Department of Information and Computing Sciences

Departement Informatica Onderwijs
Bachelor Informatica Informatiekunde Kunstmatige intelligentie Master Computing Science Game&Media Technology Artifical Intelligence Human Computer Interaction Business Informatics

Onderwijs Informatica en Informatiekunde

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Technologies for learning

Website:website containing additional information
Course code:INFOMTFL
Credits:7.5 ECTS
Period:period 1 (week 36 through 45, i.e., 3-9-2020 through 6-11-2020; retake week 1)
Participants:up till now 46 subscriptions
Schedule:Official schedule representation can be found in MyTimetable
lecture          Sergey Sosnovsky
Johan Jeuring
Matthieu Brinkhuis
Note:No up-to-date course description available.
Text below is from year 2019/2020
Literature:May change!
All papers are listed on BlackBoard.
Course form:Lectures, reading sessions/paper presentations and discussions, research project, periodic quizzes and exam.
Exam form:
  • periodic quizzes, presentations and paper discussions: 25% (individual)
  • group project: 45% (group)
  • written exam: 30% (individual)
Minimum effort to qualify for 2nd chance exam:To qualify for the retake exam, the grade of the original must be at least 4.
Description: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 analyse 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
The list of topics we will research includes but is not limited to:
  • student modelling technologies for representing knowledge, metacognitive skills and strategies and affective state of a student working with an adaptive education system
  • technologies for adaptive learning support, such as intelligent tutoring systems and adaptive educational hypermedia
  • technologies for supporting collaborative, group-based and social learning scenarios;
  • technologies exploiting big data set in education for empowering student and teachers, as well as improving the behaviour of intelligent educational software
  • modern HCI methods used in education for creating effective learning interfaces including dialog systems, learning companions, serious games and virtual reality
This academic field is extremely interdisciplinary. Hence, the background necessary to study and work with these technologies can be very diverse: knowledge of data mining and machine learning, parsing and rewriting, artificial intelligence and HCI are all useful. The course material as well as topics for group project will be adjusted to the background of the students in order to use the cumulative expertise of the class as much as possible.