Educational data mining has several advantages and one should know the details about EDM
Many educational institutions’ data collection and storage became too large, and educational data analysis could no longer be done manually. Educational data mining (EDM) is a new field that arose from the use of data mining tools to analyze educational data. Data mining and machine learning, pattern recognition, psychometrics and other areas of statistics, artificial intelligence, information visualization, and computational modeling are all growing fields in educational data mining. EDM‘s ultimate objective is to improve the educational process and to explain educational techniques to help people make better decisions.
The Educational data mining process is divided into four stages. The initial process of data mining is issue definition, which involves translating a specific problem into a data mining problem. The project aims and goals, as well as the major research topics, are developed at this phase. The second step, Data preparation, and collection are the most time-consuming. It can consume up to 80% of the total analysis time. In data mining, data quality is a key issue. Source data must be discovered, cleansed and formatted in a predetermined manner at this step. Following that, there is a Modeling and Evaluating phase in which the parameters are adjusted to their ideal values and various modeling approaches are chosen and implemented. The deployment phase is the final step in the data mining process, during which the findings are organized and displayed in graphs and reports.
Methods and Techniques
Educational data mining employs a variety of approaches, algorithms, and strategies. Classification, clustering, prediction, and association are the most common applications. Neural networks and decision trees are the most often utilized data mining algorithms.
Classification is a data mining process that divides data into desired categories or classes in a collection. It aids in the analysis of data and the prediction of consequences. Classification’s objective is to correctly anticipate the target class for each example in the data. This approach is frequently used in the educational industry to classify pupils based on factors such as age, gender, grades, knowledge, academic achievements, motivation, behavior, demographic or regional features, and so on.
Clustering analysis is a technique for grouping comparable data into previously undefined groups. It’s beneficial for identifying homogenous groups that may be utilized as input for other models during the data preparation step. Cluster analysis, like classification, may be used to look at similarities and differences amongst students, courses, professors, and so on.
Calculated assumptions for specific events are established based on available processed data in the form of predictions. The regression approach may be used to model the connection between one or more independent variables and dependent variables to make predictions. It is used in the educational industry to forecast student academic performance, enrollment, final grade, drop-outs, and other things.
Association is a data mining approach for determining the likelihood of elements in a collection occurring together. This method can be used to introduce new courses or to establish new institutions.
Neural networks are a class of computer algorithms inspired by the human central nervous system that are meant to identify complicated patterns and solve prediction problems without the use of programming. Artificial neurons are the nodes that make up neural networks. Artificial neural networks are most commonly used for voice and picture recognition, computer vision, machine translation, and video game play.
A decision tree is a decision-making tool that classifies data using a tree-shaped graph or model. It is a way of learning that is supervised.
Importance and the benefits of the Applications of Educational Data Mining
- Educational data mining has a wide range of advantages and uses. The most common applications of EDM include: improving the study process, increasing course completion, assisting students in course selection, student profiling, identifying problems that lead to dropout, student targeting, curriculum development, predicting student performance, and as a support for decision-making at the time of student enrolment.
- Student modeling, forecasting student performance, data visualization, social network analysis, feedback for support management, planning and scheduling, grouping students, and detection of undesired behaviors are all applications of EDM.
- Mining educational data to assess students’ performance highlights the possibilities of data mining techniques in the context of higher education by providing a data mining model for the higher education system. To assess a student’s performance, the decision tree technique was employed. This study might aid educators in detecting dropouts and kids who require particular attention early on so that necessary advice or counseling can be provided.
- To distinguish between high and slow learners, Bayes classification can be employed in the building of a prediction model.
- The classification task may be used to assess previous years’ student dropout data to identify students who are most likely to drop out of their first year of engineering. Information about prior education, student’s family income, parents’ education, and other factors were utilized to forecast the list of kids that require particular attention to minimize the drop-out rate. The results indicate that using current student dropout data, the machine learning algorithm can create an excellent prediction model.
- In higher education, data mining technique scan help save money and increase efficiency.
- Based on a student’s personal, pre-university, and university attributes, several data mining approaches may be used to create enrolment prediction models.
- Data mining may be used to report and evaluate data to assist in the development of marketing campaigns for specific pupils.
- Step-wise regression and decision trees can be used to uncover a variety of characteristics that influence university teachers’ teaching ability.
- Students may be clustered into various groups depending on their computer literacy using three different cluster approaches (k-means, self-organizing maps, and two-step clustering). The decision tree technique can derive important rules from each group after grouping students into distinct groups. Universities can use data mining techniques to identify certain groups that require more instruction to pass a computer competence exam.
Educational data mining or EDM is a new field with a lot of promise for everyone involved in the educational process. Data mining techniques were created to find hidden knowledge and detect patterns in educational data automatically. Educational data mining may be used to attract, maintain, and retain students for a university to be profitable. Discovering, recognizing, and comprehending which educational approaches are effective requires analyzing student data.