- Title
- Real-time feedback model for supporting individualised learning of programming students
- Creator
- Keen, Charne
- Subject
- Port Elizabeth (South Africa)
- Subject
- Eastern Cape (South Africa)
- Subject
- South Africa
- Date Issued
- 2021-12
- Date
- 2021-12
- Type
- Master's theses
- Type
- text
- Identifier
- http://hdl.handle.net/10948/53624
- Identifier
- vital:45691
- Description
- Feedback is crucial to the enhancement of the learning and teaching environment, especially in those environments that suffer from a number of extrinsic challenges. The growing demands for educators to provide academic interventions throughout the lecture session and the need for continuous improvement of the quality of university education make it necessary to find and apply more effective and efficient educational technologies and practices based on the correlation of teaching with a student’s conceptual understanding and individual learning preference. Following a combination of Design Science Research (DSR) and Case Study Methodology, this research addresses this problem by designing a technology based real-time feedback (TBRTF) model that can easily be implemented in a South African University. The model designed followed a layered architecture pattern. The architecture describes the data, technology and user support layers of the model. The data support layer incorporates the collection of student academic data and learning preferences. The technology incorporates a machine learning component. The machine learning component covers two technological aspects: the prediction component and the clustering component. This TBRTF model provides the guidelines needed to develop a system that supports individualised real-time feedback in the learning and teaching environment of programming students. The aim of the model is that as the students partake in learning activities where the student data is updated, the monitoring component will fire, updating the probability of failure prediction and in turn the student clusters are regenerated. This will notify the educator of a change and provide decision making support. The student will be allocated individualised feedback in the form of learning materials based on the cluster that the student is allocated to. Through a demonstration and evaluation, this study showed that by following the proposed architecture of the TBRTF model, a model that supports individualised realtime feedback in the learning and teaching environment of programming students can be developed. The validation used an artificial neural network as the prediction component and a k-means clustering algorithm as the clustering component.
- Description
- Thesis (MSc) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 2021
- Format
- computer
- Format
- online resource
- Format
- application/pdf
- Format
- 1 online resource (x, 169 pages)
- Format
- Publisher
- Nelson Mandela University
- Publisher
- Faculty of Science
- Language
- English
- Rights
- Nelson Mandela University
- Rights
- All Rights Reserved
- Rights
- Open Access
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