Paper Title :Using Big Data and Learning Analytics in India Higher Education to Build Organizational and Analytical Framework for Evaluating Student Engagement
Author :Chhavi Rana
Article Citation :Chhavi Rana ,
(2019 ) " Using Big Data and Learning Analytics in India Higher Education to Build Organizational and Analytical Framework for Evaluating Student Engagement " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 25-29,
Volume-7, Issue-5
Abstract : Higher Education is at a point of unparalleled ambiguity and transformation with financial changes leading to
increased focus on student focused model that emphasize on student engagement that leads them to better performance and
employability [5,6,8]. The stakeholder in Indian Higher Education system faces stiff competition from International
Universities and other organizations that are offering flexible education online. The use of Big Data analytics in higher
education is a relatively new area of practice and research [4, 8]. Learning analytics (LA) is the process of using this data to
improve learning and teaching and refers to the measurement, collection, analysis and reporting of data about the progress of
learners and the contexts in which learning takes place. In this paper, a comparative study is carried out using the output
from projects implementing learning analytics around the world and there is an attempt to outline how it can be used in
Indian Higher Education for evaluating student engagement. The paper proposes a GCM based framework that train the
classifier using various machine-learning algorithms – Naive Bayes and Maximum Entropy. The framework for quality
indicators for learning analytics aims to standardise the evaluation of learning analytics tools and to provide a mean to
capture evidence for the impact of learning analytics on educational practices in a standardised manner. The criteria of the
framework and its quality indicators are based on the results of a Group Concept Mapping study conducted with experts
from the field of learning analytics. Furthermore, we use different feature sets and machine learning classifiers to determine
the best combination for sentiment analysis of student engagement with courses and their performance. The results indicate
that the proposed approach could utilize underutilized knowledge, such as distant relationship embedded in PPI graph and
provide novel insights about student engagement.
Keywords - Student Engagement; Critical Thinking; Achievement; Student Learning, Pedagogy, Learning Analytics,
Quality Indicators, Group Concept Mapping, Framework
Type : Research paper
Published : Volume-7, Issue-5
DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-15524
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Copyright: © Institute of Research and Journals
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Published on 2019-07-26 |
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