Subject

Learning Analytics

Course Title

Learning Analytics
Educational data analytics

Code

m23_s_031

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

Boban Joksimovski, Ivan Chorbev

Prerequisites for enrolling in the course

—

10.

Objectives of the course programme (competences)

To understand the key analytics of learning and educational data mining (EDM/EDM-PROP) and apply them to real-world problems across various educational settings. To learn about the relevant political, legal, and ethical issues involved in conducting educational data analytics. Using learning analytics methods to improve education. 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. 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 examine how the same statistical and mathematical approaches are used in different ways in these research communities.

11.

Course content

Introduction to Learning Data Analysis and Educational Data Mining (LA / EDM), Predictive Regression in Educational Data, Classification Algorithms in Educational Data, Behavior Detection, Diagnostic Metrics, Feature engineering and distillation in educational data, Advanced estimation and validation, Bayesian algorithms, Performance factor analysis in educational data, Advanced BKT, Knowledge structure discovery, Network Analysis in Educational Data, Correlation and Causal Mining, Model-based Discovery, Clustering and Factor Analysis, Association Rule Mining in Educational Data, Sequential Pattern Mining, Text Mining, Visualization of Educational Data.

12.

Learning methods

Lectures supported by slide presentations, interactive lectures, practical exercises, teamwork, case studies, guest speakers, independent project work and seminar papers, and e-learning.

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

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

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	45 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	15 hours

16.

Other forms of activities

16.1.	Project assignments	50 hours
16.2.	Independent assignments	30 hours
16.3.	Home study	40 hours

17.

Assessment method

17.1.	Tests	45 points
17.2.	Seminar paper / 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.

Requirement for obtaining a signature and taking the final exam

completed activities

20.

Language of instruction

Macedonian and English

21.

Method for monitoring the quality of teaching

Internal evaluation and survey mechanism

22.

Literature

22.1.

Required literature

22.2.

Additional literature

No. Author Title Publisher Year