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
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 continuatio...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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AOSIS
2022-08-01
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| Series: | South African Journal of Communication Disorders |
| Subjects: | |
| Online Access: | https://sajcd.org.za/index.php/sajcd/article/view/912 |
| _version_ | 1811211074583658496 |
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| author | Milka C. Madahana Katijah Khoza-Shangase Nomfundo Moroe Otis Nyandoro John Ekoru |
| author_facet | Milka C. Madahana Katijah Khoza-Shangase Nomfundo Moroe Otis Nyandoro John Ekoru |
| author_sort | Milka C. Madahana |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T05:06:28Z |
| format | Article |
| id | doaj.art-bce8c04ce3234276b18796c4a33f1d53 |
| institution | Directory Open Access Journal |
| issn | 0379-8046 2225-4765 |
| language | English |
| last_indexed | 2024-04-12T05:06:28Z |
| publishDate | 2022-08-01 |
| publisher | AOSIS |
| record_format | Article |
| series | South African Journal of Communication Disorders |
| spelling | doaj.art-bce8c04ce3234276b18796c4a33f1d532022-12-22T03:46:52ZengAOSISSouth African Journal of Communication Disorders0379-80462225-47652022-08-01692e1e1310.4102/sajcd.v69i2.912677Application 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 audiologyMilka C. Madahana0Katijah Khoza-Shangase1Nomfundo Moroe2Otis Nyandoro3John Ekoru4School of Electrical and Information Engineering, Faculty of Engineering and the Built Environment, University of the Witwatersrand, JohannesburgDepartment of Audiology, School of Human and Community Development, University of the Witwatersrand, JohannesburgDepartment of Audiology, School of Human and Community Development, University of the Witwatersrand, JohannesburgSchool of Electrical and Information Engineering, Faculty of Engineering and the Built Environment, University of the Witwatersrand, JohannesburgSchool of Electrical and Information Engineering, Faculty of Engineering and the Built Environment, University of the Witwatersrand, JohannesburgBackground: 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.https://sajcd.org.za/index.php/sajcd/article/view/912artificial intelligenceaudiologyhybrid learningcontactcovid-19educationmachine learningemergency remote teachingspeech–language pathologyteachingblended learning |
| spellingShingle | Milka C. Madahana Katijah Khoza-Shangase Nomfundo Moroe Otis Nyandoro John Ekoru 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 South African Journal of Communication Disorders artificial intelligence audiology hybrid learning contact covid-19 education machine learning emergency remote teaching speech–language pathology teaching blended learning |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | artificial intelligence audiology hybrid learning contact covid-19 education machine learning emergency remote teaching speech–language pathology teaching blended learning |
| url | https://sajcd.org.za/index.php/sajcd/article/view/912 |
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