Innovative models for enhanced student adaptability and performance in educational environments

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.

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.

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.

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.

Analytic tools are useful for detecting patterns in education data and providing insights about student performance and learning. This study compared six supervised learning algorithms (linear regression, ridge regression, the lasso, regression trees, random forests regression, gradient boosted regression) and identified features important for predicting student performance. The dataset consisted of N=1044 observations from two secondary schools in Portugal (UCI-MLR, Cortez & Silva, 2008). Performance was assessed by final grades (range: 0-20) in two courses, mathematics and Portugese. The models were fit to training data with 27 independent variables and evaluated on a testing subset. Overall, performance was lower for students in mathematics than Portugese. The models selected a similar set of variables as important for predicting performance: mother's education level, student plans for higher education, and weekly study time were positively related to predicted performance, whereas course subject, school educational support, and romantic relationships were associated with decreased student performance. The models differed in the number, weighting, order and importance given to predictor variables. Linear regression provided a model with 13 predictors. Ridge regression shrank the coefficient estimates toward zero; the lasso performed variables selection for a model with 20 predictors. There was a tradeoff between model complexity and interpretability. The single pruned regression tree provided a simple, interpretable non-linear model with four features. Random forests regression and gradient boosting reduced overfitting, but were more difficult to interpret. Advantages and limitations of the different models are discussed. Applications for educational data mining (EDM) and learning analytics (LA) are considered.

In the educational landscape, educational data mining has emerged as an indispensable tool for institutions seeking to deliver exceptional and high-quality education. However, education data revealed suboptimal academic performance among a significant portion of the student population, which consequently resulted in delayed graduation. This experimental research generally aims to evaluate student graduation outcomes. Meanwhile, the specific aim is to predict student academic performance by applying the support vector machine (SVM) model based on sampling techniques. The proposed model is evaluated using datasets originating from one of the State Islamic Universities. The dataset has both on-time and delayed graduation status. The results show that the support vector machine model based on the shuffle sampling on the Arabic language and literature (BSA) dataset produces excellent performance on both tests with accuracy values above 90% and area under the curve (AUC) above 0.9. Meanwhile, the Islamic education management (MPI) dataset produces excellent performance when applying a support vector machine based on stratified sampling with accuracy values above 90% and AUC above 0.9. Therefore, it could be concluded that the proposed model has excellent and reliable performance.

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.

The remarkable growth of electronic data in the Mathematics department provides the opportunities to extract some meaningful information from this large capacity of data. The development of the data in the mining field ensures the educational data to improve the quality of the educational processes and to classify the components that affected the performance of the students in engineering statistics course. Thus, in this study, Kanri Distance Calculator (KDC) was utilized to analyze the performance of undergraduate students in Universiti Malaysia Perlis (UniMAP) during the academic session 2015/2016 in semester 2. The performance of the students was evaluated based on two aspects. First, the academic achievement of the students from the engineering statistics course during semester 2 2015/2016 was analyzed. Second, the possible variables that affect the performance of students in this course were classified. Thus, in this study, two significant groups of students were recognized i.e. (i) the students who achieved below 60% average, and (ii) the students who achieved above 60% average that can be referred as off-target and target group respectively. Based on the analysis of this study, the two variables (i.e. variables X3 and X7) affect the performance of the students and cause the students to obtain average marks lower than 60%. The results indicate that, by focusing on the possible variables that affect the academic performance of the students, it is possible to provide an appropriate warning and support to the students below average achievement, and at the same time to provide advice and opportunities to high-performance students based on the past experience about the obtained components.

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.

Student performance is one of the crucial and necessary part in the educational system due to its large volumes of real-time data that cannot be analyzed manually by the humans. So, here comes machine learning that is used to easily predict the performance of the students. This job is being performed by educational data mining (EDM). EDM contains the methods that are used to discover the data in the educational environment and are used for understanding the student and their learning environment. The educational institutions are responsible and also at the same time curious to know about the performance of the students and teachers to know the current status of the functioning of the institution towards a better path. In this project, 3 models namely students assessments grades, final student performance, teachers performance. The result shows that both all the models will be showing an accurate result. A system is designed to predict the performance of the teachers based on the average of the grades of all the students, Students review and the staff members review are taken as input and the performance of the teachers is predicted accordingly. And at the same time, we will also be predicting the performance of the students based on their previous academic performance and gives suggestions.

Educational data mining (EDM) in online learning involves data mining techniques to analyze data from online environments to gain insights into student behavior, performance, and engagement. This study explored EDM in online learning publication trends and focuses. It involved a bibliometric analysis of 615 scholarly works related to EDM in online learning as recorded in Scopus, the largest peer-reviewed citation database, on February 1, 2023. The study examined EDM in online learning publications regarding its evolution and distribution, key focus areas, impact and performance, and prominent authors and collaborations in the last decade, in which the timespan is the period from 2012 to 2022. This bibliometric analysis shows that EDM in online learning is a dynamic area of scientific research as related publications grow steadily throughout the years and involve worldwide collaborations. The study reveals current research trends, offering valuable insights for future researchers to guide their investigations in this field.

With the rapid development of the Internet and communication technology, online education has drawn more and more attention, online learning platforms, on the other hand, store massive learner behavioral data and educational data. How to effectively analyze and utilize the data to improve the quality of online education has become a key issue urgently needed to be solved in the field of big data in education(BDE), educational data mining(EDM) is exactly an effective and practical method and means of applying BDE. Therefore, EDM is an important academic research hotspot in the field of EDM. Firstly, the paper introduces the basic concepts of BDE, EDM and online learning platform, and then elaborates on the process of how educational data mining transforms raw data into knowledge. Finally, the key technologies of data mining are classified according to their uses, and gives its application in online education scene. The paper can provide some guidance for the research and application of educational data mining based on online education.

Educational Data Mining (EDM) is a novel concept associated with developing methods for exploring the specific types of data produced by educational settings and using those approaches to effectively understand students and the environments in which they learn. Prediction attempts to shape trends that will allow it to predict results or learning outcomes based on available data. Predicting student success has become an appealing challenge for researchers. They develop an understandable and efficient model using supervised and unsupervised EDM techniques. This assists decision-makers in improving student performance. The task of deciding the best model leads to the emergence of various techniques from both EDM techniques. The numerous research models used to solve the problem of student success prediction using educational data mining are discussed in this paper. The primary purpose of this paper is to explain the methodology for implementing the proposed solution for student performance prediction, as well as to present the findings of a study aimed at evaluating the performance of various data mining classification algorithms on the given dataset in order to assess their potential usefulness for achieving the goal and objectives.

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) evaluates and predicts students' performance that assists to discover important factors affecting students' academic performance and also guides educational managers to make appropriate decisions accordingly. The most common technique for discovering meaningful information from the educational database is classification. The accuracy of classification algorithms on educational data can be increased by applying feature selection algorithms. Feature selection algorithms help in selecting robots and meaningful features for predicting students' performance with high accuracy. This paper presents different EDM approaches for forecasting students' performance using different data mining techniques. In addition, this paper also presents an evaluation of recent classification algorithms and feature selection algorithms used in educational data mining. Furthermore, the paper will guide the researchers on new and possible dimensions in building a prediction model in EDM.