Home/Vakken/Technologies for learning
INFOMTFL7.5 ECTSQ1EnglishMaster

Technologies for learning

FaculteitFaculty of Science
NiveauMaster
Studiejaar2026-2027

Beschrijving

Course goals

Over the last several decades, educational technology has been rapidly transitioning from a research subject studied by academics to a branch of industry attracting startups and big companies. Many factors have facilitated this development. Proliferation of Web technology and connected devices, explosion of user-generated data have been among the factors. The Covid-19 pandemic has had a transformative effect on the global education system affecting hundreds of millions of students and teachers and awakening the society at large to the necessity of educational innovations. The recent rise of generative AI tools and conversational interfaces supported by them opens up new possibilities for scaling up advanced educational technologies and making them available to wider audiences.

In this course, we will look into some of these innovations. We will briefly study the theoretical underpinnings of computer-based education. We will delve deeper into the set of technologies built on top of the existing theories. Finally, we will analyse both classic and modern systems implementing these technologies, including their core models and components. We will pay special attention to the systems that are designed to provide adaptive support to individual students.

After this course you should be able to:

  • identify, relate and explain fundamental concepts in the field of computer-based education with a particular focus on adaptive and intelligent technologies
  • apply these concepts in practice by designing and developing components of adaptive and intelligent educational systems
  • use relevant literature to analyze existing projects and form an opinion about innovations in the field
  • investigate a problem within the field of computer-based educational technologies and set up a plan for a group project targeting it.

Assessment

There are two main grading components in the course:

  • Course project (group-based): 60%. It further breaks down into intermediate reports, final report, and final poster presentation.
  • Final exam (individual, digital, closed-book): 40%

To pass the course a student needs to:

  • participate in all course activities (get a non-zero grade for both course components)
  • get an exam grade of 4.5 or higher;
  • get an overall course grade of 5.5 or higher.

Content

The course content is broken into 5 main themes:

  • Learning Theories: covers the main pedagogical and psychological frameworks that explain how people learn in different contexts
  • Assessment: explains the main approaches towards development and validation of tests as well estimation of students' proficiency
  • Student Modelling: presents the challenges of inferring and representing information about students that is necessary to make educational applications adaptive, describes technologies for modelling knowledge, affective state and meta-cognitive processes, talks about dealing with uncertainty in the context of student modelling
  • Learning Support: outlines a wide range of technologies for adaptive learning support, such as intelligent tutoring systems and adaptive educational hypermedia, introduces the state-of-the-art in generative AI in Education, specifically talks about Programming Tutors.
  • Educational Data: provides an overview of the use of large educational datasets from the points of evaluation, learning analytics and data mining. 

Reviews0 reviews

Nog geen reviews voor dit vak. Wees de eerste!

Heb jij dit vak gevolgd?

Deel je ervaring met toekomstige studenten. Inloggen met je Universiteit Utrecht mailadres duurt één minuut.

Schrijf een review