Learning Analytics
Lecture: Learning Analytics (WS 19/20)
Semester: Winter Semester 2019/20
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
- 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
Lecture:
- Wed, 12:00 – 14:00
- LK 052
- Starts on October 16, 2019
Lab Session:
- Thu, 10:00 – 12:00
- LC 140
- Starts on October 17, 2019
Prerequisites
- Interest in data science and/or learning technologies.
- High motivation and commitment.
Registration
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 Dr. Arham Muslim 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.
Organization
- Course material in Moodle
- Show in course catalogue (LSF, Module Database)
Lecturers
Prof. Dr. Mohamed Chatti (Lecturer)
Dr. Arham Muslim (Teaching Assistant)
Student Projects
Open University Learning Analytics
Group name: Data Titans
Group members: Aashish Agarwal, Sameh Frihat, Seyedemarzie Mirhashemi, Shoeb Joarder
Project description:
The project idea is to develop an intelligent grade prediction system called 'Open University Learning Analytics' that analyze students' performance and predict their results (e.g. Distinction, Fail, Pass, Withdrawn) by providing real-time analysis and determining factors affecting the students' results.
Student Educational Performance Analysis (grade prediction)
Group name: Orchids
Group members: Farnoosh shahabbaspour, Maral Goudarzi, Meijie Li, Mohaimn Al-Alshekh Alsagara
Project description:
The tool (Student Educational Performance Analysis SEPA) was developed in order to explore and predict the educational performance of students, by applying machine learning algorithms on a prepared dataset to perform some initial data visualization.
Analysis and prediction of students' academic performance
Group name: Group 3
Group members: Marleen Matjeka, Germán Calleja Rider, Mengfan Liu, Xiaozhong He, Boyuan Han
Project description:
The project idea is to visualize the relationship between some feature data and student performance. Then, determine which feature has a greater impact on student performance. Machine learning algorithms were used to learn and analyze student feature sets.
I-Recommend
Group name: Data Miners
Group members: Furough Zarei, Swarna Sri Teja Rampalli, Sriram Anil Kumar, Elham Valipour, Joyce Kandja
Project description:
This tool allows to identify the most frequent courses taken by the students and provide course recommendations based on the courses chosen by the students. Moreover, it provides effective visualizations to give insights into existing student data.
Course Recommender (CoRe)
Group name: H.A.N.A
Group members: Nina Laabs, Hoda Ghanbarzadeh, Anusha Bangaru, Hasan Halacli
Project description:
Course Recommendation is a web application built for the ISE students of the University of Duisburg-Essen to predict their grade and recommend the course based on the grade. This project focus on giving students an opportunity to analyze their study method based on survey questions and improve their methodology to achieve good grades in their desired courses.
Tag Recommendation system (TRS)
Group name: IPT
Group members: Aalli Mahmood, Atefeh Safarkhah, Mohammad Armoun, Muhammad Dawar
Project description:
Asking questions is part of the learning process. Therefore, in this project, we want to make sure that every question will be answered through recommending proper and related tags for a question in order to guide the question to the right points, make the question clearer for the community, and make search more precise and faster.