Using Few-Shot Learning Materials of Multiple SPOCs to Develop Early Warning Systems to Detect Students at Risk

Yung-Hsiang Hu

doi:10.19173/irrodl.v22i4.5397

Abstract

Early warning systems (EWSs) have been successfully used in online classes, especially in massive open online courses, where it is nearly impossible for students to interact face-to-face with their teachers. Although teachers in higher education institutions typically have smaller class sizes, they also face the challenge of being unable to have direct contact with their students during distance teaching. In this research, we examined the online learning trajectories of students participating in four small private online courses that were all taught by one teacher. We collected relevant data of 1,307 students from the campus learning management system. Subsequently, we constructed 18 prediction models, one for each week of the course, to develop an EWS for identifying students in online asynchronous learning at risk of failing (i.e., students who fail their final examination). Our results indicated that the fifth-week model successfully predicted student performance, with an accuracy exceeding 83% from the eighth week onward.

Highlights

Learning management systems (LMSs) are used to quantify the learning behavior of students, enabling teachers to obtain data that are unavailable through face-to-face teaching in physical classrooms
Massive open online courses (MOOCs) are suitable for learning analytics or building prediction models because they involve the accumulation of large amounts of student data, which is helpful for the early detection of students who may be unable to complete such an online course (He et al, 2015) or for predicting academic results (Li et al, 2017)
Teachers have become adept at running online courses, which may include setting up decision support systems (Kotsiantis, 2011), conducting instructional interventions at the most appropriate time by using Early warning systems (EWSs) (Howard et al, 2018), and predicting academic failure (Costa et al, 2017)

Summary

Introduction

Learning management systems (LMSs) are used to quantify the learning behavior of students, enabling teachers to obtain data that are unavailable through face-to-face teaching in physical classrooms. Teachers have become adept at running online courses, which may include setting up decision support systems (Kotsiantis, 2011), conducting instructional interventions at the most appropriate time by using EWSs (Howard et al, 2018), and predicting academic failure (Costa et al, 2017) Research in this field has focused on collecting data on students who are “at risk” or “off track” and determining why they failed or ceased learning; studies have focused on the period following the completion of courses, which is too late to provide adequate support to these students (Hu et al, 2014). We collected small-sample data from different courses taught by the same teacher, while the courses were running, to build a portable student learning prediction model that can act as a warning system in SPOCs. Students who are at risk can be identified by analyzing data from the students’ online learning trajectory that are accumulated and entered weekly into the LMS. We addressed the primary research question—How can teachers use few-shot learning materials from multiple SPOCs to develop an EWS to detect students at risk?—as well as the following two related research questions: 1. How far in advance can the model predict a student’s academic performance?

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Conclusion