Assessment of student competency for personalised online learning using objective distance

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.

Design, development, and evaluation of a virtual reality game-based application to support computational thinking

“…After initiating the VR game, players' learning log data are instantiated and constantly updated on the database throughout the gameplay session. In addition, the iThinkSmart VR game computes the learners' CT competency by applying Chaichumpa and Temdee's (2018) objective distance (OD) model. The OD model measures the player's competence by applying the formula presented in Eq.…”

“…The OD model proposed by Chaichumpa and Temdee (2018) was used to evaluate students' competency while learning in a smart learning environment to enhance digital literacy (Temdee, 2021). OD measures the distance between the expected/satisfactory learner's score ( S i ) and the current learner's score ( C i ), mainly used to provide personalized support and intelligent feedback to a learner.…”

Smart Learning Environment: Paradigm Shift for Online Learning

“…Recently, personalized learning has become the most popular learning paradigm because it focuses on learners' interests. It is also tailored to promote online learning as personalized online learning [27]. Generally, personalized online learning can be described as the teaching and learning paradigm applying intelligent technology for matching the learners with the content or learning activities accordingly to their proficiency level, learning styles, and interests through different types of learning environments.…”

“…Many machine learning techniques have been widely applying K-nearest neighbor (KNN) for different purposes such as learning analytics supporting instructor decision [44], predicting successful learning for the learners [45,46], classifying learning patterns [27,47], predicting the learning outcomes from historical learning behavior of each learner [45], etc. Another popular method for pattern classification problem is an artificial neural network (ANN).…”

“…It usually is one of the comparative alternative techniques for learner classification [48]. Lastly, decision tree has also gained much attention for grouping or matching different types of learners to particular recommendation or feedback [27,49].…”