Learning Analytics

Lecture: Learning Analytics (WS 21/22)

Semester: Winter Semester 2021/22

Lecture language: English

Exam language: English

Exam type: Oral examination

Maximum number of participants: 30


The course will officially start on October 13th, 2021 and will take place in a Blended Learning format (online + in person). More information will be provided in the Moodle courseroom for the registered students. Registration is possible until October 1st 8th, 2021 (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 course, 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 (NoSQL Databases, 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/Lab Session:

  • Wed, 14:00 – 16:00
  • Online / LK 052
  • Starts on October 13, 2021

Lecture/Lab Session:

  • Thu, 10:00 – 12:00
  • Online / LC 140
  • Starts on October 14, 2021


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


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. Shoeb Joarder by October 1st 8th, 2021 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.



Prof. Dr. Mohamed Chatti (Lecturer)
M. Sc. Shoeb Joarder (Teaching Assistant)

Student Projects


Group name: Cassandra Spark

Group members: Nurmalita Manggali, Nave Wibowo, Jean Qussa, Ali Amini

Project description:

The project idea is to provide a platform that works as a matchmaking app for students to look for an appropriate course to take in a MOOC platform. This platform provides a list of courses according to the student's preferences, connect with other learners via a dedicated forum, and shows overviews of multiple platforms on a single page

Links: GitHub, Live Demo


Group name: Data Divas

Group members: Hendrik Eckhoff, Yasmine Taha Mokhtar, Nahid Hosseininezhad, Roshan Asim

Project description:

The project idea is to provide a recommendation platform for prospective students at a university in Germany. This platform provides recommendation of study programs based on the interests of the students, and allow various filtering options to compare such as Bachelor's/Masters's, language, degree, rating, etc. The platform provides interactive visualizations based on the degree of similarity between courses and students' interests.

LinksGitHubLive Demo

NYC School Explorer

Group name: Educational Detectives

Group members: Mahsasadat Nezamanadi, Laura Sielenkemper, Clara Siepmann

Project description:

The project idea is to provide a platform for exploring and getting insights into the graduation rates in schools in New York City. The users of the platform can explore by tuning various parameters to predict the outcome of graduation rates

LinksGitHubLive Demo