FI:IV127 Adaptive Learning Sem. - Course Information

IV127 Adaptive Learning Seminar

Extent and Intensity

0/2/0. 2 credit(s) (plus 1 for the colloquium). Type of Completion: k (colloquium).

Teacher(s)

doc. Mgr. Radek Pelánek, Ph.D. (lecturer)

Guaranteed by

doc. Mgr. Radek Pelánek, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Fri 18. 2. to Fri 20. 5. Fri 10:00–11:50 A220

Prerequisites (in Czech)

SOUHLAS
Účast na semináři vyžaduje schopnost číst odborný anglický text a samostatně naprogramovat analýzu dat (typicky Python, pandas a další knihovny, ale není to nezbytné).

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Image Processing and Analysis (programme FI, N-VIZ)
• Bioinformatics and systems biology (programme FI, N-UIZD)
• Computer Games Development (programme FI, N-VIZ_A)
• Computer Graphics and Visualisation (programme FI, N-VIZ_A)
• Computer Networks and Communications (programme FI, N-PSKB_A)
• Cybersecurity Management (programme FI, N-RSSS_A)
• Formal analysis of computer systems (programme FI, N-TEI)
• Graphic design (programme FI, N-VIZ)
• Graphic Design (programme FI, N-VIZ_A)
• Hardware Systems (programme FI, N-PSKB_A)
• Hardware systems (programme FI, N-PSKB)
• Image Processing and Analysis (programme FI, N-VIZ_A)
• Information security (programme FI, N-PSKB)
• Informatics (programme FI, B-INF) (2)
• Informatics in education (programme FI, B-IVV) (2)
• Information Security (programme FI, N-PSKB_A)
• Quantum and Other Nonclassical Computational Models (programme FI, N-TEI)
• Computer graphics and visualisation (programme FI, N-VIZ)
• Computer Networks and Communications (programme FI, N-PSKB)
• Principles of programming languages (programme FI, N-TEI)
• Programming and development (programme FI, B-PVA)
• Cybersecurity management (programme FI, N-RSSS)
• Services development management (programme FI, N-RSSS)
• Software Systems Development Management (programme FI, N-RSSS)
• Services Development Management (programme FI, N-RSSS_A)
• Software Systems Development Management (programme FI, N-RSSS_A)
• Software Systems (programme FI, N-PSKB_A)
• Software systems (programme FI, N-PSKB)
• Machine learning and artificial intelligence (programme FI, N-UIZD)
• Teacher of Informatics and IT administrator (programme FI, N-UCI)
• Informatics for secondary school teachers (programme FI, N-UCI) (2)
• Computer Games Development (programme FI, N-VIZ)
• Processing and analysis of large-scale data (programme FI, N-UIZD)
• Natural language processing (programme FI, N-UIZD)

Course objectives

Students gain experience with data analysis and machine learning in the context of educational applications.

Learning outcomes

At the end of the course, students will be able to apply methods of data analysis and machine learning in the area of educational applications.

Syllabus

• Presentation of research papers from research areas relevant to the Adaptive learning lab - educational data mining, machine learning, cognitive psychology. Development of educational systems, analysis of educational data, presentation and discussion of results.

Literature

• Advances in intelligent tutoring systems. Edited by Roger Nkambou - Jacqueline Bourdeau - Riichiro Mizoguchi. Berlin: Springer, 2010, xxii, 508. ISBN 9783642143625. info
• WOOLF, Beverly Park. Building intelligent interactive tutors : student-centered strategies for revolutionizing e-learning. Burlington, MA: Morgan Kaufmann Publishers, 2009, xii, 467. ISBN 9780123735942. info

Teaching methods

Student presentations, moderated discussion.

Assessment methods

Project work, presentation, active participation.

Language of instruction

Czech

Further Comments

Study Materials
The course is taught each semester.

• Enrolment Statistics (Spring 2022, recent)
  
• Permalink: https://is.muni.cz/course/fi/spring2022/IV127