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For service implementations to be widely adopted, it is necessary for the expectations of the key stakeholders to be considered. Failure to do so may lead to services reflecting ideological gaps, which will inadvertently create dissatisfaction among its users. Learning analytics research has begun to recognise the importance of understanding the student perspective towards the services that could be potentially offered; however, student engagement remains low. Furthermore, there has been no attempt to explore whether students can be segmented into different groups based on their expectations towards learning analytics services. In doing so, it allows for a greater understanding of what is and is not expected from learning analytics services within a sample of students. The current exploratory work addresses this limitation by using the three-step approach to latent class analysis to understand whether student expectations of learning analytics services can clearly be segmented, using self-report data obtained from a sample of students at an Open University in the Netherlands. The findings show that student expectations regarding ethical and privacy elements of a learning analytics service are consistent across all groups; however, those expectations of service features are quite variable. These results are discussed in relation to previous work on student stakeholder perspectives, policy development, and the European General Data Protection Regulation (GDPR). • Student expectations of learning analytics can be segmented based on service items. • Ethical and privacy expectations were relatively consistent across the sample. • Provides a discussion on how to integrate findings into institutional policies on learning analytics implementations. [ABSTRACT FROM AUTHOR]

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 contextsA discussion of localized sources of strainAn assessment of measurement quality in each contextA 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. [ABSTRACT FROM AUTHOR]

Background: Learning Analytics (LA) is an emerging field concerned with measuring, collecting, and analysing data about learners and their contexts to gain insights into learning processes. As the technology of Learning Analytics is evolving, many systems are being implemented. In this context, it is essential to understand stakeholders' expectations of LA across Higher Education Institutions (HEIs) for large‐scale implementations that take their needs into account. Objectives: This study aims to contribute to knowledge about individual LA expectations of European higher education students. It may facilitate the strategy of stakeholder buy‐in, the transfer of LA insights across HEIs, and the development of international best practices and guidelines. Methods: To this end, the study employs a 'Student Expectations of Learning Analytics Questionnaire' (SELAQ) survey of 417 students at the Goethe University Frankfurt (Germany) Based on this data, Multiple Linear Regressions are applied to determine how these students position themselves compared to students from Madrid (Spain), Edinburgh (United Kingdom) and the Netherlands, where SELAQ had already been implemented at HEIs. Results and Conclusions: The results show that students' expectations at Goethe University Frankfurt are rather homogeneous regarding 'LA Ethics and Privacy' and 'LA Service Features'. Furthermore, we found that European students generally show a consistent pattern of expectations of LA with a high degree of similarity across the HEIs examined. European HEIs face challenges more similar than anticipated. The HEI experience with implementing LA can be more easily transferred to other HEIs, suggesting standardized LA rather than tailor‐made solutions designed from scratch. Lay Description: What is currently known about this topic: Implementing Learning Analytics is a promising but challenging task.SELAQ is a questionnaire that can be used to determine students' expectations of Learning Analytics.Several institutions in Europe applied SELAQ to implement Learning Analytics that meet the needs of their students. What this paper adds to this: This study applies SELAQ for the first time at a German university and compares the results to studies from the UK, Spain, and the Netherlands.The participating students at the German university show relatively homogeneous expectations of Learning Analytics.The expectations towards Learning Analytics of students across Europe are comparable. The implications of study findings for practitioners: Learning Analytics systems should be shared across institutions rather than being implemented separately as tailored solutions.More standardized or modular Learning Analytics systems are reasonable. [ABSTRACT FROM AUTHOR]

Learning analytics (LA) employs educational data to improve the timeliness of support for learners. Apart from technical aspects, there is a need to understand social complexities brought about by different stakeholders, so as to systematise the adoption of LA in Higher Education (HE). We present an analysis of the situation, needs and challenges of LA in the context of Spanish HE, considering managers', teachers' and students' perspectives. Qualitative research is employed using recursive abstraction. Results reveal that the level of institutional adoption is low and none of the analysed institutions had an LA policy. Furthermore, only two of these institutions had an initial LA strategy. While the institutions shared some commonalities in their objectives for LA, chosen tools and adoption challenges, the distinct differences in the political contexts and institutional practices among the institutions reaffirmed that LA solutions and services cannot be implemented in the same manner. Moreover, different needs for LA and concerns are identified about its adoption among managers, students and teachers. These observations lead to our conclusion that the main challenges to implement LA in Spain are not related to technological issues but to the social and cultural issues rooted in institutions and those associated with different stakeholders. [ABSTRACT FROM AUTHOR]

Student engagement within the development of learning analytics services in Higher Education is an important challenge to address. Despite calls for greater inclusion of stakeholders, there still remains only a small number of investigations into students' beliefs and expectations towards learning analytics services. Therefore, this paper presents a descriptive instrument to measure student expectations (ideal and predicted) of learning analytics services. The scales used in the instrument are grounded in a theoretical framework of expectations, specifically ideal and predicted expectations. Items were then generated on the basis of four identified themes (Ethical and Privacy Expectations, Agency Expectations, Intervention Expectations, and Meaningfulness Expectations), which emerged after a review of the learning analytics literature. The results of an exploratory factor analysis and the results from both an exploratory structural equation model and confirmatory factor analysis supported a two‐factor structure best accounted for the data pertaining to ideal and predicted expectations. Factor one refers to Ethical and Privacy Expectations, whilst factor two covers Service Feature Expectations. The 12‐item Student Expectations of Learning Analytics Questionnaire (SELAQ) provides researchers and practitioners with a means of measuring of students' expectations of learning analytics services. Lay Description: What is already known about this topic: Understanding student expectations of learning analytics is an important challenge for higher education institutions to address.Research has measured student beliefs regarding the features and ethical procedures of a learning analytics service. What this paper adds: This study builds on prior work by developing an instrument to measure student expectations of learning analytics services.This study proposes that student expectations of learning analytics can be measured using two subscales: (a) ethical and privacy expectations and (b) service expectations. Implications for practice and/or policy: Higher education institutions should understand what students expect from learning analytics services before any implementations are actioned.Higher education institutions have a validated instrument to gauge and understand student expectations of learning analytics services.Results obtained from the instrument can be used to inform the development of specific learning analytics policies for each higher education institution. [ABSTRACT FROM AUTHOR]

Learning Analytics (LA) is perceived to be a promising strategy to tackle persisting educational challenges in Latin America, such as quality disparities and high dropout rates. However, Latin American universities have fallen behind in LA adoption compared to institutions in other regions. To understand stakeholders' needs for LA services, this study used mixed methods to collect data in four Latin American Universities. Qualitative data was obtained from 37 interviews with managers and 16 focus groups with 51 teaching staff and 45 students, whereas quantitative data was obtained from surveys answered by 1884 students and 368 teaching staff. According to the triangulation of both types of evidence, we found that (1) students need quality feedback and timely support, (2) teaching staff need timely alerts and meaningful performance evaluations, and (3) managers need quality information to implement support interventions. Thus, LA offers an opportunity to integrate data-driven decision-making in existing tasks. • The adoption of Learning Analytics (LA) services is still incipient in Higher Education Institutions in Latin America • We identified needs for LA in Latin American universities from the perspectives of students, teaching staff, and managers • Study findings highlight areas of interest in LA including quality feedback and timely data-driven support for students • It is believed that LA can provide quality and actionable information to inform educational interventions [ABSTRACT FROM AUTHOR]