Aim/Purpose: The purpose of this paper is to illuminate the learning that happens in assuming a supervisee’s role during the postgraduate study. Background: The facilitators and barriers students encountered while pursuing postgraduate studies, strategies to achieve success in postgraduate studies, and how to decrease attrition rates of students, have been sufficiently explored in literature. However, there is little written about the personal and professional impact on students when they are being supervised to complete their postgraduate studies. Methodology: Autoethnographic method of deep reflection was used to examine the learning that transpired from the supervisee’s perspective. Two lecturers (a Senior Lecturer in Nursing and an Aboriginal Tutor) focused on their postgraduate journeys as supervisees, respectively, with over 30 years of study experience between them, in Australia and abroad. Contribution: Future postgraduate students, researchers, would-be supervisors and experienced supervisors could learn from the reflections of the authors’ postgraduate experiences. Findings: Four themes surfaced, and these were Eureka moments, Critical friend(s), Supervisory relationship, and Transformative learning. The authors highlighted the significance of a supervisory relationship which is key to negotiating the journey with the supervisor. Essential for these students also were insights on finding the path as well as the destination and the transformative aspects that happened as a necessary part of the journey. Conclusion. The postgraduate journey has taught them many lessons, the most profound of which was the change in perspective and attitude in the process of being and becoming. Personal and professional transformative learning did occur. At its deepest level, the authors’ reflections resulted in self-actualization and a rediscovery of their more authentic selves. Recommendations for Practitioners: This article highlights the importance of the supervisory relationship that must be negotiated to ensure the success of the candidate. Reflections of the transformation are recommended to support the students further. Recommendation for Researchers: Quality supervision can make a significant influence on the progress of students. Further research on the supervisory relationship is recommended. Impact on Society: The support in terms of supervision to ensure postgraduate students’ success is essential. Postgraduate students contribute to the human, social, professional, intellectual, and economic capital of universities and nations globally. Future Research: Further reflections of the transformative learning will advance the understanding of the personal and professional changes that occur with postgraduate supervision.

Aim/Purpose: In the midst of COVID-19, classes are transitioned online. Instructors and students scramble for ways to adapt to this change. This paper shares an experience of one instructor in how he has gone through the adaptation. Background: This section provides a contextual background of online teaching. The instructor made use of M-learning to support his online teaching and adopted the UTAUT model to guide his interpretation of the phenomenon. Methodology: The methodology used in this study is action research through participant-observation. The instructor was able to look at his own practice in teaching and reflect on it through the lens of the UTAUT conceptual frame-work. Contribution: The results helped the instructor improve his practice and better under-stand his educational situations. From the narrative, others can adapt and use various apps and platforms as well as follow the processes to teach online. Findings: This study shares an experience of how one instructor had figured out ways to use M-learning tools to make the online teaching and learning more feasible and engaging. It points out ways that the instructor could connect meaningfully with his students through the various apps and plat-forms. Recommendations for Practitioners: The social aspects of learning are indispensable whether it takes place in person or online. Students need opportunities to connect socially; there-fore, instructors should try to optimize technology use to create such opportunities for conducive learning. Recommendations for Researchers: Quantitative studies using surveys or quasi-experiment methods should be the next step. Validated inventories with measures can be adopted and used in these studies. Statistical analysis can be applied to derive more objective findings. Impact on Society: Online teaching emerges as a solution for the delivery of education in the midst of COVID-19, but more studies are needed to overcome obstacles and barriers to both instructors and students. Future Research: Future studies should look at the obstacles that instructors encounter and the barriers with technology access and inequalities that students face in online classes.

Aim/Purpose: This study examined learning during the COVID-19 crisis, as perceived by preservice teachers at the time of their academic studies and their student teaching experience. Background: The COVID-19 crisis is unexpected. On one hand, it disrupted learning in all learning frameworks, on the other, it may create a change in learning characteristics even after the end of the crisis. This study examined the pro-ductive, challenging, and thwarting factors that preservice teachers encountered during their studies and in the course of their student teaching during the COVID-19 period, from the perspective of preservice teachers. Methodology: The study involved 287 students studying at teacher training institutions in Israel. The preservice teachers were studying online, and in addition experienced online teaching of students in schools, guided by their own teacher. The study used a mixed method. The questionnaire included closed and open questions. The data were collected in 2020. Contribution: Identifying the affecting factors may deepen the understanding of online learning/teaching and assist in the optimal implementation of online learning. Findings: Online learning experience. We found that some of the lessons at institutions of higher learning were delivered in the format of online lectures. Many pre-service teachers had difficulty sitting in front of a computer for many hours—“Zoom fatigue.” Preservice teachers who had difficulty self-regulating and self-mobilizing for study, experienced accumulating loads, which caused them feelings of stress and anxiety. The word count indicated that the words that appeared most often were “load” and “stress.” Some preservice teachers wrote that collaborating in forums with others made it easier for them. Some suggested diversifying by digital means, incorporating asynchronous units and illustrative films, and easing up on online lectures, as a substitute for face-to-face lectures. Online teaching experience in schools. The preservice teachers' descriptions show that in lessons taught in the format of lectures and communication of content, there were discipline problems and non-learning. According to the preservice teachers, discipline problems stemmed from difficulties concentrating, physical distance, load, and failure to address the students' difficulties. Recommendations for Practitioners: In choosing schools for student teaching, it is recommended to reach an understanding with the school about the online learning policy and organization. It is important to hold synchronous sessions in small groups of 5 to 10 students. The sessions should focus on the mental wellbeing of the students, and on the acquisition of knowledge and skills. Students should be prepared for participation in asynchronous digital lessons, which should be produced by professionals. It should be remembered that the change of medium from face-to-face to online learning also changes the familiar learning environment for all parties and requires modifying the ways of teaching. Recommendations for Researchers: A change in the learning medium also requires a change in the definition of objectives and goals expected of each party—students, teachers, and parents. All parties must learn to view online learning as a method that enables empowerment and the application of 21st century skills. Impact on Society: Teachers' ability to deploy 21st century skills in an online environment de-pends largely on their experience, knowledge, skills, and attitude toward these skills. Future Research: This study examined the issue from the perspective of preservice teachers. It is recommended to examine it also from the perspective of teachers and students.

Aim/Purpose: The aim of this study is to explore the role of service-learning in Information Systems (IS) education. Background: While the use of modern technologies presents many operational benefits, such as the lowering of the costs, it may also aggravate social-economic is-sues. IS professionals should account for these issues as well as exhibit the skills demanded by modern-day employers. Hence, why there is a need for IS educators to adopt a new pedagogy that supports the development of more holistic and socially responsible IS graduates. Methodology: In this qualitative exploratory case study, two IS service-learning courses at a South African university were studied. Interviews, course evaluations, and reflection essays were analyzed to gain insight into the implications that service-learning may have for students. Contribution: This study contributes to IS education research by advancing discussions on the role of service-learning in providing learning outcomes such as the development of important skills in IS, civic-mindedness, and active participation in society. Findings: The findings showed that the courses had different implications for students developing skills that are important in IS and becoming civic-minded due to the variation in their design and implementation. Recommendations for Practitioners: It is recommended that IS educators present their courses in the form of service-learning with a careful selection of readings, projects, and reflection activities. Recommendations for Researchers: IS education researchers are advised to conduct longitudinal studies to gain more insight into the long-term implications that service-learning may have for IS students. Impact on Society: This paper provides insight into how IS students may gain social agency and a better understanding of their role in society. Future Research: It is recommended that future research focus on mediating factors and the implications that service-learning may have for IS students in the long-term.

Aim/Purpose: In the United States, the Centers for Disease Control and Prevention (CDC) tracks the disease activity using data collected from medical practice's on a weekly basis. Collection of data by CDC from medical practices on a weekly basis leads to a lag time of approximately 2 weeks before any viable action can be planned. The 2-week delay problem was addressed in the study by creating machine learning models to predict flu outbreak. Background: The 2-week delay problem was addressed in the study by correlation of the flu trends identified from Twitter data and official flu data from the Centers for Disease Control and Prevention (CDC) in combination with creating a machine learning model using both data sources to predict flu outbreak. Methodology: A quantitative correlational study was performed using a quasi-experimental design. Flu trends from the CDC portal and tweets with mention of flu and influenza from the state of Georgia were used over a period of 22 weeks from December 29, 2019 to May 30, 2020 for this study. Contribution: This research contributed to the body of knowledge by using a simple bag-of-word method for sentiment analysis followed by the combination of CDC and Twitter data to generate a flu prediction model with higher accuracy than using CDC data only. Findings: The study found that (a) there is no correlation between official flu data from CDC and tweets with mention of flu and (b) there is an improvement in the performance of a flu forecasting model based on a machine learning algorithm using both official flu data from CDC and tweets with mention of flu. Recommendations for Practitioners: In this study, it was found that there was no correlation between the official flu data from the CDC and the count of tweets with mention of flu, which is why tweets alone should be used with caution to predict a flu out-break. Based on the findings of this study, social media data can be used as an additional variable to improve the accuracy of flu prediction models. It is also found that fourth order polynomial and support vector regression models offered the best accuracy of flu prediction models. Recommendations for Researchers: Open-source data, such as Twitter feed, can be mined for useful intelligence benefiting society. Machine learning-based prediction models can be improved by adding open-source data to the primary data set. Impact on Society: Key implication of this study for practitioners in the field were to use social media postings to identify neighborhoods and geographic locations affected by seasonal outbreak, such as influenza, which would help reduce the spread of the disease and ultimately lead to containment. Based on the findings of this study, social media data will help health authorities in detecting seasonal outbreaks earlier than just using official CDC channels of disease and illness reporting from physicians and labs thus, empowering health officials to plan their responses swiftly and allocate their resources optimally for the most affected areas. Future Research: A future researcher could use more complex deep learning algorithms, such as Artificial Neural Networks and Recurrent Neural Networks, to evaluate the accuracy of flu outbreak prediction models as compared to the regression models used in this study. A future researcher could apply other sentiment analysis techniques, such as natural language processing and deep learning techniques, to identify context-sensitive emotion, concept extraction, and sarcasm detection for the identification of self-reporting flu tweets. A future researcher could expand the scope by continuously collecting tweets on a public cloud and applying big data applications, such as Hadoop and MapReduce, to perform predictions using several months of historical data or even years for a larger geographical area.

Aim/Purpose: The purpose of this paper is to address the issue of gaps in students’ knowledge at the time they enter a comprehensive Information Systems cap-stone course. This problem of knowledge gaps was exacerbated by the forced remote learning and isolation caused by the COVID-19 pandemic. The aim was to find a technique that would identify and fill those gaps. Ideally, the method would also reinforce the students’ interpersonal soft skills. Background: Many universities have a capstone course where students may apply their knowledge from the curriculum to a project, and they are evaluated on their retention of knowledge from the core classes. Over the past two years, students have experienced course interruptions and modifications due to the pandemic, resulting in learning gaps on topics covered in the core courses. Depending on the project’s scope and curriculum, this may prevent students from conversing on many essential concepts during the capstone course. By requiring students to create “Teach Back” tutorials on materials from their core courses, faculty may ensure that the key concepts are discussed multiple times within the curriculum. Methodology: We present a case study to identify key concepts and compare cohort results before and after implementation. Contribution: A process for identifying potential knowledge gaps is identified, and a method to repeatedly expose students to concepts is introduced. Findings: There were improvements to the overall capstone scores after the tutorial implementation. While many factors influence changes in scores across cohorts, the initial findings are promising, supporting the concept that teaching back helps to close knowledge gaps. Recommendations for Practitioners: Faculty should collaborate to identify knowledge areas that need to be rein-forced later in their students’ academic careers. Teaching back essential concepts that may not be prioritized in implementing a capstone project ensures a repeated exposure to the identified concepts. Recommendations for Researchers: There needs to be a priority to teach students to be lifelong learners and to teach the skills needed to share their knowledge with future coworkers. There needs to be more research into a pedagogy that builds these essential soft skills within our curriculum. Finally, research into alumni feedback on course topics and pedagogy is needed. Impact on Society: Introducing pedagogy that improves both knowledge and soft skills, this re-search looks to build individuals who will learn independently and be able to communicate with and improve others. Future Research: There needs to be additional research to study the changes in technical knowledge before and after Teach Back, the consequences of elective sequencing, the consideration of elective versus required courses, and the use of Teach Back to capture student knowledge gained from completing diverse electives prior to the capstone course.

Aim/Purpose. The most crucial aspects of teaching a foreign language to more advanced learners are building an awareness of discourse modes, how to regulate discourse, and the pragmatic properties of discourse components. However, in different languages, the connections and structure of discourse are ensured by different linguistic means which makes matters complicated for the learner. Background. By uncovering regularities in a foreign language and comparing them with patterns in one’s own tongue, the corpus research method offers the student unique opportunities to acquire linguistic knowledge about discourse markers. This paper reports on an investigation of the functions of multi-word discourse markers. Methodology. In our research, we combine the alignment model of the phrase-based statistical machine translation and manual treatment of the data in order to examine English multi-word discourse markers and their equivalents in Lithuanian and Hebrew translations by researching their changes in translation. After establishing the full list of multi-word discourse markers in our generated parallel corpus, we research how the multi-word discourse markers are treated in translation. Contribution. Creating a parallel research corpus to identify multi-word expressions used as discourse markers, analyzing how they are translated into Lithuanian and Hebrew, and attempting to determine why the translators made the choices add value to corpus-driven research and how to manage discourse. Findings. Our research proves that there is a possible context-based influence guiding the translation to choose a particle or other lexical item integration in Lithuanian or Hebrew translated discourse markers to express the rhetorical domain which could be related to the so-called phenomenon of “over-specification.” Recommendations for Practitioners. The comparative examination of discourse markers provides language instructors and translators with more specific information about the roles of discourse markers. Recommendations for Researchers. Understanding the multifunctionality of discourse markers provides new avenues for discourse marker application in translation research. Impact on Society. The current study may be a useful method to strengthen students’ language awareness and analytic skills and is particularly important for students specializing in English philology or translation. Beyond the empirical research, an extensive parallel data resource has been created to be openly used. Future Research. It should be noted that the observed phenomenon of “over-specification” could be analyzed further in future research.

Aim/Purpose. Higher education institutions face difficulties and challenges when it comes to distance learning. The purpose of this paper is to examine self-efficacy indicators and student satisfaction during online English classes. Background. E-learning has been very relevant since the Covid-19 era and is still relevant today. It is possible for students to study regardless of their location or time. By measuring students’ self-efficacy, instructors can gain valuable insights into their students’ ability to create social interaction, cope with technology, and acquire knowledge and tools to manage the learning process. Methodology. This study uses mixed methods along with two measurements. Before and after the course, quantitative and qualitative data were collected. Higher education students in Israel participated. A total of 964 students enrolled in English as a foreign language courses at the pre-basic, basic, and advanced levels. Contribution. Analyzing self-efficacy from several angles provides insight into students. What influences students’ confidence and belief in their ability to succeed in online courses. Moreover, how students perceive their own learning and how they cope with challenges. Findings. Compared to the measurement before the course, self-efficacy decreased on average. Most significant decreases occurred in ‘creating social interactions’ and ‘acquirement of knowledge and tools’ to manage the learning process. A slight decrease was observed in the ability to cope with technology. Additionally, self-efficacy and satisfaction with the course were positively correlated. Recommendations for Practitioners. An overview is provided of the most effective tools and techniques for teaching languages in digital format in this paper. This will allow instructors to design and deliver courses in a more effective way. Thus, they will be able to make better informed decisions, resulting in better outcomes for students. Recommendations for Researchers. Distance Learning courses should resemble the common digital environments in everyday life, rather than imitating face-to-face courses mainly in the field of social interaction. Impact on Society. Digital tools should be encouraged that facilitate effective learning processes instead of sticking to traditional methods that characterize face-to-face courses. Using common interfaces in daily use among the general population will enable the implementation of these recommendations. Future Research. Future studies could be helpful if they compared the English courses developed in the CEFR model with those taught face-to-face as well as those taught online. In addition, motivation and self-monitoring should be examined in both synchronous and asynchronous courses as well.

Aim/Purpose. Business schools need to design, develop and deliver courses that are relevant to business problem-solving. Current pedagogies do not often provide the insight – or experience – necessary to close the gap between theory and practice. Background. The paper describes an initiative to design, develop and deliver courses in business-technology problem-solving that thoroughly immerses students in the actual world of business. Methodology. The methodology included case-based analysis where actual cases where selected to model problem-solving scenarios. Contribution. Several courses are developed that immerse students into actual problem-solving experiences. Findings The courses will be delivered to business students to assess the impact of immersive/experiential learning. Recommendations for Practitioners. Additional courses should be informed by actual cases; the commitment to relevance should be expanded. Recommendations for Researchers. Ongoing research to measure the impact of immersive/experiential learning is recommended. Impact on Society. Business schools should rethink the content of their courses and the pedagogies that have dominated business schools for many decades. Future Research. Additional research will include more courses and additional immersive/experiential pedagogies.

The purpose of this paper was to explore the current and potential use of Facebook for learning purposes by Jordanian university students. The paper attempted to compare such use with other uses of Facebook. Further, the paper investigated Jordanian university students’ attitudes towards using Facebook as a formal academic tool, through the use of course-specific Facebook groups. To that end, quantitative data were collected from a sample of 451 students from three Jordanian public universities. Findings indicated that the vast majority of Jordanian students had Facebook accounts, which echoes its popularity amongst Jordanian youth compared to other types of online social networking sites. While both “social activities” and “entertainment” were the primary motivators for Jordanian students to create and use Facebook accounts, a growing number of them were using Facebook for academic purposes too. Further, Jordanian students had a positive attitude toward the use of “Facebook groups” as an educational tool for specific courses, and under specific conditions. Based on its findings, the paper provides suggestions for Jordanian higher institutions to invest in the application of Facebook as a formal academic tool.

Together, the fields of education and information technology have identified the need for an online solution to training. The introduction of e-learning has optimised the learning process, allowing organisations to realise the many advantages that e-learning offers. The importance of user involvement in the success of e-learning makes it imperative that the forces driving intention to use e-learning and satisfaction thereof be determined. The purpose of this paper is to investigate the relationships between the metrics influencing intention to use and the satisfaction of using e-learning in companies. The results of a survey distributed amongst a South African software development company’s customer base revealed that the 94 respondents have positive enjoyment and self-efficacy levels, and low computer anxiety levels. Correlation analysis revealed significant relationships between enjoyment and self-efficacy and between enjoyment and satisfaction. Companies should therefore ensure that users enjoy using e-learning as it can directly influence satisfaction and self-efficacy.

Aim/Purpose: This study investigates the factors that affect the post implementation success of a web-based learning management system at the University of Professional Studies, Accra (UPSA). Background: UPSA implemented an LMS to blend Web-based learning environment with the traditional methods of education to enable working students to acquire education. Methodology: An explanatory sequential mixed method was adopted, under the pragmatic paradigm, to investigate the level of acceptance of web-based learning by students. The effects of perceived usefulness, perceived ease of use, and other social factors were investigated. In all, 4500 final and third-year undergraduate students of UPSA made up the population. A sample size of 870 was used for this study. Contribution: This paper contributes to the body of knowledge by identifying the factors that hinder post-implementation of LMS at the tertiary level in Ghana and adds to the general literature available. Findings: The level of acceptance of LMS seems very low due to poor IT infrastructure, inadequate training, and the relevance of the system to quality lecture delivery. However, students’ intention to use LMS and the usefulness of LMS were perceived to be high, especially among students in higher levels. Recommendations for Practitioners: The authors recommend that IT infrastructure, especially reliable and fast internet connectivity, and adequate training should be provided. Recommendation for Researchers: Further research should be done to confirm if the provision of a more reliable internet system will boost students’ internet proficiency, which in turn will improve their utilisation of the LMS. Impact on Society: Help create awareness of schooling while pursuing a career and also improve interactions between students and lecturers. It will also improve enrolment and possibly reduce the cost of education in the long-run. Future Research: Researchers can look at the possibility of implementing total virtual learning systems at the tertiary level in Ghana.

Aim/Purpose: This work aims to present a knowledge modeling technique that supports the representation of the student learning process and that is capable of providing a means for self-assessment and evaluating newly acquired knowledge. The objective is to propose a means to address the pedagogical challenges in m-learning by aiding students’ metacognition through a model of a student with the target domain and pedagogy. Background: This research proposes a framework for social and meta-cognitive support to tackle the challenges raised. Two algorithms are introduced: the meta-cognition algorithm for representing the student’s learning process, which is capable of providing a means for self-assessment, and the social group mapping algorithm for classifying students according to social groups. Methodology : Based on the characteristics of knowledge in an m-learning system, the cognitive knowledge base is proposed for knowledge elicitation and representation. The proposed technique allows a proper categorization of students to support collaborative learning in a social platform by utilizing the strength of m-learning in a social context. The social group mapping and metacognition algorithms are presented. Contribution: The proposed model is envisaged to serve as a guide for developers in implementing suitable m-learning applications. Furthermore, educationists and instructors can devise new pedagogical practices based on the possibilities provided by the proposed m-learning framework. Findings: The effectiveness of any knowledge management system is grounded in the technique used in representing the knowledge. The CKB proposed manipulates knowledge as a dynamic concept network, similar to human knowledge processing, thus, providing a rich semantic capability, which provides various relationships between concepts. Recommendations for Practitioners: Educationist and instructors need to develop new pedagogical practices in line with m-learning. Recommendation for Researchers: The design and implementation of an effective m-learning application are challenging due to the reliance on both pedagogical and technological elements. To tackle this challenge, frameworks which describe the conceptual interaction between the various components of pedagogy and technology need to be proposed. Impact on Society: The creation of an educational platform that provides instant access to relevant knowledge. Future Research: In the future, the proposed framework will be evaluated against some set of criteria for its effectiveness in acquiring and presenting knowledge in a real-life scenario. By analyzing real student interaction in m-learning, the algorithms will be tested to show their applicability in eliciting student metacognition and support for social interactivity.

Aim/Purpose: The purpose of this study is to investigate the relationship between knowledge management (KM) and organizational innovation (OI). It also enriches our understanding of the mediating effect of organizational learning (OL) in this relationship. Background: KM’s relationship with OL and OI has been tackled extensively in developed countries’ literature. Nowadays, the challenges of developing countries lie in the process of knowledge application. This study attempts to develop a new managerial knowledgeable tool and present a theoretical model and empirical analysis of the relationship between KM and innovation in Jordan, a developing country. To the knowledge of the author, no attempt has been taken to investigate this relationship in any Jordanian sector. Methodology: The sample of this study consists of 457 managers representing strategic, tactical, and operational levels randomly selected from 56 manufacturing companies in Jordan. A questionnaire-based survey has been developed based on KM, OL and OI literature to collect data. A structural equation modeling (SEM) approach was applied to investigate the proposed research model. Contribution: This study contributes to the literature in different ways. First, it asserts that OL assists in improving OI in manufacturing organization of developing countries. Second, it highlights the substantial benefits of applying KM, OL and OI in manufacturing companies in Jordan. Furthermore, it enhances the relationship between KM and innovativeness’ literature by providing empirical evidence, suggesting that OL is as important as KM to advance organizational innovation. Most importantly, it identifies the problem of a developing economy which is not promoting OL or taking care of it as much as they attended to KM in their organizational practices. Findings: Study findings indicate that the relationship between KM and OI is significantly positive. Results also reveal that the relationship between KM and organizational learning is significantly positive. Empirical results emerging from this study indicate that there is partial mediation to support the relationship between OL and OI. Recommendations for Practitioners: This study suggests that managers ought to recognize that organizational learning is equally important to KM. This entails that OL should be utilized within organizations to achieve organizational innovation. Moreover, managers ought to comprehend their importance and encourage their employees to adopt knowledge from various sources; which, if implemented correctly, will enhance the OL environment. Recommendation for Researchers: The research model can be used or applied in different manufacturing and service sectors across the globe. The findings of the current study can serve as a foundation to perform different studies to understand KM processes and recognize its antecedence. Impact on Society: This study presents insights on how to apply KM, OL and OI methodologies in Jordanian manufacturing companies to achieve a competitive advantage; hence, positively influencing society. Future Research: Future research may include conducting a similar study in the context of developed countries and developing countries which allows for comparison. Also, future research may examine the impact of KM on organizational performance applying both OL and OI as mediating variables.

Aim/Purpose: The aim of this study was to analyze various performance metrics and approaches to their classification. The main goal of the study was to develop a new typology that will help to advance knowledge of metrics and facilitate their use in machine learning regression algorithms Background: Performance metrics (error measures) are vital components of the evaluation frameworks in various fields. A performance metric can be defined as a logical and mathematical construct designed to measure how close are the actual results from what has been expected or predicted. A vast variety of performance metrics have been described in academic literature. The most commonly mentioned metrics in research studies are Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), etc. Knowledge about metrics properties needs to be systematized to simplify the design and use of the metrics. Methodology: A qualitative study was conducted to achieve the objectives of identifying related peer-reviewed research studies, literature reviews, critical thinking and inductive reasoning. Contribution: The main contribution of this paper is in ordering knowledge of performance metrics and enhancing understanding of their structure and properties by proposing a new typology, generic primary metrics mathematical formula and a visualization chart Findings: Based on the analysis of the structure of numerous performance metrics, we proposed a framework of metrics which includes four (4) categories: primary metrics, extended metrics, composite metrics, and hybrid sets of metrics. The paper identified three (3) key components (dimensions) that determine the structure and properties of primary metrics: method of determining point distance, method of normalization, method of aggregation of point distances over a data set. For each component, implementation options have been identified. The suggested new typology has been shown to cover a total of over 40 commonly used primary metrics Recommendations for Practitioners: Presented findings can be used to facilitate teaching performance metrics to university students and expedite metrics selection and implementation processes for practitioners Recommendation for Researchers: By using the proposed typology, researchers can streamline development of new metrics with predetermined properties Impact on Society: The outcomes of this study could be used for improving evaluation results in machine learning regression, forecasting and prognostics with direct or indirect positive impacts on innovation and productivity in a societal sense Future Research: Future research is needed to examine the properties of the extended metrics, composite metrics, and hybrid sets of metrics. Empirical study of the metrics is needed using R Studio or Azure Machine Learning Studio, to find associations between the properties of primary metrics and their “numerical” behavior in a wide spectrum of data characteristics and business or research requirements

Aim/Purpose: This study aimed to investigate the impact of innovation management and learning orientation as the mechanisms playing the role of an intermediate relationship between entrepreneurial leadership and organisational performance of small and medium enterprises (SMEs) in Kuwait. Background: SMEs are currently among the principal economic instruments in most industrialised and developing countries. The contribution of SMEs can be viewed from various perspectives primarily related to the crucial role they play in developing entrepreneurial activities, employment generation, and improving innovativeness. Developing countries, including Kuwait and other countries, in the Gulf Cooperation Council (GCC), have recognised the key role played by SMEs as a strong pillar of growth. Consequently, many governments have formulated policies and programmes to facilitate the growth and success of SMEs. Unfortunately, the organisational performance of SMEs in developing countries, particularly in Kuwait, remains below expectations. The lagged growth could be due to a lack of good managerial practices and increasing competition that negatively impact their performance. Numerous researchers discovered the positive effect of entrepreneurial leadership on SMEs’ performance. However, a lack of clarity remains regarding the direct impact of entrepreneurial leadership on SMEs’ performance, especially in developing countries. Therefore, the nexus between entrepreneurial leadership and organisational performance is still indecisive and requires further studies. Methodology: This study adopted a quantitative approach based on a cross-sectional survey and descriptive design to gather data within a specific period. The data were collected by distributing a survey questionnaire to Kuwaiti SMEs’ owners and Chief Executive Officers (CEOs) via online and on-hand instruments. A total of 384 useable questionnaires were obtained. Moreover, the partial least square-structural equation modelling (PLS-SEM) analysis was performed to test the hypotheses. Contribution: The current study contributed to the existing literature by developing a moderated mediation model integrating entrepreneurial leadership, innovation management, and learning orientation. The study also investigated their effect on the organisational performance of SMEs. The study findings also bridged the existing significant literature gap regarding the role of these variables on SMEs’ performance in developing countries, particularly in Kuwait, due to the dearth of studies linking these variables in this context. Furthermore, this study empirically confirmed the significant effect of innovation management and learning orientation as intermediate variables in strengthening the relationship between entrepreneurial leadership and organisational performance in the settings of Kuwait SMEs, which has not been verified previously. Findings: The study findings showed the beneficial and significant impact of entrepreneurial leadership and innovation management on SME’s organisational performance. The relationship between entrepreneurial leadership and SMEs’ organisational performance is fundamentally mediated by innovation management and moderated by learning orientation. Recommendations for Practitioners: The present study provides valuable insights and information regarding the factors considered by the government, policymakers, SMEs’ stakeholders, and other authorities in the effort to increase the organisational performance level and facilitate the growth of SMEs in Kuwait. SMEs’ owners or CEOs should improve their awareness and knowledge of the importance of entrepreneurial leadership, innovation management, and learning orientation. These variables will have beneficial effects on the performance and assets to achieve success and sustainability if adopted and managed systematically. This study also recommends that SMEs’ entrepreneurs and top management should facilitate supportive culture by creating and maintaining an organisational climate and structure that encourages learning behaviour and innovation mindset among individuals. The initiative will motivate them towards acquiring, sharing, and utilising knowledge and increasing their ability to manage innovation systemically in all production processes to adapt to new technologies, practices, methods, and different circumstances. Recommendation for Researchers: The study findings highlighted the mediating effect of innovation management on the relationship between entrepreneurial leadership (the independent variable) and SMEs’ organisational performance (the dependent variable) and the moderating effect of learning orientation in the same nexus. These relationships were not extensively addressed in SMEs of developing countries and require further validation. Impact on Society: This study aims to influence the management strategies and practices adopted by entrepreneurs and policymakers who work in SMEs in developing countries. The effect will be reflected in the development of their firms and the national economy in general. Future Research: Future research should investigate the conceptual research framework against the backdrop of other developing economies and in other business settings to generalise the results. Future investigation should seek to establish the effect of entrepreneurial leadership style on other mechanisms, such as knowledge management processes, which could function with entrepreneurial leadership to improve SMEs’ performance efficiently. In addition, future studies may include middle and lower-level managers and employees, leading to more positive outcomes.

Aim/Purpose: This paper aimed to examine the impact of learning orientation on organizational performance of small and medium enterprises (SMEs) via the mediating role of total quality management (TQM) practices and the moderating role of innovation culture. Background: SMEs’ organizational performance in developing countries, particularly in Kuwait, remains below expectation due to increasing competition and inadequate managerial practices that negatively impact their performance. Although several studies had revealed a significant effect of learning orientation on SMEs’ performance, the direct impact of learning orientation on their performance is still unclear. Thus, the link between learning orientation and organizational performance remains inconclusive and requires further examination. Methodology: This study adopted a quantitative approach based on a cross-sectional survey and descriptive design to gather the data in a specific period. The data were collected by distributing a survey questionnaire to the owners and Chief Executive Officers (CEOs) of Kuwaiti SMEs using online and on-hand instruments with 384 useable data obtained. Furthermore, the partial least square-structural equation modeling (PLS-SEM) analysis was performed to test the hypotheses. Contribution: This study bridged the significant gap in the role of learning orientation on SMEs’ performance in developing countries, specifically Kuwait. In this sense, a conceptual model was introduced, comprising a learning orientation, TQM practices, innovation culture, and organizational performance. In addition, this study confirmed the significant influence of TQM practices and innovation culture as intermediate variables in strengthening the relationship between learning orientation and organizational performance, which has not yet been verified in Kuwait. Findings: The results in this study revealed that learning orientation had a significant impact on organizational performance of SMEs in Kuwait. It could be observed that TQM practices play an important role in mediating the relationship between learning orientation and performance of SMEs, as well as that innovation culture plays an important moderating role in the same relation. Recommendations for Practitioners: This study provided a framework for the decision-makers of SMEs on the significant impact of the antecedents that enhanced the level of organizational performance. Hence, owners/CEOs of SMEs should improve their awareness and knowledge of the importance of learning orientation, TQM practices, and innovation culture since it could significantly influence their performance to achieve success and sustainability when adopted and managed systematically. The CEOs should also consider building an innovation culture in the internal environment, which enables them to transform new knowledge and ideas into innovative methods and practices. Recommendation for Researchers: The results in this study highlighted the mediating effect of TQM practices on the relationship between learning orientation (the independent variable) and organizational performance (the dependent variable) of SMEs and the moderating effect of innovation culture in the same nexus. These relationships were not extensively addressed in SMEs and thus required further validation. Impact on Society: This study also influenced the management strategies and practices adopted by entrepreneurs and policymakers working in SMEs in developing countries, which is reflected in their development and the national economy. Future Research: Future studies should apply the conceptual framework of this study and assess it further in other sectors, including large firms in developing and developed countries, to generalize the results. Additionally, other mechanisms should be introduced as significant antecedents of SMEs’ performance, such as market orientation, technological orientation, and entrepreneurial orientation, which could function with learning orientation to influence organizational performance effectively.

Aim/Purpose: Systematic literature reviews seek to locate all studies that contain material of relevance to a research question and to synthesize the relevant outcomes of those studies. The primary aim of this paper was to synthesize both research and practice reports on micro-credentials (MCRs). Background: There has been an increase in reports and research on the plausibility of MCRs to support dynamic human skills development for an increasingly impatient and rapidly changing digital world. The integration of fast-paced emerging technologies and digitalization necessitate alternative learning paradigms. MCRs offer time, financial, and space flexibility and can be stacked into a larger qualification, thereby allowing for a broader range of transdisciplinary competencies within a qualification. However, MCRs often lack the academic rigor required for accreditation within existing disciplines. Methodology: The study followed the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta Analyses), which offers a rigorous method to enhance reporting quality. The study used both academic research and practice reports. Contribution: The paper makes a theoretical contribution to the discourse about the need for innovation within existing educational paradigms for continued relevance in a changing world. It also contributes to the debate on the role of MCRs in bridging the gap between practice and academia despite the growing difference between their interests, and the role that MCRs play in the social-economic plans of countries. Findings: The key findings are that investments in MCRs are mainly in the Science, Technology, Engineering and Mathematics (STEM) and Education sectors, and have taken place mainly in high-income countries and regions – contexts that particularly value practice-accredited MCRs. Low-income countries, by contrast, remain traditional and insist on MCRs that are formally accredited by a recognized academic institution. This contributes to a widening skills gap between low- and high-income countries or regions, which results in greater global disparities. There is also a growing divide between academia and practice concerning their interest in MCRs (a reflection of the rigor versus relevance debate), which partially explains why many global and larger organizations have gone on to create their own learning institutions. Recommendations for Practitioners: We recommend that educational mechanisms consider the critical importance of MCRs as part of innovative efforts for life-wide (different sectors) and life-long (same sector) learning, especially in low-income countries. MCRs provide dynamic mechanisms to fill skills gaps in an increasing ruthless international battle for talent. Recommendation for Researchers: We recommend focused research into skills and career pathways using MCRs while at the same time remaining responsive to transdisciplinary efforts and sensitive to global and local changes within any sector. Impact on Society: Work and society have transformed over time, and more so in the new digital age, yet academia has been slow in adapting to the changes, forcing organizations to create their own learning institutions or to use MCRs to fill the skills gap. The purpose of education goes beyond preparing individuals for work, extending further to creating an environment where individuals and governments seek their own social and economic outcomes. MCRs provide a flexible means for co-creation between individuals, education, organizations, and government that could stem global rising unemployment, social exclusion, and redundancy. Future Research: Future research should focus on the co-creation of MCRs between practitioners and academia.

Aim/Purpose: To predict the change-proneness of software from the continuous evolution using machine learning methods. To identify when software changes become statistically significant and how metrics change. Background: Software evolution is the most time-consuming activity after a software release. Understanding evolution patterns aids in understanding post-release software activities. Many methodologies have been proposed to comprehend software evolution and growth. As a result, change prediction is critical for future software maintenance. Methodology: I propose using machine learning methods to predict change-prone classes. Classes that are expected to change in future releases were defined as change-prone. The previous release was only considered by the researchers to define change-proneness. In this study, I use the evolution of software to redefine change-proneness. Many snapshots of software were studied to determine when changes became statistically significant, and snapshots were taken biweekly. The research was validated by looking at the evolution of five large open-source systems. Contribution: In this study, I use the evolution of software to redefine change-proneness. The research was validated by looking at the evolution of five large open-source systems. Findings: Software metrics can measure the significance of evolution in software. In addition, metric values change within different periods and the significance of change should be considered for each metric separately. For five classifiers, change-proneness prediction models were trained on one snapshot and tested on the next. In most snapshots, the prediction performance was excellent. For example, for Eclipse, the F-measure values were between 80 and 94. For other systems, the F-measure values were higher than 75 for most snapshots. Recommendations for Practitioners: Software change happens frequently in the evolution of software; however, the significance of change happens over a considerable length of time and this time should be considered when evaluating the quality of software. Recommendation for Researchers: Researchers should consider the significance of change when studying software evolution. Software changes should be taken from different perspectives besides the size or length of the code. Impact on Society: Software quality management is affected by the continuous evolution of projects. Knowing the appropriate time for software maintenance reduces the costs and impacts of software changes. Future Research: Studying the significance of software evolution for software refactoring helps improve the internal quality of software code.

Aim/Purpose: The aim of this study is to develop and evaluate a comprehensive model that predicts students’ engagement with and intent to continue using mobile-Learning Management Systems (m-LMS). Background: m-LMS are increasingly popular tools for delivering course content in higher education. Understanding the factors that affect student engagement and continuance intention can help educational institutions to develop more effective and user-friendly m-LMS platforms. Methodology: Participants with prior experience with m-LMS were employed to develop and evaluate the proposed model that draws on the Technology Acceptance Model (TAM), Task-Technology Fit (TTF), and other related models. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was used to evaluate the model. Contribution: The study provides a comprehensive model that takes into account a variety of factors affecting engagement and continuance intention and has a strong predictive capability. Findings: The results of the study provide evidence for the strong predictive capability of the proposed model and supports previous research. The model identifies perceived usefulness, perceived ease of use, interactivity, compatibility, enjoyment, and social influence as factors that significantly influence student engagement and continuance intention. Recommendations for Practitioners: The findings of this study can help educational institutions to effectively meet the needs of students for interactive, effective, and user-friendly m-LMS platforms. Recommendation for Researchers: This study highlights the importance of understanding the antecedents of students’ engagement with m-LMS. Future research should be conducted to test the proposed model in different contexts and with different populations to further validate its applicability. Impact on Society: The engagement model can help educational institutions to understand how to improve student engagement and continuance intention with m-LMS, ultimately leading to more effective and efficient mobile learning. Future Research: Additional research should be conducted to test the proposed model in different contexts and with different populations to further validate its applicability.

Aim/Purpose: Previous research has generally concentrated on identifying the variables that most significantly influence customer churn or has used customer segmentation to identify a subset of potential consumers, excluding its effects on forecast accuracy. Consequently, there are two primary research goals in this work. The initial goal was to examine the impact of customer segmentation on the accuracy of customer churn prediction in the banking sector using machine learning models. The second objective is to experiment, contrast, and assess which machine learning approaches are most effective in predicting customer churn. Background: This paper reviews the theoretical basis of customer churn, and customer segmentation, and suggests using supervised machine-learning techniques for customer attrition prediction. Methodology: In this study, we use different machine learning models such as k-means clustering to segment customers, k-nearest neighbors, logistic regression, decision tree, random forest, and support vector machine to apply to the dataset to predict customer churn. Contribution: The results demonstrate that the dataset performs well with the random forest model, with an accuracy of about 97%, and that, following customer segmentation, the mean accuracy of each model performed well, with logistic regression having the lowest accuracy (87.27%) and random forest having the best (97.25%). Findings: Customer segmentation does not have much impact on the precision of predictions. It is dependent on the dataset and the models we choose. Recommendations for Practitioners: The practitioners can apply the proposed solutions to build a predictive system or apply them in other fields such as education, tourism, marketing, and human resources. Recommendation for Researchers: The research paradigm is also applicable in other areas such as artificial intelligence, machine learning, and churn prediction. Impact on Society: Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. Future Research: Build a real-time or near real-time application to provide close information to make good decisions. Furthermore, handle the imbalanced data using new techniques.

Aim/Purpose: This study presents a novel deep learning-based framework designed to enhance spontaneous micro-expression recognition by effectively increasing the amount and variety of data and balancing the class distribution to improve recognition accuracy. Background: Micro-expression recognition using deep learning requires large amounts of data. Micro-expression datasets are relatively small, and their class distribution is not balanced. Methodology: This study developed a framework using a deep learning-based model to recognize spontaneous micro-expressions on a person’s face. The framework also includes several technical stages, including image and data preprocessing. In data preprocessing, data augmentation is carried out to increase the amount and variety of data and class balancing to balance the distribution of sample classes in the dataset. Contribution: This study’s essential contribution lies in enhancing the accuracy of micro-expression recognition and overcoming the limited amount of data and imbalanced class distribution that typically leads to overfitting. Findings: The results indicate that the proposed framework, with its data preprocessing stages and deep learning model, significantly increases the accuracy of micro-expression recognition by overcoming dataset limitations and producing a balanced class distribution. This leads to improved micro-expression recognition accuracy using deep learning techniques. Recommendations for Practitioners: Practitioners can utilize the model produced by the proposed framework, which was developed to recognize spontaneous micro-expressions on a person’s face, by implementing it as an emotional analysis application based on facial micro-expressions. Recommendation for Researchers: Researchers involved in the development of a spontaneous micro-expression recognition framework for analyzing hidden emotions from a person’s face are playing an essential role in advancing this field and continue to search for more innovative deep learning-based solutions that continue to explore techniques to increase the amount and variety of data and find solutions to balancing the number of sample classes in various micro-expression datasets. They can further improvise to develop deep learning model architectures that are more suitable and relevant according to the needs of recognition tasks and the various characteristics of different datasets. Impact on Society: The proposed framework could significantly impact society by providing a reliable model for recognizing spontaneous micro-expressions in real-world applications, ranging from security systems and criminal investigations to healthcare and emotional analysis. Future Research: Developing a spontaneous micro-expression recognition framework based on spatial and temporal flow requires the learning model to classify optimal features. Our future work will focus more on exploring micro-expression features by developing various alternative learning models and increasing the weights of spatial and temporal features.

Aim/Purpose: This research utilized a learn-to-rank algorithm to provide medical recommendations to prescribers. The algorithm has been utilized in other domains, such as information retrieval and recommender systems. Background: Ranking the possible medical treatments according to diagnoses of the medical cases is very beneficial for doctors, especially during the coding process. Methodology: We developed two deep learning pointwise learn-to-rank models within one prediction pipeline: one for predicting the top possible active ingredients from disease features, the other for ranking actual medicines codes from diseases and the ingredients features. Contribution: A new learn-to-rank deep learning model has been developed to rank medical procedures based on datasets collected from insurance companies. Findings: We ran 18 cross-validation trials on a confidential dataset from an insurance company. We obtained an average normalized discounted cumulative gain (NDCG@8) of 74% with a 5% standard deviation as a result of all 18 experiments. Our approach outperformed a known approach used in the information retrieval domain in which data is represented in LibSVM format. Then, we ran the same trials using three learn-to-rank models – pointwise, pairwise, and listwise – which yielded average NDCG@8 of 71%, 72%, and 72%, respectively. Recommendations for Practitioners: The proposed model provides an insightful approach to helping to manage the patient’s treatment process. Recommendation for Researchers: This research lays the groundwork for exploring various applications of data science techniques and machine learning algorithms in the medical field. Future studies should focus on the significant potential of learn-to-rank algorithms across different medical domains, including their use in cost-effectiveness models. Emphasizing these algorithms could enhance decision-making processes and optimize resource allocation in healthcare settings. Impact on Society: This will help insurance companies and end users reduce the cost associated with patient treatment. It also helps doctors to choose the best procedure and medicines for their patients. Future Research: Future research is required to investigate the impact of medicine data at a granular level.

Aim/Purpose: While existing literature has extensively explored factors influencing the success of big data projects and proposed big data maturity models, no study has harnessed machine learning to predict project success and identify the critical features contributing significantly to that success. The purpose of this paper is to offer fresh insights into the realm of big data projects by leveraging machine-learning algorithms. Background: Previously, we introduced the Global Big Data Maturity Model (GBDMM), which encompassed various domains inspired by the success factors of big data projects. In this paper, we transformed these maturity domains into a survey and collected feedback from 90 big data experts across the Middle East, Gulf, Africa, and Turkey regions regarding their own projects. This approach aims to gather firsthand insights from practitioners and experts in the field. Methodology: To analyze the feedback obtained from the survey, we applied several algorithms suitable for small datasets and categorical features. Our approach included cross-validation and feature selection techniques to mitigate overfitting and enhance model performance. Notably, the best-performing algorithms in our study were the Decision Tree (achieving an F1 score of 67%) and the Cat Boost classifier (also achieving an F1 score of 67%). Contribution: This research makes a significant contribution to the field of big data projects. By utilizing machine-learning techniques, we predict the success or failure of such projects and identify the key features that significantly contribute to their success. This provides companies with a valuable model for predicting their own big data project outcomes. Findings: Our analysis revealed that the domains of strategy and data have the most influential impact on the success of big data projects. Therefore, companies should prioritize these domains when undertaking such projects. Furthermore, we now have an initial model capable of predicting project success or failure, which can be invaluable for companies. Recommendations for Practitioners: Based on our findings, we recommend that practitioners concentrate on developing robust strategies and prioritize data management to enhance the outcomes of their big data projects. Additionally, practitioners can leverage machine-learning techniques to predict the success rate of these projects. Recommendation for Researchers: For further research in this field, we suggest exploring additional algorithms and techniques and refining existing models to enhance the accuracy and reliability of predicting the success of big data projects. Researchers may also investigate further into the interplay between strategy, data, and the success of such projects. Impact on Society: By improving the success rate of big data projects, our findings enable organizations to create more efficient and impactful data-driven solutions across various sectors. This, in turn, facilitates informed decision-making, effective resource allocation, improved operational efficiency, and overall performance enhancement. Future Research: In the future, gathering additional feedback from a broader range of big data experts will be valuable and help refine the prediction algorithm. Conducting longitudinal studies to analyze the long-term success and outcomes of Big Data projects would be beneficial. Furthermore, exploring the applicability of our model across different regions and industries will provide further insights into the field.

Aim/Purpose: This conceptual review analyzes the designs of global health virtual communities of practice (VCoPs) programming reported in the empirical literature and proposes a new typology of their functioning. The purpose of this review is to provide clarity on VCoP learning stages of (co)evolution and insight into VCoP (re)development efforts to best meet member, organization, and network needs against an ever-evolving landscape of complexity in global health. Background: Since the COVID-19 pandemic, the field of global health has seen an uptick in the use of VCoPs to support continuous learning and improve health outcomes. However, evidence of how different combinations of programmatic designs impact opportunities for learning and development is lacking, and how VCoPs evolve as learning networks has yet to be explored. Methodology: Following an extensive search for literature in six databases, thematic analysis was conducted on 13 articles meeting the inclusion criteria. This led to the development and discussion of a new typology of VCoP phases of learning (co)evolution. Contribution: Knowledge gained from this review and the new categorization of VCoPs can support the functioning and evaluation of global health training programs. It can also provide a foundation for future research on how VCoPs influence the culture of learning organizations and networks. Findings: Synthesis of findings resulted in the categorization of global health VCoPs into five stages (slightly evolving, somewhat revolving, moderately revolving, highly revolving, and coevolving) across four design domains (network development, general member engagement before/after sessions, general member engagement during sessions, and session leadership). All global health VCoPs reviewed showed signs of adaptation and recommended future evolution. Recommendations for Practitioners: VCoP practitioners should pay close attention to how the structured flexibility of partnerships, design, and relationship development/accountability may promote or hinder VcoP’s continued evolution. Practitioners should shift perspective from short to mid- and long-term VCoP planning. Recommendation for Researchers: The new typology can stimulate further research to strengthen the clarity of language and findings related to VCoP functioning. Impact on Society: VCoPs are utilized by academic institutions, the private sector, non-profit organizations, the government, and other entities to fill gaps in adult learning at scale. The contextual implementation of findings from this study may impact VCoP design and drive improvements in opportunities for learning, global health, and well-being. Future Research: Moving forward, future research could explore how VCoP evaluations relate to different stages of learning, consider evaluation stages across the totality of VCoP programming design, and explore how best to capture VCoP (long-term) impact attributed to health outcomes and the culture of learning organizations and networks.

Aim/Purpose: This study aims to evaluate the success of deep learning in building user profiles for personalized information access. Background: To better express document content and information during the matching phase of the information retrieval (IR) process, deep learning architectures could potentially offer a feasible and optimal alternative to user profile building for personalized information access. Methodology: This study uses deep learning-based models to deduce the domain of the document deemed implicitly relevant by a user that corresponds to their center of interest, and then used predicted domain by the best given architecture with user’s characteristics to predict other centers of interest. Contribution: This study contributes to the literature by considering the difference in vocabulary used to express document content and information needs. Users are integrated into all research phases in order to provide them with relevant information adapted to their context and their preferences meeting their precise needs. To better express document content and information during this phase, deep learning models are employed to learn complex representations of documents and queries. These models can capture hierarchical, sequential, or attention-based patterns in textual data. Findings: The results show that deep learning models were highly effective for building user profiles for personalized information access since they leveraged the power of neural networks in analyzing and understanding complex patterns in user behavior, preferences, and user interactions. Recommendations for Practitioners: Building effective user profiles for personalized information access is an ongoing process that requires a combination of technology, user engagement, and a commitment to privacy and security. Recommendation for Researchers: Researchers involved in building user profiles for personalized information access play a crucial role in advancing the field and developing more innovative deep-based networks solutions by exploring novel data sources, such as biometric data, sentiment analysis, or physiological signals, to enhance user profiles. They can investigate the integration of multimodal data for a more comprehensive understanding of user preferences. Impact on Society: The proposed models can provide companies with an alternative and sophisticated recommendation system to foster progress in building user profiles by analyzing complex user behavior, preferences, and interactions, leading to more effective and dynamic content suggestions. Future Research: The development of user profile evolution models and their integration into a personalized information search system may be confronted with other problems such as the interpretability and transparency of the learning-based models. Developing interpretable machine learning techniques and visualization tools to explain how user profiles are constructed and used for personalized information access seems necessary to us as a future extension of our work.

Aim/Purpose: This study examines the adoption and continued use of m-learning in Iraqi universities amidst an unstable environment by extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and Expectation-Confirmation Model (ECM) models. The primary goal is to address the specific challenges and opportunities in Iraq’s higher education institutions (HEIs) due to geopolitical instability and understand their impact on student acceptance, satisfaction, and continued m-learning usage. Background: The research builds on the growing importance of m-learning, especially in HEIs, and recognizes the unique challenges faced by institutions in Iraq, given the region’s instability. It identifies gaps in existing models and proposes extensions, introducing the variable “civil conflicts” to account for the volatile context. The study aims to contribute to a deeper understanding of m-learning acceptance in conflict-affected regions and provide insights for improving m-learning initiatives in Iraqi HEIs. Methodology: To achieve its objectives, this research employed a quantitative survey to collect data from 399 students in five Iraqi universities. PLS-SEM is used for the analysis of quantitative data, testing the extended UTAUT2 and ECM models. Contribution: The study’s findings are expected to contribute to the development of a nuanced understanding of m-learning adoption and continued usage in conflict-affected regions, particularly in the Iraqi HEI context. Findings: The study’s findings may inform strategies to enhance the effectiveness of m-learning initiatives in Iraqi HEIs and offer insights into how education can be supported in regions characterized by instability. Recommendations for Practitioners: Educators and policymakers can benefit from the research by making informed decisions to support education continuity and quality, particularly in conflict-affected areas. Recommendation for Researchers: Researchers can build upon this study by further exploring the adoption and usage of m-learning in unstable environments and evaluating the effectiveness of the proposed model extensions. Impact on Society: The research has the potential to positively impact society by improving access to quality education in regions affected by conflict and instability. Future Research: Future research can expand upon this study by examining the extended model’s applicability in different conflict-affected regions and assessing the long-term impact of m-learning initiatives on students’ educational outcomes.

DSSP stands as a foundational tool in the domain of protein secondary structure prediction, yet it encounters notable challenges in accurately annotating irregular structures, such as β-turns and γ-turns, which constitute approximately 25%-30% and 10%-15% of protein turns, respectively. This limitation arises from DSSP's reliance on hydrogen-bond analysis, resulting in annotation gaps and reduced consensus on irregular structures. Alternatively, PROMOTIF excels at identifying these irregular structure annotations using phi-psi information. Despite their complementary strengths, previous methodologies utilised DSSP and PROMOTIF separately, leading to disparate prediction methods for protein secondary structures, hampering comprehensive structure analysis crucial for drug development. In this work, we bridge this gap using an annotation fusion approach, combining DSSP structures with beta, and gamma turns. We introduce IRNN-SS, a model employing deep inception and bidirectional gated recurrent neural networks, achieving 77.4% prediction accuracy on benchmark datasets, outpacing current models.

The most common kind of blood cancer in people of all ages is leukaemia. The fractional mayfly optimisation (FMO) based DenseNet is proposed for the identification and classification of leukaemia in federated learning (FL). Initially, the input image is pre-processed by adaptive median filter (AMF). Then, cell segmentation is done using the Scribble2label. After that, image augmentation is accomplished. Finally, leukaemia classification is accomplished utilising DenseNet, which is trained using the FMO. Here, the FMO is devised by merging the mayfly algorithm (MA) and the fractional concept (FC). Following local training, the server performs local updating and aggregation using a weighted average by RV coefficient. The results showed that FMO-DenseNet attained maximum accuracy, true negative rate (TNR) and true positive rate (TPR) of 94.3%, 96.5% and 95.3%. Moreover, FMO-DenseNet gained minimum mean squared error (MSE) and root mean squared error (RMSE) of 5.7%, 9.2% and 30.4%.

In this paper, two hypothetical frameworks are proposed through the application of quality function deployment (QFD) to integrate the current institutional level and program level student learning focus areas with the relevant institutional and program specific stakeholder expectations. A generic skillset proficiency expected of all the graduating students at the institutional level by the stakeholders is considered in the first QFD application example and a program specific knowledge proficiency expected at the program level by the stakeholders is considered in the second QFD application example. Operations management major/option is considered for illustration purposes at the program level. In addition, an assurance of learning based approach rooted in continuous improvement philosophy is proposed to align the stakeholder expectations with the relevant student learning outcomes at different learning tiers.