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

3.4 Ethical Frameworks in Learning Analytics 23
Slade and Prinsloo (2013) took a socio-critical perspective on the use of learning
analytics and proposed six principles to address ethics and privacy challenges:
Learning Analytics as a Moral Practice Key variables are effectiveness, appropriateness,
and practical necessity, and the goal is to understand, rather than to
measure.
Students as Agents Students should be involved in the learning analytics process
as collaborators and co-interpreters, so a student-centric approach to learning analytics
is recommended.
Student Identity and Performance Are Temporal Dynamic
Constructs Learning analytics often provides only a snapshot of a learner over a
limited time span and in a particular context and do not reflect long-term information
unless longitudinal data are used.
Student Success Is a Complex and Multidimensional Phenomenon Learning
progress and learning success are determined by multidimensional, interdependent
interactions and activities. The data used for learning analytics is always incomplete
and may lead to misinterpretation or bias.
Transparency Information about the purpose of data use, data controllers/processors,
and measures to protect the data should be disclosed in suitable forms.
Education Cannot Afford Not to Use Data Educators cannot ignore the information
that learning analytics may provide if the best possible results for individual
learners are to be reached.
Pardo and Siemens’ (2014) analysis of solutions for privacy- and ethics-related
issues in educational organizations resulted in a set of four principles that can serve
as a basis for setting up appropriate mechanisms for meeting social, ethical, and legal
requirements when developing and deploying learning analytics.
Transparency All stakeholder groups in learning analytics (e.g., learners, teachers,
educational administrator) should receive information about what type of data is
collected and how it is processed and stored.
Right to Access The security of data must be guaranteed and rights to access a
dataset clearly defined.
Student Control Over Data Students shall be granted access rights to data to
control and, if necessary, require a correction of data.
Accountability and Assessment The analytics process should be reviewed and the
responsible entities identified for each aspect of the learning analytics scenario.
Another general approach is Sclater and Bailey’s (2015) code of practice for
learning analytics. The code of practice, which is based on an extensive literature
review of legal and ethical issues in learning analytics (Sclater 2014a), addresses
eight themes: