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In the last five years, there has been an increased interest across the globe, and in the United Kingdom in particular, to define, conceptualise and measure learning gains. The concept of learning gains, briefly summarised as the improvement in knowledge, skills, work-readiness and personal development made by students during their time spent in higher education, has been hailed by some as an opportunity to measure ‘excellence’ in teaching. This chapter will review some of the common definitions and the methods employed in research on learning gains. Secondly, we will provide a critical evaluation of the computational aspects of learning gains (e.g., raw gain, normalised gain). Finally, we will critically reflect upon the lessons learnt and what is not yet known in terms of learning gains.

Little is known about why children participate in activities that are labeled worst forms of child labor (WFCL). Case–control approaches common in medicine are adapted to consider the correlates of participation in worst forms in the context of two WFCL in Nepal: portering and ragpicking. Paternal disability is a strong predictor of entry into each of the worst forms, and the presence of productive assets within the child's home reduces the risk a child is observed in a worst form. We argue that our findings are consistent with a model where there are negative amenities associated with these jobs that induce the poor and those with the fewest alternative earnings options to select into these WFCL in Nepal.

Ubiquitous access to digital technologies is becoming an integral part of our business, home, and leisure environments, yet despite a quarter century of educational technology initiatives, ubiquitous computing remains conspicuously absent from our schools. In this chapter, we argue that simply putting more computers in schools will not solve the problem, but rather that teaching, learning, and technology integration need to be reconceptualized within a ubiquitous computing framework before the full educational possibilities inherent in digital technologies can be realized. Using examples from our laboratory classroom, we discuss how teaching needs to be reconceived more as “conducting” than “instructing”; how learning needs to become more the responsibility of the student, and located with her in an expanded space and time that extends beyond the classroom; and how technology integration needs to be understood not as an add-on, device-driven enterprise, but one motivated by teaching and learning needs and in which multiple technology choices are readily available to teachers and students both within and beyond the classroom.

The Computing Research Association (CRA) was formed in 1972 as the Computer Science Board (CSB), which provided a forum for the chairs of Ph.D.-granting computer science departments to discuss issues and share information (CRA, 2009). Since 1989, women have never accounted for more than 24% of the computer science faculty at any given rank (e.g., assistant, associate, or full professor). Currently, women represent 21.7%, 15.4%, and 11.7% of computer science faculty at the assistant, associate, and full professor ranks, respectively. Women have been as much as 24% of the Ph.D. graduates in computing in a single year. Since 1998, African Americans have never accounted for more than 2.0%, 1.4%, and 0.7% of the assistant, associate, and full professors, respectively, in computer science. Furthermore, African Americans have never accounted for more than 2% of the Ph.D. graduates in computer science in a single year over that same time period. It appears women and African Americans overall are underrepresented among the ranks of computer science faculty, but to what extent?

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

Research studies indicate African American males face multiple and reinforcing obstacles by choosing to pursue science, technology, engineering, and mathematics (STEM) related majors and professions. Though participation in STEM fields has increased, African American males remain underrepresented in STEM academic programs and occupations as a whole, and in the computing sciences specifically. In the STEM field of computing sciences, isolation, inadequate advisement, among other complex factors, perpetuate the underrepresentation and low persistence of African American males in academic programs. Utilizing viable social identity and communities of practice as theoretical underpinnings, this qualitative study into the lives of aspiring and current African American male computer scientists produced findings that illuminate the significance of what I call STEMfluences, or social interactions that promote socialization, STEM identity, confidence, and success in science, technology, engineering, and mathematics related disciplines like the computing sciences, and promote persistence through degree attainment in homogeneous, unwelcoming STEM academic environments. These STEMfluences are social constructs that include positive peer interactions and modeling, parental and familial nurturing, and multifaceted mentorship.

Purpose: The study aims to explore the wider acceptance of blockchain technology and growing faith in this technology among all business domains to mitigate the chances of fraud in various sectors.

Design/Methodology/Approach: The authors focus on studies conducted during 2015–2022 using keywords such as blockchain, fraud detection and financial domain for Systematic Literature Review (SLR). The SLR approach entails two databases, namely, Scopus and IEEE Xplore, to seek relevant articles covering the effectiveness of blockchain technology in controlling financial fraud.

Findings: The findings of the research explored different types of business domains using blockchains in detecting fraud. They examined their effectiveness in other sectors such as insurance, banks, online transactions, real estate, credit card usage, etc.

Practical Implications: The results of this research highlight (1) the real-life applications of blockchain technology to secure the gateway for online transactions; (2) people from diverse backgrounds with different business objectives can strongly rely on blockchains to prevent fraud.

Originality/Value: The SLR conducted in this study assists in the identification of future avenues with practical implications, making researchers aware of the work so far carried out for checking the effectiveness of blockchain; however, it does not ignore the possibility of zero to less effectiveness in some businesses which is yet to be explored.