Subject

Recommender Systems, Virtual Guidance and Virtual Self-Help in Knowledge Acquisition

Course Title

Recommender Systems, Virtual Guidance and Virtual Self-Help in Knowledge Acquisition
Recommender systems, virtual guidance, and virtual self-help in mastering knowledge

Code

m23_s_041

Study Programme

—

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

Second cycle

Academic year / semester

10 / Summer

Number of ECTS credits

Teacher

Vangel Ajanoski

Prerequisites for enrolling in the course

—

10.

Objectives of the course programme (competences)

The goal of the course is for the student to become familiar with advanced methods and technologies that enable virtual guidance in mastering knowledge, recommending topics of interest, suggesting pathways, and providing self-help systems aimed at preventing potential problems. Besides the educational sphere, the course is also useful for students in commercial fields, especially for better human resource organization based on an assessment of knowledge mastery.

Competencies the student is expected to acquire upon completion of the course:
Understanding of methods and techniques for knowledge mapping
Understanding methods and techniques for assessing success and risk in mastering knowledge.
Use of data analysis technologies for knowledge mapping, performance assessment, and risk assessment.
- Using technologies for the visualization of the knowledge space
Implementing integrated systems for mapping, visualization, navigation, recommendation, routing, and self-help using off-the-shelf technologies

11.

Course content

Topics covered in this course:
Introduction to automated interest discovery and virtual guidance.
- Methods for mapping areas, domains, topics, and competencies within a field of interest.
Methods for assessing the success of knowledge acquisition.
Methods for detecting interest in mastering knowledge.
Visualization and navigation through the knowledge space.
Social navigation and collaborative interest definition.
Methods for spatial self-orientation in the space of knowledge.
Methods for recommending paths of movement through the knowledge space.
- Methods for recommending interesting areas and topics for learning personalized for the user.
Evaluation of the quality of mapping the knowledge space.
Evaluation of the quality of recommendations. .
Career Guides.
- Indicators of problematic regions in the knowledge space, impact assessment.
Implementation of integrated systems for virtual guidance through knowledge mastery, recommendation, and self-help.

12.

Learning methods

- Lectures and exercises with case-based discussions, analysis of various available examples - Computer-assisted learning - E-learning and distance learning - Group research and development - Use of relevant software tools - Project development and defense

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

30 + 30 + 15 + 90 + 15 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	30 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	30 hours

16.

Other forms of activities

16.1.	Project assignments	90 hours
16.2.	Independent assignments	15 hours
16.3.	Home study	15 hours

17.

Assessment method

17.1.	Tests	0 points
17.2.	Seminar paper / project (presentation: written and oral)	90 points
17.3.	Activities and learning	30 points
17.4.	Final exam	15 points

18.

Grading criteria (points / grade)

up to 50 points	5 (five) (F)
from 51 to 60 points	6 (six) (E)
from 61 to 70 points	7 (seven) (D)
from 71 to 80 points	8 (eight) (C)
from 81 to 90 points	9 (nine) (B)
from 91 to 100 points	10 (ten) (A)

19.

Requirement for obtaining a signature and taking the final exam

50% of the activities and the initial version of the project

20.

Language of instruction

Macedonian, English

21.

Method for monitoring the quality of teaching

internal evaluation and survey mechanism

22.

Literature

22.1.

Required literature

1. Ricci, Francesco, Rokach, Lior, Shapira, Bracha (Eds.) | Recommender Systems Handbook | Springer | 2015
2. Aggarwal, Charu C. | Recommender Systems The Textbook | Springer | 2016
3. Manouselis, N., Drachsler, H., Verbert, K., Santos, O.C. (Eds.) | Recommender Systems for Technology Enhanced Learning | Springer | 2014
4. R. Sottilare, A. Graesser, X. Hu, and A.M. Sinatra (Eds.). | Design Recommendations for Intelligent Tutoring Systems Vol. 1-7 | US Army CCDC | 2019
5. Selection of significant and current research papers in the field—provided in printed or electronic form for use in the activities.
6. Electronic documentation from the websites of the system manufacturers used in the activities.

22.2.

Additional literature

No. Author Title Publisher Year