Learning Analytics

Lecture: Learning Analytics (WS 20/21)

Semester: Winter Semester 2020/21

Lecture language: English

Exam language: English

Exam type: Oral examination

Maximum number of participants: 30

Notice

The course will officially start on November 4th, 2020 and will take place fully online. More information will be provided in the Moodle courseroom for the registered students. Registration is still possible until October 16th, 2020 (see the registration section below).

About this Course

Learning Analytics (LA) has attracted a great deal of attention as practitioners, institutions, and researchers are increasingly seeing the potential that LA has to shape the future technology-enhanced learning landscape. LA is an emerging data science field that represents the application of big data and analytics in education. It deals with the development of methods that harness educational data sets to support the learning process. LA is an interdisciplinary field involving competences from computer science, cognitive psychology, and pedagogy. It leverages various computer science methods. These include statistics, big data, machine learning, data/text mining, information visualization, visual analytics, and recommender systems. The first part of the course will provide a systematic overview on this emerging field and its key concepts through a reference model for LA based on four dimensions, namely data, environments, context (what?), stakeholders (who?), objectives (why?), and methods (how?). In the second part of this courses, we will discuss various methods and techniques required to develop innovative LA systems, in relation to each dimension of the LA reference model. In the last part of the course, current topics and trends in LA research will be presented and discussed in invited talks. The presented methods and technologies will be further investigated and applied in small student projects carried out throughout the course. The course topics will include:

  • Learning analytics and related areas (e.g. educational data mining)
  • A reference model for learning analytics
  • Big Data (Hadoop ecosystem)
  • Learner modeling
  • Ethics and privacy in learning analytics
  • Assessment and feedback
  • Machine Learning / Data Mining (classification, clustering, association rule mining)
  • Recommender systems
  • Information visualisation and visual analytics
  • Current topics in learning analytics (invited lectures)

Getting credits for this course requires a successful completion of all assignments, project, and oral exam at the end of the semester. The final grade will be calculated as follows: assignments and project (50%) and oral exam (50%).

Target audience

  • Master Applied Computer Science
  • Master ISE
  • Master Komedia

Date and location

Lecture:

  • Wed, 12:00 – 14:00
  • Online
  • Starts on November 4, 2020

Lab Session:

  • Thu, 10:00 – 12:00
  • Online
  • Starts on November 5, 2020

Prerequisites

  • Interest in data science and/or learning technologies.
  • High motivation and commitment.

Registration

Due to didactical methods, we have a limit of 30 students for this class (first come first serve). To register, please send an email to Mr. Mouadh Guesmi by October 16th, 2020 with your contact information, your study program, and if available your knowledge/experience in Data Science and Learning Technologies. If the maximum number of participants is reached, we will use a waiting list.

Organization

Lecturers

Prof. Dr. Mohamed Chatti (Lecturer)