Predicting student performance using data mining and learning analysis technique in Libyan Higher Education

Education can be utilized as a tool to face many problems, overcome many hurdles in life. The knowledge obtained from education helps to enhance opportunities in one's employment development. To extract useful information from the knowledge obtained, Educational Data Mining is widely used. Educational data mining provides the process of applying different data mining tools and techniques to analyze and visualize the data of an institution (school) and can be used to discover a unique pattern of students' academic performance and behavior. The present work intends to enhance students' academic performance in secondary school using data mining techniques. Real data was collected using school reports and questionnaire method by the Portugal school which has been used in this paper. Naive Bayesian algorithm can be easily implemented to predict the students' academic performance and behavior. Classification of students into two classes, pass and fail, involves training phase and testing phase. In training phase, Naive Bayes classifier is built and in the testing phase, Naive Bayes classifier is used to make the prediction. The accuracy of the classifier is calculated using WEKA tool in which confusion matrix is generated. The accuracy of the classifier obtained is 87% which can be further improved by the selection of appropriate attributes. Developing the classification algorithms in this way helps to obtain a more efficient student performance predictor tool using other data mining algorithms and it also helps to improve the quality of education in an educational institution.

Educational Data Mining (EDM) is a current trending multidisciplinary research area that explores the data and develops the methods in educational environments. The challenges in the EDM includes predicting and improving the student's performance which helps both instructors and students, identifying the key factors that affect the performance of students, understanding the learning process of the students, providing the best suggestions to students for their future development. The accurate prediction of student's performance helps the instructors to take proactive measures, improve teaching methodologies and guide the students in a right way. This also helps the student's to be active and encourage them to put extra effort on their studies. In this work, to achieve the goals of EDM various statistical and data mining techniques are used. The raw datasets obtained through various resources are first explored and the goals are identified. Afterwards, the data mining techniques are used to identify the hidden relationships and extracting the knowledge from these datasets.

Advances in educational data mining and learning analytics techniques allow instructors and institutions to analyze log data generated from learning management systems to inform themselves about student learning and success. Over the years, several machine learning techniques have been developed, used, and researched to provide more accurate predictions of students’ performance in courses. These techniques commonly need and are focused on large sample sizes and low dimensionality which is not the case for university courses in blended contexts. In some studies, student cohorts across years and programs are merged together to increase sample sizes and achieve better prediction accuracies. While there have been other recent studies experimenting with lower samples and fewer dimensions, they do not focus on cohort influences on prediction. There is a need to study this area of analysis and prediction in multi-cohort university courses both to inform (a) instructors, of key course features, prediction insights, and student study behavior to offer relevant interventions to specific cohorts and (b) curriculum designers, who can gain insights into improving program designs. In this study, we look for empirical evidence if performance metrics and key features in prediction are influenced by cohorts in multi-cohort university courses.KeywordsPredictive learning analyticsHigher educationMulti-cohort university courses

The online version contains supplementary material available at 10.1007/s40200-021-00884-2.

Every year students success rate was analysed by the Educational Institutions to develop their Academic standard. To identify the success rate many kinds of techniques are used such as statistics, physical examination and currently ongoing datamining techniques. Data mining Techniques was widely used in many fields, it is also used in the Educational environment known as Educational Data Mining (EDM). Educational data mining generate prototype in solving the research problems in students data and used to locate the unseen patterns in the students detailed dateset. This paper uses the EDM to characterize the distinct factors affecting the students performance by making predictions with efficient algorithms. Educational professionals have to identify the causes for the student failure in academic performance and the students not succeed in completing their education which becomes a social problem these days. The machine learning algorithms help the researchers for evaluation of student’s learning habits, their academic performance and added enhancement if required. This paper would discuss different kinds of algorithms to analyse the economic background of the students which mainly affects the students performance. The dataset was utilized from the UCI Repository of secondary school students performance and analysed using the Weka tool for the datamining process.

Objectives: Data mining techniques are implemented in many organizations as a standard procedure for analyzing the large volume of available data, extracting useful information and knowledge to support the major decision-making processes. Data mining can be applied to wide variety of applications in the educational sector for the purpose of improving the performance of students as well as the status of the educational institutions. Educational data mining is rapidly developing as a key technique in the analysis of data generated in the educational domain. Methods: The aim of this study presents an analysis of final year results of UG degree students using data mining technique, which carried out in three of the private colleges in Tamil Nadu state of India. The primary objective of this research work is to apply the classification techniques to the prediction of the performance of students in end semester university examinations. Particularly, the decision tree algorithm C4.5 (J48), Bayesian classifiers, k Nearest Neighbor algorithm and two rule learner’s algorithms namely OneR and JRip are used for classifying the performance of students as well as to develop a model of student performance predictors. Results: The result of this study reveals that overall accuracy of the tested classifiers is above 60%. In addition classification accuracy for the different classes reveals that the predictions are worst for distinction class and fairly good for the first class. The JRip produces highest classification accuracy for the Distinction. Classification of the students based on the attributes reveals that prediction rates are not uniform among the classification algorithms. Also shows that selected data attributes have found to be influenced the classification process. The results showed to be satisfactory. Improvements: The study can be extended to draw the performance of other classification techniques on an expanded data set with more distinct attributes to get more accurate results.

PurposeThis paper aims to evaluate educational data mining methods to increase the predictive accuracy of student academic performance for a university course setting. Student engagement data collected in real time and over self-paced activities assisted this investigation.Design/methodology/approachClassification data mining techniques have been adapted to predict students’ academic performance. Four algorithms, Naïve Bayes, Logistic Regression, k-Nearest Neighbour and Random Forest, were used to generate predictive models. Process mining features have also been integrated to determine their effectiveness in improving the accuracy of predictions.FindingsThe results show that when general features derived from student activities are combined with process mining features, there is some improvement in the accuracy of the predictions. Of the four algorithms, the study finds Random Forest to be more accurate than the other three algorithms in a statistically significant way. The validation of the best-known classifier model is then tested by predicting students’ final-year academic performance for the subsequent year.Research limitations/implicationsThe present study was limited to datasets gathered over one semester and for one course. The outcomes would be more promising if the dataset comprised more courses. Moreover, the addition of demographic information could have provided further representations of students’ performance. Future work will address some of these limitations.Originality/valueThe model developed from this research can provide value to institutions in making process- and data-driven predictions on students’ academic performances.

Educational data mining: A survey from 1995 to 2005

One of the main problems in dynamic customer segmentation is finding the dominant patterns of customer movements between different segments via time. Accordingly, we concentrate on the customer dynamics in this paper and try to find different groups of customers in transmissions between segments via time. The dominant characteristics of these groups are also investigated. To obtain this objective, a new hybrid technique based on the K-means algorithm, hierarchical clustering and association rule mining is presented and implemented on the data of one of the main telecommunication corporations in Iran. The results show that there are seven different groups of customers. Furthermore, the impact of customer dynamics on segments’ changes via time is investigated. In this regard, a new approach of categorizing customers is proposed according to their impact on the structure and the content of segments’ changes. These new groups include “the customers who preserve the structure”, “the ones who are consistent with the structure” and “the customers who destroy the structure”.

Predicting student academic performance has been an important research topic in Educational Data Mining (EDM) which uses machine learning and data mining techniques to explore data from educational settings. However measuring academic performance of students is challenging since students academic performance hinges on diverse factors. The interrelationship between variables and factors for predicting performance participate in complicated nonlinear ways. Traditional data mining and machine learning techniques may not be applied directly to these types of data and problems. In this study we develop a classification model to predict student performance using Deep Learning which automatically learns multiple levels of representation. We pre-train hidden layers of features layerwisely using an unsupervised learning algorithm sparse auto-encoder from unlabeled data, and then use supervised training for finetuning the parameters. We train model on a relatively large real world students dataset, and the experimental results show the effectiveness of the proposed method which can be applied into academic pre-warning mechanism.

The main goal of our study is to analyze behavior of the online shopping customers in a particular company, which is distributor of electronic components for industrial production. To reach the main goal we perform a data analysis of the data obtained from the companýs online sales process by using data mining technique - mining association rules. The purpose of the analysis is to identify and describe the significant association rules in order to provide information to support management decision making.

Crime is one of the biggest and dominating problems in today’s world and it is not only harmful to the person involved but also to the community and government. Due to escalation in crime frequency, there is a need for a system that can detect and predict crimes. This paper describes the summary of the different methods and techniques used to identify, analyze and predict upcoming and present crimes. This paper shows, how data mining techniques can be used to detect and predict crime using association mining rule, k-means clustering, decision tree, artificial neural networks and deep learning methods are also explained. Most of the researches are currently working on forecasting the occurrence of future crime. There is a need for approaches that can work on real-time crime prediction at high speed and accuracy. In this paper, a model has been proposed that can work on real-time crime prediction by recognizing human actions.

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

This paper brings in an insight of the number of ways the students’ performance can be predicted. The performance of a student plays a vital role in any institution. It is the measure for assessing the academic excellence of any esteemed educational body. Not only the institute but the academic performance matters to the student as well. There is a tough competition in the field of academics. Students spend their sleepless nights turning up the pages of their books. Well, amidst of this, a student tussle with many hardships. At present, there are various methodologies to predict the student's performance and Data mining provides the most scientific and dependable method for predicting the same. Educational data mining is the process behind the evaluation of useful patterns out of huge education databases. These patterns help the educators in predicting the performance of the student. In a way it also helps in advancing effective teaching. Data mining techniques can be useful in culling out the data concerted to specific needs. This paper presents the study to find out the gap in the existing performance prediction methodologies.

By maximising the greatest results and decreasing the number of students who fail, educational institutions aim to improve the quality of education. Improving and developing academic achievement requires the ability to accurately estimate student performance in advance. Using data mining techniques, educational institutions can get valuable insights from the study of educational data. The purpose of this research is to present educational data mining approaches and incorporate them into a web-based platform for forecasting low-performing pupils. Prediction models were compared in a comparative study. High-performance models were then designed to get better results. Hybrid RF, a hybrid random forest algorithm, is the most effective at classifying data. In order to enhance the learning outcomes and intervention methods, a new feature selection approach called MICHI is presented, which is the mixture of similarity measure and chi-square techniques based on ranking feature scores. Using the suggested methodologies for educational data mining, a system for predicting student academic performance will be constructed for academic entrepreneurs to obtain an early forecast of student achievement for prompt treatment. Experiments and surveys demonstrate the utility and efficacy of the academic prediction system that was built. Use of the system aids educators in intervening and enhancing student outcomes.