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Four-quarter required seminar for the LDT master's program. Discussions and activities related to designing for learning with technology. Support for master's project. Theoretical and practical perspectives, hands-on development, and collaborative efforts. (LDT)

Four-quarter required seminar for the LDT master's program. Discussions and activities related to designing for learning with technology. Support for master's project. Theoretical and practical perspectives, hands-on development, and collaborative efforts. (LDT)

How do we design artificial systems that learn as we do early in life -- as "scientists in the crib" who explore and experiment with our surroundings? How do we make AI "curious" so that it explores without explicit external feedback? Topics draw from cognitive science (intuitive physics and psychology, developmental differences), computational theory (active learning, optimal experiment design), and AI practice (self-supervised learning, deep reinforcement learning). Students present readings and complete both an introductory computational project (e.g. train a neural network on a self-supervised task) and a deeper-dive project in either cognitive science (e.g. design a novel human subject experiment) or AI (e.g. implement and test a curiosity variant in an RL environment). Prerequisites: python familiarity and practical data science (e.g. sklearn or R).

Most successful machine learning algorithms of today use either carefully curated, human-labeled datasets, or large amounts of experience aimed at achieving well-defined goals within specific environments. In contrast, people learn through their agency: they interact with their environments, exploring and building complex mental models of their world so as to be able to flexibly adapt to a wide variety of tasks. One crucial next direction in artificial intelligence is to create artificial agents that learn in this flexible and robust way. Students will read and take turns presenting current works, and they will produce a proposal of a feasible next research direction. Prerequisites: CS229, CS231N, CS234 (or equivalent).

This is an ongoing workshop and reading group for students and faculty engaged in research in higher education. Topics include but are not limited to postsecondary access; late adolescent and college student development; adult and lifelong learning; teaching and learning in postsecondary settings; leadership and management; federal, state, and institutional policy; student and organizational culture; and finance and economics. The focus of the workshop is to provide a faculty led community of support for students, faculty, and others conducting research on higher education. Accordingly, the schedule of topics each quarter will be determined by participants in the workshop to flexibly focus on current research papers and projects. The workshop will provide a space for participants to present research ideas, workshop conceptual and methodological issues, give and receive feedback on conference paper proposals and drafts, and engage in focused discussion of papers and studies relevant to their work. It is open to master's and doctoral students, and with instructor approval, undergraduates working on theses and senior papers.

Is Artificial Intelligence really disrupting the EdTech industry or do its business impacts remain elusive? Despite booming interest around AI, concrete examples of how this is actually changing the way EdTech businesses operate remain scant. In this course, students will have the opportunity to engage with EdTech entrepreneurs, board members and venture capitalists around real business cases that illustrate the opportunities and challenges linked to incorporating AI into their product when it comes to driving sales, market share, and profitability.

This is a hands-on course that introduces students to digital fabrication and 'maker' technologies used to develop prototypes of new objects and artifacts for learning. Technologies include 3D printers, electronic textiles, laser cutters, low-threshold programming environments, and micro controllers. Constructionist learning theory and current practices for design are covered. No prior technical or prototyping experience is required.

How do adolescents develop their identities, manage their inner and outer worlds, and learn? Presuppositions: that fruitful instruction takes into account the developmental characteristics of learners and the task demands of specific curricula; and that teachers can promote learning and motivation by mediating among the characteristics of students, the curriculum, and the wider social context of the classroom. Prerequisite: STEP student or consent of instructor. (STEP)

How to best manage a classroom. Student and teacher roles in developing a classroom community. Strategies for classroom management within a theoretical framework. STEP elementary only.