(SpringerBriefs in Business Process Management) Learning Analytics Cookbook_ How to Support Learning Processes Through Data Analytics and Visualizatio

24 3 Responsible Cooking with Learning Analytics
Responsibility Identifying who is responsible for the data and data processing for
learning analytics in an institution.
Validity Ensuring the validity of algorithms, metrics, and processes.
Privacy Ensuring protection of individual rights and compliance with data protection
legislation.
Transparency and Consent Ensuring openness on all aspects of using learning
analytics and meaningful consent.
Minimizing Adverse Impacts Avoiding potential pitfalls for and harm to students.
Access Providing data subjects access to their data and analytics.
Enabling Positive Interventions Handling interventions based on analytics in a
positive, appropriate, and goal-oriented way.
Stewardship of Data Handling, processing, and storing of data appropriately and
with accountability.
One of the most important frameworks of privacy and ethics issues in learning
analytics is the DELICATE checklist (Drachsler and Greller 2016). The checklist
was developed after intensive studies of EU law and various expert workshops on
ethics and privacy for learning analytics. DELICATE stands for
D-etermination Determine the purpose of learning analytics for your institution.
E-xplain Explain the scope of data collection and usage.
L-egitimate Explain how you operate within legitimate legal frameworks, referring
to the essential legislation.
I-nvolve Involve stakeholders and give assurances about the data’s distribution
and use.
C-onsent Seek consent through clear consent questions.
A-nonymise Anonymize individual data as much as possible.
T-echnical Aspects Monitor who has access to data, especially in areas with high
staff turnover.
E-xternal Partners Make sure external partners provide the highest data-security
standards.
Learning analytics solutions that are specifically designed for the K-12 school
context have been developed in the context of the European project called Lea’s Box
(http://www.learning-analytics-toolbox.org/tools/). The project emphasized the
extreme diversity and heterogeneity of daily school practices in terms of multiple
and overlapping learner groups, multiple and overlapping subjects, main lessons and
afternoon care, multiple teachers in one subject area, long time periods to be covered,
a generally technology-lean setting, and the use of all types of electronic tools, apps,
and devices. The Lea’s Box solutions build on bringing all the scattered data sources