CS-411 / 4 crédits

Enseignant(s): Dillenbourg Pierre, Jermann Patrick

Langue: Anglais


Summary

This course addresses the relationship between specific technological features and the learners' cognitive processes. It also covers the methods and results of empirical studies on this topic: do student actually learn due to technologies?

Content

Keywords

learning, pedagogy, teaching, online education, MOOCs 

Learning Prerequisites

Recommended courses

One of these courses is recommended:

- Machine Learning (Jaggi / Urbanke)

- Applied Data Analysis (West)

Learning Outcomes

By the end of the course, the student must be able to:

  • Describe the learning processes triggered by a technology-based activity
  • Explain how a technology feature influences learning processes
  • Elaborate a study that measures the learning effects of a digital environment
  • Select appropriately a learning technology given the target audience and the expected learning outcomes
  • Apply machine learning methods to educational traces

Transversal skills

  • Set objectives and design an action plan to reach those objectives.

Teaching methods

The course will combine participatory lectures with a project around learning analytics

 

Expected student activities

The project will include several milestones to be delivered along the semester.

Assessment methods

  • Project + exam
  • 50 / 50

Supervision

Office hours No
Assistants Yes
Forum Yes

Dans les plans d'études

  • Semestre: Automne
  • Forme de l'examen: Oral (session d'hiver)
  • Matière examinée: Digital education & learning analytics
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Oral (session d'hiver)
  • Matière examinée: Digital education & learning analytics
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Oral (session d'hiver)
  • Matière examinée: Digital education & learning analytics
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Oral (session d'hiver)
  • Matière examinée: Digital education & learning analytics
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Oral (session d'hiver)
  • Matière examinée: Digital education & learning analytics
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Oral (session d'hiver)
  • Matière examinée: Digital education & learning analytics
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Oral (session d'hiver)
  • Matière examinée: Digital education & learning analytics
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Oral (session d'hiver)
  • Matière examinée: Digital education & learning analytics
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Oral (session d'hiver)
  • Matière examinée: Digital education & learning analytics
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Oral (session d'hiver)
  • Matière examinée: Digital education & learning analytics
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines

Semaine de référence

 LuMaMeJeVe
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22