Learning Analytics

Lecture: Learning Analytics (WS 18/19)

Semester: Winter Semester 2018/19

Lecture language: English

Exam language: English

Exam type: Oral examination

Maximum number of participants: 30

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
  • Social Network Analysis
  • 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

Date and location


  • Wed, 12:00 – 14:00
  • LK 052
  • Starts on October 10, 2018

Lab Session:

  • Thu, 10:00 – 12:00
  • LC 140
  • Starts on October 11, 2018


  • 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 Prof. Dr. Mohamed Chatti 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)

Arham Muslim, M.Sc. (Teaching Assistant)

Student Projects

Intelligent Grade Prediction System (I-Grade)

Group name: Group 1

Group members: Kiarash Moazzez, Mohamed Nagi, Shadi Zumor, Noah Seo, Vidhya Vijayaraman, Abdulrazzak Moustafa

Project description:

This project is developed to predict the final grade of a student based on his/her provided habits on the user interface of the I-Grade. In order to predict the grade of a student, a prediction model was generated based on sample data. Some visualizations are provided based on Chartjs library to show an overview of the available dataset.

Links: GitHub, Live Demo

Student Academic Performance Analysis

Group name: Group 2

Group members: Fangzheng Ji, Jihao Zhang, Jaleh Ghorban, Shahrzad Amini, Damera Ritesh

Project description:

This is an analytics and visualization project developed as part of the Learning Analytics course at the University of Duisburg-Essen. The main objective is to allow users to explore the students' performance dataset through data visualization. Additionally, machine learning algorithms are applied on the dataset to determine the most important features and to predict the student's performance based on these features.

Links: GitHub, Live Demo

Students Performance

Group name: Lorem

Group members: Hasan Mhd Amin, Mhd Yazan Al Zaeem, Bilawal Wajid Ali, Muhammad Faizan Riaz, Sammar Javed, Nimesh Ghimire

Project description:

The goal of this project is to help teachers to make better decisions about choosing the exam type (i.e. oral or written) based on the level of education of students and type of course to ensure good performance by the students.

Links: GitHub, Deployed Version

Student Enrollment and Performance Analysis (SEPA)

Group name: Zusammen

Group members: Abdul-Rahman Khan, Piush Aggarwal, Muhammad Zeeshan , A. B. M. Rocknuzzaman, Amin Shahin, Tianyu Zhu

Project description:

The aim of this project is to analyze student performance and enrolment. Data was collected from students based on their previous grades and classified into two categories, namely Good or Bad. The results are provided with interactive visualization to allow students to explore the data.

Links: GitHub, Live Demo

Learning Analytics Insights (LaiS)

Group name: Insight

Group members: Volkan Yücepur, Ankita Mandal, Florian Richtscheid, Hadis Fouladikia, Negin Ahmadian, Moloud Kordestani

Project description:

Learning Analytics Insights is developed as part of the Learning Analytics lecture (WS 18/19). This R-based application can be used for predicting student grades based on inputs taken from a survey and an open-source dataset. The results are shown with interactive visualizations to support exploratory experience.

Links: GitHub, Live Demo