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.

Premalatha M

Computer Science

School of Computer Science and Engineering

Chennai Campus

Viswanathan V

Suganya G

Kaviya M

Vijaya A.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Educational Data Mining and Recommender Systems Survey

International Journal of Web Portals

AbstractChoice Based Course Selection (CBCS) allows students to select courses based on their preferred sequence. This preference in selection is normally bounded by constraints set by a university like pre-requisite(s), minimum and maximum number of credits registered per semester. Unplanned course sequence selection affects the performance of the students and may prolong the time to complete the degree. Course Difficulty Index (DI) also contributes to the decline in the performance of the students. To overcome these difficulties, we propose a new Subset Sum Approximation Problem (SSAP) aims to distribute courses to each semester with approximately equal difficulty level using Maximum Prerequisite Weightage (MPW) Algorithm, Difficulty Approximation (DA) algorithm and Adaptive Genetic Algorithm (AGA). The three algorithms have been tested using our university academic dataset and DA algorithm outperforms with 98% accuracy than the MPW and AGA algorithm during course distribution.

Fulltext

Cybernetics and Information Technologies

Course Sequence Recommendation with Course Difficulty Index Using Subset Sum Approximation Algorithms

Anais dos Workshops do VI Congresso Brasileiro de Informática na Educação (CBIE 2017)

Application of clustering techniques on data generated by an Online Educational Network

2017 IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM)

Pattern analysis of blooms knowledge level students performance using association rule mining

IEEE Internet of Things Journal

Survey on Prediction Algorithms in Smart Homes

2017 8th International Conference on Information Technology (ICIT)

Students performance prediction using KNN and Naïve Bayesian

Journal	International Journal of Web Portals
Publisher	IGI Global
ISSN	1938-0194
Open Access	0