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

16 3 Responsible Cooking with Learning Analytics
person’s even being aware of it. Data collection and use under such circumstances is,
of course, ethically and legally questionable (Greller and Drachsler 2012).
Learning analytics is the mechanism that brings such perils of digitization into
education, classrooms, and lecture halls. Often, educators are either not fully aware
of these issues when they apply various technologies or they are over-anxious and
hesitant to use them. Ethical and data protection issues in learning analytics include
the collection of data, informed consent, privacy, de-identification of data, transparency,
data security, interpretation of data, data classification and management, and
potential harm to the data subject (cf. Sclater 2014a; Slade and Prinsloo 2013). A
number of researchers have worked on establishing an agreed set of guidelines with
respect to the features of learning analytics, such as the ownership of data and
analytic models and rights and responsibilities (Berg 2013; Ferguson 2012).
The more interlinked, multimodal, and multifaceted learning analytics becomes,
and the more frequent and widespread its use, the more important are issues related
to privacy and data protection, particularly since the introduction of the 2018 EU
General Data Protection Regulation (GDPR). These strict regulations and rules
establish a specific code of conduct when applying learning analytics in educational
settings. The following nine categorizations and dimensions can guide users through
the process of integrating solid data protection and privacy measures into their
learning analytics applications (Sclater 2014a; Slade and Prinsloo 2013; Willis
2014).
Privacy Privacy refers to “the quality or state of being apart from company or
observation” (Merriam Webster Dictionary 2019). Aggregating and analyzing personal
data may violate this principle. An example is that learning analytics may
allow teachers to view all of an individual’s working steps, rather than just the
assigned outcome.
Transparency Transparency refers to informing learners about all of the features,
processes, and mechanisms of the application of learning analytics and to making the
often hidden analytics processes and means of automatic grading understandable to
the learners.
Informed Consent Informed consent refers to the process of ensuring that individual
learners understand the features, processes, and mechanisms of the application of
learning analytics and the potential consequences and to seeking the consent of
individual learners (or their legal representatives) to apply learning analytics.
De-identification of Data Data de-identification refers to separating performance
data from data that can identify a particular individual.
Location and Interpretation of Data Educational activities that are tracked by
learning analytics are usually spread over multiple tools and locations. Learning
analytics brings these data sources together for a more complete picture of learning.
Questions about the implications of using multiple and perhaps noninstitutionalized
sources arise concerning whether the data is representative of a particular student.