Learning Analytics

Lecture: Learning Analytics (WS 22/23)

Semester: Winter Semester 2022/23

Lecture language: English

Exam language: English

Exam type: Oral examination

Maximum number of participants: 30


The course will officially start on the 12th of October 2022 and will take place in a Blended Learning format (online + in-person). More information will be provided in the Moodle course room for the registered students. Registration is possible until the 3rd of October 2022 (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 competencies 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 of 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 visualization and visual analytics
  • Current topics in learning analytics (invited lectures)

Getting credits for this course requires successful completion of all assignments, projects, and oral exams at the end of the semester. The final grade will be calculated as follows: assignments and projects (50%) and oral exams (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 the 12th of October 2022

Lecture/Lab Session:

  • Thu, 10:00 – 12:00
  • Online / LC 140
  • Starts on the 13th of October 2022


  • 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 served). To register, please email Shoeb Joarder by the 3rd of October 2022 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)


Group name: Amigos

Group members: Louis Born, Nicodemus Aprianto, Jordy Zeufack Assongmo, and Inga Rittmann

Project description:

QUESTUP is a web application that aims to motivate students to engage with learning content by incorporating game design elements and principles into a non-game context. The application provides an interactive and personalized learning experience that is tailored to the individual needs of each user. By utilizing various game elements such as points, badges, and progress, QUESTUP provides students with feedback on their progress and encourages them to continue their learning journey. With its user-friendly interface and wide range of educational materials, QUESTUP offers a fun and innovative way for students to stay engaged and committed to their learning goals.

GitHub 1 
GitHub Frontend 


Group name: Databits

Group members: Hla Abuhamra, Heiner Ploog, Hadil Khbaiz, Ruidan Liu, Yifei Yao

Project description: GoalEvaluator is a goal evaluator tool designed to identify problematic studying behaviors of students and provide personalized advice on learning optimization. The tool analyzes a student's study goals and behavior, then generates a report that highlights areas that need improvement and provides actionable advice on how to optimize their study habits. With GoalEvaluator, students can take control of their learning process and work towards achieving their academic goals more efficiently.


The Ducks' School

Group name: RubberDucks

Group members: Hong Yang, Saba Darbandi, Chengcheng Li, Chau Le, Zaid A.R Abdulmohsin, Ahmed Abdelbary

Project description: The Ducks' School is an innovative web-based platform designed to help students choose their perfect course based on different approaches. The platform utilizes a Recommender System approach, taking into account the subjects the students have studied, and the subjects taken by other students. Additionally, The Ducks' School incorporates a non-personalized approach by visualizing different attributes of courses to students. The platform is user-friendly, not only for IT students but also for students in other subjects. With The Ducks' School, students can easily and efficiently navigate through different courses and identify the most suitable option for their academic goals.


BTS - Books to Search

Group name: Neurons

Group members: Farnaz Arghavan, Divyangana Kothari, Manoj Kumar Dara, Julian Stülp

Project description: BTS is a book recommendation system that utilizes embedding to provide personalized recommendations for its users. The system analyzes the reading preferences of users and generates recommendations based on their reading history. BTS offers interactive representations of the recommended books to make the experience more engaging and informative. The system is designed to provide users with a seamless and intuitive experience, making it easy for them to explore new books and authors that match their interests. With BTS, users can discover new books that align with their preferences and expand their reading experience.


Advanced Review Analyzer

Group name: LATFinders

Group members: Mahdyar Safarianbarmi, ​ William Kana Tsoplefack​, Nikolas Gur, Yusra Abdulrahman​, Ghazal Abbasi​

Project description: The Advanced Review Analyzer is a web-based platform designed to provide an overall and detailed view of courses, allowing users to compare courses from different universities. The system utilizes machine learning algorithms to predict future trends in the course's overall ratings based on previous students' ratings and visualizes both future and current trends. The system calculates the mean average of both trends and gives recommendations on the suitability of the course. The recommendation is based on a scale from 0 to 50, with scores from 0 to less than 20 being labeled as "Not recommended," scores from 20 to less than 40 as "Recommended," and scores from 40 to 50 as "Highly recommended." With the Advanced Review Analyzer, users can make informed decisions about their courses and stay up-to-date with the latest trends in their respective fields.