Prediction Of Student Academic Performance Research Articles

Predicting Student Academic Performance using Data Generated in Higher Educational Institutes

The analysis of data generated by higher educational institutes has the potential of revealing interesting facets of student learning behavior. Classification is a popularly explored area in Educational Data Mining for predicting student performance. Using student behavioral data, this study compares the performance of a broad range of classification techniques in an attempt to find a qualitative model for the prediction of student performance. Rebalancing of data has also been explored to verify if it leads to the creation of better classification models. The experimental results, validated using well-established evaluation matrices, presented potentially significant outcomes, which may be used for reshaping the learning paradigm.

An Intelligent System to predict Students academic performance using Data Mining

An Intelligent System to predict Students academic performance using Data Mining

Modeling of student academic achievement in engineering education using cognitive and non-cognitive factors

Purpose The retention and success of engineering undergraduates are increasing concern for higher-education institutions. The study of success determinants are initial steps in any remedial initiative targeted to enhance student success and prevent any immature withdrawals. This study provides a comprehensive approach toward the prediction of student academic performance through the lens of the knowledge, attitudes and behavioral skills (KAB) model. The purpose of this paper is to aim to improve the modeling accuracy of students’ performance by introducing two methodologies based on variable selection and dimensionality reduction. Design/methodology/approach The performance of the proposed methodologies was evaluated using a real data set of ten critical-to-success factors on both attitude and skill-related behaviors of 320 first-year students. The study used two models. In the first model, exploratory factor analysis is used. The second model uses regression model selection. Ridge regression is used as a second step in each model. The efficiency of each model is discussed in the Results section of this paper. Findings The two methods were powerful in providing small mean-squared errors and hence, in improving the prediction of student performance. The results show that the quality of both methods is sensitive to the size of the reduced model and to the magnitude of the penalization parameter. Research limitations/implications First, the survey could have been conducted in two parts; students needed more time than expected to complete it. Second, if the study is to be carried out for second-year students, grades of general engineering courses can be included in the model for better estimation of students’ grade point averages. Third, the study only applies to first-year and second-year students because factors covered are those that are essential for students’ survival through the first few years of study. Practical implications The study proposes that vulnerable students could be identified as early as possible in the academic year. These students could be encouraged to engage more in their learning process. Carrying out such measurement at the beginning of the college year can provide professional and college administration with valuable insight on students perception of their own skills and attitudes toward engineering. Originality/value This study employs the KAB model as a comprehensive approach to the study of success predictors. The implementation of two new methodologies to improve the prediction accuracy of student success.

Classification and Prediction of Student Academic Performance in King Khalid University- A Machine Learning Approach

Objectives: Universities accumulate huge amount of student’s data in electronic form. Based on the information stored in the database filtering a data on certain criteria becomes difficult, when executed manually. Hence implementing tools that analyses the data in statistical, descriptive or computational ways are quite important to be considered. Methods/ Statistical Analysis: This study presents an analysis on top ten machine learning algorithms used in classification and prediction. WEKA tool is used to conduct the experiment to know the accuracy and other result parameters on evaluating the categorical prediction of student performance. Also an analysis has been done to estimate the parameters based on the number of samples. Findings: The comparative analysis on the classification accuracy of around 12 classifiers of WEKA involving Rep Tree, Naive Bayes, J48, Bagging, lBK, Multilayer Perceptron, Random Forest, Random Tree, Stacking, AdaBoost, Logistic and SMO were analysed on datasets in varying number of instances. Based on the results obtained best 5 methods are chosen and compared on the entire dataset for prediction results. Ten machine learning algorithms were considered wherein the results such as accuracy in classification, Kappa statistic, and Mean absolute error are considered and compared. Bagging, Random Forest, lBK, Random Tree was filtered at the first level based on kappa statistic. In the second level filter based on accuracy lBK, Random Tree was considered as the final suitable models for the provided dataset. Application/Improvements: Developing a questionnaire among students and teachers is to be done to evaluate and predict the results in various angles based on various parameters. The positive factors and the negative factor contribution for the result of the institution are to be analysed. Keywords: Educational Data Mining, Analysis, Prediction, Machine Learning, Student Performance, WEKA

Classification and Prediction of Student Academic Performance using Machine Learning A Review

Classification and Prediction of Student Academic Performance using Machine Learning A Review

Relief-F and Budget Tree Random Forest Based Feature Selection for Student Academic Performance Prediction

Relief-F and Budget Tree Random Forest Based Feature Selection for Student Academic Performance Prediction

Neural-fuzzy with representative sets for prediction of student performance

In this paper, a new method for handling the Multi-Input Multi-Output Student Academic Performance Prediction (MIMO SAPP) problem is proposed. The MIMO SAPP aims to predict the future performance of a student after being enrolled into a university. The existing methods have limitations of using a parameter set and an unsuitable training strategy. Thus, the new method called MANFIS-S (Multi Adaptive Neuro-Fuzzy Inference System with Representative Sets) uses multiple parameter sets and a special learning strategy to resolve those weaknesses. Specifically, the idea of multiple parameter sets is to approximate the MANFIS-S model with many meaningful parameters to ensure the performance of system. This is regarded as the representative problem, which is mathematically formulated and theoretically validated. The idea for the special learning strategy is to use global and local training. In global training, a random parameter set is trained from the first to the last record of the database. Each time of training results in a set of parameters. Global training will rectify and achieve a meaningful subset of parameters by the last training process. In local training, there are 2 types of parameters in MANFIS-S namely premise and consequent that are trained by the gradient descent and Particle Swarm Optimization in a hybrid way. Lastly, for a new record in the testing set, Fuzzy K-Nearest Neighbor is used to find which group it belongs to. The proposed MANFIS-S model is experimentally validated against ANFIS, MANFIS, OneR and Random Tree in a benchmark student performance dataset from UCI, a real student performance dataset from VNU University of Science, Vietnam, and 3 educational datasets taken from KDD Cup. The experiments demonstrated the superiority of MANFIS-S over the related algorithms in term of accuracy.

Predicting Student Academic Performance in Computer Science Courses: A Comparison of Neural Network Models

Predicting Student Academic Performance in Computer Science Courses: A Comparison of Neural Network Models

Prediction of Student Academic Performance using Neural Network, Linear Regression and Support Vector Regression: A Case Study

Prediction of Student Academic Performance using Neural Network, Linear Regression and Support Vector Regression: A Case Study

A Study of Feature Selection Algorithms for Predicting Students Academic Performance

The main aim of all the educational organizations is to improve the quality of education and elevate the academic performance of students. Educational Data Mining (EDM) is a growing research field which helps academic institutions to improve the performance of their students. The academic institutions are most often judged by the grades achieved by the students in examination. EDM offers different practices to predict the academic performance of students. In EDM, Feature Selection (FS) plays a vital role in improving the quality of prediction models for educational datasets. FS algorithms eliminate unrelated data from the educational repositories and hence increase the performance of classifier accuracy used in different EDM practices to support decision making for educational settings. The good quality of educational dataset can produce better results and hence the decisions based on such quality dataset can increase the quality of education by predicting the performance of students. In the light of this mentioned fact, it is necessary to choose a feature selection algorithm carefully. This paper presents an analysis of the performance of filter feature selection algorithms and classification algorithms on two different student datasets. The results obtained from different FS algorithms and classifiers on two student datasets with different number of features will also help researchers to find the best combinations of filter feature selection algorithms and classifiers. It is very necessary to put light on the relevancy of feature selection for student performance prediction, as the constructive educational strategies can be derived through the relevant set of features. The results of our study depict that there is a 10% difference of prediction accuracies between the results of datasets with different number of features.

A new proposal Classification method based on Fuzzy Association Rule Mining for Student Academic Performance Prediction

Predicting student academic performance (SAPP) is an important task in moderneducation system. Proper prediction of student performance improves construction of educationprinciples in universities and helps students select and pursue suitable occupations. Theprediction approaching fuzzy association rules (FAR) give advantages in this circumstancebecause it gives the clear data-driven rules for prediction outcome. Applying fuzzy conceptbrings the linguistic terms that are close to people thought over a quantitative dataset, howeveran efficient mining mechanism of FAR requires a high computing effort normally. The existingFAR-based algorithms for SAPP often use Apriori-based method for extracting fuzzy associationrules, consequently they generate a huge number of candidates of fuzzy frequent itemsets andvarious redundant rules. This paper presents a new proposal model of predictor using FAR toelevating prediction performance and avoids extraction of the fixed set of FAR beforepredictions progress. Indeed, a modification tree structure of a FP-growth tree is used in fuzzyfrequent itemset mining, when a new requirement rises, the proposed algorithm mines directly inthe tree structure for the best prediction results. The proposal model does not require to predeterminethe antecedents of prediction problem before the training phrase. It avoids searchingfor non-relative rules and prunes the conflict rules easily by using a new rule relatednessestimation.

The Influence of Social Media and Environmental Factors on Academic Performance of Student Nurses in South-West Nigeria

The growing popularity and consideration of social networking sites having the ability to enhance interaction, academic participation and performance among lecturers and students respectively underscored an investigation into the influence of social media and environmental factors on academic performance of student nurses in Southwest Nigeria. The study is a survey design that adopted correlational research type. The population comprised all Student Nurses in all the Nursing Schools in Southwest Nigeria. Six schools purposively selected and a sample of 300 students randomly selected participated in the study. Two instruments: Social Media and Environmental Factor Questionnaire (SMEFQ) r=0.85 and Student Nurses Achievement Test (SNAT), r=0.72 was used to collect data. Three research questions guided the study and data collected was analysed using descriptive and inferential statistics. Result revealed that the three predictor variables correlated with each other in predicting students’ academic achievement. It also showed that Facebook ß = (-.162), t(-1.436) = .155, p>.005 contributed most among the predictor variables while the two predictors (social media and environmental factors) could not predict students academic performance in Nursing (F(2,77) =1.214, P= 0.303). It is recommended that students be encouraged to use social media adequately for academic purposes while government improve school physical structures and provide conducive learning environment for academic excellence.

Student Academic Performance Prediction on Problem Based Learning Using Support Vector Machine and K-Nearest Neighbor

Academic evaluation is an important process to know how well the learning process was conducted and also one of the decisive factors that can determine the quality of the higher education institution. Though it usually curative, the preventive effort is needed by predicting the performance of the student before the semester begin. This effort aimed to reduce the failure rate of the students in certain subjects and make it easier for the PBL tutor to create appropiate learning strategies before the tutorial class begin. The purpose of this work is to find the best data mining technique to predict student academic performance on PBL system between two data mining classification algorithms. This work applied and compared the performance of the classifier models built from Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). After preprocessed the dataset, the classifier models were developed and validated. The result shows that both algorithms were giving good accuracy by 97% and 95,52% respectively though SVM showing the best performance compared to KNN in F-Measure with 80%. The further deployment is needed to integrate the model with academic information system, so that academic evaluation can be easily done.

Predicting Student Academic Performance in KSA using Data Mining Techniques

The main objective of higher education institutions is to provide quality education to its students. One way to achieve highest level of quality is to identify factors affecting academic performance and then trying to resolve weakness of these factors. The specific objective of the proposed research work is to find out if there are any patterns in the available data (student and courses records) that could be useful for predicting students’ performance. The study involved a sample of 150 students collected from Najran University students in Saudi Arabia. The data was captured and arranged with the use of statistical package for social sciences (SPSS) and data mining tool (clementine). Developing an accurate student’s performance prediction model is challenging task. Data mining based model were used to identify which of the known factors can give an early indicator of expected performance. This paper employs both feature reduction and classification technique to reduce error rate. The experimental results reveal significant relationships between including both practical work and assignments in course and its success rate. But, on the other hand the number of given assignment has a negative impact on course academic performance. In context of factors affect student academic performance, the most affecting factor is student attendance in class in addition to final exam and mid exam grades.

Predicting Student Academic Performance in Blended Learning Using Artificial Neural Networks

Predicting Student Academic Performance in Blended Learning Using Artificial Neural Networks

Student Academic Performance Prediction Model Using Decision Tree and Fuzzy Genetic Algorithm

The research on the educational field that involves Data Mining techniques is rapidly increasing. Applying Data Mining techniques in an educational background are known as Educational Data Mining that aims to discover hidden knowledge and patterns about student's performance. This work aims to develop student's academic performance prediction model, for the Bachelor and Master degree students in Computer Science and Electronics and Communication streams using two selected classification methods; Decision Tree and Fuzzy Genetic Algorithm. Parameters like internal marks, sessional marks and admission score were selected to conduct this work. Internal marks are the combination of attendance marks, average marks obtained from two sessional exams and assignment marks. Admission score for degree students is the weighted score obtained from 10th and 12th examination marks and entrance marks. In the case of master's degree students, it includes degree examination marks and entrance marks. Resultant prediction model can be used to identify student's performance for each subject. Thereby, the lecturers can classify students and take an early action to improve their performance. Systematic approaches can be taken to improve the performance with time. Due to early prediction and solutions are done, better results can be expected in final exams. Students can view their academic information and updates. Reputed companies having a tie-up with the institution can search students according to their requirements.

A Decision Tree Approach for Predicting Students Academic Performance

This research is on the use of a decision tree approach for predicting students’ academic performance. Education is the platform on which a society improves the quality of its citizens. To improve on the quality of education, there is a need to be able to predict academic performance of the students. The IBM Statistical Package for Social Studies (SPSS) is used to apply the Chi-Square Automatic Interaction Detection (CHAID) in producing the decision tree structure. Factors such as the financial status of the students, motivation to learn, gender were discovered to affect the performance of the students. 66.8% of the students were predicted to have passed while 33.2% were predicted to fail. It is observed that much larger percentage of the students were likely to pass and there is also a higher likely of male students passing than female students.

Predicting Student Academic Performance at Degree Level: A Case Study

Universities gather large volumes of data with reference to their students in electronic form. The advances in the data mining field make it possible to mine these educational data and find information that allow for innovative ways of supporting both teachers and students. This paper presents a case study on predicting performance of students at the end of a university degree at an early stage of the degree program, in order to help universities not only to focus more on bright students but also to initially identify students with low academic achievement and find ways to support them. The data of four academic cohorts comprising 347 undergraduate students have been mined with different classifiers. The results show that it is possible to predict the graduation performance in 4th year at university using only pre-university marks and marks of 1st and 2nd year courses, no socio-economic or demographic features, with a reasonable accuracy. Furthermore courses that are indicators of particularly good or poor performance have been identified.

Predicting Student Academic Performance: A Comparison of Two Meta-Heuristic Algorithms Inspired by Cuckoo Birds for Training Neural Networks

Predicting student academic performance with a high accuracy facilitates admission decisions and enhances educational services at educational institutions. This raises the need to propose a model that predicts student performance, based on the results of standardized exams, including university entrance exams, high school graduation exams, and other influential factors. In this study, an approach to the problem based on the artificial neural network (ANN) with the two meta-heuristic algorithms inspired by cuckoo birds and their lifestyle, namely, Cuckoo Search (CS) and Cuckoo Optimization Algorithm (COA) is proposed. In particular, we used previous exam results and other factors, such as the location of the student’s high school and the student’s gender as input variables, and predicted the student academic performance. The standard CS and standard COA were separately utilized to train the feed-forward network for prediction. The algorithms optimized the weights between layers and biases of the neuron network. The simulation results were then discussed and analyzed to investigate the prediction ability of the neural network trained by these two algorithms. The findings demonstrated that both CS and COA have potential in training ANN and ANN-COA obtained slightly better results for predicting student academic performance in this case. It is expected that this work may be used to support student admission procedures and strengthen the service system in educational institutions.

Predicting Student Academic Performance

The biggest challenge in nurturing an academic community is encouraging knowledge sharing among its members. Literature on communities, however, has paid less attention on the role of outcome expectations in encouraging the knowledge sharing behaviors. This study examines the effects of Personal Outcome Expectations (POE) and Community-related Outcome Expectations (COE) on the knowledge sharing behaviors of students and its consequent impact on their academic performance. In order to study these relationships a survey of university students was conducted. Based on structural equation modeling approach, it was found that COE have significant impact on knowledge sharing among the students.