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Machine Learning and Human Learning

University of Illinois at Urbana-Champaign via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

4 weeks, 10 hours a week
Sessions

Self-Paced
Level

Beginner
Subtitles

English, Kazakh, Arabic, German, Korean, Portuguese, Japanese, Italian, Indonesian, French, Spanish

Found in

Part of

Artificial Intelligence in Finance and Wealth Management

Overview

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This course examines the differences between machine and human learning and the ways in which machines can complement human learning. It examines technical definitions of supervised and unsupervised machine learning, as well as broader views of mechanical intelligence able to replicate or exceed human intelligence. The course will also explore practical applications of learning analytics and artificial intelligence in learning management systems and other educational tools and critically interrogate the applications of AI in education. The course is designed to aspiring and current educators, and anyone who is interested in the intersection of human and machine learning, and AI applications in education.

Syllabus

Course Orientation and Introduction
- This course examines the differences between machine and human learning and the ways in which machines can complement human learning. It examines technical definitions of supervised and unsupervised machine learning, as well as broader views of mechanical intelligence able to replicate or exceed human intelligence. The course will also explore practical applications of learning analytics and artificial intelligence in learning management systems and other educational tools and critically interrogate the applications of AI in education.
Cyber-Social Perspectives
- To understand what machines can and cannot do with meaning, we need to hold two questions together: how humans make meaning, and how computers process it. This module does exactly that. We begin with human learning, and the insight of Piaget and Vygotsky that people build understanding actively, and always with the help of others and the tools their culture provides. We then turn to the machine, tracing the word "cyber" from Norbert Wiener's cybernetics to what we call cyber-social intelligence: not a machine that replaces human thinking, but one that forms a complementary partnership with it. In the last two lessons, we look closely at how computers handle meaning through binary notation, which lets them name, classify, measure, act, and locate with astonishing precision, and we find the edges of what binary can do. What emerges is a clearer view of the human capacities to mean that no machine has, and of the cyber-social relationship in which people and machines each contribute what the other cannot.
Educational Data Mining
- Whenever we learn in a digital space, we leave a trail. Every click, every pause, every attempt and retry is recorded as data. This module asks what we can learn from that trail. Educational data mining is the study of these traces of learning, and of what they can tell us about how people come to understand. We begin with the data itself: the interaction logs that capture not just what a learner finally produced, but the whole process of getting there. We then turn to the methods that make sense of it, some that look for what we already know to seek, such as whether a student has mastered a skill, and others that explore the data to discover patterns we did not know were there, such as the different ways learners move through a course. Throughout, we hold onto a responsibility this work carries: because these traces come from real students, they must be used with care for privacy, equity, and fairness.
Framing the AI Discussion
- Having seen how machines can learn from the traces of our learning, we step back in this final module to ask the questions underneath. What is knowledge? How do humans acquire skill? And what, really, is the relationship between a human mind and a machine? We begin with the cognitive science of learning: the kinds of knowledge we hold, the journey from novice to expert, and the architectures researchers have built to model the mind. We then look at how these ideas have been put to work in education, from the earliest tutoring systems to the intelligent tutors of today, and at how the mobile devices in our pockets might carry learning to those with less access to other technologies. Finally, we return to a question that has run quietly through this whole course: if computers can now do so much of what we once called thinking, what should learning become? Our answer, developed here and across our work, is that the task is neither to resist these machines nor to surrender to them, but to build a new kind of learning with them, what we have called cyber-social learning.