14 August 2020
Learning Analytics (LA)
Due to the covid-19 outbreak, all programmes for 2020 have been cancelled.
The first section:
Information about the course and how things will go.
Introduction to the learning analytics field.
What and why is learning analytics
How is learning analytics different form educational data mining and academic analytics.
What are the different types and techniques of learning analytics (Brief introduction)
Dispositional learning analytics
Multimodal learning analytics
Social network analytics (SNA)
Predictive learning analytics
Visualization and dashboards
Does learning analytics make a difference: the evidence.
The second section : Theory and learning analytics
Is analytics a new paradigm ?
Why theory matters?
What are the common theories implemented in learning analytics and how were they operationalized,examples ?
The third section: Two main components of learning analytics will be discussed in details, the process and the details.
The process of learning analytics will be discussed, mainly
The data capture and refining stage, including a discussion of the sources of data, linking, cleaning and ethics.
The analysis, prediction and reporting stage: the different analysis methods will be discussed, how analytics results are presented, reported or visualized.
The general framework of learning analytics
1. Stakeholders: The subjects (learners, teachers and administrators)
2. Objectives: the Goal of using analytics and the questions needing an answer.
3. Data: The possible sources of information and the data available.
4. Instruments: Technologies and tools used to collect, store, analysis, report and display.
The fourth section: Ethics and privacy:
This week, general issues about ethics and privacy will be discussed.
What are the standards that govern the process of privacy protection in learning analytics and how compliance with these standards could be measured?
What are the negative consequences learning analytics can have on students, instructors and institutions from a privacy perspective?
How can analytics be translated into action without compromising a learner or institution privacy or reputation.
Access issues: Who has the right to access learner’s activities, logs or other sources of data?
Data Issues: How can data be stored, handled, transferred, classified, managed or shared without undermining privacy of users.
Who owns the data and who have control over it, who can authorize research, exchange, analysis and who has the ability to revoke such authorizations?
Consent: General principles and governing rules.
Ilkka Jormanainen, ilkka.jormanainen(at)uef.fi
This Learning Analytics (LA) Course will provide a framework for the understanding of the field of LA and how data is used in education. The course will address the taxonomy of learning analytics and related terms such as educational data mining and academic analytics. The course will also discuss the theoretical background behind learning analytics and the concepts of big data paradigm shift. The learning analytics main steps and procedures will be discussed in details, including data gathering, analysis and generation of insights. The main ethical and privacy issues will also be discussed.
ECTS. 1 week + additional pre/post-assignments
EUR 300: Course fee. There is a discount of 20 % for the UEF partner university students.
EUR 200: Course fee for exchange students starting in autumn 2020.