A Review on Feature Selection Methods for Improving the Performance of Classification in Educational Data Mining

In the present state of affairs, the motive behind every educational organization is to uplift the academic achievement of students. Educational data mining (EDM) is an upward field of research, and it is very helpful for academic institutions to predict the academic performance of the students. Educational datasets are the basis of various predictive models. Quality of these models can be improved by using feature selection (FS). To get the required benefits from the available data, there must be some tools for analysis and prediction. In lieu of the above, machine learning/data mining are most suitable. In educational data mining, for better accuracy of prediction models’ and quality of various educational datasets, feature selection (FS) plays a vital role. Feature selection (FS) algorithms abolish inappropriate information from the repositories of educational background so that performance of classifier in terms of accuracy could be increased and the same could be used for better decision. In lieu of the above, a best feature selection algorithm must be selected. In this paper, two filter selection approaches namely correlation feature selection (CFS) and wrapper-based feature selection have been used to demonstrate the importance of selection of a feature subset for a classification problem. The present paper aims to find the detailed investigation of filter feature selection algorithms along with the classification algorithms on a given dataset. We found result with numerous numbers of features from various Feature selection algorithms and classifiers which will help the researcher to discover the most excellent mixture of filter feature selection algorithms and its associated classifiers. The result indicates that SMO and J48 have the highest accuracy measures with the correlation feature selection algorithms, while Naïve Bayes has the highest accuracy measures with the wrapper subset feature selection algorithms for predicting high, medium and low grade for the students.

In recent years, research on Educational Data Mining (EDM) has developed rapidly. However, most researches focus on data source issues, and ignore the importance of data preprocessing and data mining algorithms. This paper has studied EDM, with a special focus on educational big data mining algorithms. Firstly, it analyzed the relevant elements of EDM and introduces big data technology based on the requirements of educational data application. Then it introduced the common educational big data mining algorithms and their applications, and finally discussed the development trend of educational big data mining algorithms.

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.

Data mining is a technique for extraction of valuable patterns from multiple sources. Data mining plays an important role in marketing, electronic-commerce, business intelligent, healthcare and social network analysis. Advancement in these applications, many researchers show their interest in development of data mining applications in educational context. Educational data mining is a technique defined as a scientific area making inventions within rear types of data that derived from educational surroundings. This Paper reviews different case studies based on data mining educational systems. These systems and mining methods are considered for gathering and analysis of information. Due to huge amount of data in Educational databases, it becomes very challenging to evaluate student performance. Currently in Pakistan, there is dire need to monitor and examine student’s academic progress. There are two main causes of why existing systems were not able to analyze performance of students. First, the study on present evaluation methods is still not satisfactory to analyze the appropriate methods for evaluating the progress and performance of students in institutions of Pakistan. Second is because of absence of investigations on parameters; that effects student’s success in specific courses. Thus, a comprehensive review is proposed on evaluation of student’s performance by using techniques of Data Mining methods to progress student’s achievements. The aim of paper is to improve students’ academic performance by identifying most suitable attributes by using techniques of EDM.

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.

University students face challenges with time management skills and stress that affects academic performance.Furthermore, the lack of awareness and access to resources for healthy sleep patterns is also a major challenge for university students to achieve academic success and overall well-being.The purpose of this study is to perform educational big data mining that analyzes the relationship between sleep management, time management, food management,, mental / mental management and academic performance and to explore the role of friends in mediation through the large ICPSR dataset (originally >90,000 sample size, 42 datasets).The study selected, merged, cleansed, and transformed the most relevant ICPSR datasets 8, 16, and 17.Pearson's correlation and mediation analysis are used to explore the relationship between student life management and academic performance by using SPSS and the PROCESS macro.Mediation analysis revealed that hangout with friends played a significant role as a mediator in the relationship between life management and academic performance.Life management also positively impacts CGPA and significantly reduces the failure rate.The results also state that sleep and time management correlate positively with academic performance, while mental and food management correlate negatively with academic performance.Mental and physical health are important contributors to overall well-being, including academic performance.Therefore, it is important to ensure that both are managed effectively.This study provides insight into the steps involved in large educational data mining, which help the researcher make a decision for future potential data mining research using large datasets.

Due to the growing interest in data mining and the educational system, educational data mining is the emerging topic for research community. The various techniques of data mining like classification and clustering can be applied to bring out hidden knowledge from the educational data. Web video mining is retrieving the content using data mining techniques from World Wide Web. There are two approaches for web video mining using traditional image processing (signal processing) and metadata based approach. In this paper, we focus on the education data mining and precisely MOOCs which constitute a new modality of e-learning and clustering techniques. We present a methodology that can be used for mining Moocs videos using metadata as leading contribution for knowledge discovery.

Educational data mining (EDM) is an up-coming interdisciplinary research field, in which data mining (DM) techniques applying in educational data. Its objective is to better understand how students gain knowledge and recognize the settings in which they learn to improve educational outcomes. Educational systems can store a huge amount of data that coming from multiple sources in different formats. Each particular educational problem requires different types of the mining techniques because traditional DM techniques cannot be applied directly to these types of data and problems. There are many general data mining tools are available but these are not designed for deal with educational data and an educator cannot use these tools without knowledge of data mining concepts. To overcome from this problem many authors provided educational data mining tools with different data mining techniques for different purposes. This paper surveys the EDM Tools from 2001 to 2016 and presents the consolidated list of tools in one place to help the EDM researchers. Then categorizes the tools based on data mining methods and explains the usage of EDM Tools.

The increasing amount of educational data has rapidly in the latest few years. The Educational Data Mining (EDM) techniques are utilized to detect the valuable pattern so that improves the educational process and to obtain high performance of all educational elements. The proposed work contains three stages: preprocessing, features selection, and an active classification stage. The dataset was collected using EDM that had a lack in the label data, it contained 2050 records collected by using questionnaires and by using the students’ academic records. There are twenty-five features that were combined from the following five factors: (curriculum, teacher, student, the environment of education, and the family). Active learning had been utilized in the classification. Four techniques had been applied for classifying the features: Random Forest (RF) algorithm, Label Propagation (LP), Logistic Regression (LR), and Multilayer Perceptron (MLP). The accuracies of prediction were 95.121%, 92.195%, 92.292%, and 93.951% respectively. Also, the RF algorithm has been utilized for assorting the features depending on their importance.

Educational Data Mining (EDM) is an emerging, promising and interdisciplinary research area, which has an immense impact on student’s academic performance by analyzing educational data for improving teaching, learning experiences and, of course improving the institutional effectiveness. One of the most key facts in educational data is the significant growth of data and these statistics are rising swiftly without any benefit to the institution. In this short study, different studies conducted on the theme “EDM and interdisciplinary” were consulted and examined. Most of the authors have conducted studies on different systems which mostly include e-learning systems, student performance recommender systems (SPRS), conventional educational systems, adaptive and intelligent web based educational systems. The study of the past research has revealed that immense progress has been done in various domains of education and in different phases as well, but there still exists reasonable gap which needs to be examined in details as a future research study. Keywords: Educational Data Mining; Neural Networks; Classification; Association rules; Clustering; Prediction; k-means.

This bibliometric study on data mining in education synonymous with big educational data utilizes VOSviewer and Harzing’s Publish and Perish to analyze the metadata of 1,439 journal articles found in Scopus from 2010 to 2022. As bibliometric analyses in this field are lacking, this study aims to provide a comprehensive outlook on the current developments and impact of research in this field. This study employs descriptive and trends analysis, co-authorship analysis, co-citation analysis, co-occurrences of keywords, terms map analysis, and analysis of the impact and performance of publications. It also partially replicates a similar study conducted by Wang et al. (2022), who used the Web of Science (WoS) database. The study is reported in an article entitled ‘Big data and data mining in education: A bibliometrics study from 2010 to 2022’. Results show that data mining in education is a growing research field. There is also a significant difference between the publications in Scopus and WoS. The study found several research areas and topics, such as student academic performance prediction, e-learning, machine learning, and innovative data mining techniques, to be the core basis for collaborating and continuing current research in this field. These results highlight the importance of continuing research on data mining in education, guiding future research in tackling educational challenges.

In recent years, educational data mining has emerged as a growing discipline focused on developing models for predicting academic performance. The primary objective of this research was to tune classification models to predict academic performance in secondary school. The dataset employed for this study encompassed information from 19,545 high school students. We used descriptive statistics to characterise information contained in personal, school, and socioeconomic variables. We implemented two data mining techniques, namely artificial neural networks (ANN) and support vector machines (SVM). Parameter optimisation was conducted through five–fold cross–validation, and model performance was assessed using accuracy and F1–Score. The results indicate a functional dependence between predictor variables and academic performance. The algorithms demonstrated an average performance exceeding 80% accuracy. Notably, ANN outperformed SVM in the dataset analysed. This type of methodology could help educational institutions to predict academic underachievement and thus generate strategies to improve students’ academic performance.

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 way we learn has changed as a result of advances in science and technology and the growing influence of information technology on daily life. E-learning is a relatively new concept in educational settings, but it has progressively earned a spot in modern training techniques. Data mining methods are used in many applications, particularly in elearning. Comparing direct communications difficult to track the student involvement in online courses. To overcome the above mentioned issues the existing methods are analyzed various techniques of data mining and optimization methods which can be applied to bring out hidden knowledge from the educational data? In the e-learning work, preprocessing, feature selection and classification process are performed to improve the Kalboard 360 educational dataset accuracy significantly. This study focused on educational data mining (EDM) and employed decision trees, support vector machines (SVM), multilayer perceptrons, and the naive bayesian C4.5 algorithm among other classification techniques to assess the features for the prediction of students’ behaviour and academic achievement. This paper analyzes the advantages and shortcomings of each algorithm applied to data set on the basis of accuracy for various classification techniques like Multilayer Perceptron based ANN algorithm (MP-ANN), Decision Tree (DT) C4.5 and Hybrid Regression model with Multi Label Classification (HR-MLC) approach. The experimental result shows that the HR-MLC algorithm provides better performance in terms of higher accuracy, precision, recall and f-measure rather than the other existing methods.

Data mining methodology can analyze relevant information results and produce different perspectives to understand more about the students' activities. When designing an educational environment, applying data mining techniques discovers useful information that can be used in formative evaluation to assist educators establish a pedagogical basis for taking important decisions. Mining in education environment is called Educational Data Mining. Educational Data Mining is concerned with developing new methods to discover knowledge from educational database and can used for decision making in educational system. In this study, we collected the student's data that have different information about their previous and current academics records and then apply different classification algorithm using Data Mining tools (WEKA) for analysis the student's academics performance for Training and placement. This study presents a proposed model based on classification approach to find an enhanced evaluation method for predicting the placement for students. This model can determine the relations between academic achievement of students and their placement in campus selection.