The views and opinions of Rhodes University lecturers towards isiXhosa as a language of learning and teaching (LOLT) in higher education
- Authors: Nkunzi, Zintle
- Date: 2023-10-13
- Subjects: Multilingualism , Bilingualism , Language and education , Xhosa language , College teachers South Africa Makhanda Attitudes , Code switching (Linguistics)
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/424963 , vital:72197
- Description: This research sought to investigate how African languages function as a communicative tool in a university where English is the medium of instruction. The study's purpose is to provide a systematic review of research that has been carried out on language attitudes towards isiXhosa as a language of learning and teaching (LoLT) in higher education. The study reveals that South African higher education institutions such as Rhodes University mostly use English as the LoLT- a language which for most lecturers is not their first/home language but helps ease communication in a multilingual community. Rhodes University is characterised by multilingualism because the university community is made up of diversity in culture, language, and educational background of the people. Previously explored language attitude studies are based on students’ views and this study investigated RU lecturer views and opinions towards isiXhosa as a LoLT. The study focused on the importance and the need (if any) of isiXhosa in a multilingual higher education institution. The study reveal that language barriers are one of the difficulties, but academic cultural differences seem to play a crucial role that can impact on the learning and teaching outcomes. This can lead to negative experiences and the forming of stereotypical views. These views include how lecturers are and should be trained to teach mathematics, science, and academic studies in African languages. The SA higher education practices and language use (i.e., monolingual language policy) are one of the reasons that the implementation of indigenous languages in education policies in SA is fraught with difficulties due to several factors. Amongst the factors is the fact that indigenous languages are not yet fully developed as academic languages. The study further reveals that lecturers find it difficult to teach mathematical studies in isiXhosa because of lack of terminology in the language for academic purposes particularly at a tertiary level. Furthermore, existing literature highlights the importance of the use of code-switching which is a beneficial practice for lecturers in assisting their students who struggle with English as a LoLT at RU. The lecturer views towards isiXhosa as a LoLT at RU is not only on language barrier but also about the lack of development in the language use in spaces where only English is believed to be the best such as language for academics. The study also reveals an integration of Information Communication Technology in education and how language appears as a barrier. , Thesis (MA) -- Faculty of Humanities, School of Languages and Literatures, 2023
- Full Text:
- Date Issued: 2023-10-13
- Authors: Nkunzi, Zintle
- Date: 2023-10-13
- Subjects: Multilingualism , Bilingualism , Language and education , Xhosa language , College teachers South Africa Makhanda Attitudes , Code switching (Linguistics)
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/424963 , vital:72197
- Description: This research sought to investigate how African languages function as a communicative tool in a university where English is the medium of instruction. The study's purpose is to provide a systematic review of research that has been carried out on language attitudes towards isiXhosa as a language of learning and teaching (LoLT) in higher education. The study reveals that South African higher education institutions such as Rhodes University mostly use English as the LoLT- a language which for most lecturers is not their first/home language but helps ease communication in a multilingual community. Rhodes University is characterised by multilingualism because the university community is made up of diversity in culture, language, and educational background of the people. Previously explored language attitude studies are based on students’ views and this study investigated RU lecturer views and opinions towards isiXhosa as a LoLT. The study focused on the importance and the need (if any) of isiXhosa in a multilingual higher education institution. The study reveal that language barriers are one of the difficulties, but academic cultural differences seem to play a crucial role that can impact on the learning and teaching outcomes. This can lead to negative experiences and the forming of stereotypical views. These views include how lecturers are and should be trained to teach mathematics, science, and academic studies in African languages. The SA higher education practices and language use (i.e., monolingual language policy) are one of the reasons that the implementation of indigenous languages in education policies in SA is fraught with difficulties due to several factors. Amongst the factors is the fact that indigenous languages are not yet fully developed as academic languages. The study further reveals that lecturers find it difficult to teach mathematical studies in isiXhosa because of lack of terminology in the language for academic purposes particularly at a tertiary level. Furthermore, existing literature highlights the importance of the use of code-switching which is a beneficial practice for lecturers in assisting their students who struggle with English as a LoLT at RU. The lecturer views towards isiXhosa as a LoLT at RU is not only on language barrier but also about the lack of development in the language use in spaces where only English is believed to be the best such as language for academics. The study also reveals an integration of Information Communication Technology in education and how language appears as a barrier. , Thesis (MA) -- Faculty of Humanities, School of Languages and Literatures, 2023
- Full Text:
- Date Issued: 2023-10-13
Benthic habitat mapping using marine geophysics and machine learning on the continental shelf of South Africa
- Authors: Pillay, Talicia
- Date: 2021-04
- Subjects: Gqeberha (South Africa) , Eastern Cape (South Africa) , Marine geophysics
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/52061 , vital:43452
- Description: A method to map seafloor substrates using machine learning, based primarily on hydroacoustic data including multibeam bathymetry, backscatter, and side-scan sonar, has been developed. The aim was to produce a customdesigned benthic habitat classification method that digitally integrates marine geophysics and biological science data, with relevance to all elements of the local substrate, and this was the first time it was attempted in a South African context. The algorithm developed is able to produce bio-physical benthic habitat maps and this can be extended along the continental shelf of South Africa as new data setsare collected and the algorithm is supplemented. At the outset, this work has focused on broad categories of rock and detailed categories of sediment. Four study sites with varying substrate were selected to holistically build the algorithm that followed a tiered approach of machine learning: Table Bay, Clifton, Koeberg Harbour and Cape St Francis. Table Bay was used to develop a new method of physical seafloor classification, by comparing and contrasting a number of statistical algorithms and software programs. Clifton was used to test the developed clustering algorithm, and Koeberg which is 35 km to the north was used to validate the algorithm because sediment samples, along with drop camera footage, were integrated to better define the results. The resultant verified algorithm was tested at Cape St Francis, where Remotely Operated Vehicle (ROV) footage was acquired in addition to hydroacoustic data. In the first phase of the process towards developing an algorithm, a customised tool was created within ArcGIS using python scripting language to classify seafloor bathymetry, which can be applied to any area of seafloor whatsoever. The tool was based on pioneering work done by the National Oceanic and Atmospheric Administration (NOAA) on a benthic terrain modelling toolbox and adapted to include side-scan sonar data. In the second phase of work, multibeam bathymetry, backscatter and side-scan sonar data that were processed using Qimera, Fledermaus Geocoder Toolbox, and Navlog processing software, were classified using different machine learning techniques including Decision Trees, Random Forests, and k-means clustering computer algorithms. The results from these algorithms were compared to manually-digitised polygons which were created to classify the seafloor substrate distribution by identification of different textures. Integrating all results facilitated a quantitative comparison that illuminated advantages and disadvantages of each machine learning technique and ultimately the k-means clustering techniques were found to be the simplest to implement and understand and worked most efficiently based on their seafloor segmentation capabilities in Table Bay, against all three hydroacoustic data sets (multibeam bathymetry, backscatter and side-scan sonar). In the third phase of work, ground-truthed seafloor characterisation maps were produced for the two study areas of Clifton and Koeberg Harbour. This applied multibeam bathymetry and backscatter data that were collected and processed with machine learning clustering techniques. , Thesis (MSc) -- Faculty of Science, Ocean Sciences, 2021
- Full Text: false
- Date Issued: 2021-04
- Authors: Pillay, Talicia
- Date: 2021-04
- Subjects: Gqeberha (South Africa) , Eastern Cape (South Africa) , Marine geophysics
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/52061 , vital:43452
- Description: A method to map seafloor substrates using machine learning, based primarily on hydroacoustic data including multibeam bathymetry, backscatter, and side-scan sonar, has been developed. The aim was to produce a customdesigned benthic habitat classification method that digitally integrates marine geophysics and biological science data, with relevance to all elements of the local substrate, and this was the first time it was attempted in a South African context. The algorithm developed is able to produce bio-physical benthic habitat maps and this can be extended along the continental shelf of South Africa as new data setsare collected and the algorithm is supplemented. At the outset, this work has focused on broad categories of rock and detailed categories of sediment. Four study sites with varying substrate were selected to holistically build the algorithm that followed a tiered approach of machine learning: Table Bay, Clifton, Koeberg Harbour and Cape St Francis. Table Bay was used to develop a new method of physical seafloor classification, by comparing and contrasting a number of statistical algorithms and software programs. Clifton was used to test the developed clustering algorithm, and Koeberg which is 35 km to the north was used to validate the algorithm because sediment samples, along with drop camera footage, were integrated to better define the results. The resultant verified algorithm was tested at Cape St Francis, where Remotely Operated Vehicle (ROV) footage was acquired in addition to hydroacoustic data. In the first phase of the process towards developing an algorithm, a customised tool was created within ArcGIS using python scripting language to classify seafloor bathymetry, which can be applied to any area of seafloor whatsoever. The tool was based on pioneering work done by the National Oceanic and Atmospheric Administration (NOAA) on a benthic terrain modelling toolbox and adapted to include side-scan sonar data. In the second phase of work, multibeam bathymetry, backscatter and side-scan sonar data that were processed using Qimera, Fledermaus Geocoder Toolbox, and Navlog processing software, were classified using different machine learning techniques including Decision Trees, Random Forests, and k-means clustering computer algorithms. The results from these algorithms were compared to manually-digitised polygons which were created to classify the seafloor substrate distribution by identification of different textures. Integrating all results facilitated a quantitative comparison that illuminated advantages and disadvantages of each machine learning technique and ultimately the k-means clustering techniques were found to be the simplest to implement and understand and worked most efficiently based on their seafloor segmentation capabilities in Table Bay, against all three hydroacoustic data sets (multibeam bathymetry, backscatter and side-scan sonar). In the third phase of work, ground-truthed seafloor characterisation maps were produced for the two study areas of Clifton and Koeberg Harbour. This applied multibeam bathymetry and backscatter data that were collected and processed with machine learning clustering techniques. , Thesis (MSc) -- Faculty of Science, Ocean Sciences, 2021
- Full Text: false
- Date Issued: 2021-04
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