CS-411 / 4 credits

Teacher(s): Dillenbourg Pierre, Jermann Patrick

Language: English


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 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

In the programs

  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Digital education & learning analytics
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Digital education & learning analytics
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Digital education & learning analytics
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Digital education & learning analytics
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Digital education & learning analytics
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Digital education & learning analytics
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Digital education & learning analytics
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Digital education & learning analytics
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Digital education & learning analytics
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Digital education & learning analytics
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Exam form: Oral (winter session)
  • Subject examined: Digital education & learning analytics
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9 CHB330   
9-10    
10-11 CHB330   
11-12    
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     

Tuesday, 8h - 10h: Lecture CHB330

Tuesday, 10h - 12h: Project, other CHB330