An intelligent distance learning framework: Assessment-driven approach

ABSTRACT

Online education is popular for various remote trainings, or it becomes inevitable in anomalous situations, for example, COVID-19 pandemic. One of the well-recognized online education platforms is massive online open courses, whose contents are developed by world-famous experts. However, the learning effectiveness of online education is not yet higher than face-to-face classroom education. It is in part because the online contents are uniformly designed for all heterogeneous online students, even to the students who need the same course topic at different levels. Also when a student needs to revisit a course content to strengthen his or her weakness, typical online infrastructures do not pinpoint their areas of weakness to go over. This chapter proposes both online lecture and mobile assessment platforms to elevate the quality of distance learning. The online platform proposed is the three-layers of four quadrant panes. The online platform has three layers (1) basic, (2) advanced, and (3) application per course module. Each layer is divided into four quadrants (a) slides quadrant, (b) videos quadrant, (c) summary quadrant, and (d) quizlet quadrant panes. Students begin with the basic layer first, dive into the advanced layer, and apply the application layer. The other platform is a mobile assessment. One of the critical issues in distance learning is fair assessment management. Smartphone-based assessment of online student learning performance disables the high chance of cheating schemes and enables the building of student learning patterns. Its analytics eventually leads to the reorganization of online course module sequences. The contribution is (1) an accurate recognition of student weakness;(2) an intelligent and automatic answering to student questions;and (3) a mobile phone application-based assessment. © 2021 Elsevier Inc. All rights reserved.