pioneered by James McGregor Burns and supported by Bass. The study was anchored on the positivism philosophy and guided by a correlation survey design with a quantitative research method. A multi-stage sampling technique was used to select 163 public secondary schools from a total sample of 253 public secondary schools, registered with the Ministry of Education and Sports in Uganda's central region. Primary data was collected using structured questionnaires. Data was analysed through descriptive and inferential statistics. Hypothesis testing was done at 5% significance level using the multiple linear regression model. The study established that transformational leadership is a significant predictor of academic performance both at Uganda Certificate Education (Ordinary level) and Uganda Advanced Certificate Education (Advanced level) through idealised influence, intellectual stimulation, individualised consideration, and the control variables. The study concludes that transformational leadership has a significant influence on academic performance. The study recommends that policymakers and strategists in Uganda's Education Ministry should closely monitor the school's alignment with the transformational leadership practices of head teachers for sustainable academic performance in public secondary schools

Tri-Branch Convolutional Neural Networks for Top-k Focused Academic Performance Prediction.

Academic performance prediction aims to leverage student-related information to predict their future academic outcomes, which is beneficial to numerous educational applications, such as personalized teaching and academic early warning. In this article, we reveal the students' behavior trajectories by mining campus smartcard records, and capture the characteristics inherent in trajectories for academic performance prediction. Particularly, we carefully design a tri-branch convolutional neural network (CNN) architecture, which is equipped with rowwise, columnwise, and depthwise convolutions and attention operations, to effectively capture the persistence, regularity, and temporal distribution of student behavior in an end-to-end manner, respectively. However, different from existing works mainly targeting at improving the prediction performance for the whole students, we propose to cast academic performance prediction as a top-k ranking problem, and introduce a top-k focused loss to ensure the accuracy of identifying academically at-risk students. Extensive experiments were carried out on a large-scale real-world dataset, and we show that our approach substantially outperforms recently proposed methods for academic performance prediction. For the sake of reproducibility, our codes have been released at https://github.com/ZongJ1111/Academic-Performance-Prediction.