Application of machine learning approaches to analyse student success for contact learning and emergency remote teaching and learning during the COVID-19 era in speech–language pathology and audiology

Abstract

Background: The onset of the COVID-19 pandemic across the globe resulted in countries taking several measures to curb the spread of the disease. One of the measures taken was the locking down of countries, which entailed restriction of movement both locally and internationally. To ensure continuation of the academic year, emergency remote teaching and learning (ERTL) was launched by several institutions of higher learning in South Africa, where the norm was previously face-to-face or contact teaching and learning. The impact of this change is not known for the speech–language pathology and audiology (SLPA) students. This motivated this study. Objectives: This study aimed to evaluate the impact of the COVID-19 pandemic on SLPA undergraduate students during face-to-face teaching and learning, ERTL and transitioning towards hybrid teaching and learning. Method: Using course marks for SLPA undergraduate students, K means clustering and Random Forest classification were used to analyse students’ performance and to detect patterns between students’ performance and the attributes that impact student performance. Results: Analysis of the data set indicated that funding is one of the main attributes that contributed significantly to students’ performance; thus, it became one of the priority features in 2020 and 2021 during COVID-19. Conclusion: The clusters of students obtained during the analysis and their attributes can be used in identification of students that are at risk of not completing their studies in the minimum required time and early interventions can be provided to the students.

Keywords

• artificial intelligence
• audiology
• hybrid learning
• contact
• covid-19
• education
• machine learning
• emergency remote teaching
• speech–language pathology
• teaching
• blended learning