Title: Assessment of student competency for personalised online learning using objective distance

Authors: Sataworn Chaichumpa; Punnarumol Temdee

Addresses: School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand ' School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand

Abstract: Like traditional learning, online learning also requires effective personalised learning so that the appropriate feedback can be given individually for the students to achieve their goals. This paper proposes the objective distance which is the measurement representing the distance between current status of student's competency to the satisfied competency level required for accomplishing the entire course. This paper aims to study to what extent the proposed objective distance can be used for effective classification of student's competency comparing to raw score data and its combination. The experiments are conducted with two different online courses including computer skill and English language course having 55 and 111 students respectively. The students are classified with three classifiers including K-nearest neighbour, artificial neural network and decision tree into different classes accordingly to different competency levels. The classification results show that the proposed objective distance can be effectively used for competency classification of online students.

Keywords: online learning; personalised learning; classification; objective distance; K-nearest neighbour; KNN; artificial neural network; ANN; decision tree.

DOI: 10.1504/IJIL.2018.088789

International Journal of Innovation and Learning, 2018 Vol.23 No.1, pp.19 - 36

Received: 04 Aug 2016
Accepted: 04 Feb 2017
Published online: 19 Dec 2017 *

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