E-learning Technologies

Data is displayed for academic year: 2023./2024.

Lecturers

Assoc. Prof.
Predrag Pale
Asst. Prof.
Juraj Petrović

Laboratory exercises

Asst. Prof.
Juraj Petrović
Lucija Petricioli
mag. ing.

Course Description

E-education or the application of ICT (information and communication technology) in educational processes is a hot topic today. However, one needs to be well acquainted with the properties of both technologies and the educational processes in order for this compound to be effective. Do you know when it’s better to use text than an image to explain something, or a series of images instead of a video, spoken word instead of text? Everyone is recording lectures today, but what should the recording be like and what else needs to be added to make it useful for learning? What is the difference between a lecture recording and an educational video? What properties must an educational video have? What technologies are used to achieve this? How is quality audio and video recording technically achieved? What technologies can be used to improve the interactivity of lectures and student collaboration in learning? Can a computer objectively check competencies, as a human would do, or is there still a lack of technology for that? In which cases and what kind? What is the difference between a virtual and a remote lab? What are they for? How can they be built? What are “Programmed Learning” and “Guided Learning”? How are they achieved?

Study Programmes

University graduate
[FER3-HR] Audio Technologies and Electroacoustics - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Communication and Space Technologies - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Computational Modelling in Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Computer Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Computer Science - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Control Systems and Robotics - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Data Science - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Electrical Power Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Electric Machines, Drives and Automation - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Electronic and Computer Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Electronics - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Information and Communication Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Network Science - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Software Engineering and Information Systems - profile
Elective Course of the profile (3. semester)
Elective Course of the Profile (1. semester)
Elective Courses (1. semester) (3. semester)

Learning Outcomes

  1. Describe how humans learn
  2. Create short educational video
  3. Create computer based assesment of factual, procedural and conceptual knowledge
  4. Choose and apply approrpiate audience response tools for classroom.
  5. Define required technical properties of a virtual laboratory or simulator

Forms of Teaching

Lectures

Lectures are held weekly. There is a class preparation assignment for each lecture.

Seminars and workshops

Within the course students work on a practical assignment thematically related to the course.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Homeworks 50 % 15 % 0 % 0 %
Class participation 50 % 20 % 0 % 0 %
Seminar/Project 50 % 45 % 50 % 60 %
Mid Term Exam: Written 0 % 1 % 0 %
Final Exam: Written 50 % 19 %
Exam: Written 50 % 40 %

Week by Week Schedule

  1. Introduction
  2. Learning theories
  3. Computer assisted and automated knowledge assessment
  4. Lecture captures
  5. Educational video
  6. Classroom response systems (1/2)
  7. Classroom response systems (2/2)
  8. Midterm exam
  9. Virtual laboratories and simulations (1/2)
  10. Virtual laboratories and simulations (2/2)
  11. Media application (1/2)
  12. Media application (2/2)
  13. Programmed and guided learning (1/2)
  14. Programmed and guided learning (2/2)
  15. Final exam

Literature

Peter Jarvis, John Holford, Colin Griffin (2003.), The Theory & Practice of Learning, Psychology Press
Isaac I. Bejar (2016.), Automated Scoring of Complex Tasks in Computer-based Testing, Psychology Press
Spratt, Christine, Lajbcygier, Paul (2009.), E-Learning Technologies and Evidence-Based Assessment Approaches, IGI Global

For students

General

ID 223089
  Winter semester
5 ECTS
L1 English Level
L2 e-Learning
30 Lectures
0 Seminar
0 Exercises
10 Laboratory exercises
0 Project laboratory
0 Physical education excercises

Grading System

90 Excellent
80 Very Good
70 Good
60 Sufficient

Similar Courses