Overview and Logistics
T509- Massive: The Future of Learning at Scale
|Introduction:||Week 1: What is Massive?
Week 2: The Long History of Distance and Online Education
|Part I: Under the Hood||Week 3: Connectivism and Syndication
Week 4: The LMS and Algorithms
Week 5: Assessment and Credentials: Self, Peer, Expert, AI
Week 6: Blended Learning
|Part II: Implications||Week 7: Production: How the MOOCs are Made
Week 8: Research and the Science of Learning
Week 9: Economics and Policy
Week 10: Worst Case Scenarios
Week 11: Equity and Openness
|Coda||Week 12: Project Presentations and Closure|
IMPORTANT: The syllabus is tentative as of August 21. I expect the required readings will only change in minimal ways, but the exact activities on each class session will depend greatly upon the exact enrollment of the course.
Video lectures, peer networks, and intelligent tutors now enable teachers to lecture to mass audiences, software platforms to train millions, and vast online communities to support one another’s growth. Optimists see new innovations as harbingers of transformation in educational systems, and critics see these cheap technologies as poor substitutes for human experts. This course will explore the opportunities, limits, and tradeoffs of new technologies that support teaching and learning at scale. Through examining MOOCs, intelligent tutors, blended learning environments, and interest based learning communities, students will examine the implications of these new modes of learning for classrooms, software, equity, opportunity, research, and policy. Through readings, discussions, encounters with experts in the field, and immersion in the technologies of large scale learning, students will work collectively to understand where technologies of large scale learning might enhance or transform learning and where they fall short or might even harm learners or systems. Ideally, the course community will include students with a deep understanding of education (but perhaps little background in technology) and students with extensive experience with computing technologies (but perhaps little background in education). Framing questions for the course include: Where can large-scale learning technologies most enhance student learning, and where do they offer no value? Who benefits and who is harmed from the increasing use of large scale learning environments? What pedagogical theories areimplicitly or explicitlybaked into the architecture of different massive learning platforms? How does the utility of large scale learning platforms vary across different domains of learning and sectors of education? What do computers do best? What do peer networks do best? What do expert teachers do best? What are the policy implications for new technology and new models of learning in K-12, higher education, and other sectors?
Welcome to T509-Massive: The Future of Learning at Scale
What will we study?
This course is a collaborative investigation of large-scale learning environments where there are many learners and few expert instructors, environments such as connectivist MOOCs, xMOOCs, intelligent tutors, blended learning environments, and interest-based learning communities. The goal of the course is to create a networked and residential learning community that supports one another in more deeply understanding the underlying features of these environments and their implications for educational systems.
Who should take this course?
Since large-scale learning environments are poised to reshape nearly every sector of education—from professional development for early childhood educators to ongoing training for lifelong learners—the course is designed to serve the widest possible community of learners. Students with deep backgrounds in technology or deep backgrounds in education, students with interests in early childhood, K-12, higher ed, and lifelong adult learning, students with great excitement for online learning and students with deep skepticism are all invited into the course. Of course, such a diverse student community will have diverse needs and interests. The challenge will be to create a learning community where we support each other’s specific inquiry into a broad topic.
What is the model of learning in this course?
The central metaphor of T509-Massive is the potluck. As an instructional team, our job is to lay out a spread of opportunities, but students in the course will also need to bring dishes to the table. Each of us then as learners will need to create a plate for ourselves—a meal that meets our individual needs. There will be too much on the buffet line for anyone to eat everything, so part of the learning experience will involve figuring out how to select the most satisfying possible meal for ourselves and how to help our fellow learners do the same. The main prerequisite for the course, therefore, is a willingness to contribute to an emergent learning community and an understanding that a central part of the experience is navigating a variety of learning options rather that following a clearly demarked path.
What is the design of the course?
The course proceeds in three parts. The first part is an introduction to our (unconventional) approach to learning and to the history of distance education. The second part, “Under the Hood,” investigates some of the key technological and pedagogical theories underlying large-scale learning environments. The third part, “Implications” investigates the implication of these technologies for research, equity, openness, economics, and other features of our system of education.
When will we meet?
We will have two kinds of meeting times and spaces. HGSE calls these times “lecture” and “lab,” but they are probably better understood as “whole group” and “small group.”
Every week we will meet as a whole group from 4pm-6pm on Wednesdays, currently scheduled for Larsen G-08.
Some weeks, we will then continue to meet from 6-7pm. Other weeks, we will have “labs” or “sections,” where we can work on issues in smaller groups. These will be 90 minute sessions, with times and places to be scheduled. So every week, we will get together for about 3-3.5 hours per week.
Having some extra time scheduled for class meetings will allow students to self-organize sessions—birds-of-a-feather meetings, project meetings, guest lecturers, etc.—and allow us to meet in a variety of configurations.
How will students be evaluated?
Students will receive self, peer, and instructor feedback on their activities throughout the course. Grades will be computed holistically, but roughly 40% of grades will be based on the course project, 40% on participation in the online network, and 20% on pre-class assignments such as peer evaluations.
Academic integrity is a cornerstone of the intellectual enterprise, and plagiarism is a serious violation of that integrity. Don’t. You might find two tutorials helpful in this regard: Principles of Paraphrasing (http://isites.harvard.edu/icb/icb.do?keyword=paraphrasing) and APA Exposed (http://isites.harvard.edu/icb/icb.do?keyword=apa_exposed). If you are struggling to complete an assignment, please reach out to the instructional team about an extension.
Who is teaching the course?
The lead learner for the course is Justin Reich, the Richard L. Menschel HarvardX Research fellow. Justin does research for HarvardX, is an affiliate of the Berkman Center for Internet and Society, teaches at HGSE and in the MIT Scheller Teacher Education program, founded a professional development consultancy called EdTechTeacher, serves on the Massachusetts Digital Learning Advisory Council, and writes the EdTech Researcher blog for Education Week. You can find out more about Justin from his about page, portfolio, Twitter feed, or blog.
More details about our teaching/learning fellows will follow.