Model Development in Predicting Academic Performance of Students Based on Self-Efficacy Using K-Means Clustering

A presente pesquisa objetivou analisar os estilos intelectuais, o desempenho acadêmico e a adaptação ao planejamento de carreira de estudantes do ensino superior e averiguar se os estilos intelectuais podem prever essas variáveis. Participaram 316 universitários de três estados do Brasil (média de idade [MIdade] = 22,2; desvio padrão [DP] = 4,7) que responderam coletivamente ao Inventário de Estilos Intelectuais e à subescala Adaptação ao Planejamento de Carreira do Questionário de Adaptação Acadêmica do Ensino Superior. As médias de desempenho foram obtidas nas secretarias de graduação das universidades, somente dos alunos que consentiram informá-las. Os resultados indicaram a existência de correlações positivas e significativas entre as variáveis, bem como alguns dos estilos intelectuais da amostra pesquisada puderam prever a adaptação ao planejamento de carreira e o desempenho acadêmico. Também foi observado que a adaptação ao planejamento da carreira pode prever, em certa medida, o desempenho dos estudantes do ensino superior. Os resultados são discutidos em vias de suas implicações psicoeducacionais com base na literatura científica.

The aim of this study is to undertake an empirical inquiry and comparison of the effectiveness of various classifiers with ensembles classifiers in the prediction of student academic performance. A single classifier algorithm will be compared against the performance and efficiency of ensemble classifiers. Reducing student attrition is a major problem for educational institutions all over the world. The search for solutions to increase student retention and graduation rates continues for educators. This is only possible if at-risk students are identified and intervened with as soon as possible. However, the majority of regularly used prediction models are inefficient and inaccurate as a result of inherent classifier limitations and the inclusion of insignificant inputs in their calculations. The majority of data mining and machine learning researcher focused on developing an algorithm that can extract useful information from massive amounts of data after being processed by a computer. The most difficult problem in predictive modelling is identifying the most effective prediction algorithms that are also accurate enough to be useful. Therefore, a multi-level homogeneous ensemble predictive (MLHoEP) model is designed, which uses the different techniques of data mining like feature selection, ensemble learning techniques like boosting and bagging. Seven distinct machine learning algorithms were used on this model to predict and analyse the academic performance of the students. The performance of the classification algorithms in terms of prediction was evaluated using k-fold cross-validation. The study contributes to the body of knowledge by suggesting the development of homogeneous classifiers that may be used to accurately predict students’ academic success. It also proposes the construction of homogeneous classifiers, which may be deployed for accurate student performance prediction, in order to provide a better explanation for the poor performance prediction. As a result of this research, it has been demonstrated that the technique of applying homogeneous ensemble approaches is incredibly efficient and accurate in terms of predicting student performance and assisting in identifying students, who are in danger of dropping out of school. The study compared the accuracy and efficiency of single classifiers to ensembles of classifiers in terms of performance. It was discovered in the research that a homogeneous model with excellent accuracy and efficiency might be developed for anticipating student performance. These key problems have been successfully addressed by the findings of this research study: Which characteristics of students are the most effective predictors of academic performance? How accurate are approaches such as bagging and boosting ensembles for predicting student academic performance? The approach offered in this study will aid educational administrators and policymakers in designing new policies and curriculum-linked to student retention in higher education. This research can also aid in the identification of students who are at risk of dropping out of school early, providing for timely intervention and support. Prospective research will examine the creation and implementation of an automated prediction system known as the students’ academic performance forecast framework, which will collect data from students via online submission and produce a prediction result for their academic performance.KeywordsEducational data miningEnsemble learningMultilayer perceptronRandom forestNaïve BayesCorrelation attribute evaluationInformation gainGain ratio

In the ever-changing world of higher education services, examining factors that impact students’ performance is important in making sure that student’s learning experience can be improved to reach academic success. This research investigates the impact of University Servicescape (USc) and Information Systems Implementation Quality (ISQ) on student performance in higher education institutions in Malang. USc refers to the physical environment and facilities provided by the university. At the same time, the ISQ pertains to the effectiveness and efficiency of the technological systems employed for academic and administrative purposes. Both factors have been deeply researched separately in the context of higher education service management, but no research has delved into both at the same time. A quantitative empirical survey-based study is employed to examine this topic. The sample consists of university students studying in Malang, East Java, Indonesia. The results empirically show that USc and ISQ significantly impact university student performance in higher education, particularly within institutions in Malang, East Java, Indonesia. These findings should be useful for policymakers, university administrators, and educators in enhancing the delivery of educational services in higher education institutions across Indonesia

Student performance in higher education has become one of the most widely studied area. While modelling students' performance, data plays a pivotal role in forecasting their performance and this is where the data mining applications are now becoming widely used. There are various factors which determine the student performance. In this study, eight attributes are used as inputs which are considered most influential in determining students' performance in the Pacific. Statistical analysis is done to find out which attribute has the highest influence to student performance. In this research, different algorithms are utilized for building the classification model, each of them using various classification techniques. The classification techniques used are Artificial Neural Network, Decision Tree, Decision Table, and Naïve Bayes. The dataset of 651 records used in this research is an imbalanced set, which is later transformed to balance set through under sampling. Neural Network is one of the classification techniques that has performed well on both, imbalanced and balanced datasets with the highest prediction accuracy of 96.8%. The analysis further shows that internal assessment has weak positive relationship with student performance while demographic data has no significant relationship.

Purpose This study aims to identify the role of teacher competency in enhancing student engagement and performance. The moderating roles of teacher creativity and student achievement motivation are employed to see their effects. Design/methodology/approach Questionnaires are distributed to undergraduate students of the management program in Indonesia, and 233 responses are used for hypothesis testing. Partial least squares structural equation modeling with SmartPLS 4 is then used to analyze the research model. Findings This study finds that student engagement mediates the relationship between teacher competency and student performance and that teacher creativity can strengthen the positive relationship between teacher competency and student engagement. Surprisingly, the positive relationship between engaged students and their performance is stronger when their achievement motivation is low. Originality/value The moderating roles of teacher creativity and student achievement motivation, not thoroughly examined in prior research, are presented to complement our model of the relationships among teacher competency, student engagement and performance.

Effects of co-curricular activities on student's academic performance by machine learning

Background: Interactive learning resources developed with the H5P platform have been progressively adopted to support autonomous learning and conceptual consolidation. However, empirical evidence regarding their impact on academic performance in theoretically demanding university courses remains limited. The primary aim of this study was to examine the effect of the structured integration of an interactive digital pedagogical resource developed with multiple-choice H5P on the academic performance of higher education students enrolled in a Motor Control course. Methods: A quasi-experimental study was conducted to compare two independent groups: a control group (CG; n = 90) and an intervention group (IG; n = 115), which had access throughout the semester to a multiple-choice interactive resource developed using the H5P platform. Academic performance was operationalized as the score obtained on a written summative assessment. Baseline equivalence between groups was assessed using an initial diagnostic test. Between-group comparisons were performed using robust non-parametric statistical procedures and further examined using a linear regression model adjusted for relevant covariates. Results: No statistically significant differences were found between groups in the baseline diagnostic test (p > 0.05), indicating comparable starting levels. At the end of the intervention period (≈2 months), the intervention group obtained significantly higher scores in the summative assessment (p < 0.001), with a large effect size (d = 0.87). Conclusions: The findings suggest that the structured integration of multiple-choice H5P resources may positively contribute to academic performance when used as a complementary tool alongside traditional teaching. These results reinforce the pedagogical potential of multiple-choice H5P to support autonomous learning and conceptual consolidation, while also highlighting the need for future research employing more rigorous experimental designs and process-based measures to better understand the underlying learning mechanisms.

This review article addresses the influence of the virtual learning environment (VLE) on the academic performance of higher education students, exploring students' perceptions of this environment in the context of higher education. The aim of this study is to analyze how different dimensions of the VLE impact academic performance, based on a systematic review methodology that followed the PRISMA approach. Empirical studies published between 2013 and 2024 were included, and they were analyzed in four categories: perception of the technological environment, pedagogical factors, socio-emotional dimension, and the relationship between perception and performance. The main findings indicate that the technological quality of the VLE, pedagogical interaction, and emotional support are determining factors for academic performance. Furthermore, inequalities in access to quality technologies and challenges in integrating traditional teaching with digital methodologies were identified. Finally, it is concluded that an appropriate VLE design, considering the emotional, pedagogical, and technological needs of students, is essential to improving the learning experience and academic performance.

Data analysis is the emerging research field that relies on methods and techniques to make insights on the data sets. Data analysis on student's academic Performance based on their Health status such as nutritious food intake, hygienic life style and frequency of health issues is the main objective of the research. The datasets were obtained by Questionnaire method and the analysis were carried out initially with clustering algorithms such as K-means algorithm, Hierarchical clustering and EM Method. In the second phase, Genetic search was performed and the outputs were generated. The statistical output representation for the important attributes are given using orange software. The algorithmic Experimental setup was also carried out with weka datamining tool on student's dataset that has 113 instances and 93 attributes. The findings of the research work were that K-means algorithm outperformed well when compared with EM method and Hierarchical clustering. Genetic search method predicted correlated attributes for the selected class attribute and the outputs are generated. The statistical data analysis shows that nutrition and health issues of female students has an impact on the academic performance of students.

Student performance is related to complex and correlated factors. The implementation of a new advancement of technologies in educational displacement has unlimited potentials. One of these advances is the use of analytics and data mining to predict student academic accomplishment and performance. Given the existing literature, machine learning (ML) approaches such as Artificial Neural Networks (ANNs) can continuously be improved. This work examines and surveys the current literature regarding the ANN methods used in predicting students’ academic performance. This study also attempts to capture a pattern of the most used ANN techniques and algorithms. Of note, the articles reviewed mainly focused on higher education. The results indicated that ANN is always used in combination with data analysis and data mining methodologies, allowing studies to assess the effectiveness of their findings in evaluating academic achievement. No pattern was detected regarding selecting the input variables as they are mainly based on the context of the study and the availability of data. Moreover, the very limited tangible findings referred to the use of techniques in the actual context and target objective of improving student outcomes, performance, and achievement. An important recommendation of this work is to overcome the identified gap related to the only theoretical and limited application of the ANN in a real-life situation to help achieve the educational goals.

PurposeCreating suitable learning conditions in university classrooms continues to be a topical issue in higher education. The above means that a continued search for innovative teaching and learning strategies in universities remains a priority issue. The study therefore investigated conditions that facilitate the effective implementation of cooperative learning (CL) as well as how CL influences the academic performance of students in universities in Zimbabwe.Design/methodology/approachThe research adopted a quantitative research approach located in the descriptive research design. A sample of 341 undergraduate students selected using a simple random sampling strategy from a sample frame of 701 undergraduate students participated in the study. The study first used a pre-test and later, a post-test to determine the initial and end of semester (EOS) academic performance levels of students, respectively, on a Calculus 1 course. A self-constructed structured questionnaire was also used for data collection on the views of students towards the use of CL with particular reference to the 5 dimensions of positive interdependence (PDI), promotive interaction (PI), individual and group accountability (IGA), social and interpersonal skills (SS) and group processing (GP). The questionnaire was validated using confirmatory factor analysis. Structural equation modelling was used to test proposed relationships. A total of 341 questionnaires were administered through an email survey. Of the total, 149 completed questionnaires were received from the students, giving a return rate of 43.7%.FindingsThe results showed that positive interdependence, promotive interaction, social and interpersonal skills, individual and group accountability as well as group processing were important antecedents to the effective implementation of CL in universities. The results further showed that CL plays a significant role in improving the academic performance of university students; hence, academic performance was viewed as a significant consequence of CL.Research limitations/implicationsThis is the first study on the application of CL to enhance the academic performance of university students in the context of Zimbabwe. As a result, caution should be exercised when generalising the results as more studies to either confirm or disconfirm these results in the context of Zimbabwe may still be required.Practical implicationsThese results have implications on policy and practice with regards to active teaching strategies in universities. With regards to practice, the results demonstrated that the use of group activities that allow students to share knowledge and support each other while the teacher plays a facilitating role is important for enhancing students' academic performance. With regards to policy, the results showed that universities can enhance the academic performance of students if policies that promote student-centered teaching and learning approaches to ensure that teaching in universities is more student-led than lecturer-led are developed.Originality/valueWhile many studies have been conducted in other contexts, to establish the influence of CL on the academic performance of university students, there is no known study on the influence of CL that has been conducted in the context of Zimbabwean universities. The results of this study therefore are an eye opener on the role of CL in enhancing the academic performance of students in Zimbabwean universities.

Background : Various educational approaches to learning have been proposed. It is, of course, of vital importance to study those that have resulted in effective educational achievements. Purpose : To assess the student's attitudes and performance in order to evaluate educational approaches and identify the variables leading to students gaining good marks. Methods : The subjects of the study were medical students at pathophysiology phase. A multiple-choice questionnaire was prepared, the reliability and validity of which were confirmed. A paired-sample T­ test was used to compare and analyze the student's Basic Sciences Comprehensive Exam scores in each discipline and their average score during the basic sciences course. Results: With respect to attitudes, the students highly valued the importance of teacher's methodology and mutual respect, as an influential factor in Learning. Furthermore, the majority of the students relied heavily on their textbooks as the main source of information and preferred to study at home, rather than any other places. Most of the students prefer to study in the morning. They believed that mid-term exams, quizzes, and active class participation do not have much effect on learning. However, it was of high importance to them to attend practical claSSfS (labs). With respect to performance, most of the students used lecture notes as the main references, and considered their home as a good place to study and preferred to study in the morning, but have participated in theoretical and practical classes regularly. Conclusion: Most successful students attributed their success to active class participation, taking mid-term exams, quizzes, and using library. Having analyzed the data, we recommend the authorities to provide more methodology workshops for teachers, sufficient number of textbooks, expanding or increasing the number of the reading rooms with essential equipment. Keywords : students' attitudes, student performance, learning

Higher educational institutions (HEIs) are striving to improve their student's performance. Classification of students to predict student's performance could be useful in identifying bad performers or late completers to improve at an early stage by forecasting their final outcome. In this study, three student's performance prediction models were investigated. J48 decision tree algorithm was used for the study. When the performance of these models was evaluated, it was found that the third prediction model provided better performance compared to the other models. Also, there was variation in the course-taking patterns of students and contextual attribute course difficulty depending on their completion time. This demonstrates the significance of students course-taking patterns on performance and time-to-degree. The most significant finding is that unlike easy course delivering high GPAs interestingly some very difficult courses too lead to optimum time and GPA which could be attributed to other hidden factors which need to be investigated.

Academic result measured by Grade Point Average is often considered as the most dominant performance indicator of a student in most of developing countries like Bangladesh. Several prior studies have been conducted to determine the factors which act as control measures to the academic performance in tertiary level education. But the obtained results varied largely depending on the socio-economic context and education policy of the nations. The study aims at finding out the factors that affect the academic performance of students of a public university situated in the south-western part of Bangladesh and to assess the relative predictability of various factors on academic performance. A sample of 314 graduate students from different disciples was chosen based on a random sampling method. Principal Component Analysis (PCA) as a statistical tool has been used to extract factors affecting academic performance. Multiple Linear regression was run to predict the influence of these factors on the dependent variable. The academic factor that includes conventional exam system, teachers’ evaluation system, and academic study environment appeared as the most dominant determinant factors affecting academic performance. Other important factors such as the financial condition of students, family income, social media affiliation, part-time job, extra-curricular activities also dictate academic performance. Regression coefficient indicates that gender, higher secondary examination grades, sleeping duration, academic study hours have a significant influence on academic achievement. The study results will be useful to the students, academicians, guardians of the students, policymakers, and researchers to have a better understanding of determinants of academic achievement and taking appropriate measures accordingly. Keywords: Academic performance, PCA, Academic factors, demographic and socio-economic factors, Public university students, Bangladesh. DOI: 10.7176/JEP/12-5-07 Publication date: February 28 th 2021

This paper conducts a methodological research on the academic performance of online learners. This paper applies classification models commonly used in data mining algorithms, such as random forests based on decision trees, support vector machines, neural networks, K nearest neighbor algorithms, etc., combined with data mining tools S software and statistical analysis tools, and analyzes the University of Finance and Economics Tools scores of online learners in the class of 2019. It studies the important factors that affect the academic performance of college students' online learners, and uses these factors to predict students' academic performance. Based on the distance calculation method in mathematics, the article separately studied the application of Euclidean distance correlation analysis algorithm and correlation coefficient correlation algorithm in curriculum relevance, and compared several correlation algorithms. Experimental research results show that in the era of big data, learners will accumulate a large amount of structured and unstructured data during online learning. We can explore the influencing factors of online learners' academic performance through data mining technology, and we can also use machine learning to automatically learn from the data to the academic performance prediction model.