scholarly journals Technical and Vocational Education Analytics Using Punjab TEVTA Students’ Data

2018 ◽  
pp. 37-48
Keyword(s):  
Data Mining ◽  
Vocational Education ◽  
Teaching And Learning ◽  
Teaching Method ◽  
Technical Education ◽  
Educational Data Mining ◽  
Vocational Education And Training ◽  
Classification Problems ◽  
Data Set ◽  
Data Mining Techniques

The development of informative workforce that is skilled in a specific profession is considered as the most recommended and desirable feature of any advanced state. Technical Education & Vocational Trainings provide golden opportunity of growth regarding the output of individuals and prosperity of employers. Subsequently it is the dire need of developing countries to invest in public vocational education and training sector (VET) for the progression of skillful societies. Process of manual predictions and analysis on the basis of students’ data to make decisions that will improve the overall teaching and learning is very difficult and tiring. Data mining is exceptionally helpful when we are talking about education data analysis and prediction. Data mining techniques are being used successfully in different areas especially in student educational and learning analytics called as Educational Data Mining (EDM). In this work, TEVTA students’ data is shaped as a ready-to-mine data set and then various data mining techniques are applied to derive interesting patterns that can potentially derive important decisions for improvement of learning process, enhancement of teaching method and overall development of whole system of technical education and vocational trainings. Besides presenting interesting analytics of TEVTA data, we develop classification problems to predict status of students after completing TEVTA courses. This classification can also help in evaluating success of TEVTA programs. This work can help in analyzing and predicting the aspects affecting students’ as well as institutes’ performance from different dimensions.

Educational Data Mining and Indian Technical Education System

2017 ◽  
pp. 18-34 ◽  
Author(s):  
Nancy Kansal ◽  
Vijender Kumar Solanki ◽  
Vineet Kansal
Keyword(s):  
Data Mining ◽  
Teaching And Learning ◽  
Technical Education ◽  
Educational Data Mining ◽  
Literature Survey ◽  
Learning Systems ◽  
Individual Counseling ◽  
Rule Mining ◽  
Support Students ◽  
Students Satisfaction

Educational Data Mining (EDM) is emerged as a powerful tool in past decade and is concerned with developing methods to explore the unique types of data in educational settings. Using these methods, to better understand students and the settings in which they learn. Different unknown patterns using classification, Clustering, Association rule mining, decision trees can be discovered from this educational data which could further be beneficial to improve teaching and learning systems, to improve curriculum, to support students in the form of individual counseling, improving learning outcomes in terms of students' satisfaction and good placements as well. Therefore a literature survey has been carried out to explore the most recent and relevant studies in the field of data mining in Higher and Technical Education that can probably portray a pathway towards the improvement of the quality education in technical institutions.

Educational Data Mining and Indian Technical Education System

2021 ◽  
pp. 884-899
Author(s):  
Nancy Kansal ◽  
Vijender Kumar Solanki ◽  
Vineet Kansal
Keyword(s):  
Data Mining ◽  
Teaching And Learning ◽  
Technical Education ◽  
Educational Data Mining ◽  
Literature Survey ◽  
Learning Systems ◽  
Individual Counseling ◽  
Rule Mining ◽  
Support Students ◽  
Students Satisfaction

Educational Data Mining (EDM) is emerged as a powerful tool in past decade and is concerned with developing methods to explore the unique types of data in educational settings. Using these methods, to better understand students and the settings in which they learn. Different unknown patterns using classification, Clustering, Association rule mining, decision trees can be discovered from this educational data which could further be beneficial to improve teaching and learning systems, to improve curriculum, to support students in the form of individual counseling, improving learning outcomes in terms of students' satisfaction and good placements as well. Therefore a literature survey has been carried out to explore the most recent and relevant studies in the field of data mining in Higher and Technical Education that can probably portray a pathway towards the improvement of the quality education in technical institutions.

Educational Data Mining and Indian Technical Education System

2021 ◽  
pp. 1691-1706
Author(s):  
Nancy Kansal ◽  
Vijender Kumar Solanki ◽  
Vineet Kansal
Keyword(s):  
Data Mining ◽  
Teaching And Learning ◽  
Technical Education ◽  
Educational Data Mining ◽  
Literature Survey ◽  
Learning Systems ◽  
Individual Counseling ◽  
Rule Mining ◽  
Support Students ◽  
Students Satisfaction

Educational Data Mining (EDM) is emerged as a powerful tool in past decade and is concerned with developing methods to explore the unique types of data in educational settings. Using these methods, to better understand students and the settings in which they learn. Different unknown patterns using classification, Clustering, Association rule mining, decision trees can be discovered from this educational data which could further be beneficial to improve teaching and learning systems, to improve curriculum, to support students in the form of individual counseling, improving learning outcomes in terms of students' satisfaction and good placements as well. Therefore a literature survey has been carried out to explore the most recent and relevant studies in the field of data mining in Higher and Technical Education that can probably portray a pathway towards the improvement of the quality education in technical institutions.

scholarly journals DATA MINING IN EDUCATION FOR STUDENTS ACADEMIC PERFORMANCE: A SYSTEMATIC REVIEW

2014 ◽  
Vol 13 (9) ◽  
pp. 5020-5028
Author(s):  
Anurag Jindal ◽  
Er. Williamjeet Singh
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Academic Performance ◽  
Nearest Neighbor ◽  
Educational Data Mining ◽  
Educational Systems ◽  
Good Prediction ◽  
Data Set ◽  
Data Mining Techniques ◽  
Higher Education System

Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. Higher education, throughout the world is delivered through universities, colleges affiliated to various universities and some other recognized academic institutes. The main objective of higher education institutes is to provide quality education to its students. Indian education sector has a lot of data that can produce valuable information which can be used to increase the quality of education. Good prediction of student’s success in higher learning institution is one way to reach the higher level of quality in higher education system. In this paper we analyzed the potential use of data mining in education section and survey the most relevant work in this area. Data Mining can be used for dropout students, student’s academic performance, teacher’s performance and student’s complaints. As we know large amount of data is stored in educational database, so in order to get required data and to find the hidden relationship, different data mining techniques are developed & used. Various algorithms and data mining techniques like Classification, Clustering, Regression, Artificial Intelligence, Neural Networks, Association Rules, Decision Trees (CART and CHIAD), Genetic algorithms, Nearest Neighbor method etc. are used for knowledge discovery from databases and helps in prediction of students academic performance. In future work we can apply different data mining techniques on an expanded data set with more distinct attributes to get more accurate results.

Workplace learning for pre – service teachers’ practice and quality teaching and learning in technical vocational education and training: key to professional development

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Theresa Chinyere Ogbuanya ◽  
Taiwo Olabanji Shodipe
Keyword(s):  
Professional Development ◽  
Vocational Education ◽  
Workplace Learning ◽  
Teaching And Learning ◽  
Education And Training ◽  
Vocational Education And Training ◽  
Quality Teaching ◽  
Content Type ◽  
Technical Vocational Education ◽  
And Training

Purpose With critical reviews of previous studies in workplace learning, this paper aims to investigate workplace learning for pre-service teachers’ practice and quality teaching and learning in technical vocational education and training: key to professional development. Design/methodology/approach The study adopted multistage sampling technique to select sample for the study. Empirical analysis was adopted to analyse the data collected from technical vocational education and training pre-service teachers. Findings The result of the study revealed that the constructs of social learning theory had a stronger linkage with the constructive teaching than traditional management. Originality/value This study emphasizes the need to adequately train pre-service teachers on instructional delivery processes, building strong relationship with learners and build the ability to organize and execute necessary actions required to successfully carry out a specific educational task in a particular context.

scholarly journals Failure Analysis in University and Computer Science Contexts With Data Mining

2020 ◽  
Author(s):  
Daniela De Souza Gomes ◽  
Marcos Henrique Fonseca Ribeiro ◽  
Giovanni Ventorim Comarela ◽  
Gabriel Philippe Pereira
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Failure Analysis ◽  
Computer Science ◽  
Educational Administration ◽  
Intelligent Systems ◽  
Data Set ◽  
Data Mining Techniques ◽  
Study Case ◽  
Support Students

High failure rates are a worrying and relevant problem in Brazilian universities. From a data set of student transcripts, we performed a study case for both general and Computer Science contexts, in which Data Mining Techniques were used to find patterns concerning failures. The knowledge acquired can be used for better educational administration and also build intelligent systems to support students’ decision making.

scholarly journals Privacy Preservation using (L, D) Inference Model Based on Dependency Identification Information Gain

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1170-1173
Keyword(s):  
Data Mining ◽  
Information Gain ◽  
Original Data ◽  
Perturbation Approach ◽  
Sensitive Information ◽  
Functional Dependencies ◽  
Inference Model ◽  
Data Set ◽  
Data Mining Techniques ◽  
Original Dataset

The improvement of an information processing and Memory capacity, the vast amount of data is collected for various data analyses purposes. Data mining techniques are used to get knowledgeable information. The process of extraction of data by using data mining techniques the data get discovered publically and this leads to breaches of specific privacy data. Privacypreserving data mining is used to provide to protection of sensitive information from unwanted or unsanctioned disclosure. In this paper, we analysis the problem of discovering similarity checks for functional dependencies from a given dataset such that application of algorithm (l, d) inference with generalization can anonymised the micro data without loss in utility. [8] This work has presented Functional dependency based perturbation approach which hides sensitive information from the user, by applying (l, d) inference model on the dependency attributes based on Information Gain. This approach works on both categorical and numerical attributes. The perturbed data set does not affects the original dataset it maintains the same or very comparable patterns as the original data set. Hence the utility of the application is always high, when compared to other data mining techniques. The accuracy of the original and perturbed datasets is compared and analysed using tools, data mining classification algorithm.

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