Learning Analytics (LA)
Time: 10-14 August 2020
Duration and credits: 1 week + additional pre/post-assignments, 5 ECTS
Teaching language: English
Level: Master, doctoral
Maximum number of attendees:
Course coordinator: Ilkka Jormanainen, Ilkka.email@example.com
Responsible department: School of Computing
Learning outcomes: 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.
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
- Discourse 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.
- Action stage
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.
- UEF policy
Modes of study: The course’s main strategy will be building students’ knowledge and skills in the field of learning analytics through:
- Lectures and presentations by the instructors, literature reading and group discussions·
- Participatory knowledge building through collaborative interactions (discussions in the classroom, online discussions and group assignments). The aim of these interactions and assignments is to help students reflect, develop critical capacity and deep understanding of the field of LA.
- Online exercises and assignments.
Study materials/suggested references: Introduction
· Siemens, G. (2013). Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist, 57(10), 1380–1400. https://doi.org/10.1177/0002764213498851
· Shum, S. B. (2012). Learning Analytics (UNESCO Policy Brief). Retrieved from http://iite.unesco.org/pics/publications/en/files/3214711.pdf
· Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic analytics: A new tool for a new era. EDUCAUSE review, 42(4), 40.
· Blikstein, P., & Worsley, M. (2016). Multimodal Learning Analytics and Education Data Mining: Using Computational Technologies to Measure Complex Learning Tasks. Journal of Learning Analytics, 3(2), 220–238. https://doi.org/10.18608/jla.2016.32.11
· Wong, J., Baars, M., de Koning, B. B., van der Zee, T., Davis, D., Khalil, M., … Paas, F. (2019). Educational Theories and Learning Analytics: From Data to Knowledge. Utilizing Learning Analytics to Support Study Success, 3–25. https://doi.org/10.1007/978-3-319-64792-0_1
· Knight, S., & Buckingham Shum, S. (2017). Theory and Learning Analytics. Handbook of Learning Analytics, 17–22. https://doi.org/10.18608/hla17.001
· Friend Wise, A., & Williamson Schaffer, D. (2015). Why theory matters more than ever in the age of big data. Journal of Learning Analytics, 2(2), 5–13. https://doi.org/10.18608/jla.2015.22.2
· Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired magazine, 16(7), 16-07.
· Drachsler, H., & Greller, W. (2016, April). Privacy and analytics: it's a DELICATE issue a checklist for trusted learning analytics. In Proceedings of the sixth international conference on learning analytics & knowledge (pp. 89-98). ACM. Chicago
· Prinsloo, P., & Slade, S. (2017). Ethics and Learning Analytics: Charting the (Un)Charted. Handbook of Learning Analytics, 49–57. https://doi.org/10.18608/hla17.004
· Sclater, N., & Bailey, P. (2015). Code of practice for learning analytics.
· Pardo A. (2014) Designing Learning Analytics Experiences. In: Larusson J., White B. (eds) Learning Analytics. Springer, New York, NY
· Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2013). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5-6), 318-331.
· Marbouti, F., Diefes-Dux, H. A., & Madhavan, K. (2016). Models for early prediction of at-risk students in a course using standards-based grading. Computers & Education, 103, 1-15.
· Ferguson, R., & Clow, D. (2017). Where is the evidence? A call to action for learning analytics. ACM International Conference Proceeding Series, 56–65. https://doi.org/10.1145/3027385.3027396.
Evaluation criteria: Participation in lectures and exercises 20 %, Participation in online activities and group project, 20 %, the review assignment 30 %, data assignments 30 %.
Teacher: Dr Mohammed Saqr (UEF) + international guest lecturers