A Survey and Future Vision of Data Mining in Educational Field

2012 ◽  
Author(s):  
R. Barahate Sachin ◽  
M. Shelake Vijay
Keyword(s):  
Data Mining ◽  
Future Vision ◽  
Educational Field

scholarly journals DATA MINING TECHNIQUES FOR EDUCATIONAL DATA: A REVIEW

2020 ◽  
Vol 5 (2) ◽  
pp. 166-177 ◽  
Author(s):  
Pragati Sharma ◽  
Dr. Sanjiv Sharma
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Making ◽  
Educational Institutions ◽  
Data Set ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
New Knowledge ◽  
Hidden Patterns ◽  
Educational Field

Recently, data mining is gaining more popularity among researcher. Data mining provides various techniques and methods for analysing data produced by various applications of different domain. Similarly, Educational mining is providing a way for analyzing educational data set. Educational mining concerns with developing methods for discovering knowledge from data that come from educational field and it helps to extract the hidden patterns and to discover new knowledge from large educational databases with the use of data mining techniques and tools. Extracted knowledge from educational mining can be used for decision making in higher educational institutions. This paper is based on literature review of different data mining techniques along with certain algorithms like classification, clustering etc. This paper represents the effectiveness of mining techniques with educational data set for higher education institutions.

A Semi-Supervised Regression Algorithm for Grade Prediction of Students in Distance Learning Courses

2019 ◽  
Vol 28 (04) ◽  
pp. 1940001 ◽  
Author(s):  
Georgios Kostopoulos ◽  
Sotiris Kotsiantis ◽  
Nikos Fazakis ◽  
Giannis Koutsonikos ◽  
Christos Pierrakeas
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Undergraduate Students ◽  
Educational Data Mining ◽  
Predictive Performance ◽  
Educational Process ◽  
Training Set ◽  
Final Grade ◽  
Future Outcomes ◽  
Educational Field

Applying data mining methods in the educational field has gained a lot of attention among researchers in recent years. Educational Data Mining has turned into an effective tool for uncovering hidden relationships in educational data and predicting students’ learning outcomes. Several supervised methods have been successfully applied with the purpose of identifying students at risk of failing or of predicting their academic performance. Recently, the implementation of Semi-Supervised Learning (SSL) methods in the educational process indicated their superiority over the supervised ones. SSL is an emerging subfield of machine learning seeking to effectively exploit a small pool of labeled examples together with a large pool of unlabeled ones. On this basis, a small number of students’ data from previous years may be used as the training set of a learning model to predict future outcomes of current students. A number of rewarding studies deal with the implementation of classification methods in the educational field in contrast to regression, which is deemed to be a slightly touched task. In this paper, a novel semi-supervised regression (SSR) algorithm is presented for predicting the final grade of undergraduate students in a distance online course. To the best of our knowledge there is no study dealing with the implementation of SSR methods in the educational field. A plethora of attributes related to students’ characteristics, academic performance and interaction within the course online platform form the training set, while several experiments were carried out confirming the superiority of the proposed algorithm over familiar regression methods. The experiment results show that the predictive performance of the proposed algorithm is increasing significantly over time, achieving a MAE value of less than 1.2358 before the middle of the academic year, which provides the advantage of early warnings and interventions.

Educational Data Mining Review

2017 ◽  
pp. 149-165 ◽  
Author(s):  
Rashmi Agrawal ◽  
Neha Gupta
Keyword(s):  
Data Mining ◽  
Network Formation ◽  
At Risk Students ◽  
Educational Data Mining ◽  
Learning Needs ◽  
Institutional Performance ◽  
Data Mining Techniques ◽  
Curriculum Renewal ◽  
Analysis Of Social Networks ◽  
Educational Field

In today's era, educational data mining is a discipline of high importance for teaching enhancement. EDM techniques can reveal useful information to educators to help them design or modify the structure of courses. EDM techniques majorly include machine learning and data mining techniques. In this chapter of the book, we will deliberate upon various data mining techniques that will help in identifying at-risk students, identifying priority learning needs for different groups of students, increasing graduation rates, effectively assessing institutional performance, maximizing campus resources, optimizing subject curriculum renewal. Various applications of data mining are also discussed by quoting example of various case studies. Analysis of social networks in educational field to understand student network formation in classrooms and the types of impact these networks have on student is also discussed.

Educational Data Mining and Recommender Systems Survey

2018 ◽  
Vol 10 (1) ◽  
pp. 39-53
Author(s):  
M. Premalatha ◽  
V. Viswanathan ◽  
G. Suganya ◽  
M. Kaviya ◽  
Aparna Vijaya
Keyword(s):  
Data Mining ◽  
Recommender Systems ◽  
Educational Data Mining ◽  
Educational Needs ◽  
Data Mining Techniques ◽  
Educational Field

Data mining techniques are widely used for various educational researches. This article depicts the survey of various data mining techniques and tools which are used to guide students, course instructors, course developers, course administrators and organizations in respective fields based on future scope. This article also highlights how recommender systems rule the educational field though it's filtering mechanisms in recommending courses for students. It also illustrates future scope of data mining in educational needs.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining
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