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

2.4 Data: Educational Datasets and the Environments in Which They Occur 11
Greller and Drachsler 2012; Nistor et al. 2015; Papamitsiou et al. 2014; Sin and
Muthu 2015) occurred in 362 papers published in Learning Analytics & Knowledge
Conference and 39 papers in Journal of Educational Measurement between 2010
and 2016. More detailed descriptions of each objective are available in the Learning
Analytics Toolbox at http://www.learning-analytics-toolbox.org/home/objectives/.
The most common objective is to monitor the student’s performance. This refers
to tracking how active students are and how they perform during a course or their
studies. The second most common objective in the core of learning analytics
literature is to distill and visualize various patterns in students’ behavior that could
improve learning and teaching activities via better understanding of the learning
situation. It is clear that these two objectives (Fig. 2.4) are broader than the others.
When learning analytics are applied in practice, the first objective is usually something
other than just making visualizations (e.g., classifying students into certain
groups, predicting students’ performance). For example, the goal of applying learning
analytics could be to estimate students’ competencies and then to create a
dashboard that presents insights from the data in a convenient and comprehensible
way. However, one could also just estimate students’ competencies without creating
sophisticated visualizations or dashboards.
Many stakeholders are probably interested in implementing learning analytics
solutions, but it is not easy to know where to start. An overview of these objectives
indicates the types of objectives learning analytics could address. Understanding the
potential areas of implementation makes identifying the tools and data that are
available in a certain teaching or learning environment easier.
After an objective for applying learning analytics is clear, it still might not be easy
to choose the right method to meet it. Learning analytics applies known methods and
models that have been used in other types of analytics to address issues that affect
students’ learning processes and the organizational learning system. The list
of learning analytics methods is long (and would be longer if all of the variations
of such methods were taken into account). For a high-level view of what kind of
methods are out there and what they are good for, we present a list of methods and
present them in the Learning Analytics Toolbox (http://www.learning-analyticstoolbox.org/methods/).
2.4 Data: Educational Datasets and the Environments
in Which They Occur
Typical scenarios for learning analytics include e-learning courses and perhaps
popular MOOCs, where a learner produces data with every mouse click. However,
students usually do their exercises and homework with paper and pencil, which
matches the social, interactive, and personal process of learning, an analogue
process. Because of the scarcity of digital traces from students, we rarely have
access to educational “big data” and sometimes do not even find “little data,” as