Exploring the development of computational thinking among pre-service teachers using visual programming: an interventionist case study
- Authors: Sepula, Chikondi
- Date: 2025-04-03
- Subjects: Computational thinking , Visual programming (Computer science) , Sociocultural perspective , Problem solving , Scratch (Computer program language) , Student teachers South Africa
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/479996 , vital:78387
- Description: Due to its cognitive effect on learners, computational thinking (CT) has gained significant attention and has been increasingly integrated into primary and secondary education worldwide. The integration of CT into educational curricula offers several benefits, including improved learning outcomes, enhanced problem-solving abilities, and the development of skills necessary for the digital landscape of the 21st century. Recognizing these benefits, South Africa introduced CT in primary schools in 2023 through a dedicated subject, coding and robotics. However, teacher upskilling remains a major challenge, as many teachers lack the necessary skills to teach this subject effectively. This problem is particularly pronounced at the foundational phase, where delivering similar content to young learners presents additional pedagogical complexities. Thus, this study explored the development of CT skills with visual programming among foundational phase pre-service teachers. Situated within the interpretive paradigm, a qualitative case study methodology was employed, owing to its effectiveness in exploring contextual factors and complexities that influence human experiences. The study involved 49 first-year pre-service teachers in an Introduction to Technology module at Rhodes University. Purposive sampling was used to select the foundational phase pre-service teachers at Rhodes University. Data was collected using CT reflective tool, semi-structured interviews, focus-group discussions, and reflective journals. This study was grounded in Vygotsky’s sociocultural theory (SCT) to understand and mediate the development of CT through visual programming. It utilized the “Code, Connect, Create” professional development (PD) model as a structured teacher training approach for CT development through visual programming. Additionally, the “Use, Modify, Create” pedagogical model was implemented as a CT framework specific to visual programming to guide and regulate pedagogical decisions during the intervention. Brennan and Resnick’s 3D CT framework was employed to identify and analyse the CT concepts and skills incorporated in the study. Thematic analysis, which involved coding was used to generate themes from qualitative data to address the research questions. Results indicated that visual programming approach enhances CT by fostering interest, creativity, and collaboration. Key enablers included contextualised project-based learning, a clear rationale for CT, and prior programming exposure. Conversely, lack of a clear CT rationale, prior programming experience, and the multimedia nature of Scratch were identified as hindrances. The study concluded that visual programming effectively enhances CT among foundational phase pre-service teachers and recommended its early integration into their training programs to enhance CT skills. , Thesis (MEd) -- Faculty of Education, Secondary and Post School Education, 2025
- Full Text:
- Date Issued: 2025-04-03
- Authors: Sepula, Chikondi
- Date: 2025-04-03
- Subjects: Computational thinking , Visual programming (Computer science) , Sociocultural perspective , Problem solving , Scratch (Computer program language) , Student teachers South Africa
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/479996 , vital:78387
- Description: Due to its cognitive effect on learners, computational thinking (CT) has gained significant attention and has been increasingly integrated into primary and secondary education worldwide. The integration of CT into educational curricula offers several benefits, including improved learning outcomes, enhanced problem-solving abilities, and the development of skills necessary for the digital landscape of the 21st century. Recognizing these benefits, South Africa introduced CT in primary schools in 2023 through a dedicated subject, coding and robotics. However, teacher upskilling remains a major challenge, as many teachers lack the necessary skills to teach this subject effectively. This problem is particularly pronounced at the foundational phase, where delivering similar content to young learners presents additional pedagogical complexities. Thus, this study explored the development of CT skills with visual programming among foundational phase pre-service teachers. Situated within the interpretive paradigm, a qualitative case study methodology was employed, owing to its effectiveness in exploring contextual factors and complexities that influence human experiences. The study involved 49 first-year pre-service teachers in an Introduction to Technology module at Rhodes University. Purposive sampling was used to select the foundational phase pre-service teachers at Rhodes University. Data was collected using CT reflective tool, semi-structured interviews, focus-group discussions, and reflective journals. This study was grounded in Vygotsky’s sociocultural theory (SCT) to understand and mediate the development of CT through visual programming. It utilized the “Code, Connect, Create” professional development (PD) model as a structured teacher training approach for CT development through visual programming. Additionally, the “Use, Modify, Create” pedagogical model was implemented as a CT framework specific to visual programming to guide and regulate pedagogical decisions during the intervention. Brennan and Resnick’s 3D CT framework was employed to identify and analyse the CT concepts and skills incorporated in the study. Thematic analysis, which involved coding was used to generate themes from qualitative data to address the research questions. Results indicated that visual programming approach enhances CT by fostering interest, creativity, and collaboration. Key enablers included contextualised project-based learning, a clear rationale for CT, and prior programming exposure. Conversely, lack of a clear CT rationale, prior programming experience, and the multimedia nature of Scratch were identified as hindrances. The study concluded that visual programming effectively enhances CT among foundational phase pre-service teachers and recommended its early integration into their training programs to enhance CT skills. , Thesis (MEd) -- Faculty of Education, Secondary and Post School Education, 2025
- Full Text:
- Date Issued: 2025-04-03
Darknet Traffic Detection Using Histogram-Based Gradient Boosting
- Brown, Dane L, Sepula, Chikondi
- Authors: Brown, Dane L , Sepula, Chikondi
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464063 , vital:76472 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-99-1624-5_59"
- Description: The network security sector has observed a rise in severe attacks emanating from the darknet or encrypted networks in recent years. Network intrusion detection systems (NIDS) capable of detecting darknet or encrypted traffic must be developed to increase system security. Machine learning algorithms can effectively detect darknet activities when trained on encrypted and conventional network data. However, the performance of the system may be influenced, among other things, by the choice of machine learning models, data preparation techniques, and feature selection methodologies. The histogram-based gradient boosting strategy known as categorical boosting (CatBoost) was tested to see how well it could find darknet traffic. The performance of the model was examined using feature selection strategies such as correlation coefficient, variance threshold, SelectKBest, and recursive feature removal (RFE). Following the categorization of traffic as “darknet” or “regular”, a multi-class classification was used to determine the software application associated with the traffic. Further study was carried out on well-known machine learning methods such as random forests (RF), decision trees (DT), linear support vector classifier (SVC Linear), and long-short term memory (LST) (LSTM). The proposed model achieved good results with 98.51% binary classification accuracy and 88% multi-class classification accuracy.
- Full Text:
- Date Issued: 2023
- Authors: Brown, Dane L , Sepula, Chikondi
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464063 , vital:76472 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-99-1624-5_59"
- Description: The network security sector has observed a rise in severe attacks emanating from the darknet or encrypted networks in recent years. Network intrusion detection systems (NIDS) capable of detecting darknet or encrypted traffic must be developed to increase system security. Machine learning algorithms can effectively detect darknet activities when trained on encrypted and conventional network data. However, the performance of the system may be influenced, among other things, by the choice of machine learning models, data preparation techniques, and feature selection methodologies. The histogram-based gradient boosting strategy known as categorical boosting (CatBoost) was tested to see how well it could find darknet traffic. The performance of the model was examined using feature selection strategies such as correlation coefficient, variance threshold, SelectKBest, and recursive feature removal (RFE). Following the categorization of traffic as “darknet” or “regular”, a multi-class classification was used to determine the software application associated with the traffic. Further study was carried out on well-known machine learning methods such as random forests (RF), decision trees (DT), linear support vector classifier (SVC Linear), and long-short term memory (LST) (LSTM). The proposed model achieved good results with 98.51% binary classification accuracy and 88% multi-class classification accuracy.
- Full Text:
- Date Issued: 2023
- «
- ‹
- 1
- ›
- »