FOLCS

Future Of Learning in Computer Science

FOLCS was previously documented at folcs.wp.imt.fr. That content is now maintained here.

The FOLCS lab is an interdisciplinary research and innovation group exploring the future of teaching and learning in the computer science field. It brings together researchers, educators, designers and edtech developers with a common interest in advancing computer science education. We design high-quality and inclusive tools, architectures and models to make learning computer science more accessible to everyone, and we work with interdisciplinary and international teams so that the research stays actionable.

The group is hosted by Télécom Paris, under the umbrella of the Computer Science and Networks department, and the team meets regularly at the Centre for Educational Experimentation of the Institut Villebon — Georges Charpak in Orsay.

Origins

A new research group was born in June 2021. After six years of fruitful collaboration, Rémi Sharrock, Ella Hamonic, Mathias Hiron and Gérard Memmi decided to found a research group dedicated to the future of computer science education, made possible by several learning projects run with colleagues and institutions in France (France-IOI, IMT, Fondation Mines-Télécom) and around the world, especially at Dartmouth College.

The key figures at the time of founding: more than 300,000 learners had used and validated the tools and technologies developed; two grants from the Mines-Télécom Foundation (Patrick and Lina Drahi endowment, Alumni Prestige Dinner); two grants from the Île-de-France region (edtech awards 2019 and 2020); one grant from the French Programme d’investissements d’avenir; and two international prizes.

Objectives and research questions

  1. Evidence-based learning experiments for CS education. We teach computer science online, but do people learn? We evaluate the integration of new learning tools with pre/post tests, and we build analytics and dashboards to measure and monitor learning.

  2. Cognitive science. Does learning how to program promote the development of higher cognitive skills, or transfer across knowledge domains — abstract thinking, problem solving, reasoning, designing, planning, algorithmic thinking? How do we measure procedural thinking, conditional reasoning, mathematical ability and memory capacity?

  3. System architectures for better scalability, interoperability, discoverability and traceability in learning. We design and prototype modular learning systems that scale massively through self-healing, self-deploying, self-optimizing and self-protecting architectures, and we explore the open protocols, standards and formats that enable genuine reuse of learning resources.

  4. Training CS educators at scale. How might we better train computer science educators in order to scale up programming teaching?

  5. Inclusive by design in CS education. How do we design learning experiences that promote diversity and inclusion, for teachers and learners alike?

  6. Better feedback with AI. Can artificial intelligence help provide relevant, instant and personalised feedback in CS learning?

  7. Ethics by design in CS. While developing learning interfaces, how do we implement ethical frameworks and policy guidance for developers and computer scientists?

  8. Teaching basic AI ethics for all. Designing tools that foster the critical thinking any citizen needs around algorithms and artificial intelligence.

The problems we start from

There is a considerable need for up-to-date technology to learn computer science, and even when new tools are integrated, their efficiency is rarely evaluated.

Existing learning management systems usually ship with content editors that produce resources which are not interoperable with other systems, and not indexable by search engines. Reuse therefore raises questions of duplication and traceability: attribution, hosting, and compensation models for authors all need answers.

Grading programming skills automatically — and fairly — remains an open machine learning problem.

And underneath all of it sits a policy question. How do we find enough teachers? What tools should they use? How should CS curricula be scoped and sequenced? What do teachers need to know, and how do they get trained? Why does computer science education matter for the next generations?

People

Founding members

  • Rémi Sharrock — researcher and associate professor, Télécom Paris, LTCI, Institut Polytechnique de Paris, IMT
  • Gérard Memmi — head of the Networks and Computer Science department, Télécom Paris
  • Ella Hamonic — edtech consultant and learning designer
  • Mathias Hiron — president of France-IOI

Collaborators

  • Petra Bonfert-Taylor — professor, Thayer School of Engineering, Dartmouth College
  • Michael Goudzwaard — associate director of learning innovation, Dartmouth College
  • Olivier Berger — research engineer, computer science department, Télécom SudParis, IP Paris, IMT
  • Jeanne Parmentier — physicist, Institut Villebon — Georges Charpak
  • Tony Février — mathematician, Institut Villebon — Georges Charpak
  • Alain Virouleau — mathematician, Institut Villebon — Georges Charpak

Learning

The C Programming with Linux professional certificate, by DartmouthX and IMTx, won the edX Prize in 2019. Access the courses

Contact

Please copy in both remi.sharrock@telecom-paris.fr and hamonic.ella@gmail.com.

19 Place Marguerite Perey, 91120 Palaiseau, France