Student Achievement Prediction Research Articles

A New Method Based on Belief Rule Base with Balanced Accuracy and Interpretability for Student Achievement Prediction

In the field of education, the accurate prediction of students’ future performance is essential for personalized instruction and efficient allocation of resources. Such predictions not only help education professionals develop targeted educational strategies but also identify students’ learning needs at an early stage so that timely interventions and support can be provided. To gain the trust of educational experts and ensure the practical application value of the prediction results, the prediction methods used must be highly interpretable. However, there are two problems with the current belief rule base (BRB) applied to student performance prediction. First, there is a current lack of effective strategies for enhancing the interpretability of the optimization process. Second, BRB models that overemphasize accuracy tend to exhibit characteristics of black-box models. To overcome these challenges, this paper proposes a new method based on BRB with balanced accuracy and interpretability (BRB-Bai) for student achievement prediction. First, an attribute selection method is proposed to filter out important features associated with student performance. Then, expert knowledge credibility is calculated, and four interpretability strategies are proposed to ensure the interpretability of the model and to achieve a balance between interpretability and accuracy on the basis of expert knowledge credibility. The effectiveness of the proposed model is demonstrated by conducting experiments on the student achievement dataset.

Prediction Of Student Achievement Using Artificial Neural Network And Support Vector Regression At SMK TELKOM Lampung

The analysis of student performance is crucial in vocational schools because it helps identify the challenges students face in preparing themselves for the workforce. By integrating data mining techniques such as Artificial Neural Networks (ANN), educators can enhance their understanding of factors that improve student learning outcomes. An artificial neural network (ANN) is composed of interconnected artificial neurons that can learn from input data and make complex predictions, including academic achievements. The structure and function of the human brain inspire ANN. This study compares the effec- tiveness of the artificial neural network (ANN) method with other methodologies, such as support vector regression (SVR), to predict student achievement at SMK Telkom Lampung. Primary data collected from SMK Telkom Lampung includes 4939 examples with 550 cases, 26 features, and 4 meta-attributes. Performance evaluation involves metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2). The coefficient of determination (R2) value of the Neural Network at 0.001 is higher than the R2 value of SVR, which reaches -0.036. Research find- ings indicate that the Artificial Neural Network model slightly outperforms the Support Vector Regression model, with lower prediction error rates and better ability to explain data variability.

Comparison and analysis of the accuracy of Lasso regression, Ridge regression and Elastic Net regression models in predicting students' teaching quality achievement

In this paper, the "Student Performance Data Set" data set of Kaggle competition is used to conduct correlation analysis on multiple attributes such as students' personal information, school information and students' school performance, and a correlation heat map is drawn. The results show that there is a strong positive correlation among the attributes. We then divided the data set into training, validation, and test sets in a 6:2:2 ratio, and used the Lasso regression model, the Elastic Net model, and the Ridge regression model to make predictions. After training 50 epochs, we evaluated and compared the models. The results show that Lasso regression model has the lowest prediction error and the best error effect. Elastic Net was the next best predictor, while Ridge regression model had the largest prediction error and was the worst. To sum up, Lasso regression model has the best Performance in grade prediction based on the "Student Performance Data Set" dataset. This conclusion is of great significance for schools and educational institutions, as it can help them better understand students' learning and improve the quality and effectiveness of teaching. At the same time, this conclusion is also valuable for data scientists and machine learning researchers, because it can guide them to choose the most appropriate model and algorithm on similar data sets, improving the accuracy and effectiveness of predictions. In general, this paper analyzes and discusses the problem of Student achievement prediction, puts forward the Lasso regression model based on the "Student Performance Data Set" data set, which has the best performance prediction effect, and analyzes the principles of the three models. This conclusion has practical applications for schools and educational institutions, as well as providing a reference for data scientists and machine learning researchers.

Predictive assessment of learners through initial interactions with encoding techniques in deep learning

<p>Recent academic research has prominently focused on predicting student achievements in online learning. However, ongoing challenges persist for teachers and researchers, primarily revolving around the selection of relevant features. To address this issue, the study aims to predict the correlation between the use of a Virtual Learning Environment (VLE) and students’ performance based solely on their initial interactions with the platform during the initial segment of the course module within the annual study sequence of the academic year 2014–2015. The assessment employed a specialized data model that integrates the chronological order of a learner’s interactions with the educational materials provided on the online learning platform throughout the sequence employing data from the Open University Learning Analytics Dataset (OULAD), which included 32,593 undergraduates in all courses. Innovative methods, such as the VLE, and Long Short-Term Memory (LSTM) neural network rooted in deep learning, were applied to ensure the prediction of student achievement. Preliminary findings demonstrated a 60% accuracy of the model using only learner interaction data within the first third of the course duration and employing various variable encoding techniques. The successful integration of the esteemed OULAD, combined with the implementation of an LSTM neural network architecture in our data model, proved to be a highly effective strategy. This approach yielded valuable perceptions facilitating the identification of crucial data points essential for predicting student success and contributing to informed decision-making in formulating strategies to assess and support student performance effectively.</p>

Aplikasi Prediksi Prestasi pada Siswa Menggunakan Algoritma C4.5

The development of computer technology that is increasingly modern, fast and practical greatly affects the lives of the wider community. With the existence of increasingly sophisticated technology, a country's education can foster people's abilities, form personalities and progress a dignified country in the context of educating the country's life. Therefore, education is becoming an increasingly large impact that encourages students to achieve the best achievements. One of the factors that support student achievement is craft, adequate facilities, academic grades and also quality educators. The purpose of this study is to determine the achievement of students by predicting using the C4.5 algorithm on the designed web-based application. This research uses the RAD (Rapid Application Development) method in the process of designing student achievement prediction applications using the C4.5 algorithm. The results of the designed application aim to make it easier for students to increase their potential. The application designed has become an alternative problem solving for student problems regarding achievements, both academic and non-academic. So that when students know it, they can easily continue to hone their respective interests and talents.

A Novel Student Achievement Prediction Method Based on Deep Learning and Attention Mechanism

A Novel Student Achievement Prediction Method Based on Deep Learning and Attention Mechanism

Categorization of Learning Analytics Models: Brief Literature Review

Learning analytics is one of the technological tools aiming to investigate educational database collected during the learning delivery process for further purpose of use in decision making or process update. Various types of methods on learning analytics are originated by scholars with their own ambition to contribute the field study. It is emerging study field since 2010s. This paper review literature papers which focused on categorization of learning analytics models with focus of its’ criteria. The papers are chosen from open scholar databases. The selected papers reviewed learning analytics model related studies to bring up their suggested categorization. The category based on the learning analytics models’ main objective as well as used approach. It is observed that prediction of student achievement or success is significant method among learning analytics models.

Research on Abnormal Behavior Prediction by Integrating Multiple Indexes of Student Behavior and Text Information in Big Data Environment

With the wide application of information technologies such as big data, the Internet of Things, and cloud computing, college students have accumulated a large amount of personal information and daily behavior data in their daily studies and life. How to dynamically integrate multidimensional information of students to build accurate student portraits, using multi-indicator data of student behavior and comment texts, and finding out students with abnormal behavior from among many students has become an important problem to be solved. This paper proposes an abnormal behavior prediction method integrating multiple indicators of student behavior and text information (ABPM-IMISBTI) for the problem of abnormal behavior prediction of college students in the big data environment. First, given the problems of multidimensionality, timeliness, and dynamics of student behavior information fusion in the construction of student behavior portraits, by integrating students’ objective tags and subjective tags, an optimized K-means algorithm based on a cloud platform environment is proposed. Second, aiming at the problem of insufficient text information analysis in the analysis of students’ abnormal behavior, the ABPM-IMISBTI method is proposed to solve the prediction of students’ abnormal behavior through long and short-term memory networks (LSTM) combined with student behavior multi-index data and text information. Finally, this paper takes student achievement prediction as an example for verification. The experimental results show that, compared with other prediction methods, the ABPM-IMISBTI method proposed in this paper can improve the accuracy of student behavior prediction, and then quickly determine the abnormal behavior of students, to improve the level of education management in universities and promote the development of safe campuses, smart campuses, and smart education.

Prediction of Student Achievement through Teacher's Professional Characteristics by using Artificial Intelligence

Teacher characteristics are important for teaching learning and are important predictors of students’ achievement. The present study aims to predict students’ academic achievement criteria on teachers’ characteristics with the help of artificial intelligence (AI). The data were collected from 100students from one of the universities in Lahore, Pakistan, through a questionnaire comprising of teachers’ characteristics effective for teaching, against a 4-point scale varying from strongly agree to strongly disagree to the find extant of the existence of certain characteristics in the teachers. The analysis was done to predict the student grade based on teacher characteristics in the class with the help of artificial intelligence. Two machine learning models were built, and their accuracy was compared. Thek-Nearest Neighbor (KNN) and Multiple Linear Regression (MLR) models were built. The accuracies of the KNN and MLR were 83 Percent and 91 Percent, respectively. This work concluded that the MLR model could be effectively used to predict student performance.

Markov Model-Based Curriculum Association Classification Model and Student Achievement Prediction

With the increasing popularity of higher education and the increasing scale of students in schools, the educational administration management system of various colleges and universities has also accumulated a large amount of performance data. In the face of these massive data, many teaching managers still stay in the simple operation of adding, deleting, modifying, and checking the data and cannot effectively extract and analyze the useful knowledge and information hidden behind the data. Therefore, Markov model is proposed. The advantage of the Markov model is that it has better prediction effect on random series and data series with large volatility; that is, grey prediction model is used to reveal the overall trend of development and change of prediction data series. This paper studies the curriculum association classification model and student achievement prediction based on the Markov model. According to the student’s historical achievement, the average missed detection rate of future grade point is 48.65%, the average missed detection rate of Apriori algorithm is 35.5%, the average missed detection rate of FP growth algorithm is 43.2%, the average missed detection rate of this algorithm is 37.5%, and only 17 of the 40 grade point interval predictions match the actual interval. Make full use of the association rules between courses to provide students with early warning and teacher guidance for specific courses, which can effectively reduce the failure rate while reducing the academic burden. The Markov model can mine students’ data inside and outside the classroom, establish and improve college students’ achievement index system, then deeply analyze and discuss the development of college students’ achievement and ability, and finally give each student’s achievement results.

Student achievement prediction using deep neural network from multi-source campus data

Finding students at high risk of poor academic performance as early as possible plays an important role in improving education quality. 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 experience and knowledge. However, owing to an increase in the varieties and overall volume of behavioral data, it has become more and more challenging to identify high-quality handcrafted features. In this paper, we propose an end-to-end deep learning model that automatically extracts features from students’ multi-source heterogeneous behavior data to predict academic performance. The key innovation of this model is that it uses long short-term memory networks to capture inherent time-series features for each type of behavior, and it takes two-dimensional convolutional networks to extract correlation features among different behaviors. We conducted experiments with four types of daily behavior data from students of the university in Beijing. The experimental results demonstrate that the proposed deep model method outperforms several machine learning algorithms.

A Predictive Model for Student Achievement Using Spiking Neural Networks Based on Educational Data

Student achievement prediction is one of the most important research directions in educational data mining. Student achievement directly reflects students’ course mastery and lecturers’ teaching level. Especially for the achievement prediction of college students, it not only plays an early warning and timely correction role for students and teachers, but also provides a method for university decision-makers to evaluate the quality of courses. Based on the existing research and experimental results, this paper proposes a student achievement prediction model based on evolutionary spiking neural network. On the basis of fully analyzing the relationship between course attributes and student attributes, a student achievement prediction model based on spiking neural network is established. The evolutionary membrane algorithm is introduced to learn hyperparameters of the model, so as to improve the accuracy of the model in predicting student achievement. Finally, the proposed model is used to predict student achievement on two benchmark student datasets, and the performance of the prediction model proposed in this paper is analyzed by comparing with other experimental algorithms. The experimental results show that the model based on spiking neural network can effectively improve the prediction accuracy of student achievement.

Student Performance Prediction in Mathematics Course Based on the Random Forest and Simulated Annealing

Educational data mining is becoming a more and more popular research field in recent years, mainly with the help of cross research conducted by various disciplines, so as to solve various difficult problems in the teaching and education process. In this paper, we proposed a hybrid approach for student performance prediction. We collected the dataset, including 15 characteristics of students from three categories (individual basic information, individual education information, and individual behavior information). Based on the random forest (RF) and simulated annealing (SA) algorithms, we binary encode the relevant parameters (number of features, tree size, and tree decision weights) as the target variables for algorithm optimization, use the out-of-bag error as the optimization objective function, and then propose the IRFC (improved random forest classifier) algorithm in this paper. Compared with other mainstream improved random forest algorithms, the research results demonstrate that the proposed algorithm in this paper has higher generalization ability and smaller OOB error. This study provides a methodological reference for the prediction of student achievement and also makes a marginal contribution to student management work.

An Improved Deep Learning Algorithm for Student Achievement Prediction

Educational Data Mining (EDM) research has risen to prominence because it aids in the discovery of relevant knowledge from educational data sets that can be used for a variety of reasons, such as forecasting students' academic performance and outcomes. Predicting student accomplishment may be beneficial in the development and implementation of a variety of improvements in education settings as a response to current educational systems. Machine learning has been used to predict students' achievement in a huge amount of existing research, which has taken a variety of factors into account, including family income, students' gender, students' absence, and stage-by-stage characteristics. In this proposal, an attempt is made to investigate the usefulness of applying the Deep Learning Algorithm (DLA), more specifically the Optimal Deep Neural Network (ODNN), to forecast students' progress, which could aid in determining whether or not students would be able to complete their degree. Using experimental data, it was discovered that the suggested framework outperformed the current one that was within reach in terms of accuracy of prediction.

Naïve Bayes for Analysis of Student Learning Achievement

Student achievement is measured by the achievement index value obtained every semester,student achievement is measured by several factors, and in this research the author takes several factors including study paths, choice of majors, monthly living expenses, relationships with friends, relationships with family, motivation study, employment, scholarships, transportation, and internet services. Analysis and prediction of student achievement using Naïve Bayes Algorithm classification method, the result is this algorithm works very well using 14 student datasets to determine the grades of the 15th student. Based on theAnalysis, variables that affect student achievement include choice of majors, residence, relationships with friends, relationships with family, job, and scholarships. The accuracy of the naïve bayes algorithm for this student achievement case study model reaches 60%, precision 25%, and recall 100%.

Analisa Jaringan Saraf Tiruan Backpropagation Untuk Memprediksi Prestasi Siswa SMA Muhammadiyah Serbelawan

Achievements achieved by graduates from an educational institution show the quality and quality. One of them is seen from one of the assessment criteria for assessing the achievement of graduates at the secondary school level, namely through the average score. This average value is often used as a measure to assess students who will enter the next level of education. In addition, the acceptance of students at a level of education is also adjusted to the capacity of the school in question. The high average score at the high school level does not guarantee student achievement at the tertiary level. So that this study aims to obtain an output architecture prediction of student achievement at SMA Muhammadiyah Serbelawan which correlates between the average value and the total score of class XII (twelve) high school students according to the data trained using Artificial Neural Network Analysis using the Backpropagation method. The data taken in the form of the average value of students and the total value of the second semester of class XII students. Furthermore, the data were analyzed using Backpropagation ANN method, with the help of MATLAB software. From the results of testing the Student Achievement data above, we can see in the 5-5-5-1 architecture which shows from the target minus the ANN output that SSE is 0.17625 which shows that there is a measuring tool in predicting the best students using academic value data as a target. From the data obtained, the computational performance of artificial neural networks with the Backpropagation Algorithm is 85%.

Design and Implementation of Intelligent Educational Administration System Using Fuzzy Clustering Algorithm

The present work aims to solve the problems that the traditional educational administration management system has, such as low efficiency in analyzing big data, and the analysis results have low value, which is based on manual rules definition in big data analysis and processing. The work proposes a student achievement prediction model FCM-CF based on Fuzzy C-means (FCM) and Collaborative Filtering (CF). The work also introduces it into the research of educational administration management to construct an intelligent educational administration management system. At the beginning, the FCM-CF model is described in detail. Then, the system requirements and specific design methods are described in detail. Eventually, with the students’ performance prediction as an example, the performance of the system is tested by designed simulation experiments. The result shows that the students’ achievement in study is closely related to their daily study performance such as preparation before class, classroom performance, attendance, extracurricular study, and homework completion. Generally, the examination scores of students are significant to their daily performances. Under the same experimental conditions, the prediction error of the FCM-CF model proposed here is less than 10.8% of that of other algorithms. The model has better prediction performance and is more suitable for the prediction of middle school students’ examination scores in educational administration management system. The innovation of intelligent educational administration management system is that, in addition to the basic information management function, it also has two other functions: students’ performance prediction analysis and teacher evaluation prediction. It can provide data support for improving teaching quality. The research purpose is to provide important technical support for more intelligent educational administration and reduce the loss of human resources in educational administration.

Comparison J48 And Naïve Bayes Methods in Educational Analysis

In the present, data mining can be applied in various fields. One of them is the field of education. By applying data mining in the field of education, education providers can make an analysis of students in their schools. Schools can predict student achievement, make assessments of students more thoroughly, and can also predict students’ interests and talents. This study will discuss the prediction of student learning habits and the prediction of student achievement in order to find out the right steps to take afterwards. In this study, two (2) classification algorithms were used, namely J48 and NaiveBayes. This is done to find the best results from each algorithm.

Prediction of Students Performance using Machine learning

An enormous measure of computerized information is being produced over a wide assortment in the field of data mining strategies. The creation of student achievement prediction models to predict student performance in academic institutions is a key area of the development of Education Data Mining. A prediction system has been proposed by using their 10th, 12th and previous semester marks. The study is evaluated using Binomial logical regression, Decision tree, and Entropy and KNN classifier. In order to attain their higher score, this framework would assist the student to recognize their final grade and improve their academic conduct.

Grade Prediction Model Based on DeepCycle Neural Network Classification Algorithm

There are many factors affecting students’ grades, which make the prediction of students’ grades present high dimensional and nonlinear characteristics. Therefore, the traditional method has a large error in the prediction results, which is difficult to meet the practical needs. With the rapid development of artificial neural network (Ann), the deep cycle neural network algorithm based on Ann provides a new approach for student achievement prediction. In order to further improve the accuracy of student achievement prediction, this paper proposes a performance prediction model based on deep cycle neural network algorithm. First, principal component analysis is used for data dimensionality reduction processing of the established student writing evaluation system, and the first five principal components are extracted. Then, these principal components are taken as the input of the neural network to construct a three-layer neural network prediction model. The experimental results show that, compared with the single RBF neural network and BP neural network, the prediction model under deep cycle neural network is simple in structure, fast in convergence and 21.6% higher in prediction accuracy, which verifies the effectiveness of the model proposed in this paper.