Educational data analytics

1.

Subject title

Educational data analytics

Аналитика на образовни податоци

2.

Code

m23_s_031

3.

Study program

Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Inteligent Systems, Internet Technologies and cyber security, Computer Science, Statistics and Data Analytics, Software for embedded systems, Cloud Computing, Bioinformatics, Security, Cryptography and Coding, Statistics and Data Analytics, Eco-informatics, IT management, Data science in computer science and engineering, Еducation with ICT, Software Engineering, Software Engineering,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Летен

7. Number of ECTS credits

6.0

8.

Instructor

ворн. проф. д-р Бобан Јоксимоски проф. д-р Иван Чорбев

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

To understand key learning data analytics and educational data mining (APU / PROP) and apply them to real-world problems across a variety of educational environments. Learning about relevant political, legal, and ethical issues involved in implementing educational data analytics. Using learning analysis methods to change education for the better. This course covers basic methods in educational data mining. Students will learn how to implement these methods in standard software packages and the limitations of existing implementations of these methods. Equally important, students will learn when and why to use these methods. A discussion of how EDM differs from traditional statistical and psychometric approaches will be a key part of this course; in particular, we will study how the same statistical and mathematical approaches are used in different ways in these research communities

11.

Subject content:

Introduction to Learning Data Analysis and Educational Data Mining (LA / EDM), Predictive Regression in Educational Data, Classification Algorithms in Educational Data, Behavior Discovery, Diagnostic Metrics, Engineering and Feature extraction in Educational Data, Advanced Assessment and Validation, Bayesian Algorithms, Performance Factor Analysis in Educational Data, Advanced BKT, Knowledge Structure Discovery, Network Analysis in Educational Data, Correlation Mining and Causal Mining, Pattern Discovery, Clustering and Factor Analysis, Mining Associative rules in educational data, Mining of sequential patterns, Text mining, Visualization of educational data.

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, практични вежби, тимска работа, пример случаи, поканети предавачи, самостојна изработка на проектна задача и семинарска работа и електронско учење.

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

45 + 15 + 30 + 50 + 40 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

45 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

15 hours

16.

Other forms of activities

16.1.

Project tasks

50 hours

16.2.

Independent tasks

30 hours

16.3.

Homework

40 hours

17.

Grading method

17.1.

Tests

45 points

17.2.

Seminar work / project (presentation: written and oral)

50 points

17.3.

Activities and learning

10 points

17.4.

Final exam

0 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.

Condition for signature and taking final exam

реализирани активности

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

Механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature