The use of data science for education: The case of social-emotional learning

Smart Learning Environments

Table 2 Quality indicators for learning analytics. Source: summarized from Scheffel et al. (2014)

Topic	Criterion	Representative statements
Objectives	Teacher awareness	Teachers change their behavior in some respects. Teachers react in a more personalized way to how their students are dealing with learning material.
Objectives	Student awareness	Students become more self-regulated in their learning processes. Students are more aware of their learning progress.
Learning Support	Learning support	An early detection of students at risk. The ability to explain what could help them to improve further. Students regularly utilize the tools provided.
Learning Measures and Output	Learning outcome	If teachers can gain new insights using the given methods. Results are compared with other (traditional) measures.
Learning Measures and Output	Learning performance	Change in workplace learning is measurable. The extent to which the achievement of learning objectives can be demonstrated.
Data Aspects	Open access	Data are open access. Portability of the collected data.
Data Aspects	Privacy	Privacy is ensured. Learners can influence which data are provided.
Organizational Aspects	Acceptance & uptake	Administrators invest in scaling successful tools across their programming.