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Purpose: This study aims to offer a bibliometric and coding analysis of blockchain articles published in the accounting, auditing and accountability fields. Design/methodology/approach: The data were collected using the Scopus database and a bibliometric and qualitative coding analysis with the keywords "blockchain" and "accounting" or "auditing" or "accountability." Of the 514 initial sources, 93 peer-reviewed papers, book chapters and conference proceedings in the areas of business, management and accounting were finally selected. Nonscientific sources such as nonpeer-reviewed books and white papers were excluded. Findings: This study reveals a promising and multidisciplinary field of research dominated by scholars and less by practitioners. Qualitative research, especially discourse analysis, is the most used method among authors. This study gives some useful insights about blockchain's definition and characteristics, business models, processes involved, connection with other technologies and relationships with accounting theories. Among the most interesting insights, the results confirm that technology as an external force can create an intersection among several research areas: accounting, auditing, accountability, business, management, computer science and engineering fields. Finally, in terms of research themes, although blockchain has a clear effect on auditing accounting, the links with the area of accountability are less clear and validated. Originality/value: This study highlights the current state of the field, combining methodological approaches and providing valuable future research insights. Additionally, it is also a starting point for professionals to fully understand blockchain's characteristics and potential with a constructive and systemic approach. [ABSTRACT FROM AUTHOR]

Purpose: Digital learning has become a global trend. Partly or fully automatic learning systems are integrated into education in schools and universities on a previously unseen scale. Learning systems have a lot of potential for re-education, life-long learning and for increasing access to educational resources. Learning systems create massive amounts of data about learning behaviour. Analysing that data for educational decision making has become an important track of research. The purpose of this paper is to analyse data from an intermediate-level computer science course, which was taught to 141 students in spring 2018 at University of Turku, Department of Future Technologies, Finland. Design/methodology/approach: The available variables included number of submissions, submission times, variables of groupwork and final grades. Associations between these variables were looked at to reveal patterns in students' learning behaviour. Findings: It was found that time usage differs per different grades so that students with grade 4 out of 5 used most time. Also, it was found that studying at night is connected to weaker learning outcomes than studying during daytime. Several issues in relation to groupwork were revealed. For example, associations were found between prior skills, preference for individual vs groupwork, and course learning outcomes. Research limitations/implications: The research was limited by the domain of available variables, which is a common limitation in learning analytics research. Practical implications: The practical implications include important ideas for future research and interventions in digital learning. Social implications: The importance of research on soft skills, social skills and collaboration is highlighted. Originality/value: The paper points a number of important ideas for future research. One important observation is that some research questions in learning analytics need qualitative approaches, which need to be added to the toolbox of learning analytics research. [ABSTRACT FROM AUTHOR]

Purpose - Recent educational initiatives by the Obama Administration have highlighted the need for more racial and ethnic diversity in Science, Technology, Engineering, and Mathematics (STEM) fields (The White House, 2011). While African Americans are underrepresented in faculty positions nationally, accounting for only 5.2% of all academic faculty across all disciplines (Harvey, W. B.,& Anderson, E. L. (2005). Minorities in higher education: Twenty-first annual status report. Washington, DC: American Council on Education), the underrepresentation of African Americans in STEM fields such as computing science is even more severe. According to a recent Computing Research Association (CRA) Taulbee Survey, African Americans represent just 1.3% of all computing sciences faculty (CRA, 2006). Design/methodology/approach - This paper examines the benefits of one program that specifically seeks tofulfill the Obama Administration's initiatives by addressing this disparity in higher education. Findings - The program helps prepare doctoral students for the academic job search process in an effort to increase the ranks of African American faculty in computing sciences. [ABSTRACT FROM AUTHOR]