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Collaboration Analytics: Investigating Methods for Education and Work
Provider: Faculty of Science

Activity no.: 5193-24-02-01There are 20 available seats 
Enrollment deadline: 26/08/2024
Date and time05.09.2024, at: 00:00 - 14.11.2024, at: 16:00
Regular seats20
ECTS credits5.00
Contact personAmanda Lybke Rasmussen    E-mail address: amra@di.ku.dk
Enrolment Handling/Course OrganiserAmanda Lybke Rasmussen    E-mail address: amra@di.ku.dk
Teaching languageEnglish
Exam formWritten assignment
Course workload
Course workload categoryHours
E-learning10.00
Theoretical exercises20.00
Preparation17.50
Laboratory20.00
Practical exercises20.00
Class Instruction25.00
Lectures25.00
Sum137.50
Content
The course will be a hybrid mix with remote options, so physical presence is not required.

Course Days: 

September 5, 2024, from 0900-1200 & 1300-1500
September 6, 2024 from 0900-1200 & 1300-1500
September 26, 2024 from 0900-1200 & 1300-1500
September 27, 2024 from 0900-1200 & 1300-1500
October 17, 2024 from 0900-1200 & 1300-1500
October 18, 2024 from 0900-1200 & 1300-1500
November 14, 2024 from 0900-1200 & 1300-1500

Registration fee:
There is no registration fee for this course.

Questions:
Please contact course coordinator Daniel Spikol (ds@di.ku.dk) if you have any questions.

Aim and content
The pervasive integration of digital technology in society and education influences both teaching and learning practices and how we work. These technologies allow access to data, mainly available from emerging online work and learning environments, that can be used to improve learning conditions and work. Increased access to previously unavailable digital data allows us to perform new analyses that measure chosen learning and work activities more objectively than using more traditional methods, often based on learners’ and workers’ perceived attitudes and observations. These new forms of analyses constitute the field of Collaboration (CA) and Learning Analytics (LA), defined as the measurement, collection, analysis, and reporting of data about people and their contexts, for understanding and optimizing learning and work in the environments in which it occurs.

These fields of research and practice are built on the developments and success of other domains and disciplines and the rapid growth of data and analytics methods. For instance, LA has achieved significant advances in multiple areas: student recommender systems, learning dashboards, adaptive feedback, early warning systems, and personalized student support.

This course provides a sound ground for understanding these areas of Collaboration and Learning for research and practice. The course will address the taxonomy of collaboration and learning analytics and related terms such as educational data mining and academic analytics. The course will also present the theoretical background behind collaboration analytics and the significant data paradigm shift concepts. The process and procedures will be discussed in detail, including data gathering, analysis, and generation of insights. The critical ethical and privacy issues will also be covered. The practical aspect of the course will enable the students to practice using different methods, including epistemic network analysis, social network analysis, process- and sequence mining, multimodal data, and visualization basics.

Formel requirements
Basic experience with statistics, quantitative and qualitative research, some basic programming experience in Python or R, with a focus on interactive notebooks.

Learning outcome
Knowledge:
• Identify the taxonomy of learning analytics, the main themes, and applications.
• Recognize the different theoretical models underpinning the learning analytics process and apply such theories to different problems.

Skills:
• Describe the learning analytics data cycle as well as how to apply these principles in research and practice.
• Identify key epistemological, pedagogical, ethical, and technical factors informing the design and implementation of learning analytics.

Competences:
• Apply the basics of collecting, cleaning, transforming, and analyzing educational data with real-life examples.
• Apply popular data analytic techniques, including predictive models, epistemic network analysis, multimodal learning analytics, relationship mining, social network analysis, and visualizations.
• Perform a research project using the learned methodological research skills in learning analytics empirically as well as theoretically.

Literature
Lang, C., Siemens, G., Wise, A., & Gasevic, D., Merceron, A. (Eds.). (2022). Handbook of learning analytics, Second Edition. New York: SOLAR, Society for Learning Analytics and Research.
10 Selected Research Papers

Target group
Students interested in Human-Centered Computing, Computer-Supported Collaborative Work, Business and Organizational Learning, Education, Psychology, Cognitive Science.

Teaching and learning methods
Course participants will meet twice during the course (three days + two days). Between these meetings, they will prepare and hand in a paper describing their own Collaboration and Learning Analytics research project. The course combines independent research and synthesis, interactive lectures, hands-on workshops, group work, and Master Classes.

• The workshops have diverse participants from computer science, learning sciences, and other fields. Ph.D. students will engage and actively participate in hands-on workshops with data using various techniques to collect, analyze, and understand data.
• The Master Classes will involve PhD students presenting their five-page papers (to be handed in before the Class) and their reflections on collaboration and analytics. PhD students receive formative feedback on their own ideas from international experts and their peers and engage in constructive discussions about the ideas of the other PhD students.

Lecturers

Lecturers:
- Olga Viberg, KTH Sweden
- Barbara Wasson, University of Bergen
- Mohammad Khalil, University of Bergen

Potential guests:
- Mutlu Cukorova, UCL UK
- Xavier Ochoa, NYU, USA
- Maria Rodriuez-Traina, University of Valladolid
- Sasha Poquet, TUM, Germany
- Malgorzata Agnieszka Cyndecka, University of Bergen

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