Seminar Big Data and Learning Analytics

Seminar: Big Data and Learning Analytics (WS 17/18)

Semester: Winter Semester 2017/18

Seminar language: English

Exam language: English

Exam type: Seminar paper + presentation / discussion

Maximum number of participants: 7


Big data is an umbrella term to cover various aspects of handling large amounts of data (volume) produced at a high speed (velocity) with a wide range of data types and sources (variety). The possibilities of big data continue to evolve rapidly, driven by innovation in the underlying technologies, platforms, and analytics capabilities. Learning analytics represent the application of big data and analytics in education. The most commonly cited  definition  of  learning  analytics  which  was  adopted  by  the first international conference on learning analytics and knowledge (LAK11) is ”the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs”.

This seminar will give insights about the state of the art in big data analytics and current research topics in learning analytics. Topics will include, but are not limited to:

  • The Hadoop Ecosystem
  • Big Data Storage
  • Big Data Processing
  • Big Data Visualization
  • Visual Analytics
  • Visual Interactive Recommender Systems
  • Ethics and Privacy in Learning Analytics

The topics will be assigned in the kick-off meeting. We will provide one-two starting papers for each topic. The students will be asked to write a seminar paper (8-10 pages ACM style, a template will be provided) and prepare a 30 minute talk about one topic.

After the seminar, the students will have some in-depth knowledge about one topic in big data and learning analytics and will have a good overview on other topics in this field. The seminar also gives students the opportunity to refine their scientific research and presentation skills. This seminar is highly recommended for students interested in doing their thesis projects on topics related to data science and learning technologies at the Social Computing Group.

Target audience

  • Master Applied Computer Science
  • Master Komedia
  • Master ISE

Date and location

Kick-off meeting (only for registered students):

  • November 14, 2017, 10:00-12:00

Presentation dates:

  • January 17, 2018, 14:00-17:00
  • January 18, 2018, 14:00-17:00


  • Interest in data science and learning technologies, dedication, self-reliance.


To register for the seminar, please send an email to Prof. Dr. Mohamed Chatti with your contact information, your study program, a copy of your grade transcript from the university so far, and (if applicable) your knowledge/experience in data science and related topics. If the maximum number of participants is reached, we will use a waiting list. The deadline for registration is November 9th, 2017.


  • The seminar will be held as a block seminar
  • Details and submission milestones will be provided in the kick-off meeting
  • Latex will be used to write the seminar paper
  • Course material in Moodle
  • Show in course catalogue (LSF)


Prof. Dr. Mohamed Chatti