Learning analytics (LA) involves collecting, processing, and visualizing big data to help teachers optimize learning conditions. Despite its contributions, LA has not yet been able to meet teachers’ needs because it does not provide sufficient actionable insights that emphasize more on analytics and less on learning. Our work uses specific analytics for student guidance to evaluate an instructional design that focuses on LA agency between teachers and students. The research goal is to investigate whether the minimal and strong guidance provided by the LA learning approach has the same impact on student outcomes. The research questions are as follows “Does the LA-based minimal and strong guidance learning approach have the same impact on student performance and SRL skills? What are the students’ learning perceptions and satisfaction under LA-based guidance?” A mixed methods study was conducted at a university in which LA-based strong guidance was applied to the experimental group and minimal guidance was given to the control group. When strong guidance was applied, the results indicated increased final grades and SRL skills (metacognitive activities, time management, persistence, and help seeking). Furthermore, student satisfaction was high with LA-based guidance. Future research could adapt our study to nonformal education to provide nuanced insights into student outcomes and teachers’ perceptions.

Learning analytics (LA) is an educational innovation that enhances teaching practices and facilitates student learning. However, the degree of LA adoption across schools remains limited, and teachers who adopt LA do not engage with it consistently. Based on the unified theory of acceptance and use of technology (UTAUT) as a framework, we conducted a perception survey and five focus group interviews with K-12 teachers, aiming to identify their expectations, needs, and perceptions of LA adoption. This study focuses on aspects related to LA’s technology acceptance in school education. The research question is, “Using the UTAUT framework as a theoretical perspective, which factors explain the adoption of LA in schools? How do teachers describe their motivation and readiness for LA adoption?” The findings indicated that the factors facilitating LA adoption were performance expectancy, social influence, and feelings. Furthermore, the factors inhibiting the adoption of LA were effort expectancy, self-efficacy, facilitating conditions, and culture change. The implications and potential future research for adopting LA in schools are discussed.

The broader aim of this research is to examine how Learning Analytics (LA) can become ethically sound, pedagogically actionable, and realistically adopted in educational practice. To address this overarching challenge, the study investigates three interrelated research questions: ethics by design, learning impact, and adoption conditions. Methodologically, the research follows an exploratory sequential multi-method design. First, a meta-synthesis of 53 studies is conducted to identify key ethical challenges in LA and to derive an ethics-by-design framework. Second, a quasi-experimental study examines the impact of interface-based LA guidance (strong versus minimal) on students’ self-regulated learning skills and academic performance. Third, a mixed-methods adoption study, combining surveys, focus groups, and ethnographic observations, investigates the factors that encourage or hinder teachers’ adoption of LA in K–12 education. The findings indicate that strong LA-based guidance leads to statistically significant improvements in students’ self-regulated learning skills and academic performance compared to minimal guidance. Furthermore, the adoption analysis reveals that performance expectancy, social influence, human-centred design, and positive emotions facilitate LA adoption, whereas effort expectancy, limited facilitating conditions, ethical concerns, and cultural resistance inhibit it. Overall, the study demonstrates that ethics by design, effective pedagogical guidance, and adoption conditions are mutually reinforcing dimensions. It argues that LA can support intelligent, responsive, and human-centred learning environments when ethical safeguards, instructional design, and stakeholder involvement are systematically aligned.