Ben Shapiro (University of Colorado Boulder)
Peter Norvig (Google)
Rebecca Fiebrink (Goldsmiths University of London)
Monday, August 21, 2017 09:00-17:00
Machine learning is transforming many areas of computer science. From natural language processing and search to computer music, many systems that were once made with symbolic systems are now hybrids of symbolic and statistical machinery.
This shift presents new challenges and opportunities for learning about computer science, for studying learning about computing, and for collaborating with researchers from other education research disciplines.
In this workshop, we will collaboratively define a research agenda for CS Education Research on the topic of machine learning. Participants will give brief presentations about the ideas in their position papers (see below), then form groups to discuss and further develop related ideas. We expect that the participants in the workshop will begin outlining integrative position papers and research proposals, and that this workshop will ultimately result in published position papers and spur new lines of collaborative research by the participants.
Participation is limited to 15 people, exclusive of the organizers. To cover costs of the workshop, there is a fee of $40 for each participant.
To apply to this workshop, please email a 2-3 page position paper to email@example.com. The deadline for submission is June 16, 2017, 11:59pm, anywhere on earth (UTC-12). We will notify submitters about their acceptance by June 24, 2017.
Suggested topics for position papers include:
- What is important for students to learn about machine learning, statistics, or other related topics?
- Notional machines for thinking about machine learning systems, and hybrids of classical and ML systems.
- Probabilistic programming as a bridge between imperative and statistical systems.
- What CS Education Researchers should learn from Math Education Research.
- What does computational thinking mean in a machine learning context? How do we have to modify existing frameworks to account for ML?
- How does ML create new opportunities for CS to integrate with other subject areas?
- What theories and methods for studying learning is the domain of ML especially felicitous to?
- Implications for teacher training.
- Prior work: what research on learning about ML, AI, or other closely-related topics has already been conducted?
Other topics are also welcome.
Please send questions to firstname.lastname@example.org.
Disambiguating Note: The focus of this workshop is not the application of machine learning techniques to understand student learning about computer science.
Answer to a Frequently Asked Question: We are doing this after the conference because Ben is Co-Chairing the Doctoral Workshop before the conference.