Semester: Summer Semester 2021

Lecture language: English

Exam language: English

Exam type: Prototype, Presentation

Maximum number of participants: 15

Notice

The course will start on April 12, 2021 and will take place fully online. More information will be provided in the Moodle courseroom for the registered students. Registration is possible until March 15, 2021 (see the registration section below).

About this course

The Interactive Data Exploration and Analytics (IDEA) lab course offered at the UDE Social Computing Group focuses on the effective integration of techniques from human-computer interaction (HCI), information visualization, and machine learning to help users interactively explore data.

The aim of this semester's IDEA lab is to provide the foundation for an Exploratory Learning Analytics Toolkit for Students (ELAS) to support UDE students in their learning activities. In the previous iterations of the Learning Analytics (LA), Advanced Web Technologies (AdvWebTech), and Learning Analytics and Visual Analytics (LAVA) courses offered at the SoCo Group, different LA applications were developed as part of student projects (see the student projects section below). The task in this semester’s IDEA lab will be to improve, adapt, and integrate selected applications into the ELAS platform.

You will organize yourselves into groups of 4-5 students. In the first few weeks of the lab, we will provide a general overview of the ELAS platform, introduce the Web technologies used in ELAS (e.g. React, Flask, Axios, MongoDB, visualization libraries), and give tutorials on how to get started with the project. There will be weekly project management meetings to answer your questions and guide you with additional materials. We will also have regular sessions where you will present the progress of your projects (ideas/concepts, prototypes, demos, end product).

Grading for this lab will be based on the content and quality of the project (code, demos, presentations) as well as your performance (project management, collaboration, class participation, creativity) during the project.

Target audience

• Bachelor Applied Computer Science
• Bachelor Komedia

Date and location

• Mon, 16:00 - 18:00
• Online
• Starts on April 12, 2021

Prerequisites

• Basic knowledge and interest in developing Web applications using state-of-the-art Web frameworks
• Interest in data science
• High motivation and commitment

Registration

We have 8 places for Bachelor Komedia and 7 places for Bachelor AI (first come, first serve). Komedia students should register via the central registration system. Bachelor AI students should register by sending an email to Prof. Dr. Mohamed Chatti by March 15, 2021 with your matriculation number, study program, and, if available, your knowledge/experience in Web technologies and data science. If the maximum number of participants is reached, we will use a waiting list.

Organization

• Course material in Moodle
• Show in course catalog (LSF)

Student Projects

• Learning Analytics lecture course (WS 19/20, WS 18/19)
• Advanced Web Technologies lecture course (SS 20, SS 19, SS 18)
• LAVA lab course (SS 20, WS 19/20, SS 19)

Lecturers

Prof. Dr. Mohamed Chatti (Lecturer)

M. Sc. Shoeb Joarder (Teaching Assistant)

Group name: DIVOC

Group members: Joshua Redmann, Christoph Vorer, Sofie Kalthof

Project description:

The project idea is to provide a platform for students at the University of Duisburg-Essen to select the university’s courses within a few easy steps. Furthermore, the platform lets students easily make a schedule by observing various details such as overlapping course schedules, ratings of the courses, and links to LSF.

Links: GitHub, Advertisement, Live Demo

Group name: LivewireProblem

Group members: Charlotte Hartmann Paludo, Rouchda Pepouna Makwet, Nassim Guelbi

Project description:

The project aims to help students choose a suitable course from the E3 Module and provide insightful information that will help orientation and perhaps further specialization at the University of Duisburg-Essen. This project consists of powerful filtering, searching, and sorting capabilities to make it easy for the students to search for interesting courses that fit their needs.

Links: GitHub, Advertisement, Scraper Demo, Live Demo

Group name: Team Komedia

Group members: Dilara Ince, Clarissa Kümhof, Willi Dick

Project description:

This project aims to identify and improve students’ learning styles through David Kolb’s learning cycle (Kolb’s 40-item questionnaire) to suggest courses according to their preferred learning styles. In addition, this project allows the students to get an overview of the learning styles present in other courses, study programs, and countries.

Links: GitHub, Advertisement, Live Demo