Course

CS-411: Digital education

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: do student actually learn due to technologies? This iteration will also have a focus on AI for teaching and learning.

Lectures

Instructors

Official source

https://edu.epfl.ch/coursebook/en/digital-education-CS-411

Moodle Page

http://go.epfl.ch/CS-411

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Summary

Lectures

Instructors

Official source

https://edu.epfl.ch/coursebook/en/digital-education-CS-411

Moodle Page

http://go.epfl.ch/CS-411

About this result

Lectures in this course (81)

Introduction to Digital Education

Covers online education platforms, simulations, teamwork support, and more in digital education.

Learning & Memory

Explores learning, memory, cognitive loads, metacognition, and problem-solving tasks.

Constructivism

Delves into cognitive development, cognitive conflict, and the transition from constructivism to constructionism in education.

Mastery Learning and Instructional Design

Explores mastery learning, behaviorism, and instructional design, emphasizing personalized instruction and the effectiveness of Intelligent Tutoring Systems.

Designing Experiments and Measuring Learning

Explores experimental design challenges in social sciences, emphasizing hypothesis formulation, variable control, and bias mitigation.

Related courses (32)

CS-401: Applied data analysis

This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the data science world (pandas, scikit-learn, Spark, etc.) ...

PHYS-467: Machine learning for physicists

Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practised. ...

CS-423: Distributed information systems

This course introduces the foundations of information retrieval, data mining and knowledge bases, which constitute the foundations of today's Web-based distributed information systems. ...

MICRO-110: Probability & statistics for engineers

This course covers the basics of probability and statistical techniques for engineers, including major probability concepts, data analysis, and statistical inference.

COM-490: Large-scale data science for real-world data

This hands-on course covers tools and methods used by data scientists, from researching solutions to scaling prototypes on Spark clusters. Students engage with the full data engineering and data science pipeline, from data acquisition to extracting insight ...