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Purpose – To highlight ways to overcome challenges in conducting authentic assessments and using data effectively in program planning.

Approach – To help teachers investigate the definition and purpose of assessments available for use in today's diverse classrooms, and use assessment results to inform instruction.

Practical implications – A school team analysis framework focused on teacher collaboration when conducting evaluations of districts' reading programs, a data use cycle, and a reflective questionnaire are provided for professional development.

Social implications – Social justice and differentiated instruction require balanced assessment methods and portfolio use as an implementable and manageable method to document student progress.

Originality/value of paper – This chapter engages teachers in the reality that they can be the driving force behind assessments for learning in their classrooms, schools, and districts.

Purpose – For decades, archivists have been appraising, preserving, and providing access to digital records by using archival theories and methods developed for paper records. However, production and consumption of digital records are informed by social and industrial trends and by computer and data methods that show little or no connection to archival methods. The purpose of this chapter is to reexamine the theories and methods that dominate records practices. The authors believe that this situation calls for a formal articulation of a new transdiscipline, which they call computational archival science (CAS).

Design/Methodology/Approach – After making a case for CAS, the authors present motivating case studies: (1) evolutionary prototyping and computational linguistics; (2) graph analytics, digital humanities, and archival representation; (3) computational finding aids; (4) digital curation; (5) public engagement with (archival) content; (6) authenticity; (7) confluences between archival theory and computational methods: cyberinfrastructure and the records continuum; and (8) spatial and temporal analytics.

Findings – Each case study includes suggestions for incorporating CAS into Master of Library Science (MLS) education in order to better address the needs of today’s MLS graduates looking to employ “traditional” archival principles in conjunction with computational methods. A CAS agenda will require transdisciplinary iSchools and extensive hands-on experience working with cyberinfrastructure to implement archival functions.

Originality/Value – We expect that archival practice will benefit from the development of new tools and techniques that support records and archives professionals in managing and preserving records at scale and that, conversely, computational science will benefit from the consideration and application of archival principles.

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.

This chapter explores what managers in the library and information science workplace can do to keep stress and burnout levels low. The literature on stress and burnout in human services, or the helping professions, is surveyed and the differences between the two phenomena are explained. Research is clear that keeping stress levels low and burnout at bay in the workplace benefits both employees and the organization. Even so, managers are given little training on how to identify and deal with stress and often fail to notice that their employees are chronically stressed. When managers become aware that they do have employees who are seriously stressed or burned out, they are often unsure whether they should address the problem and how to handle it. The author explains the differences between stress and burnout and clarifies how managers can minimize their negative impact by monitoring six areas in which workers are most likely to experience them: (1) the demands of the job which include the quantity of work and the knowledge required to perform; (2) the amount of control employees are permitted to exercise in the workplace; (3) the amount of the social support employee’s feel they have from managers and colleagues; (4) the quality of workplace relationships; (5) the clarity of one’s role on the job; and (6) support and honest communication during times of change. The practical implication of this information aimed at managers is to help them create a better workplace and mentally and physically healthier staff members.

With a push toward inclusion of students with disabilities in the general education classroom, students who are d/Deaf or hard of hearing (DHH) are exposed to greater educational opportunities. Given the largely verbal nature of traditional classroom instruction, there has been a need for advancements in technology to provide more access to the material covered by teachers and in class discussions. In addition, the COVID-19 pandemic and the transition to virtual learning also brought to light many additional challenges for the DHH population, which can, in part, be lessened through technological advancements.