Big data from massive open online courses (MOOCs) have enabled researchers to examine learning processes at almost infinite levels of granularity. Yet, such data sets do not track every important element in the learning process. Many strategies that MOOC learners use to overcome learning challenges are not captured in clickstream and log data. In this study, we interviewed 92 MOOC learners to better understand their worlds, investigate possible mechanisms of student attrition, and extend conversations about the use of big data in education. Findings reveal three important domains of the experience of MOOC students that are absent from MOOC tracking logs: the practices at learners’ workstations, learners’ activities online but off-platform, and the wider social context of their lives beyond the MOOC. These findings enrich our understanding of learner agency in MOOCs, clarify the spaces in-between recorded tracking log events, and challenge the view that MOOC learners are disembodied autodidacts.

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The article substantiates the need for educational institutions to use Data Mining technology as a key to successful management decisions in modern realities. The study focuses on working with social media data. The authors emphasized the lack of attention to this issue among both foreign and Ukrainian scientists.The article outlines the algorithm for collecting and transmitting primary data obtained as a result of monitoring the activity of educational institutions in social networks to form models of various types of their actions.The model presented by the authors includes four stages. Stages one and two provide the list of factors / metrics that can be included in the model. These factors require an appropriate and high-quality data collection process. At the next stage, the authors propose data clustering as the most important process for the future use of social network data. It is emphasized that the formation of clusters will depend on the tasks facing the management teams of the educational market. The authors give several examples of such clustering but point out that the list is not exhaustive and can be significantly expanded.An important aspect of the availability of such databases is access to information not only for teachers, but also for all interested university staff. At the same time, each user (students, teachers, staff, administration) will receive data relevant to their requests and needs.The developed methodology will help increase the efficiency of management decision-making and implementation and provide an opportunity to justify the parameters of successful innovation in educational institutions in many respects, including the development of educational programs, implementation of new certification programs and disciplines, other services, etc.

Social media platforms and digital networks have enabled professionals to communicate with colleagues, disseminate information, and co-create knowledge. As the Internet has grown up and social networks have scaled, there is an increased awareness of the occupational impact of being online. Prior research in postsecondary education has examined the structure of social networks, imagined audiences online, openness of practice, and informational/communication patterns of academics. We conducted 92 semi-structured interviews with higher educational professionals beyond the faculty/academic role to understand motivations and challenges online. Participants shared how they navigate digital identity and contribute to networked practices in professional online communities. Multiple factors influence and impact the use of social media over time, specifically as professionals reflect on their digital presence, network affiliations, and informal learning opportunities. We identify seven networked practices: kinship in community, self-directed learning, digital norms, navigating context collapse, career advancement, reputation management, and risk versus reward assessment.

Guided by the question, “What are the implications of national educational evidence standards for school librarianship research?,” prevailing U.S. evidence-driven educational policies are examined to identify implications for school librarianship research; promising methods to contribute to building this evidence base are explored; and finally, progress on a long-term research agenda designed to enable school librarianship researchers to contribute evidence to educational policy is reviewed. As promising methods are explored, an actionable agenda is proposed that school library researchers can undertake to participate in a causal research environment.