Learning analytics
| Title | Ανάλυση δεδομένων μάθησης / Learning analytics |
| Code | IHST207 |
| Faculty | Sciences |
| School | Informatics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter |
| Coordinator | Stavros Demetriadis |
| Common | No |
| Status | Active |
| Course ID | 600016392 |
Programme of Study: PMS TECΗNOLOGIES DIADRASTIKŌN SYSTĪMATŌN (2018 éōs sīmera) PF
Registered students: 6
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses | 1 | 1 | 7.5 |
| Academic Year | 2024 – 2025 |
| Class Period | Winter |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Class ID | 600263320
|
Course Type 2021
Specific Foundation
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600263320
- eLearning (Moodle): https://elearning.auth.gr/course/view.php?id=11451
Language of Instruction
- Greek (Instruction, Examination)
- English (Instruction, Examination)
Prerequisites
General Prerequisites
Students who choose the course are expected to have a general education in Science/Engineering areas (e.g. from Departments of the Faculty of Sciences, Polytechnic, etc.) and to have basic programming experience (in any language) in order to understand what code and programming mean. Experience in Python programming is desirable and helps to move quickly without, however, being necessary as the instructor has prepared a special introductory MOOC in Python (in Greek) and novice students are instructed and supported to attend it.
Learning Outcomes
Upon successful completion of the course students will be able to:
a) Explain the basic concepts of the field and the relationships between them
b) Explain the different methods of analyzing learning data and the type of problems to which they apply (in general and not only in learning data)
c) They apply data analysis techniques (explanatory as well as predictive) in Python environment by writing code where they will use libraries such as: numpy, scipy, matplotlib, statsmodels, scikit.
d) They complete a data analysis project by interpreting the results in order to reach useful conclusions
e) Apply a framework for ethically sound data acquisition and utilization
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Make decisions
- Work autonomously
- Work in teams
- Work in an international context
- Work in an interdisciplinary team
- Generate new research ideas
- Design and manage projects
- Appreciate diversity and multiculturality
- Respect natural environment
- Demonstrate social, professional and ethical commitment and sensitivity to gender issues
- Be critical and self-critical
- Advance free, creative and causative thinking
Course Content (Syllabus)
The course introduces the Theory and Practice of the innovative research field of Learning Analysis that applies methods of analysis of data in interactions that occur in learning interactions (learning interactions). Students:
- a) They will know the main methods of Learning Data Analysis with emphasis on the goals set by each and the type of problems they face. They will also learn techniques for designing "Dashboard" type interfaces for analysis data.
- b) They will learn to implement data analysis techniques (explanatory and predictive, eg t-test, ANOVA, linear & logistic regression, classification, clustering, etc.) in a Python environment (will use the Jupyter Notebook and libraries such as numpy, scipy, matplotlib, pandas, and scikit). These techniques have a more general value as they can be applied in any case of data analysis and not just learning.
Special emphasis will be given to examples and analysis of data from Massive Open Online Courses (MOOCs). The various challenges from the application of Learning Analytics (eg ethics) will also be analyzed.
Prior knowledge of Python is important but in the first lessons there will be a brief reminder and audiovisual material will be available so that those who do not know can watch.
Keywords
Learning data analysis, Learning analytics, Predictive modeling, Python libraries, Ethics in Data management
Educational Material Types
- Notes
- Slide presentations
- Video lectures
- Multimedia
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Description
Due to the content of the course (digital applications) ICT is systematically used for the presentation, education, communication and evaluation of students
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 27 | ✓ | ✓ | ||
| Seminars | 6 | ✓ | ✓ | ||
| Laboratory Work | 6 | ✓ | ✓ | ✓ | |
| Reading Assigment | 68 | ✓ | ✓ | ✓ | |
| Project | 75 | ✓ | ✓ | ✓ | |
| Written assigments | 40 | ✓ | ✓ | ✓ | |
| Exams | 3 | ✓ | ✓ | ✓ | |
| Total | 225 |
Student Assessment
Student Assessment methods
- Written Assignment (Formative, Summative)
- Performance / Staging (Formative, Summative)
- Report (Formative, Summative)
- Labortatory Assignment (Formative, Summative)
Last Update
21-03-2024
