Student performance and time-to-degree analysis by the study of course-taking patterns using J48 decision tree algorithm

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

Education is the key to success which provides multiple opportunities in the life. Education not only contributes to individual success but also contributes to the success of the society. The Indian education system still follows the traditional way of the teaching learning process which lacks in interactive sessions. So, it becomes very difficult to continuously monitor student performance. Indian population is huge which makes the monitoring and analyzing student performance challenging. The Indian education system lacks in the standards for accessing the student performance and achievements. The proper methodology is required to monitor student's academic progress and development. Multiple parameters influence a student performance so it is a very challenging task to find out the significant parameters which are affecting a student performance. Another challenge in the education system is to identify the slow learners at an early stage. In this research work, various factors affecting student performance are analyzed and visualized like student's age, parent's education & occupation, health etc. The visualization technique is used to identify the weak students at an early stage to work on their improvement. To forecast the student performance, a variety of Machine Learning (ML) methods, including K Nearest Neighbors (KNN), Logistic Regression, and Support Vector Machine (SVM), are used. The SVM model with linear kernel gave the best accuracy 84.37%for the selected dataset.

<p class="MsoNormal" style="text-align: justify;"><span style="font-family: Times New Roman;">This paper explores machine learning approaches to predicting student performance using artificial neural networks. By employing educational data mining and predictive modeling techniques, accurate predictions of student outcomes were achieved. The results indicate that artificial neural networks exhibit high accuracy and reliability in forecasting student academic performance. Through comprehensive analysis and empirical testing, this approach significantly enhances the effectiveness of student performance predictions. Future research directions may include further optimization of the model's algorithms and expansion of the data sample size to improve prediction accuracy and applicability. The method demonstrated exceptional performance in predicting student outcomes, offering high accuracy and efficacy. By mining and analyzing extensive educational data, a predictive model was established and validated through experiments. We introduce a novel predictive model to the field of education, providing robust support for student learning and educational decision-making. Future enhancements can optimize the model, increase prediction precision, and expand application fields to better serve the development of educational endeavors.</span></p>

Student outcomes are of great importance in higher education institutions. Accreditation bodies focus on them as an indicator to measure the performance and effectiveness of the institution. Forecasting students’ academic performance is crucial for every educational establishment seeking to enhance performance and perseverance of its students and reduce the failure rate in the future. The main goal of this study is to predict the performance of undergraduate first-level students in the Computer Department during the years 2016 to 2021 to enhance their performance in future by discovering the best algorithm use to analyze the educational data to identify the students’ academic performance. The secondary data was collected by reviewing the Student Affairs Department at the Faculty of Specific Education at Damietta University, in addition to the Statistics Department at the university. The dataset contained 830 instances after excluding 139 instances of missing values, irrelevant rows, and outliers. The dataset was divided into train (577 instances (70%)), test (253 instances (30%)) and involved six features such year, midterm, practical exam, writing exam, final total degree, and grade. This paper use five machine learning (ML) algorithms which was selected according to the literature review and high accuracy in predicting educational data mining: For the purpose of comparison, a number of different machine learning algorithms, such as Random Forest, Decision Tree, Naive Bayes, Neural Network, and K-Nearest Neighbours, were utilized and evaluated with evaluation metrics such as confusion matrix, accuracy, precision, recall, and F-measure. The Random Forest and Decision Tree classifiers emerged as the top-performing algorithms, accurately categorizing 250 instances when predicting students' performance in the statistics course. This was determined based on the findings of the study. Out of a total of 253 instances that were included in the testing set, they only made three incorrect classifications.

Presently, Information and Communication Technologies (I&CT) is considered one of key element as services and features in higher education. Learning through traditional way is no longer the norm in higher education at present. Instead, advanced ICT is becoming a viable paradigm for fundamentally altering Higher Education Institutions (HIEs). In this regard, the Higher Education Commission (HEC) of Pakistan has launched several initiatives and made significant investments in the usage of technology in the education segment. However, it is unclear what influence ICT usage will have upon the environment, student academic performance, and learning. Evidence from literature the scarcity of research available on ICT impacts on students' academic performance, as well as the effectiveness of ICT in universities. Therefore, it needs to recognize the significance of ICT availability and the academic performance of students. The determination of this study is to explore, examine, and evaluate ICT acceptance impact on student performance at universities in Sindh, Pakistan. This study based on the extended Technology Acceptance Model (TAM) with various external factors. This is the basic model to understand user observations and behavior toward latent acceptance and denial. This research is based on a quantitative research technique for triangulate to acquire the optimal research outcome. A collection of facts in this study from students will be a cross-sectional questionnaire survey method. The Structure equation modeling (SEM) will be used to determine the rationality of the research model with Analysis of Moment Structures (AMOS) software. The estimated outcome of this study will showcase the extent at universities in Sindh, and also showcase the ICT impact on student academic performance. Based on those outcomes a critical framework would be recommended for students to integrate ICT.The significance of this framework will be recommended (HEIs) to enrich the quality of the education system by efficiently employing ICT to improve students' capacity to be more effective and quality-oriented professionals.

Performance prediction models have been proposed countless times due to the benefits that they can provide to educational stakeholders. While many factors have been taken into account when predicting student performance, teachers’ assessment or observation reports have not been commonly used. A teacher’s assessment is a fundamental part of the educational process and has a direct impact on students’ success. In this study, we analyze the topics, and psychological features in teachers’ daily written reports and apply them to the student performance prediction model. Experimental results show the capability of this approach in contributing to the accuracy of performance prediction models. KeywordsTeacher reportsTopic modellingSentiment analysisPerformance predictionText miningMachine learning

This study investigates the effectiveness of machine learning and deep learning models for early prediction of student performance in higher education institutions. Using the Open University Learning Analytics (OULA) dataset, various models, including Decision Tree, Support Vector Machine, Neural Network, and Ensemble Model, were employed to predict student performance in three categories: Pass/Fail, Close to Fail, and Close to Pass. The Ensemble Model (EM) consistently outperformed other models, achieving the highest overall F1 measure, precision, recall, and accuracy. These results highlight the potential of data-driven techniques in informing educational stakeholders’ decision-making processes, enabling targeted interventions, and facilitating personalized learning strategies tailored to students’ needs. By identifying at-risk students early in the academic year, institutions can provide additional support to improve academic outcomes and retention rates. The study also discusses practical implications, including the development of pedagogical policies and guidelines based on early predictions, which can help educational institutions maintain strong academic outcomes and enhance their reputation for academic excellence. Future research aims to investigate the impact of individual activities on student performance and explore day-to-day student behaviors, enabling the creation of tailored pedagogical policies and guidelines.

Despite of providing high quality of education, demand on predicting student academic performance become more critical to improve the quality and assisting students to achieve a great performance in their studies. The lack of existing an efficiency and accurate prediction model is one of the major issues. Predictive analytics can provide institution with intuitive and better decision making. The objective of this paper is to review current research activities related to academic analytics focusing on predicting student academic performance. Various methods have been proposed by previous researchers to develop the best performance model using variety of students data, techniques, algorithms and tools. Predictive modeling used in predicting student performance are related to several learning tasks such as classification, regression and clustering. To achieve best prediction model, a lot of variables have been chosen and tested to find most influential attributes to perform prediction. Accurate performance prediction will be helpful in order to provide guidance in learning process that will benefit to students in avoiding poor scores. The predictive model furthermore can help instructor to forecast course completion including student final grade which are directly correlated to student performance success. To harvest an effective predictive model, it requires a good input data and variables, suitable predictive method as well as powerful and robust prediction model.

Supplemental Instruction (SI) is a programme that is attached to difficult courses with the objective of increasing student performance and retention. However, an SI programme also has the potential to increase overall student performance and retention during the first critical year if applied to introductory courses. In this study the latter objective is investigated in an engineering educational environment. The study shows that an SI programme attached to difficult first semester courses for new engineering students has substantial positive effects on both first-year student performance and retention. Both male and female students appear to benefit from attending SI to the same extent. Some potential reasons for these improved first-year student performances are that attendance at SI sessions appears to lead to improved self-confidence, a broader network of study partners, improved study strategies and problem-solving skills and an increased ability to critically review material and work with others.

Many prediction models have been developed using data mining tools in order to predict the performance of the students at the early stage. The academic performance of higher education students commonly was predicted based on their results in the end of the previous semester or during the semester like test score or mid-term exam. However, there is lack of models that emphasize the use of data related to student’s behaviour for predicting the academic performance. Therefore, the aim of this study is to investigate the use of self-efficacy behaviour data to predict the academic performance of students using principal component analysis (PCA) and k-means clustering (KMC). This study focuses on the first part of the prediction which is model development. In the model development phase, the number of variables were reduced from 20 into two by using PCA. The scores for the first two principal components were clustered by using KMC. The results show that the scores can be clustered into three main clusters to differentiate the student’s self-efficacy behaviour. Next research will investigate the underlying causes of the clusters in order to predict the risky students.

This study investigates the mediating role of self-regulation in the relationship between teaching methods and students' academic performance in Cambodian higher education. The research aims to determine whether instructional strategies directly influence student outcomes and indirectly enhance performance by fostering self-regulation. A quantitative approach using SmartPLS (PLS-SEM) was applied to survey data from 320 academic staff across public and private universities. Measurement model results confirmed strong reliability and validity, while structural model analysis yielded R² values of 0.242 for self-regulation and 0.135 for students' performance, indicating weak but acceptable explanatory power. Findings: Teaching methods had a significant positive effect on students' performance (β = 0.183, t = 2.907, p = 0.004) and self-regulation (β = 0.491, t = 12.432, p = 0.000). Self-regulation positively influenced performance (β = 0.242, t = 3.867, p = 0.000) and partially mediated the relationship between teaching methods and performance (β = 0.119, t = 3.633, p = 0.000). The study highlights self-regulation as a critical mechanism linking teaching strategies to academic success. The findings suggest that higher education institutions should adopt learner-centered pedagogies and embed self-regulatory skill development into curricula to enhance student engagement and performance.

Aim/Purpose: This research aims to present a brand-new approach for student performance prediction using the Learning Fuzzy Cognitive Map (LFCM) approach. Background: Predicting student academic performance has long been an important research topic in many academic disciplines. Different mathematical models have been employed to predict student performance. Although the available sets of common prediction approaches, such as Artificial Neural Networks (ANN) and regression, work well with large datasets, they face challenges dealing with small sample sizes, limiting their practical applications in real practices. Methodology: Six distinct categories of performance antecedents are adopted here as course characteristics, LMS characteristics, student characteristics, student engagement, student support, and institutional factors, along with measurement items within each category. Furthermore, we assessed the student’s overall performance using three items of student satisfaction score, knowledge construction level, and student GPA. We have collected longitudinal data from 30 postgraduates in four subsequent semesters and analyzed data using the Learning Fuzzy Cognitive Map (LFCM) technique. Contribution: This research proposes a brand new approach, Learning Fuzzy Cognitive Map (LFCM), to predict student performance. Using this approach, we identified the most influential determinants of student performance, such as student engagement. Besides, this research depicts a model of interrelations among the student performance determinants. Findings: The results suggest that the model reasonably predicts the incoming sequence when there is a limited sample size. The results also reveal that students’ total online time and the regularity of learning interval in LMS have the largest effect on overall performance. The student engagement category also has the highest direct effect on student’s overall performance. Recommendations for Practitioners: Academic institutions can use the results and approach developed in this paper to identify students’ performance antecedents, predict the performance, and establish action plans to resolve the shortcomings in the long term. Instructors can adjust their learning methods based on the feedback from students in the short run on the operational level. Recommendation for Researchers: Researchers can use the proposed approach in this research to deal with the problems in other domains, such as using LMS for organizational/institutional education. Besides, they can focus on specific dimensions of the proposed model, such as exploring ways to boost student engagement in the learning process. Impact on Society: Our results revealed that students are at the center of the learning process. The degree to which they are dedicated to learning is the most crucial determinant of the learning outcome. Therefore, learners should consider this finding in order the gain value from the learning process. Future Research: As a potential for future works, the proposed approach could be used in other contexts to test its applicability. Future studies could also improve the performance level of the proposed LFMC model by tuning the model’s elements.

Student engagement is one of the important constructs that is used to understand the behavior of the student towards the teaching-learning process, and it determines the students’ academic performance. The aim of this study is for predictive analytics to work on the comprehension of student commitment and scholarly execution and anticipate students who are in danger of low execution or commitment right on time before the evaluation to work with conceivable mediation to further develop the learning results in advanced education. This research adopts the process of machine learning such as linear regression, decision tree, naïve Bayes, KNN, Kmeans in order to identify the most effective determinants for student academic performance prediction. The result of this study shows that after testing the five attributes, we discovered so far that the attributes that has impact on student evaluation are their Race/Ethnicity and Parental level of Education. Thus, the early prediction of student performance can trigger educators to track student dropouts in a particular course at an early stage. The model can also be used as an early warning system to identify failure students in the classroom by the course coordinators and educators, to take strategic decisions to improve student performance.

To analyse the student's academic performance, a new prediction model is developed. This proposed model collects the student's data from standard online sources. At first, these gathered data are pre-processed by certain methods like outlier removal and filling. Then, the filtered data undergoes feature mining by developing the entropy weighted fuzzy rough set mining (EW-FRSM), where certain parameters are tuned with the proposed hybrid optimisation algorithm with probability-based coyote electric fish optimisation (P-CEFO). The refined features are utilised for performance prediction, where the deep belief-Bi-LSTM (DB-Bi-LSTM) is used for student performance prediction. The parameters in DBN and Bi-LSTM are tuned with the same P-CEFO algorithm to improve the prediction performance. Simulation is performed to examine the efficacy of the designed prediction method with diverse performance metrics by evaluating with diverse baseline approaches to prove the usefulness of the model.

Advances in Information and Communications Technology (ICT) have increased the growth of Massive open online courses (MOOCs) applied in distance learning environments. Various tools have been utilized to deliver interactive content including pictures, figures, and videos that can motivate the learners to build new cognitive skills. High ranking universities have adopted MOOCs as an efficient dashboard platform where learners from around the world can participate in such courses. The students learning progress is evaluated by using set computer-marked assessments. In particular, the computer gives immediate feedback to the student once he or she completes the online assessmentsThe researchers claim that student success rate in an online course can be related to their performance at the previous session in addition to the level of engagement. Insufficient attention has been paid by literature to evaluate whether student performance and engagement in the prior assessments could affect student achievement in the next assessmentsIn this paper, two predictive models have been designed namely students’ assessments grades and final students’ performance. The models can be used to detect the factors that influence students’ learning achievement in MOOCs. The result shows that both models gain feasible and accurate results. The lowest RSME gain by RF acquire a value of 8.131 for students assessments grades model while GBM yields the highest accuracy in final students’ performance, an average value of 0.086 was achieved.