This study investigates the expectations of adult learners in online learning environments, specifically within the context of employee training for civil servants. The participant group includes 2,455 civil servants. The research employs a case study approach, collecting data using open-ended forms and conducting content analysis. According to the results, the expectations of adults are classified into nine categories: 1) interaction type, 2) content design, 3) video interaction, 4) visibility, 5) decision to enroll, 6) expectations for satisfaction, 7) content preference, 8) resource management, and 9) trainer preference. The study's findings inform the development of effective personnel training in online learning environments. Implications of these findings for the design and administration of e-learning are discussed.

Technology-and analytics-enhanced self-assessments may provide multiple benefits for learners. However, data analytics approaches currently fail to make full use of educational technology and data for self-assessment. This research focuses on the implementation of an environment for self-assessment including a data-driven dashboard in the context of higher education and examines learners’ usage behaviors of N= 100 learners in three experimentally varied conditions. Findings indicate an intensive use of the self-assessments and data-driven dashboards throughout the semester. However, no differences in the interaction of learners with the different dashboard conditions were found. In conclusion, the design of data-driven dashboards for self-assessments requires valid information about learners, assessment processes, and the context of the assessment to better support the current needs of the learner and provide meaningful feedback to foster learning processes and outcomes.

Feedback has been identified as the most powerful moderator for supporting learning. Learning analytics haven been recognized for opportunities for providing timely and informative feedback to learners when they need it. This study seeks to investigate learners' perceptions and expected benefits of different forms of learning analytics feedback from different sources. In a quasi-experimental study including 230 students, four experimental groups were confronted with five learning scenarios receiving different learning analytics feedback. Findings indicate that perceived benefits from learning analytics feedback varies across different delivery sources and requires informative recommendations. Accordingly, designing and implementing feedback in learning analytics systems is more complex than just providing visualizations of behavioral data.