An online learning platform for teaching, learning, and assessment of programming
- Robinson, Philip E., Carroll, Johnson
- Authors: Robinson, Philip E. , Carroll, Johnson
- Date: 2017
- Subjects: Online learning platform , Automated assessment , Programming
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/236077 , uj:24151 , Citation: Robinson, P.E. & Carroll, J. 2017. An online learning platform for teaching, learning, and assessment of programming.
- Description: Abstract: In this paper the use of an open-source online learning platform to aid in teaching and assessment of computer programming in large classes is discussed. The pedagogical philosophy of how the subject of computer programming is taught is presented. Based on the skills and learning processes that are identified for effective teaching of computer programming, a strategy for employing modern web technology coupled with an automated assessment capability to meet these goals is discussed. The paper describes the technology and implementation of the learning platform and new methods for automated assessment of programming assignments and exams. Finally, the application of the system to achieve the pedagogical goals and the benefits of using the system for teaching large classes is reported.
- Full Text:
- Authors: Robinson, Philip E. , Carroll, Johnson
- Date: 2017
- Subjects: Online learning platform , Automated assessment , Programming
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/236077 , uj:24151 , Citation: Robinson, P.E. & Carroll, J. 2017. An online learning platform for teaching, learning, and assessment of programming.
- Description: Abstract: In this paper the use of an open-source online learning platform to aid in teaching and assessment of computer programming in large classes is discussed. The pedagogical philosophy of how the subject of computer programming is taught is presented. Based on the skills and learning processes that are identified for effective teaching of computer programming, a strategy for employing modern web technology coupled with an automated assessment capability to meet these goals is discussed. The paper describes the technology and implementation of the learning platform and new methods for automated assessment of programming assignments and exams. Finally, the application of the system to achieve the pedagogical goals and the benefits of using the system for teaching large classes is reported.
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Blind deconvolution of Gaussian blurred images containing additive white Gaussian noise
- Robinson, Philip E., Roodt, Yuko
- Authors: Robinson, Philip E. , Roodt, Yuko
- Date: 2013
- Subjects: Gaussian blur , Gaussian noise , Bind deconvolution
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/16753 , uj:15807 , Robinson, P.E. & Roodt, Y. Blind deconvolution of Gaussian blurred images containing additive white Gaussian noise. IEEE International Conference on Industrial Technology (ICIT 2013), 2013, pp. 1092-1097.
- Description: Abstract: Image restoration algorithms are used to reconstruct the information that is suppressed when an observed image is subjected to blurring. These algorithms generally assume that knowledge of the nature of the distortion and noise contained in an observed image is available. When this information is not available and has to be directly estimated from the image being processed the problem becomes one of blind deconvolution. This paper makes use of a novel blur identification technique and a noise identification technique to perform blind deconvolution on single images that have been degraded by a Gaussian blur and contain additive white Gaussian noise.
- Full Text:
- Authors: Robinson, Philip E. , Roodt, Yuko
- Date: 2013
- Subjects: Gaussian blur , Gaussian noise , Bind deconvolution
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/16753 , uj:15807 , Robinson, P.E. & Roodt, Y. Blind deconvolution of Gaussian blurred images containing additive white Gaussian noise. IEEE International Conference on Industrial Technology (ICIT 2013), 2013, pp. 1092-1097.
- Description: Abstract: Image restoration algorithms are used to reconstruct the information that is suppressed when an observed image is subjected to blurring. These algorithms generally assume that knowledge of the nature of the distortion and noise contained in an observed image is available. When this information is not available and has to be directly estimated from the image being processed the problem becomes one of blind deconvolution. This paper makes use of a novel blur identification technique and a noise identification technique to perform blind deconvolution on single images that have been degraded by a Gaussian blur and contain additive white Gaussian noise.
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Implementation of specifications grading for assessment of programming using an online learning platform
- Robinson, Philip E., Carroll, Johnson
- Authors: Robinson, Philip E. , Carroll, Johnson
- Date: 2017
- Subjects: Computer science education , Specifications grading , Online learning platform
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/${Handle} , uj:24937 , Citation: Robinson, P.E. & Carroll, J. 2017. Implementation of specifications grading for assessment of programming using an online learning platform.
- Description: Abstract: Computer programming is inherently ill-suited to traditional assessment with partial credit, as real-world programming tasks have a binary evaluation scheme (works or does not work) allowing multiple attempts. In this study, the specifications grading paradigm is presented as an alternative method of assessing computer programming in a more authentic manner. The authors propose a specifications grading scheme for an introductory programming course, and describe the implementation of this scheme using a custom online learning platform with automated grading capabilities. A set of criteria are defined for assessing the quality of a grading system and these criteria are used to discuss the merits of the proposed grading system. The authors argue that the proposed system is superior to the traditional assessment models in terms of fostering authentic learning, providing more accurate and reliable assessment, and saving the instructor time.
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- Authors: Robinson, Philip E. , Carroll, Johnson
- Date: 2017
- Subjects: Computer science education , Specifications grading , Online learning platform
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/${Handle} , uj:24937 , Citation: Robinson, P.E. & Carroll, J. 2017. Implementation of specifications grading for assessment of programming using an online learning platform.
- Description: Abstract: Computer programming is inherently ill-suited to traditional assessment with partial credit, as real-world programming tasks have a binary evaluation scheme (works or does not work) allowing multiple attempts. In this study, the specifications grading paradigm is presented as an alternative method of assessing computer programming in a more authentic manner. The authors propose a specifications grading scheme for an introductory programming course, and describe the implementation of this scheme using a custom online learning platform with automated grading capabilities. A set of criteria are defined for assessing the quality of a grading system and these criteria are used to discuss the merits of the proposed grading system. The authors argue that the proposed system is superior to the traditional assessment models in terms of fostering authentic learning, providing more accurate and reliable assessment, and saving the instructor time.
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Robust single image noise estimation from approximate local statistics
- Roodt, Yuko, Clarke, Wimpie, Robinson, Philip E., Nel, André
- Authors: Roodt, Yuko , Clarke, Wimpie , Robinson, Philip E. , Nel, André
- Date: 2012
- Subjects: Gaussian noise , Imaging sensors
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/16706 , uj:15802 , Roodt, Y., Robinson, P.E. & Nel, A. Robust single image noise estimation from approximate local statistics. Proceedings of the Twenty-Third Annual Symposium of the Pattern Recognition Association of South Africa, November 2012, pp 47-53.
- Description: Abstract: A novel method for estimating the variance and standard deviation of the additive white Gaussian noise contained in an image will be presented. Only a single image is used to estimate the noise properties. Local image outliers are discarded, this allows us to separate the additive zero mean white Gaussian noise contained in a noisy image from the original image structure. Local variance estimates can then be calculated from the extracted noise. These local variance estimates are weak and can be influenced by misclassified image information. Robust statistics are then used to fuse the weak local variance estimates to obtain a robust global noise variance estimate. This method of estimating the noise properties is computationally efficient and provides reliable estimation results in synthetic and real-world imagery. The accuracy and processing complexity of the proposed algorithm will be compared against the current state-of-the-art noise estimators.
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- Authors: Roodt, Yuko , Clarke, Wimpie , Robinson, Philip E. , Nel, André
- Date: 2012
- Subjects: Gaussian noise , Imaging sensors
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/16706 , uj:15802 , Roodt, Y., Robinson, P.E. & Nel, A. Robust single image noise estimation from approximate local statistics. Proceedings of the Twenty-Third Annual Symposium of the Pattern Recognition Association of South Africa, November 2012, pp 47-53.
- Description: Abstract: A novel method for estimating the variance and standard deviation of the additive white Gaussian noise contained in an image will be presented. Only a single image is used to estimate the noise properties. Local image outliers are discarded, this allows us to separate the additive zero mean white Gaussian noise contained in a noisy image from the original image structure. Local variance estimates can then be calculated from the extracted noise. These local variance estimates are weak and can be influenced by misclassified image information. Robust statistics are then used to fuse the weak local variance estimates to obtain a robust global noise variance estimate. This method of estimating the noise properties is computationally efficient and provides reliable estimation results in synthetic and real-world imagery. The accuracy and processing complexity of the proposed algorithm will be compared against the current state-of-the-art noise estimators.
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