The Six Sigma (6σ) method is used to improve and optimize an organization’s products, services, and processes by constantly decreasing defects. In this paper we try implementing the 6σ methodology in Academia. The challenges and actuality of implementation of 6σ methodologies successfully in Academia are immense. However, the benefits of applying 6σ are equally great. The objective of this paper is to understand the evolution, benefits, and challenges of 6σ practices in academia through a case study of Presidency University situated in Bangalore, Karnataka, India. The problem statement in the paper is the poor performance of students in academics and by using the DMAIC approach of 6σ we try to identify and understand the causes and set out to improve them so the students can have better placement and academic opportunities. This paper is also an attempt to show the readers about the wide application of 6σ in fields other than manufacturing and how it can be beneficial.

The goal of this narrative review was to identify, synthesize and summarize research providing both institutional and personal recommendations for female leaders who aspire to hold leadership roles in academia. Existing evidence regarding factors that influence females acquiring leadership positions was first gathered and examined, noting recurring issues that have been identified as inimical to women assuming leadership roles. Recent research studies and statistical trends were then investigated, tracking and reporting on the increase in female leadership in academia that has occurred over recent years. Quantitative and qualitative literature focusing on successful strategies to support females aspiring to academic leadership positions were then elaborated upon, with specific factors identified in order to deepen understanding of shifts that may help to account for the rise of females in academic leadership roles. The recommendations resulting from the findings of this narrative review could be used to scaffold female success in the pursuit of academic leadership roles and offer insights for university executives and administrators on methods of more effectively supporting an increase in female leaders in academia.

Artificial intelligence (AI)-based models hold the potential to transform higher education if adopted properly and ethically. A prime example is ChatGPT with earlier studies indicating its widespread adoption in academia and by university students. The current study aimed to identify the factors influencing the attitude towards ChatGPT and its usage among university students in Arab countries. A previous survey instrument termed Technology Acceptance Model Edited to Assess ChatGPT Adoption (TAME-ChatGPT) was administered online using a convenience-based approach among the contacts of the authors. Confirmatory factor analysis (CFA) for the survey constructs was done using root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and Tucker-Lewis index (TLI). The final study sample comprised a total of 2240 participant divided as follows: Iraq (n=736, 32.9%), Kuwait (n=582, 26.0%), Egypt (n=417, 18.6%), Lebanon (n=263, 11.7%), and Jordan (n=242, 10.8%). A total of 1048 respondents heard of ChatGPT before the study (46.8%), of which 551 used ChatGPT (52.6%). The mean scores of TAME-ChatGPT constructs showed that the ease of ChatGPT use, positive attitude towards technology and social influence, higher perceived usefulness, the influence of behavioral/cognitive factors, low perceived risks and low anxiety were the determinants of positive attitude to ChatGPT and its use. For both the attitude and usage scales of TAME-ChatGPT, CFA collectively yielded satisfactory fit indices as indicated by low RMSEA and SRMR together with high CFI and TLI. Multivariate analysis showed that attitude to ChatGPT use was significantly influenced by country of residence, age, university type, and the latest grade point average of the students. The current study confirmed the validity of TAME-ChatGPT as a survey instrument to assess the possible determinants of ChatGPT use among university students in Arab countries. The study findings highlighted that successful adoption of ChatGPT in higher education could be dependent on perceived ease of use, usefulness, positive attitudes to technology, social influence, behavioral/cognitive factors, lower anxiety, and minimal perceived risks. The utility of ChatGPT in higher education requires policies that should be tailored for various settings, considering the differences observed in attitude towards ChatGPT among participating students in this study.

COVID-19 pandemic instigated a digital revolution in academia and higher education. Social distancing, months-long quarantine, as economic shutdown will help the majority of people working in academia and higher education not only to complete their personal transition to the fully functional and operational online tuition, but also to understand that online defences, online entrance and final exams, as well as online academic jobs are as effective and meaningful as those conducted “in real life”. Due to the crisis induced by the coronavirus epidemic, innovations in academia and higher education that would have normally taken several years due to the various contradictory administrative regulations are now introduced promptly in a matter of days. This is a clear example of the Schumpeterian ‘creative destruction’ in making that will forever change the status quo in academia and higher education.

The doctoral defense is an important step towards obtaining the doctoral degree. As such, preparing for the event is necessary. Anecdotal evidence highlights that there is a wide variety of ways in which doctoral candidates prepare for the defense. In this work, I want to explore if there is a relation between the way in which a doctoral candidate prepares for the defense and two important aspects of the defense: the outcome of the defense, and the student perception during and after the defense. For this purpose, I first reviewed the literature on the topic of the preparation for the doctoral defense. Then, I carried out an international survey on the doctoral defense and analyzed the data of the 204 completed surveys with respect to the preparation for the defense using quantitative and qualitative methods. The methods I used included the statistical tests of the correlation between on one hand the preparation and on the other hand the defense outcome and student perception. I used inductive thematic analysis of the open-ended survey questions to gain deeper insight in the way candidates prepared for their defense. I found that candidates most often prepare by making their presentation, reading their thesis, and practicing. The most effective measure is the mock defense, followed by a preparatory course. Reading blogs, books, and chapters is a less effective preparation measure. The conclusion of this work is that doctoral candidates need to understand the format of their defense in order to be able to prepare properly, and that universities should explore either individual pathways to the defense or pilots using a mock defense and/or preparatory course to give their doctoral candidates the necessary tools to prepare for their doctoral defense.

To mitigate the COVID-19 infection, many world governments endorsed the cessation of non-essential activities, such as the school attendance. Thereby, forcing the evolution of the teaching model to the virtual classroom. In the present work we show the application of a modified version of the adapted COVID-19 stress scales (ACSS) which also included teaching anxiety and preparedness, and resilience for academic professionals in Mexico, during the unprecedented transformation of the education system undergone in the COVID-19 quarantine. Most of the studied variables: gender, age, academic degree, household occupants, having a disease, teaching level, teaching mode, work hours, resilience, teaching anxiety and preparedness, and fear of being an asymptomatic patient (FOBAP), showed significant statistical correlation between each other (p<0.050) and to the 6 areas of the ACSS (danger, contamination, social economical, xenophobia, traumatic stress and compulsive checking). Our results further showed that the perceived stress and anxiety, fell into the category of absent to mild with only the danger section of the ACSS falling into the moderate category. Finally, resilience generated throughout the quarantine, seems to be a predictor of the adaptation the academic professional has undergone to cope with stress.

Machine learning is an established and frequently used technique in industry and academia but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners have a need for guidance throughout the life cycle of a machine learning application to meet business expectations. We therefore propose a process model for the development of machine learning applications, that covers six phases from defining the scope to maintaining the deployed machine learning application. The first phase combines business and data understanding as data availability oftentimes affects the feasibility of the project. The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications, as the risk of model degradation in a changing environment is eminent. With each task of the process, we propose quality assurance methodology that is suitable to address challenges in machine learning development that we identify in form of risks. The methodology is drawn from practical experience and scientific literature and has proven to be general and stable. The process model expands on CRISP-DM, a data mining process model that enjoys strong industry support but lacks to address machine learning specific tasks. Our work proposes an industry and application neutral process model tailored for machine learning applications with focus on technical tasks for quality assurance.

The doctoral defense, which is an essential requirement for the doctoral degree, is considered to have three dimensions: the scholarly dimension, the emotional (affective) dimension, and the cultural dimension. In this work, I explore the link between sociodemographic factors and students’ perception of the doctoral defense. In particular, I focus on gender, ethnicity, and age at time of defense, as well as current position, and field of study. To address the influence of these aspects on the affective dimension of the doctoral defense, I first reviewed the literature on these socio-demographic aspects as well as the affective dimension of the defense. I then carried out an international survey on doctoral defenses, defense formats, and students’ perceptions and analyzed the 204 completed surveys for this study using quantitative and qualitative methods. The analysis included cross-correlations between students’ perceptions and the studied sociodemographic aspects. The main results of these analyses are that gender affects various aspects of the students’ perception of the doctoral defense and long-term perception, and that female candidates experience more issues with their committee. Ethnicity is important as well, although the participations of non-white respondents in this survey was limited. The influence of age at the defense is limited, and only for the youngest and oldest age groups I observe some differences in perception. There is no relation between current position and perception of the candidates during the defense. Finally, field of study is correlated for various aspects of student perception, committee issues, and long-term perception. The conclusion of this work is that socio-demographic aspects, and in particular gender, ethnicity, and field of study, influence how doctoral candidates experience their defense.