Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/67898
Title: Technology-enhanced learning with data analytics
Authors: Kho, Chun Ee
Keywords: DRNTU::Engineering
Issue Date: 2016
Abstract: Learning analytics has been gaining increasingly popular and academic institutions around the world have been using data analytics to improve students’ learning. Since the studying methods and learning styles in Singapore are different, data analytic using the different types of data has to be performed. Breaking down of the learning outcome into tagging is needed to have a better understanding of the learning progress. This report aims to analyse the data of the learners’ to understand and improve the learning progress and outcome of the learners. The data used in the data analysis were characteristic, representation of their mathematical skill, quiz result and knowledge of the students. The data were used to construct the C4.5 decision tree model to predict the grade of the students, the knowledge of the students and correctness of the questions. Tagging refers to the knowledge of the student in this report. The average accuracy of the models to predict the grade of the students, the tagging of the students and the correctness of their answer were 95%, 82.32% and 56.82% respectively. The ideal tagging database, with zero wrong assumption, was created and models were created with it. The accuracies these models were compared. More data on student characteristic can be analysed. The stimulation of the math indicator can be improved to better reflect the mathematical standard of the students. Instead of analyzing if their answers are correct, the choice of the answer can be examined. Data analytics of the learners’ data have provided new insights into the learning progress and outcome.
URI: http://hdl.handle.net/10356/67898
Schools: School of Electrical and Electronic Engineering 
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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