Assessing the validity of a learning analytics expectation instrument: A multinational study

2020 
To assist higher education institutions in meeting the challenge of limited student engagement in the implementation of Learning Analytics services, the Questionnaire for Student Expectations of Learning Analytics (SELAQ) was developed. This instrument contains 12 items, which are explained by a purported two‐factor structure of “Ethical and Privacy Expectations” and “Service Feature Expectations.” As it stands, however, the SELAQ has only been validated with students from UK university, which is problematic on account of the interest in Learning Analytics extending beyond this context. Thus, the aim of the current work was to assess whether the translated SELAQ can be validated in three contexts (an Estonian, a Spanish, and a Dutch University). The findings show that the model provided acceptable fits in both the Spanish and Dutch samples, but was not supported in the Estonian student sample. In addition, an assessment of local fit is undertaken for each sample, which provides important points that need to be considered in future work. Finally, a general comparison of expectations across contexts is undertaken, which are discussed in relation to the General Data Protection Regulation (2018). Lay Description What is currently known about the subject matter: Accounting for student expectations allows for a service that is meaningful. SELAQ measures student expectations towards Learning Analytics services. The 12 items of the SELAQ can be explained by a two‐factor structure (Ethical and Privacy Expectations and Service Feature Expectations). What their paper adds to this: Validation of the SELAQ in three contexts A discussion of localized sources of strain An assessment of measurement quality in each context A general comparison of student expectations across contexts The implications of study findings for practitioners: SELAQ can be used with Spanish and Dutch students. SELAQ responses provide a much needed input from end‐users in Learning Analytics implementations.
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