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This article is the second in a series of three articles about academic fraud. The first, "Diploma Mills and Counterfeit Operations, Part 1" by Allen Ezell College and University v94 n3 Sum 2019 described the history and growth of diploma mills and counterfeit operations. This article provides an in-depth look at the operations of the world's largest diploma mill, Axact, Ltd., a criminal enterprise with more than 8 million customers worldwide that has generated several billion dollars. The third article will detail how to identify diploma mills and Axact websites. [For "Diploma Mills and Counterfeit Operations, Part 1," see EJ1225180.]

The main aim of this paper is to assess the level of knowledge, attitude and practice of university teachers regarding plagiarism in Bangladesh. An online questionnaire consisted of 20 knowledge questions, 23 attitude items, and 18 practice questions was created using Google Forms. The link to the questionnaire was sent via email to university teachers. The total correct answers for knowledge and practice questions, and the total attitude score were converted to percentile scores and categorized accordingly as poor (< mean -- 1 SD), average (mean ± 1 SD), and good (> mean + 1 SD). Bivariate analyses were conducted to compare the total knowledge, attitude and practice scores based on demographic and academic variables. Multiple linear regressions were used to identify the association between knowledge, attitude and practice scores, and other covariates. The findings revealed an average level of knowledge, attitude and practice regarding plagiarism among the majority of university teachers. The knowledge, attitude and practice scores were significantly higher for teachers who attended academic writing workshops compared to those who did not attend such events. Demographic and academic variables did not impact knowledge and attitude scores. However, the number of papers published in the last two years and their indexing in Web of Science (WoS) or Scopus significantly impacted attitude and knowledge scores. The multiple regression analyses showed that the practice score was significantly associated with age, highest education, and knowledge.

Copyright of China Brewing is the property of China Brewing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Brewing is the property of China Brewing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper reports the results of a comparative assessment concerned with the effectiveness of identifying the basic forms of partial discharges (PD) measured by the acoustic emission technique (AE), carried out by application of selected machine learning methods. As part of the re-search, the identification involved AE signals registered in laboratory conditions for eight basic classes of PDs that occur in paper-oil insulation systems of high-voltage power equipment. On the basis of acoustic signals emitted by PDs and by application of the frequency descriptor that took the form of a signal power density spectrum (PSD), the assessment involved the possibility of identifying individual types of PD by the analyzed classification algorithms. As part of the research, the results obtained with the use of five independent classification mechanisms were analyzed, namely: k-Nearest Neighbors method (kNN), Naive Bayes Classification, Support Vector Machine (SVM), Random Forests and Probabilistic Neural Network (PNN). The best results were achieved using the SVM classification tuned with polynomial core, which obtained 100% accuracy. Similar results were achieved with the kNN classifier. Random Forests and Naïve Bayes obtained high accuracy over 97%. Throughout the study, identification algorithms with the highest effectiveness in identifying specific forms of PD were established.

The provision of applied behavior analytic (ABA) services is a highly efficacious intervention approach most often used to improve the lives of individuals with autism spectrum disorder (ASD)/Autistics. Given the advancement of the field, more nuanced skill sets of behavior analysts, such as compassionate care skills, are emerging as the focus of intervention as measurable, observable, and essential. As the field progresses, the identification, refinement, and assessment of more nuanced skills become crucial to the success of our interventions. Leading with a compassionate approach that balances habilitation with client happiness, assent, and engagement is of the utmost importance. This paper discusses the current trend in existing compassionate care literature and how those evaluations may potentially be extended to direct interventionists and Registered Behavior Technicians. In this paper, we argue that a behavioral framework should be used to conceptualize, train, and evaluate the demonstration of these skills in front line ABA practitioners.

The purpose of this paper is to share insights gained from the discovery, design, and delivery phases of creating a three-tiered model of non-academic learning support in open, distance, and flexible learning (ODFL): "Learner Engagement and Success Services (LESS)", at Open Polytechnic | Te Pukenga, New Zealand. Presented as a case study, this paper discusses the early vision of the model, examines current understanding relating to learner support and engagement, and describes the successes and challenges faced in bringing this vision to reality. It outlines challenges relating to the emergent use of learning analytics (LA) in identifying "exception" learners, the contestability of ethical use and choices of data, and the rapid evolution and devolution of commercially available communication tools. The tiered model is described as a scalable blend of human and technology-enabled interventions and services, underpinned by the values of agency and equity. The components of this model could be replicated, and its success is measurable.

In today's data-sharing paradigm, personal data has become a valuable resource that intensifies the risk of unauthorized access and data breach. Increased data mining techniques used to analyze big data have posed significant risks to data security and privacy. Consequently, data breaches are a significant threat to individual privacy. Privacy is a multifaceted concept covering many areas, including the right to access, erasure, and rectify personal data. This paper explores the legal aspects of privacy harm and how they transform into legal action. Privacy harm is the negative impact to an individual as a result of the unauthorized release, gathering, distillation, or expropriation of personal information. Privacy Enhancing Technologies (PETs) emerged as a solution to address data privacy issues and minimize the risk of privacy harm. It is essential to implement privacy enhancement mechanisms to protect Personally Identifiable Information (PII) from unlawful use or access. FIPPs (Fair Information Practice Principles), based on the 1973 Code of Fair Information Practice (CFIP), and the Organization for Economic Cooperation and Development (OECD), are a collection of widely accepted, influential US codes that agencies use when evaluating information systems, processes, programs, and activities affecting individual privacy. Regulatory compliance places a responsibility on organizations to follow best practices to ensure the protection of individual data privacy rights. This paper will focus on FIPPs, relevance to US state privacy laws, their influence on OECD, and reference to the EU General Data Processing Regulation. (GDPR).

Cheating behaviors have been construed as a continuing and somewhat vexing issue for academic institutions as they increasingly conduct educational processes online and impose metrics on instructional evaluation. Research, development, and implementation initiatives on cheating detection have gained new dimensions in the advent of artificial intelligence (AI) applications; they have also engendered special challenges in terms of their social, ethical, and cultural implications. An assortment of commercial cheating-detection systems have been injected into educational contexts with little input on the part of relevant stakeholders. This paper expands several specific cases of how systems for the detection of cheating have recently been implemented in higher education institutions in the US and UK. It investigates how such vehicles as wearable technologies, eye scanning, and keystroke capturing are being used to collect the data used for anti-cheating initiatives, often involving systems that have not gone through rigorous testing and evaluation for their validity and potential educational impacts. The paper discusses accountability- and policy-related issues concerning the outsourcing of cheating detection in institutional settings in the light of these emerging technological practices as well as student resistance against the systems involved. The cheating-detection practices can place students in a disempowered, asymmetrical position that is often at substantial variance with their cultural backgrounds.

What is bias? A review of the library literature reveals no attempts to define the concept. Nor does it reveal systematic attempts to develop interventions that teach the identification and evaluation of bias. Current pedagogical approaches (checklists and bias charts) tend to assume a self-evident definition that categorises bias as unquestioningly bad and disqualifying. Current approaches, however, fail to recognise the cognitive complexity of decoding bias within a source. A decoding process includes identifying the type of bias, determining an objective baseline, recognising biased features, and analysing bias's impact. Based on work done from several fields--argumentation theory, media bias, media literacy, and history education--this paper proposes an operational definition of bias and a practical framework for conceptualising a process to identify and evaluate bias. This paper will explore the limitations of this framework, as well as existing source evaluation paradigms. If librarians want to prepare individuals to participate in a post-truth society, where disinformation weaponises bias by appealing to emotions and beliefs rather than facts, an inclusive and nuanced conception of bias is a necessary component of library instruction.

Vertebrate species knowledge is one of the predictors of pupils' understanding of biodiversity. This study will describe vertebrate species identification skills of pupils from the Czech Republic. Altogether, the research tool included 30 vertebrate species, out of which five were fish, three were amphibians, three were reptiles, nine were birds, and 10 were mammals. The research tool consisted of 22 pictures, three footprints, two silhouettes, and three sounds. In addition, we evaluated the influence of variable factors on vertebrate species knowledge, which the research tool also contained. The paper will describe the percentage success rate of vertebrate species knowledge of 1537 respondents. On average pupils could identify nearly 15 species. The results showed that differences in species knowledge were statistically significant mostly by pupils' expectations (self-efficacy) or their results and educational level. In general, younger students identified animals worse than students of higher levels of education. Moreover, significant differences were confirmed between the five classes of vertebrates. Mammals were the best-identified class, followed by amphibians and fish; reptiles and birds were the least correctly identified. While educational level played a significant role in identification skills, the results revealed that the pupils' hometown did not play a significant role.

In reaction papers, Richard H. Coop, an educational psychologist, discusses six themes evident in papers on gifted education; B. J. Cox argues that systems theory is a valuable addition to education of identified and potentially gifted students; and Gary D. Fenstermacher argues for specification of educational entitlements of any learner before special provisions for gifted learners can be justified. (Author/RH)

Deploying a Lacanian conceptual framework, this article interrogates the psychoanalytic underpinnings of Paulo Freire's dialogical method of critical pedagogy. The paper advances the claim that the transformative efficacy of Freirean dialogue is rooted in its unique ability to confront and engage the repressed element of trauma, or what Lacan calls the "real." The author suggests that the locus of trauma stands as the elusive, yet central and constitutive axis around which Freire's dialogical engagement turns. Following psychoanalysis' attention to biography, the paper first examines how Freire's personal experience of exile informs his philosophical orientation to being, politics, and education. Turning to a specific classroom event Freire outlines in "Pedagogy of Hope," the paper then develops a new interpretation of Freire's idea of naming, and through Lacanian analysis, extends Freire's insight on the relationship between psyche, ideology, and social antagonism. Pushing the idea of class subjectivity in Freire beyond its familiar determinants (namely as an 'identity'), the paper resituates the notion of radical subjectivity in critical pedagogy as the effect of a traumatic loss or gap in the sociosymbolic order of being. The author argues that the 'naming event' in Freire is formally rooted in an encounter with this unconscious gap. To conclude, the paper offers critical educators some new points of departure for conceptualizing the transformative labor of problem-posing dialogue.

This paper aims to identify and explore the learning objectives outlining the core knowledge for forensic accounting education. Bloom's taxonomy is used to outline and analyze the core knowledge for forensic accounting education (e.g. fraud examination, litigation support, business valuation, and IT forensic accounting) in 15 Australian universities that provide forensic accounting courses. Furthermore, this paper applies a qualitative method to forensic accounting curricula, handbooks, and syllabi. These educational documents were retrieved from Australian universities. The findings report learning objectives under core content knowledge distributed over Bloom's cognitive areas. This study also provides a unified set of learning objectives to harmonize forensic accounting courses' teaching and learning processes. The most promising contribution of the paper is to provide a set of learning objectives in all forensic accounting subtopics. The main implications of this paper are relevant to forensic accounting educators, students, standard setters, researchers, regulators, and curricula designers.

Background: In the past few years there has been a growing interest in the employment of verbal productions as digital biomarkers, namely objective, quantifiable behavioural data that can be collected and measured by means of digital devices, allowing for a low-cost pathology detection, classification and monitoring. Numerous research papers have been published on the automatic detection of subtle verbal alteration, starting from written texts, raw speech recordings and transcripts, and such linguistic analysis has been singled out as a cost-effective method for diagnosing dementia and other medical conditions common among elderly patients (e.g., cognitive dysfunctions associated with metabolic disorders, dysarthria). Aims: To provide a critical appraisal and synthesis of evidence concerning the application of natural language processing (NLP) techniques for clinical purposes in the geriatric population. In particular, we discuss the state of the art on studying language in healthy and pathological ageing, focusing on the latest research efforts to build non-intrusive language-based tools for the early identification of cognitive frailty due to dementia. We also discuss some challenges and open problems raised by this approach. Methods & Procedures: We performed a scoping review to examine emerging evidence about this novel domain. Potentially relevant studies published up to November 2021 were identified from the databases of MEDLINE, Cochrane and Web of Science. We also browsed the proceedings of leading international conferences (e.g., ACL, COLING, Interspeech, LREC) from 2017 to 2021, and checked the reference lists of relevant studies and reviews. Main Contribution: The paper provides an introductory, but complete, overview of the application of NLP techniques for studying language disruption due to dementia. We also suggest that this technique can be fruitfully applied to other medical conditions (e.g., cognitive dysfunctions associated with dysarthria, cerebrovascular disease and mood disorders). Conclusions & Implications: Despite several critical points need to be addressed by the scientific community, a growing body of empirical evidence shows that NLP techniques can represent a promising tool for studying language changes in pathological aging, with a high potential to lead a significant shift in clinical practice.