Mapping Computational Thinking Skills to the South African Secondary School Mathematics Curriculum
- Bradshaw, Karen L, Milne, Shannon
- Authors: Bradshaw, Karen L , Milne, Shannon
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , book
- Identifier: http://hdl.handle.net/10962/440285 , vital:73763 , ISBN 9783030950033 , https://doi.org/10.1007/978-3-030-95502-1_41
- Description: Computational thinking (CT) is gaining recognition as an important skill for learners in both Computer Science (CS) and several other disciplines, including mathematics. In addition, researchers have shown that there is a direct correlation between poor mathematical skills and the high attrition rate of CS undergraduates. This research investigates the use of nine core CT skills in the South African Grades 10–12 Mathematics curriculum by mapping these skills to the objectives given in each of the topics in the curriculum. The artefact developed shows that all the identified CT skills are used in the curriculum. With the use of this mapping, future research on interventions to develop these skills through mathematics at secondary school, should produce school leavers with better mathematical and problem solving abilities, which in turn, might contribute to better success rates in CS university courses.
- Full Text:
- Date Issued: 2021
- Authors: Bradshaw, Karen L , Milne, Shannon
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , book
- Identifier: http://hdl.handle.net/10962/440285 , vital:73763 , ISBN 9783030950033 , https://doi.org/10.1007/978-3-030-95502-1_41
- Description: Computational thinking (CT) is gaining recognition as an important skill for learners in both Computer Science (CS) and several other disciplines, including mathematics. In addition, researchers have shown that there is a direct correlation between poor mathematical skills and the high attrition rate of CS undergraduates. This research investigates the use of nine core CT skills in the South African Grades 10–12 Mathematics curriculum by mapping these skills to the objectives given in each of the topics in the curriculum. The artefact developed shows that all the identified CT skills are used in the curriculum. With the use of this mapping, future research on interventions to develop these skills through mathematics at secondary school, should produce school leavers with better mathematical and problem solving abilities, which in turn, might contribute to better success rates in CS university courses.
- Full Text:
- Date Issued: 2021
Segmentation of Tuta Absoluta’s Damage on Tomato Plants: A Computer Vision Approach
- Loyani, Loyani K, Bradshaw, Karen L, Machuze, Dina
- Authors: Loyani, Loyani K , Bradshaw, Karen L , Machuze, Dina
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/440313 , vital:73765 , xlink:href="https://doi.org/10.1080/08839514.2021.1972254"
- Description: Tuta absoluta is a major threat to tomato production, causing losses ranging from 80% to 100% when not properly managed. Early detection of T. absoluta’s effects on tomato plants is important in controlling and preventing severe pest damage on tomatoes. In this study, we propose semantic and instance segmentation models based on U-Net and Mask RCNN, deep Convolutional Neural Networks (CNN) to segment the effects of T. absoluta on tomato leaf images at pixel level using field data. The results show that Mask RCNN achieved a mean Average Precision of 85.67%, while the U-Net model achieved an Intersection over Union of 78.60% and Dice coefficient of 82.86%. Both models can precisely generate segmentations indicating the exact spots/areas infested by T. absoluta in tomato leaves. The model will help farmers and extension officers make informed decisions to improve tomato productivity and rescue farmers from annual losses.
- Full Text:
- Date Issued: 2021
- Authors: Loyani, Loyani K , Bradshaw, Karen L , Machuze, Dina
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/440313 , vital:73765 , xlink:href="https://doi.org/10.1080/08839514.2021.1972254"
- Description: Tuta absoluta is a major threat to tomato production, causing losses ranging from 80% to 100% when not properly managed. Early detection of T. absoluta’s effects on tomato plants is important in controlling and preventing severe pest damage on tomatoes. In this study, we propose semantic and instance segmentation models based on U-Net and Mask RCNN, deep Convolutional Neural Networks (CNN) to segment the effects of T. absoluta on tomato leaf images at pixel level using field data. The results show that Mask RCNN achieved a mean Average Precision of 85.67%, while the U-Net model achieved an Intersection over Union of 78.60% and Dice coefficient of 82.86%. Both models can precisely generate segmentations indicating the exact spots/areas infested by T. absoluta in tomato leaves. The model will help farmers and extension officers make informed decisions to improve tomato productivity and rescue farmers from annual losses.
- Full Text:
- Date Issued: 2021
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