Predicting student academic performance. Furthermore, results showed significant social .
Predicting student academic performance Dataset 1 consisted of 500 student records with 16 features. Based on above mentioned research questions, we propose the students academic performance prediction network (SAPPNet), which is designed to predict student academic performance by analyzing Similarly, a study in predicting students' academic performance using time on task, the total number of logins to a learning system, average assessment grades, and percentage of learning activities accessed revealed that the average assessment grade is the highest contributing variable, followed by the time on task [15]. Relief-F and budget tree random forest based feature selection for student academic performance prediction. They are interested in prediction accuracy but pay less attention to interpretability. 2 Statement of problem 1. However, one of the major drawbacks of this learning style is the lack of effective communication and feedback, which Predicting student academic performance has long been an important research topic in many academic disciplines. It is happened, students pass or fail at the end of the school period. However, individual characteristics are important in promoting Social media have become an indispensable part of peoples’ daily lives. Results showed a range of accuracy from 33% to 98% and a range of cross-validation from 30% to 37%. Here both datasets contain the data gathered from two Portuguese secondary schools of the academic year 2005–2006. An early prediction of student performance may help the responsible entities to provide solutions to the students with low performance. , 2020). One of these advances is the use of analytics and data mining to predict student academic accomplishment and performance. With the emergence of e-learning, researchers and programmers aim to find Twelve top features were identified as important features for predicting student academic performance. This type of education is significant because it ensures that all students receive the required learning. The present study is the first study that develops and compares four types of mathematical models to predict student academic performance in engineering dynamics – a high-enrollment, high-impact, and core course that many Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic for processing and analyzing student performance prediction using a machine learning algorithm. The statistical evaluations are limited in providing good predictions of the university’s e-learning quality. As a result Introduction. The in hand research work Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs. g. There are many studies that focus on predicting student performance, but comparatively fewer studies that take action based on these predictions. Utilizing the SAPEx-D (Student This work will focus on students' demographics, registrations, and virtual learning environment interactions to predict student's academic performance. In this study, we examine the association between students’ mental health and psychological attributes derived from social media interactions and academic performance. Over 300 students were surveyed to assess their learning habits and performance using the collected data. To address this issue, many research works have employed fuzzy concepts to analyze, predict, and make decisions about Prediction of students' academic performance is one of the educational problems solved by data mining. Selection of a right academic program at right time can save time, efforts and resources of both parents and educational institutions. The study proves that irrelevant features in the dataset reduce the prediction results and Student academic performance prediction is a significant area of study in the realm of education that has drawn the interest and investigation of numerous scholars. proposed a decision tree approach for predicting academic performance of students. A Neural Network (NN) was created using a diverse range of data mining methodologies to obtain a prediction The proposed framework is employed to predict student academic performance using balanced as well as, imbalanced datasets using the synthetic minority oversampling technique (SMOTE). Most of the existing prediction models are built by a machine learning method. A dataset was obtained using a questionnaire-based survey and the academic section of the chosen institution. Students’ academic performances may fluctuate [84,85] for different reasons, and we would like to investigate whether users’ psychological Predicting student academic performance by using non-explainable models entails fully trusting the prediction outcome, because the patterns used to assign the student’s success rate for a given course are unknown . Unlike the modelling concepts as outlined in the literature, the Yang et al. Most studies tend to focus on the common characteristics of students but ignore their individual characteristics. This is reached through the development of an intelligent model that predicts the right path for each student Predicting students’ academic performance using artificial neural network TABLE OF CONTENT Title page Approval page Dedication Acknowledgment Abstract Table of content CHAPETR ONE 1. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. The . / Predicting academic performance : A systematic literature review. The review results indicated that various The main objective of this paper is to highlight the recently published studies for predicting student academic performance in higher education. The variety However, there are only limited studies and tools to predict the academic performance of students, especially in smart education context. Quantifying student academic performance is challenging because academic performance of students hinges on several factors. Many studies have employed machine learning models to predict student performance using EDM [8,9]. The data were collected from 8 year period intakes from Academic performance evaluation is essential to enhance educational affection and improve educational quality and level. Recently, machine learning technology has been adopted in Educational Data Mining (EDM) to predict and In recent decades, many studies on predicting the performance of students by using machine learning algorithms have been proposed. In Generally, this research study advances the understanding of the application of ensemble techniques to predicting student performance using learner data and has successfully addressed these fundamental questions: What combination of variables will accurately predict student academic performance? Predicting students' academic performance is of great concern for both students and educational institutions. Using data from the China Education Panel Survey conducted Predicting Student's Academic performance using artificial neural network: A case study of an engineering course. With effective performance prediction Thus, the proposed approach offers a solution to predict student academic performance efficiently and accurately by comparing several ML models to the Deep Learning In this systematic review, the relevant EDM literature related to identifying student dropouts and students at risk from 2009 to 2021 is reviewed. However, although the learning outcomes are believed to improve learning and teaching, prognosticating the attainment of The prediction of student academic performance has drawn considerable attention in education. Predict the performance of students at risk in academic institutions 2. S. 5 and Naive Bayes classifiers in predicting student academic performance in a Virtual Learning Environment. Student attrition is of a great concern to, and an extraordinarily challenging issue to address for higher education (HE) providers. Data Mining is the most prevalent family of techniques to predict students’ performance and is extensively This work examines and surveys the current literature regarding the ANN methods used in predicting students’ academic performance. Numerous The present study deals with predicting students’ academic performance in a technical institution in India. predict students’ performance using 56 instances from The primary focus is understanding how machine learning algorithms, particularly in the context of AI, can predict students' academic performance in ODL environments. The research aims to help educational institutes predict future Predicting student academic performance or success is an essential concept in tackling the student academic performance crisis. The implementation of a new advancement of technologies in educational displacement has unlimited potentials. Current prediction systems struggle to effectively analyze and track student progress, often overlooking potential strong performances. Student performance analysis and prediction using datasets has become an essential component of modern education systems. The sequential minimal optimization algorithm outperforms logistic regression in accuracy. Waheed H, Hassan SU, Aljohani NR, et al. (2019). The Pacific Journal of Science and Technology, (2018), 9 (1) pp. To predict student performance, this study compared the effectiveness of label encoding, one-hot In this study, we are also interested in predicting users’ academic performance and computing their psychological attributes and mental health from different social media such as Twitter, Reddit, etc. Thus, the proposed approach offers a solution to predict student academic performance efficiently and accurately by comparing several ML models to the Deep Learning model. With effec- Predicting Academic Performance ITiCSE ’18 Companion, July 2–4, 2018, Larnaca, Cyprus Secondary Education Post-Graduation 1st Year 2nd Year Upper Years CS1, CS2 Data PDF | On May 2, 2020, Bassam Zafar and others published Predict Students’ Academic Performance based on their Assessment Grades and Online Activity Data | Find, read and cite all the research Online education has been facing difficulty in predicting the academic performance of students due to the lack of usage of learning process, summative data and a precise prediction of quantitative relations between variables and achievements. (2020) Predicting academic performance of students from VLE big data using deep learning models. Many researchers have developed models for predicting student performance at various levels Predicting Students' Academic Performance (SAP) is one of the important research areas in Higher Learning Institutions. 5 and Naive Bayes classifiers in predicting student academic performance in a Virtual Learning Environment; R. They found that linear support vector machine is the best classifier among others in terms of continuous features, and The tremendous growth of educational institutions’ electronic data provides the opportunity to extract information that can be used to predict students’ overall success, predict students’ dropout rate, evaluate the performance of teachers and instructors, improve the learning material according to students’ needs, and much more. Predicting students’ academic performance is very essential to produce high-quality students. Learn more. The raw data in OULAD consists of 7 CSV files containing different parts of the information about courses, assessments, students, the virtual learning environment, and students' interactions and Prediction of student performance is one of the most important subjects of educational data mining. Bravo-Agapito et al. Predicting student‟s performance becomes more challenging due to the large volume of data in educational databases. Azcona et al. Predictive modelling in the education domain can be utilised to significantly improve teaching and learning experiences. Of the remaining students, quite often they drop out or fail in their second or third year of studies. Predicting student academic performance is linked to developing the best educational policies in higher education, which significantly impact economic and financial development. This study proposes an Predicting students’ academic performance has long been an important area of research in education. Predicting Students’ Academic Performance by Their Online Learning Patterns in a Blended Course: To What Extent Is a Theory-driven Approach and a Data-driven Approach Consistent? Feifei Han1, 2* and Robert A. Azizah et al. Crossref View in Scopus Google Scholar. The prediction of SAP in Students’ academic performance prediction is one of the most important applications of Educational Data Mining (EDM) that helps to improve the quality of the education process. It offers important insights that can help and guide institutions to make timely decisions and changes leading to better student outcome achievements. 18-22. The experiments on a large-scale real-world Predicting students’ academic performance has long been an important area of research in education. Something went wrong and this page crashed! The purpose of this study is to predict students' academic performance and determine the relative importance of the variables (gender, grade, father variable-1 father variable-2 (education level), mother variable-1, mother In this paper, Naive Bayes algorithm is applied for predicting student’s academic performance at the end semester exams by analyzing students feedback and their performance in the mid-semester exams. Huang Computer Science & Information Engineering, National Central University, Taoyuan City, The use of Deep Learning to predict what happens in the future becomes more popular because of great availability of data. Q. 3 Objective of the study 1. In this study, we perform the first and second types. In addition, academic performance This framework captures inter-semester correlation, inter-major correlation, and integrates student similarity to predict students’ academic performance. Quantifying student academic performance is challenging because academic performance of students Student performance is related to complex and correlated factors. Targeting at-risk students using engagement and effort predictors in an introductory computer programming course; E. 2 PROBLEM STATEMENT Predicting students’ academic performance is a complex task because of the multitude of factors that can impact student Evaluating students’ academic performance is crucial for assessing the quality of education and educational strategies. We build a classification The most frequently used data sets or attributes for predicting students' academic performance and employability are their cumulative grade point average (CGPA), gender, technical, communication A statistical model for predicting students' academic performance as a dependent variable based on students' sex, course enrolled, senior high school data, and college career-guided test performance was established. The dataset is available on Kaggle. Google Scholar Yadav SK, Bharadwaj B, Pal S (2012) Data mining applications: a comparative study for predicting student’s performance. Walia N, Kumar M, Nayar N, et al. The project explores various aspects of students' academic, personal, and social life, aiming to understand the factors influencing their final grades and to predict academic outcomes. Numerous studies have been conducted for this purpose, but they are plagued by challenges including limited dataset size This repository contains a comprehensive analysis and prediction model for student performance based on a rich dataset (student-mat. Introduction. The current approaches for student academic performance prediction mainly rely on the educational information provided by educational system, ignoring the information on students’ classroom Understanding what predicts students’ educational outcomes is crucial to promoting quality education and implementing effective policies. Pac J Sci Technol 9(1):72–79. This review investigates the application of different techniques of data mining and machine learning to; 1. However, it can be challenging to predict and evaluate academic performance under uncertain and imprecise conditions. This framework captures inter-semester correlation, inter-major correlation and integrates student similarity to predict students' academic performance. Oladokun VO, Adebanjo AT, Charles-Owaba OE (2008) Predicting students’ academic performance using artificial neural network: a case study of an engineering course. Predicting students' educational outcomes has become more complex because of the large volume of data in the databases. Given the existing literature, machine learning A LMS called Kalboard 360 has been used to collect educational dataset. com under the name of BStudents’ As artificial intelligence (AI) becomes increasingly integrated into educational environments, adopting a human-centered approach is essential for enhancing student outcomes. Compare the performance of existing models and The academic performance of first-year students can be predicted using Multiple Linear Regression (MLR) analysis, a model that considers the high school GWA, strand, and EDM enables educators to predict situations such as dropping out of school or less interest in the course, analyse internal factors affecting their performance, and make statistical The prediction of student academic performance has drawn considerable attention in education. During the online mode of learning, The Programme for International Student Assessment (PISA) is a global survey conducted by the Organisation for Economic Co-operation and Development (OECD) to assess educational systems by evaluating the academic performance of 15-year-old school students in mathematics, science, and reading. The factors such as students financial status, gender and motivation to study were discovered to influence the students performance. Most existing literature have made use of traditional statistical methods that run into the Predicting Students’ Academic Performance Through Supervised Machine Learning Abstract: There are many supervised and unsupervised types of machine learning approaches that are used to extract hidden information and relationship between data, which will eventually, helps decision-makers in the future to take proper interventions. However, although the learning outcomes are believed to improve learning and teaching, prognosticating improving board performances by predicting students' academic performance before admission. Typically, about 35% of the first-year students in various engineering programs do not make it to the second year. By presenting these nuanced evaluations, we aim to provide guidance to educators, policymakers, and data The goal of this paper is to present a systematic literature review on predicting student performance using machine learning techniques and how the prediction algorithm can be used to identify the Machine learning algorithms for predicting student academic performance is an emerging field in educational data mining that can potentially solve this problem . Ellis1 1Office of Pro-Vice-Chancellor (Arts, Education and Law), Griffith University, Australia // 2Griffith Institute for To improve the accuracy of predicting students’ academic performance, machine learning algorithms have become an indispensable part of the EDM field and play an increasingly important role in educational Forecasting academic performance of student has been a substantial research inquest in the Educational Data-Mining that utilizes Machine-learning (ML) procedures to probe the data of educational setups. This paper has therefore proposed the use of ANN as an application modelling tool for predicting the academic performance among students. Understanding the key determinants of students’ academic performance is paramount for educators, policymakers, and institutions to enhance learning outcomes and facilitate targeted Predicting students’ academic performance is one of the earliest and most popular applications of EDM to predict students’ future academic performance based on existing data, such as using machine learning algorithms to predict final scores or academic performance based on students’ performance records. Research in aims to predict student performance using Artificial Intelligence, aiming to help students avoid poor results and groom them for future exams. Whereas dataset 2 contained 300 students records with 24 features. The integration of educational data mining and deep neural networks, along with the adoption of the Apriori algorithm for generating association rules, focuses to resolve the problem of misdirection of students in the university, leading to their failure and dropout. [52] proposed a model for predicting students' academic performance in blended learning system by using the activities that are homework, quizzes, and video-based learning. Student By predicting students’ future academic performance, machine learning can facilitate the early identification of students at risk, enabling targeted interventions to enhance learning outcomes and potentially improve grades (Sapare and Beelagi 2021). Google Scholar Deepika, K. To be more precise, there are three types of predictions in higher education: (i) predicting students' academic performance or GPA at a degree level, (ii) predicting students' failure or drop out of a degree, and (iii) predicting students' results in particular courses (Alturki et al. The main goal is to continuously help students to increase their ability in the learning process and Predicting student's academic performance during online learning has been considered a major task during the pandemic period. There is much academic information related to students available. Tech second-year students. In this paper, a web-based system for predicting academic performance and identifying students at risk of failure through academic and demographic factors is developed. Dagdagui published Predicting Students’ Academic Performance Using Regression Analysis | Find, read and cite all the research you need on ResearchGate This paper proposes a framework for predicting students' academic performance of first year bachelor students in Computer Science course. Through the ethical collection of student data, via academic records and surveys, a model can be created to anticipate where resources can be focused to boost student performance. 5 Significance of the study 1. This paper has two broad aims. The main objectives of Prediction methods in EDM are to study the features of model that are essential for predicting SAP and A statistical model for predicting students' academic performance as a dependent variable based on students' sex, course enrolled, senior high school data, and college career-guided test performance was established. This system gives users (students, teachers and parents) synchronous access to reach the educational resources from all devices by using an internet connection []. To do so, most existing studies have used the traditional machine learning algorithms to predict students’ achievement based on their behavior data, from which behavior features are extracted manually thanks to expert Student academic performance is a crucial indicator of educational progress, influenced by factors like gender, age, teaching staff, and learning. However, these studies mainly focus on demographic data, and prediction has been The primary objective of this project is to develop a predictive model that can forecast the performance of students in their academic projects. The objective was to design and implement a predictive model to predict academic performance to anticipate student performance. That is forcing many Predicting student academic performance is one of the important . Data-pre-processing and factor analysis have been performed on the obtained dataset to remove the anomalies in the data EDM has a primary focus on predicting students’ academic performance. Petersen, Andrew et al. This paper aims to predict student academics The focus of this work is to find a way to predict a student's academic performance in the University using the machine learning approach. Predicting students’ academic performance has long been a significant research area in educational institutes and become a challenging task due to large number performance affecting factors 1. This paper presents a methodology for predicting student performance (SPP) that leverages machine learning techniques to forecast students' academic The results validate the effectiveness of Decision Trees in predicting student academic performance. The 1D data were transformed into 2D data such that the 2D CNN could be applied to the dataset. Akinode (2016) used the Academic resul t, semester-1 and semester-2 grades for student academic . Massive Open Online Courses (MOOCs) generate a large volume of data that can be exploited to predict and evaluate student performance based on various factors. 1. Results show that the Extreme Gradient Boosting (XGBoost) can predict student academic performance with an accuracy of 97. The ability to predict students’ performance and identify at-risk students early on can be crucial, as effective interventions can be targeted to improve educational outcomes (Kovacic, 2010). This paper proposes a framework for predicting students' academic performance of first year bachelor students in Computer Science course. Within student groups, common characteristics can reveal trends in overall student learning. In Paper presented at the proceedings of the 26th international conference on world wide web companion. 6 Scope and limitation of the It is to certify that the manuscript “Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate Level Using Machine Learning” fulfilled the following: 1) This material is the authors' own original work. Previously, some other data has been used as a feature in making predictions, but TSQ result had never Download Citation | On Nov 20, 2022, Rolly T. Multivariable logistic regression was carried out to identify which factors were independently associated with academic performance. The empirical review highlights several challenges, including a lack of standardization in performance metrics, limited model generalizability, and potential bias in training data. 2. However, each algorithm possesses unique characteristics that may make it preferable under different circumstances or configurations. predicting students’ academic performance through the application of linear regres-sion. Crossref. L. This study also delves into potential gender and regional disparities that might moderate the effects of AI adoption to offer a comprehensive perspective. We propose a stacking ensemble model to predict and analyze student performance in academic competition. ITiCSE 2018 Companion - Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Explore and run machine learning code with Kaggle Notebooks | Using data from Students' Academic Performance Dataset. Three major advances are introduced by the suggested strategy. The use of machine learning for The study explores the application of data analytics and machine learning to forecast academic outcomes, with the aim of ensuring effective and sustainable e-learning. Users could explore [Show full abstract] the present study to predict student academic performance in Engineering Dynamics - a high-enrollment, high-impact, and core engineering course that almost every mechanical or From Figure 4, it can be observed that the most important feature for predicting students' academic performance is the number of times a student visited a course content, with an importance score Predicting student academic performance is a complex task that involves analyzing a variety of factors such as past performance, demographic information, online learning, video learning, and engagement with the curriculum. 1 Important attributes for predicting student academic performance Aspects of a student’s demographic and socio-economic background (e. With the increasing availability of educational Academic achievement is a multifaceted outcome influenced by a multitude of factors spanning across educational, socioeconomic, and individual characteristics. Student performance prediction has become a hot research topic. EDM has a primary focus on predicting students’ academic performance. To achieve this goal, an intelligent decision support system (IDSS) is essential to concealed insights that can enhance students' academic performance. 2) The paper reflects the authors' own research and analysis in a truthful and complete manner. To address these two obstacles, this study develops an artificial intelligence-enabled prediction model for student academic Predicting Student Academic Performance using Support Vector Machine and Random Forest ICETM 2020, December 17–19, 2020, London, United Kingdom Table 8: Binary classi cation results with Student‟s performance is an essential part in higher learning institutions. Authors’ research methodology consists a structured way of data processing which consists of cleaning, transformation and filtration of data before implementation. (2022) built a hybrid 2D CNN model using a combination of two different 2D CNN models. The study was conducted in one of the State Universities and Colleges (SUCs) in Cordillera Administrative Region (CAR). This is done by using the previous records of the student 3. One approach is to utilize machine learning to build predictive models of student performance. However, evaluating academic performance is difficult due to the complexity and nonlinear education process and learning behavior. With the help of this predictive capacity, students may make well-informed decisions about their academic and career paths, and institutions can proactively identify students who may not graduate and offer tailored support to ensure their success. They used three machine learning techniques, namely factor analysis, cluster Besides identifying the factors and attributes of the students, examined the student’s assignment submission behavior and students’ behavioral patterns before a homework due date to predict their academic performance. We built a hybrid 2D CNN model by combining two different 2D CNN models to predict academic Predicting student performance using advanced learning analytics. This review scrutinises Predicting students' academic performance is critical for educational institutions because strategic programs can be planned in improving or maintaining students' performance during their period Study namely Machine Learning Approach to Predict Student Academic Performance. The Predicting university student graduation is a beneficial tool for both students and institutions. Student performance in the final exam could be affected by many factors (e. This study also attempts to capture a pattern of the most used Predicting student performance is crucial for improving students’ future academic achievements. Decision trees and associative classifiers are the two main classes of explainable classification models. Oloruntoba, J. Educational institutions seek more information about their students to find ways to utilize their talents and address their weaknesses. 72-79. Most existing literature have made use of traditional statistical methods that run into the Forecasting academic performance of student has been a substantial research inquest in the Educational Data-Mining that utilizes Machine-learning (ML) procedures to probe the data of educational setups. To develop on education quality there is a requirement to be capable to predict students academic performance. Dropout prediction is related to student retention and has been studied extensively in recent work; Due to COVID-19, the researching of educational data and the improvement of related systems have become increasingly important in recent years. 4 Research Hypotheses 1. By the time, it is already too late to help the students. 2018 4th international conference on education and technology (ICET), IEEE (2018), pp. Authors’ dataset contains the record of 1735 B. Therefore, it is helpful to apply data mining to extract factors affecting students’ academic performance. These settings encompass critical elements that define the environment and . Comparative performance between C4. Furthermore, results showed significant social University electronic learning (e-learning) has witnessed phenomenal growth, especially in 2020, due to the COVID-19 pandemic. Moreover, this study aims to identify the most With the advances in Artificial Intelligence (AI) and the increasing volume of online educational data, Deep Learning techniques have played a critical role in predicting student Our study is aimed to provide a comprehensive review of recent studies based on student performance prediction tasks, predictor variables, methods, accuracy, and tools used in previous works Deeply understand the intelligent approaches and techniques developed to forecast student learning outcomes, which represent the student academic performance. The attainment of student outcomes in an Outcome-based Education (OBE) system adds invaluable rewards to facilitate corrective measures to the learning processes. Students are facing various PDF | Students’ Academic Performance (SAP) is an important metric in determining the status of students in any academic institution. place of birth, disability, parent academic and job background, residing region, gender, socioeconomic This systematic literature review aims to identify the recent research trend, most studied factors, and methods used to predict student academic performance from 2015 to 2021. With the increasing availability of data on student demographics, academic history, and other relevant factors, schools and universities are using advanced analytics and machine learning algorithms to gain insights into techniques to predict student’ s academic performance. The experiments on a large-scale real-world dataset show the Predicting students’ performance during their years of academic study has been investigated tremendously. 0 INTRODUCTION 1. Research work stated in presents a model for predicting students’ academic performance using supervised machine learning algorithms like support vector machine and logistic regression. In Prediction of student academic performance is an important aspect in the learning process. Several factors impact the academic performance of students, including their learning abilities, family background, peer Predicting students' academic performance in advance is of great importance for parents, management of higher education institutions and the student itself. A. In this model, This research work evaluates the use of artificial intelligence and its impact on student’s academic performance at the University of Guayaquil (UG). This work helps the educational institutions to identify the weaker studens in advance and arrange necessary training before they are going to predict students' academic performance because of the huge bulks of data stored in the environments of educational databases. [] explained their study based on the prediction of 802 undergraduate student's academic performance in e-learning. 12%. This study used several ML classification models to predict student academic performance. In this research study predicting student's academic performance in advance is of great importance for parents, student's further higher educational institutions and the student itself. This project focuses on harnessing the potential of machine learning algorithms to predict students’ academic performance, thereby enabling personalized education and enhancing educational practices. This comprehensive research focuses on predicting students’ academic success as a key aspect of Educational Data Mining (EDM). csv). It allows academic institutions to provide appropriate support . Also, consider admission policy to be incorporated to sustain quality and be globally competitive in the delivery of instruction. The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. This study investigates the role of personality factors in predicting academic performance, emphasizing the need for explainable and ethical AI systems. In this study, publicly available datasets from the University of Minho, Portugal [], are utilized to predict students’ performance. This research presents a quantitative, non-experimental, projective, and Predicting student dropout in higher education; D. Zaffar et al. This study proposes the power of Deep Learning to predict Students' Academic Performance (SAP). In the post-COVID-19 pandemic era, the adoption of e-learning has gained momentum and has increased Students’ academic records and demographic factors are the best attributes to predict performance. Drawing on the prediction of learning outcome, lecturers and institutional Univariable logistic regression was carried out summarising the association between contextual background characteristics and academic performance; this was defined as good (2:1, first classification) versus other. This research proposes a novel Student Academic Performance Predicting (SAPP) system to address these issues and enhance prediction accuracy. Anna Y. Computers in Human Behavior 104: 106189. , previous assignment grades, social life, parents’ job, and absence frequency). This paper aims to review the latest Predicting students’ academic performance at an early stage of a semester is one of the most crucial research topics in the field of Educational Data Mining (EDM). 1 Background of the study 1. The model aims to help educators and institutions identify students who may need additional support or intervention early in the project development process, ultimately enhancing overall student success. Google Scholar. Students' performance can be predicted with the help of various available techniques. Academic Performance: Academic success means that a student has satisfied the requirements for his course of The ever-increasing importance of education has driven researchers and educators to seek innovative methods for enhancing student performance and understanding the factors that contribute to academic success. compared the performance of feature selection methods using two datasets. The data were collected from 8 year period intakes from Many researchers have used traditional machine learning to predict the academic performance of students, and very little research has been conducted on the architecture of convolutional neural networks (CNNs) in the context of the pedagogical domain. This study proposes that the efforts of students, parents, and schools are interrelated and collectively contribute to determining academic achievements. Predicting Student Academic Performance Abstract: Engineering schools worldwide have a relatively high attrition rate. By identifying dependencies and course College context and academic performance are important determinants of academic success; using students’ prior experience with machine learning techniques to predict academic success before the end of the first year reinforces college self-efficacy. Selection of a right academic program and institution at right time can save time, efforts and resources of both parents and educational institutions. 1 Dataset. OK, Got it. N. S Should we care about Student Performance models? YES. (2020, April) Student’s academic performance prediction in academic using data mining techniques. Firstly, to develop and tune several Machine Kolo et al. The XGBoost Model is the (Binh & Duy, 2017) proposed a way to predict a student's academic performance by taking into account their preferred learning style. It has a better architecture that uses a combination Finding students at high risk of poor academic performance as early as possible plays an important role in improving education quality. In addition The study of learning performance prediction provides a basis for teachers to adjust their teaching methods for students who may have problems by predicting students’ performance on future exams As an emerging teaching method, online learning is becoming increasingly popular among learners. This model's performance emphasizes the importance of balanced sensitivity and specificity in predicting student academic performance. Research suggests that interactions on social media partly exhibit individuals’ personality, sentiment, and behavior. Various factors contribute to student attrition [], such as withdrawal from courses because of academic failure, peer pressure, financial issues, inter-institutional transfer, employment-related factors or myriad personal The precise prediction of student academic performance is a vital tool for educational institutions seeking to implement timely interventions and personalized learning strategies. applications of educational data mining. Determine and predict students’ dropout from on-going courses To predict academic performance (pass or fail), Poudyal et al. Technological study programs in universities often experience high dropout rates, which makes it essential to analyze and predict potential risks to reduce dropout percentages. The original source of the dataset is found in []. This study applies several machine learning models in predicting student academic performance using Time Management Skills data obtained from Time Structure Questionnaire (TSQ). Artificial neural networks are seen to be an effective tool in predicting student performance in e-learning environments. In the current educational landscape, where large amounts of data are being produced by institutions, Educational Data Mining (EDM) emerges as a critical discipline that plays a crucial role in extracting knowledge from this data to help academic policymakers make decisions. , & Sathyanarayana, N. In PISA 2022, 13,437 students from Australia participated Predicting students’ academic performance has become an important research area to take timely corrective actions, thereby increasing the efficacy of education systems.
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