What is it to use learning analytics? In this section we look more closely at the nature of artificial intelligence and machine learning in order to understand where the decisions we make have an ethical outcome. In this module we look at the entire lifecycle of an analytics application, including but not limited to the framing of the problem, the data set, application and testing.
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2021/11/22 12:00 Module 6 - Introduction2021/11/26 12:00 Module 6 - Discussion
2021/11/29 12:00 Module 7 - Introduction
2021/12/03 12:00 Module 7 - Discussion
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Larysa Visengeriyeva, et.al., MLOps, 2021/12/01
"A high-level overview of a typical workflow for machine learning-based software development ."
Web: [Direct Link] [This Post]Dragan Gašević, Shane Dawson, George Siemens, 2021/12/02
"The paper stresses that learning analytics are about learning. As such, the computational aspects of learning analytics must be well integrated within the existing educational research. "
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We consider what sort of factors are taken into an account by an AI when it performs an essay grading task or an image recognition task, relating these decisions to the accuracy of the result and the ethics of using the AI for these purposes.
Web: [This Post]Chris Anderson, Wired, 2021/12/05
"All models are wrong, but some are useful." So proclaimed statistician George Box 30 years ago, and he was right, writes Chris Anderson. He argues that in the era of big data, we have no more need for classifications and taxonomies, no more need for theories that are only broad generalizations of what the data describes precisely.
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Lecture from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012. This lecture describes Hopfield nets, and offers an easy-to-follow explanation of how neural nets can be used to remember.
Web: [Direct Link] [This Post]I. Glenn Cohen, Ruben Amarasingham, Anand Shah, Bin Xie, Bernard Lo, Health Affairs, 2021/12/13
This article has good sections on the evaluation and application of AI-generated models in health care environments, which I adapted for the current work on the testing, application, evaluation and outcomes of learning analytics.
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"Explainable artificial intelligence (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms."
Web: [Direct Link] [This Post]Tim Miller, arXiv, 2021/12/15
"There exists vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations.... This paper argues that the field of explainable artificial intelligence should build on this existing research."
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The survey covers the work of 67 papers and charts recent trends in the field. "Deep-learning pioneer Geoffrey Hinton downplayed the need for explainability, tweeting: Suppose you have cancer and you have to choose between a black box AI surgeon that cannot explain how it works but has a 90% cure rate and a human surgeon with an 80% cure rate. Do you want the AI surgeon to be illegal?"
Web: [Direct Link] [This Post]Synopsis
synopsis
- Course Outline
- Course Newsletter
- Activity Centre
- -1. Getting Ready
- 1. Introduction
- 2. Applications of Learning Analytics
- 3. Ethical Issues in Learning Analytics
- 4. Ethical Codes
- 5. Approaches to Ethics
- 6. The Duty of Care
- 7. The Decisions We Make
- 8. Ethical Practices in Learning Analytics
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