Educational Data Mining in Practice Literature Review

2020 ◽  
Vol 07 (01) ◽  
pp. 1-7
Author(s):  
Kamal Bunkar ◽  
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Structure Model ◽  
Education Data ◽  
Education Study ◽  
Objective Feedback ◽  
Student Models ◽  
Predictive Data Mining ◽  
Practice Literature

Educational Data Mining (EDM) is an evolving field with a suite of computational and psychological methods for understanding how students learn. Applying Data Mining methods to education data help us to resolve educational investigation issues. The growth of education data offers some unique advantages as well as some new challenges for education study. Some of the challenges are an improvement of student models, identify domain structure model, pedagogical support and extend educational theories. The main objective of this paper is to present the capabilities of data mining in the context of the higher educational system and their applications and progress, through a survey of literature and the classification of articles. We observed the works on investigational situation studies showed in the EDM during the recent past, in addition, we have introduced three data models based on descriptive and predictive data mining techniques. This is oriented towards students in order to recommend learners’ activities, resources, suggest path pruning and shortening or simply links that would favor and improve their learning or to educators in order to get more objective feedback for instruction.

scholarly journals Multimodal Learning Analytics and Education Data Mining: using computational technologies to measure complex learning tasks

2016 ◽  
Vol 3 (2) ◽  
pp. 220-238 ◽  
Author(s):  
Paulo Blikstein ◽  
Marcelo Worsley
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Online Courses ◽  
Educational Data Mining ◽  
Multimodal Learning ◽  
Learning Tasks ◽  
Education Data ◽  
Multimodal Learning Analytics ◽  
High Degree ◽  
Learning Analysis

New high-frequency multimodal data collection technologies and machine learning analysis techniques could offer new insights into learning, especially when students have the opportunity to generate unique, personalized artifacts, such as computer programs, robots, and solutions engineering challenges. To date most of the work on learning analytics and educational data mining has been focused on online courses and cognitive tutors, both of which provide a high degree of structure to the tasks, and are restricted to interactions that occur in front of a computer screen. In this paper, we argue that multimodal learning analytics can offer new insights into students’ learning trajectories in more complex and open-ended learning environments. We present several examples of this work and its educational application.

scholarly journals Effect of Academic Interest and Emotional Happiness on Academic Performance in Learning Environment.

2020 ◽  
Vol 9 (1) ◽  
pp. 2486-2489
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Clustering Algorithm ◽  
Reference Model ◽  
Educational Data Mining ◽  
Positive Association ◽  
Academic Interest ◽  
Education Data ◽  
Learning Techniques ◽  
And Performance

According to Bloom's Taxonomy, the motto of education is to groom the students' as a better personality in knowledge, skill set and emotions under the supervision of academicians. Development of information technology paves the way to analyse the data from the educational environment and make decisions which help to be in track to achieve the motto. i.e. Educational Data mining. Education Data mining is one of the research domains of data mining which convert the data from the educational sector as insights for decision making. This paper is to analyse the effect of student's academic interest on emotional happiness and academic performance by applying supervised and unsupervised learning techniques. Students' Emotional Happiness and students' academic performance is evaluated by the Oxford Happiness Inventory and criterion reference model. Academic interest is received as yes or no responses from the students. Naive Bayes classification algorithm and K Means clustering algorithm is applied to categorise the student participants based on their happiness scale, academic interest and academic performance. The association between academic interest and performance is determined using predictive and descriptive mining. By this research, it is witnessed the positive association between academic interest, happiness and performance. The insights of this investigation will allow the teachers' to understand the students in a better way and do the needful to enhance academic efficiency.

scholarly journals EDM – survey of performance factors and algorithms applied

2018 ◽  
Vol 7 (2.6) ◽  
pp. 93 ◽  
Author(s):  
Deepali R Vora ◽  
Kamatchi Iyer
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Educational Data Mining ◽  
Knowledge Discovery In Databases ◽  
Useful Knowledge ◽  
Performance Factors ◽  
Student’S Performance

Educational Data Mining (EDM) is a new field of research in the data mining and Knowledge Discovery in Databases (KDD) field. It mainly focuses in mining useful patterns and discovering useful knowledge from the educational information systems from schools, to colleges and universities. Analysing students’ data and information to perform various tasks like classification of students, or to create decision trees or association rules, so as to make better decisions or to enhance student’s performance is an interesting field of research. The paper presents a survey of various tasks performed in EDM and algorithms (methods) used for the same. The paper identifies the lacuna and challenges in Algorithms applied, Performance Factors considered and data used in EDM.

scholarly journals FINDING THE BEST ALGORITHMS AND EFFECTIVE FACTORS IN CLASSIFICATION OF TURKISH SCIENCE STUDENT SUCCESS

2019 ◽  
Vol 18 (2) ◽  
pp. 239-253 ◽  
Author(s):  
Enes Filiz ◽  
Ersoy Öz
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Eighth Grade ◽  
Support Vector ◽  
Educational Strategies ◽  
Vector Machines ◽  
Eighth Grade Students ◽  
Timss 2015 ◽  
Mathematics And Science

Educational Data Mining (EDM) is an important tool in the field of classification of educational data that helps researchers and education planners analyse and model available educational data for specific needs such as developing educational strategies. Trends International Mathematics and Science Study (TIMSS) which is a notable study in educational area was used in this research. EDM methodology was applied to the results of TIMSS 2015 that presents data culled from eighth grade students from Turkey. The main purposes are to find the algorithms that are most appropriate for classifying the successes of students, especially in science subjects, and ascertaining the factors that lead to this success. It was found that logistic regression and support vector machines – poly kernel are the most suitable algorithms. A diverse set of features obtained by feature selection methods are “Computer Tablet Shared”, “Extra Lessons Last 12 Month”, “Extra Lessons How Many Month”, “How Far in Education Do You Expect to Go”, “Home Educational Resources”, and “Student Confident in Science” and these features are the most effective features in science success. Keywords: classification algorithms, educational data mining, eighth grade, science success, TIMSS 2015.

scholarly journals Enhancing the Quality Education using Predictive and Descriptive Data Mining Model

2019 ◽  
Vol 8 (3) ◽  
pp. 6843-6847
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Educational Data Mining ◽  
Strong Relationship ◽  
Data Mining Techniques ◽  
Huge Data ◽  
Student’S Performance ◽  
Predictive Data Mining ◽  
Mining Model ◽  
The Given

Data mining is the trending field used to get relevant knowledge from the database given. This technique consists of subfield called educational data mining is the emerging area used to extract the hidden patterns from the huge data with the help of tools techniques developed by the researchers of the educational data mining. The purpose of extracting patterns from the educational database is to improve the quality of education can be provided to the students for their better feature. The patterns are extracted by using the existing data mining techniques to enhance student performance. Educational data mining techniques such as classification, regression, clustering are available in the field. Classification is defined as the technique used to categorize the data based on the given label and constraints. In this paper, the algorithms like naves Bayes, Random Forest and J48 algorithms used to classify the data instances under the given labels using the constraints given., the classification algorithms like naves Bayes shows best performance accuracy with the given student dataset. Clustering and apriori rule have a strong relationship in student performance. In this paper, predictive data mining used to predict the student's performance to enhance the study level of the students in the organization.

Learning Analytics and Education Data Mining in Higher Education

2021 ◽  
pp. 108-126
Author(s):  
Samira ElAtia ◽  
Donald Ipperciel
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Big Data ◽  
Learning Analytics ◽  
Large Scale ◽  
Educational Data Mining ◽  
Current Situation ◽  
Research Perspective ◽  
Education Data ◽  
The Impact

In this chapter, the authors propose an overview on the use of learning analytics (LA) and educational data mining (EDM) in addressing issues related to its uses and applications in higher education. They aim to provide meaningful and substantial answers to how both LA and EDM can advance higher education from a large scale, big data educational research perspective. They present various tasks and applications that already exist in the field of EDM and LA in higher education. They categorize them based on their purposes, their uses, and their impact on various stakeholders. They conclude the chapter by critically analyzing various forecasts regarding the impact that EDM will have on future educational setting, especially in light of the current situation that shifted education worldwide into some form of eLearning models. They also discuss and raise issues regarding fundamentals consideration on ethics and privacy in using EDM and LA in higher education.

scholarly journals Analyzing Students Performance Using Frequent Item Set Mining, Clustering & Classification

2012 ◽  
Vol 1 (2) ◽  
pp. 31-41
Author(s):  
Rudresh Shirwaikar ◽  
Nikhil Rajadhyax
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Decision Tree ◽  
Student Performance ◽  
Real World ◽  
Scientific Inquiry ◽  
Educational Data Mining ◽  
The Subject ◽  
Hidden Patterns

Educational data mining (EDM) is defined as the area of scientific inquiry centered around the development of methods for making discoveries within the unique kinds of data that come from educational settings , and using those methods to better understand students and the settings which they learn in. Data mining enables organizations to use their current reporting capabilities to uncover and understand hidden patterns in vast databases. As a result of this insight, institutions are able to allocate resources and staff more effectively. In this paper, we present a real-world experiment conducted in Shree Rayeshwar Institute of Engineering and Information Technology (SRIEIT) in Goa, India. Here we found the relevant subjects in an undergraduate syllabus and the strength of their relationship. We have also focused on classification of students into different categories such as good, average, poor depending on their marks scored by them by obtaining a decision tree which will predict the performance of the students and accordingly help the weaker section of students to improve in their academics. We have also found clusters of students for helping in analyzing student  performance and also improvising the subject teaching in that particular subject.

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