This longitudinal, quasi-experimental study investigated students’ cognitive personality type using the Myers-Briggs personality Type Indicator (MBTI) in Internet-based Online and Face-to-Face (F2F) modalities. A total of 1154 students enrolled in 28 Online and 32 F2F sections taught concurrently over a period of fourteen years. The study measured whether the sample is similar to the national average percentage frequency of all 16 different personality types; whether specific personality type students preferred a specific modality of instructions and if this preference changed over time; whether learning occurred in both class modalities; and whether specific personality type students learned more from a specific modality. Data was analyzed using regression, t-test, frequency, and Chi-Squared. The study concluded that data used in the study was similar to the national statistics; that no major differences in preference occurred over time; and that learning did occur in all modalities, with more statistically significant learning found in the Online modality versus F2F for Sensing, Thinking, and Perceiving types. Finally, Sensing and Thinking (ST) and Sensing and Perceiving (SP) group types learned significantly more in Online modality versus F2F.

The impact that mobile web-enabled devices have had on the lives and behavior of university students has been immense. Yet, many of the models used in the classrooms have remained unchanged. Although a traditional research approach of examining the literature, developing a methodology, and so on is followed, this paper’s main aim is to inform practitioners on observations and examples from courses which insist on and encourage mobiles in the classroom. The paper asked three research questions regarding the use, impact, and unintended consequences of mobile web-enabled devices in the classroom. Data was collected from observing and interacting with post graduate students and staff in two universities across two continents: Africa and Europe. The paper then focuses on observations and examples on the use, impact, and unintended consequences of mobile web-enabled devices in two classrooms. The findings are that all students used mobile web-enabled devices for a variety of reasons. The use of mobile devices did not negatively impact the class, rather students appeared to be more engaged and comfortable knowing they were allowed to openly access their mobile devices. The unintended consequences included the use of mobiles to translate text into home languages.

The study involved student teachers enrolled in early childhood teaching at a teacher training institute in Hong Kong Special Administrative Region. Seventy-four students participated in flipped classroom activities during their first semester of study. Students were told to learn from online videos related to using image editing software in their own time and pace prior to the next class. When they met in class, they were asked to apply their recently acquired editing knowledge to edit an image of their own choice related to the theme of their group project. At the end of the activity, students were asked to complete an online questionnaire. It was found that students had rated all five questions relating to generic skills highly, with self-study skills rated the highest. They particularly enjoyed the flexibility of learning on their own time and pace as a benefit of the flipped classroom. Data collected from students’ project pages show they had used average of 3.22 editing features for the theme images for their project. Most groups had inserted text followed by using the filter function. It is possible that these two functions are more noticeable than other editing functions. In conclusion, students were able to apply their self-learnt knowledge in a real-life situation and they had also developed their generic skills via the flipped classroom pedagogy.

The authors present the results of a study conducted at a comprehensive, urban, coeducational, land-grant university. A quasi-experimental design was chosen for this study to compare student performance in two different classroom environments, traditional versus flipped. The study spanned 3 years, beginning fall 2012 through spring 2015. The participants included 433 declared business majors who self-enrolled in several sections of the Management Information Systems course during the study. The results of the current study mirrored those of previous works as the instructional method impacted students’ final grade. Thus, reporting that the flipped classroom approach offers flexibility with no loss of performance when compared to traditional lecture-based environments.

Aim/Purpose: Teachers are being asked to integrate mobile technologies into their content creation and distribution tasks. This research aims to provide an understanding of teachers taking on this process and whether the use of technology has influenced their content creation and distribution in the classroom. Background: Many claim that the use of technology for content creation and distribution can only enhance and improve the educational experience. However, for teachers it is not simply the integration of technology that is of prime concern. As teachers are ultimately responsible for the success of technology integration, it is essential to understand teachers’ viewpoints and lived technology experiences. Methodology: The Task-Technology Fit (TTF) model was used to guide interpretive case study research. Six teachers were purposively sampled and interviewed from a private school where a digital strategy is already in place. Data was then analysed using directed content analysis in relation to TTF. Contribution: This paper provides an understanding of teachers’ mobile technology choices in relation to content creation and distribution tasks. Findings: Findings indicate that teachers fit technology into their tasks if they perceive the technology has a high level of benefit to the teaching task. In addition, the age of learners and the subject being taught are major influencers. Recommendations for Practitioners: Provides a more nuanced and in-depth understanding of teachers’ technology choices, which is necessary for the technology augmented educational experience of the future. Recommendations for Researchers: Provides an unbiased and theoretically guided view of mobile technology use with content creation and distribution tasks. Impact on Society: Teachers do not appear to use technology as a de facto standard, but specifically select technology which will save them time, reduce costs, and improve the educational experiences of their learners. Future Research: A mixed-method approach, including several diverse schools as well as learners would enrich the findings. Furthermore, consideration of hardware limitations and lack of software features are needed.

Aim/Purpose: The aim of this study was to develop a prototype of an information-generating computer tool designed to automatically map the dental restorations in a panoramic radiograph. Background: A panoramic radiograph is an external dental radiograph of the oro-maxillofacial region, obtained with minimal discomfort and significantly lower radiation dose compared to full mouth intra-oral radiographs or cone-beam computed tomography (CBCT) imaging. Currently, however, a radiologic informative report is not regularly designed for a panoramic radiograph, and the referring doctor needs to interpret the panoramic radiograph manually, according to his own judgment. Methodology: An algorithm, based on techniques of computer vision and machine learning, was developed to automatically detect and classify dental restorations in a panoramic radiograph, such as fillings, crowns, root canal treatments and implants. An experienced dentist evaluated 63 panoramic anonymized images and marked on them, manually, 316 various restorations. The images were automatically cropped to obtain a region of interest (ROI) containing only the upper and lower alveolar ridges. The algorithm automatically segmented the restorations using a local adaptive threshold. In order to improve detection of the dental restorations, morphological operations such as opening, closing and hole-filling were employed. Since each restoration is characterized by a unique shape and unique gray level distribution, 20 numerical features describing the contour and the texture were extracted in order to classify the restorations. Twenty-two different machine learning models were evaluated, using a cross-validation approach, to automatically classify the dental restorations into 9 categories. Contribution: The computer tool will provide automatic detection and classification of dental restorations, as an initial step toward automatic detection of oral pathologies in a panoramic radiograph. The use of this algorithm will aid in generating a radiologic report which includes all the information required to improve patient management and treatment outcome. Findings: The automatic cropping of the ROI in the panoramic radiographs, in order to include only the alveolar ridges, was successful in 97% of the cases. The developed algorithm for detection and classification of the dental restorations correctly detected 95% of the restorations. ‘Weighted k-NN’ was the machine-learning model that yielded the best classification rate of the dental restorations - 92%. Impact on Society: Information that will be extracted automatically from the panoramic image will provide a reliable, reproducible radiographic report, currently unavailable, which will assist the clinician as well as improve patients’ reliance on the diagnosis. Future Research: The algorithm for automatic detection and classification of dental restorations in panoramic imaging must be trained on a larger dataset to improve the results. This algorithm will then be used as a preliminary stage for automatically detecting incidental oral pathologies exhibited in the panoramic images.

Aim/Purpose: This paper presents an exploratory case study into using 360° videos to present small segments of lecture content for IT students in an Australian University. The aim of this study was to understand; what is the impact of incorporating 360° videos into class content for students and teaching staff? In this study the 360° videos are described as “learning atoms”. Learning atoms are short duration videos (1 to 5 minutes) captured in 360°. Background: Within this paper we conducted experiments in the classroom using 360° videos to determine if they have an impact on student's feeling of presence with class content. Additionally, to follow up, how does the inclusion of 360° impact on the teaching experience. Methodology: The methodology used in this study focused on both quantitative and qualita-tive aspects. Data was captured at the same time during the teaching period to address the research questions. In order to gauge the feeling of presence within the classroom a short survey was administered to students in the undergraduate IT class at the start (pre) and end (post) of the semester using the same questions to measure any change. Contribution: The main contributions from this study were that we demonstrated there is a potential for providing an alternative ‘immersive’ content presentation for students. This alternative content took the form of 360° learning atoms, whereas further showed our nuance process for creating and publishing of these atoms. Findings: The results show that for students, learning atoms can help improve the sense of presence, particularly for remote students, however the interactive experience can take student’s attention away from the lecturer. The results present potential for providing an alternative ‘immersive’ content presentation for students, however problems for uptake are present for both students and teachers, such as image capture quality and file size Impact on Society: We foresee this approach as being a new approach to teaching students in higher education within online spaces to increase engagement and move towards having a richer virtual experience no matter the location. Future Research: Future research will be conducted to resolve whether presence and engagement is supported by the inclusion of 360° videos in the classroom.

Aim/Purpose: The flipped classroom approach is one of the most popular active learning approaches. This paper explores the effectiveness of a new pedagogy, known as FOCUSED, for postgraduate students. Background: The flipped classroom approach is a trendy blended learning pedagogy which capitalizes on the flexibility of online learning and the stimulating nature of face-to-face discussion. This article describes a pilot study involving post-graduate students who experienced the flipped classroom approach in one of their courses. Methodology: In additional to online activities, students adopted a newly learned approach to solve a related problem that was given by another group of students during classes. Quantitative data were collected from pre- and post-tests for both self-learned online materials and group discussion during classes so that the effectiveness of the flipped classroom pedagogy could be examined from the perspective of a holistic learning experience. Findings: It was found that the average scores for the post-test for the self-learned online video were much higher than for pre-test, even though the post-tests for both online and face-to-face learning were higher than the respective pre-tests. The qualitative data collected at the end of the flipped classroom activities further confirmed the value of the flipped classroom approach. Even though students could self-learn, more students valued peer interactions in the classroom more than the flexibility of online learning.

Aim/Purpose: This brief paper will provide preliminary insight into an institutions effort to help students understand the application of the scientific method as it applies to the business discipline through the creation of a dedicated, required course added to the curriculum of a mid-Atlantic minority-serving institution. In or-der to determine whether the under-consideration course satisfies designated student learning outcomes, an assessment regime was initiated that included examination of rubric data as well as the administration of a student perception survey. This paper summarizes the results of the early examination of the efficacy of the course under consideration. Background: A small, minority-serving, university located in the United States conducted an assessment and determined that students entering a department of business following completion of their general education science requirements had difficulties transferring their understanding of the scientific method to the business discipline. Accordingly, the department decided to create a unique course offered to sophomore standing students titled Principles of Scientific Methods in Business. The course was created by a group of faculty with input from a twenty person department. Methodology: Rubrics used to assess a course term project were collected and analyzed in Microsoft Excel to measure student satisfaction of learning goals and a student satisfaction survey was developed and administered to students enrolled in the course under consideration to measure perceived course value. Contribution: While the scientific method applies across the business and information disciplines, students often struggle to envision this application. This paper explores the implications of a course specifically purposed to engender the development and usage of logical and scientific reasoning skills in the business discipline by students in the lower level of an bachelors degree program. The information conveyed in this paper hopefully makes a contribution in an area where there is still an insufficient body of research and where additional exploration is needed. Findings: For two semesters rubrics were collected and analyzed representing the inclusion of 53 students. The target mean for the rubric was a 2.8 and the overall achieved mean was a 2.97, indicating that student performance met minimal expectations. Nevertheless, student deficiencies in three crucial areas were identified. According to the survey findings, as a result of the class students had a better understanding of the scientific method as it applies to the business discipline, are now better able to critically assess a problem, feel they can formulate a procedure to solve a problem, can test a problem-solving process, have a better understanding of how to formulate potential business solutions, understand how potential solutions are evaluated, and understand how business decisions are evaluated. Conclusion: Following careful consideration and discussion of the preliminary findings, the course under consideration was significantly enhanced. The changes were implemented in the fall of 2020 and initial data collected in the spring of 2021 is indicating measured improvement in student success as exhibited by higher rubric scores. Recommendations for Practitioners: These initial findings are promising and while considering student success, especially as we increasingly face a greater and greater portion of under-prepared students entering higher education, initiatives to build the higher order thinking skills of students via transdisciplinary courses may play an important role in the future of higher education. Recommendations for Researchers: Additional studies of transdisciplinary efforts to improve student outcomes need to be explored through collection and evaluation of rubrics used to assess student learning as well as by measuring student perception of the efficacy of these efforts. Impact on Society: Society needs more graduates who leave universities ready to solve problems critically, strategically, and with scientific reasoning. Future Research: This study was disrupted by the COVID-19 pandemic; however, it is resuming in late 2021 and it is the hope that a robust and detailed paper, with more expansive findings will eventually be generated.

Aim/Purpose; Fostering student engagement is one of the great challenges of teaching, especially in online learning environments. An educators’ assumptions and beliefs about what student engagement is and how it manifests will shape the strategies they design to engage students in learning. However, there is no agreement on the definition of concept of student engagement and it re-mains a vague construct. Background: Adopting the principles of user-centered design, the author maintains that to design learning experiences which better support student engagement it is important to gain insights into how students perceive and operationalize the concept of engagement in learning. The recent challenges of teaching effectively online prompted the author to reflect more deeply on the concept of engagement and how it might be achieved. Methodology: In the tradition of reflective teaching, the author undertook an informal, qualitative inquiry in her classroom, administering a brief questionnaire to students in her online class. When the themes which emerged were integrated with other literature and findings from the author’s earlier classroom inquiry, some insights were gained into how students ‘operationalize’ the concept of engagement, and weight was added to the authors’ premise of the value of humanistic approaches to university teaching, the need for greater emphasis on student-teacher connection and the necessity of considering the affective domain alongside the cognitive domain in learning in higher education. The insights were brought together and visualized in a conceptual model of student engagement. Contribution: The conceptual model presented in the present paper reflects the author’s present ‘mental model’ of student engagement in classes online and, when the opportunity arrives, in face-to-face classes as well. This mental model shapes the authors’ course design, learning activities and the delivery of the course. Although the elements of the model are not ‘new’, the model synthesizes several related concepts necessary to a humanist approach to under-standing student engagement. It is hoped that the model and discussion presented will be stimulus for further rich discussion around the nature of student engagement. Findings: Interestingly, the affective rather than the cognitive domain framed students’ perspectives on what engagement ‘looks like to them’ and on what teachers should do to engage them. Recommendations for Practitioners: By sharing the process through which the author arrived at this understanding of student engagement, the author has also sought to highlight three key points: the importance of including the ‘student perspectives and expectations’ against which educators can examine their own assumptions as part of the process reflective teaching practices; the usefulness of integrating theoretical and philosophical frameworks in our understandings of student engagement and how it might be nurtured, and finally the necessity of affording greater influence to humanism and the affective domain in higher education. The findings emphasize the necessity of considering the affective dimension of engagement as an essential condition for cognitive engagement and as inextricable from the cognitive dimension of engagement. Recommendations for Researchers: The emphasis in research engagement learning and teaching is on how we (the educators) can do this better, how we can better engage students. While the student perspective is often formulated from data obtained through surveys and focus groups, researchers in learning engagement are working with their own understandings (albeit supported by empirical research). It is crucial for deeper insight to also understand the students’ conceptualization of the phenomena being researched. Bringing the principles of design thinking to bear on educational research will likely provide greater depth of insight. Impact on Society: Empirical, formal, and structured research is undeniably essential to advancing human endeavor in any field, including learning and teaching. It is however important to recognize informal research in the form of classroom inquiry as part of teachers’ reflexive practice is also legitimate and useful to advancing understanding of complex phenomenon such as student engagement in learning through multiple perspectives and experiences. Future Research: Further research on the nature of student engagement in different contexts and against different theoretical frameworks is warranted as is empirical investigation of the premise of the value of humanism and the affective do-main in defining and measuring student engagement in higher education.

Aim/Purpose: Designing augmented reality (AR) experiences for education, health or entertainment involves multidisciplinary teams making design decisions across several areas. The goal of this paper is to present a classification schema that describes the design choices when constructing an AR interactive experience. Background: Existing extended reality schema often focuses on single dimensions of an AR experience, with limited attention to design choices. These schemata, combined with an analysis of a diverse range of AR applications, form the basis for the schema synthesized in this paper. Methodology: An extensive literature review and scoring of existing classifications were completed to enable a definition of seven design dimensions. To validate the design dimensions, the literature was mapped to the seven-design choice to represent opportunities when designing AR iterative experiences. Contribution: The classification scheme of seven dimensions can be applied to communicating design considerations and alternative design scenarios where teams of domain specialists need to collaborate to build AR experiences for a defined purpose. Findings: The dimensions of nature of reality, location (setting), feedback, objects, concepts explored, participant presence and interactive agency, and style describe features common to most AR experiences. Classification within each dimension facilitates ideation for novel experiences and proximity to neighbours recommends feasible implementation strategies. Recommendations for Practitioners: To support professionals, this paper presents a comprehensive classification schema and design rationale for AR. When designing an AR experience, the schema serves as a design template and is intended to ensure comprehensive discussion and decision making across the spectrum of design choices. Recommendations for Researchers: The classification schema presents a standardized and complete framework for the review of literature and AR applications that other researchers will benefit from to more readily identify relevant related work. Impact on Society: The potential of AR has not been fully realized. The classification scheme presented in this paper provides opportunities to deliberately design and evaluate novel forms of AR experience. Future Research: The classification schema can be extended to include explicit support for the design of virtual and extended reality applications.

Aim/Purpose: The aim of this paper is to analyze various text classification techniques employed in practice, their strengths and weaknesses, to provide an improved awareness regarding various knowledge extraction possibilities in the field of data mining. Background: Artificial Intelligence is reshaping text classification techniques to better acquire knowledge. However, in spite of the growth and spread of AI in all fields of research, its role with respect to text mining is not well understood yet. Methodology: For this study, various articles written between 2010 and 2017 on “text classification techniques in AI”, selected from leading journals of computer science, were analyzed. Each article was completely read. The research problems related to text classification techniques in the field of AI were identified and techniques were grouped according to the algorithms involved. These algorithms were divided based on the learning procedure used. Finally, the findings were plotted as a tree structure for visualizing the relationship between learning procedures and algorithms. Contribution: This paper identifies the strengths, limitations, and current research trends in text classification in an advanced field like AI. This knowledge is crucial for data scientists. They could utilize the findings of this study to devise customized data models. It also helps the industry to understand the operational efficiency of text mining techniques. It further contributes to reducing the cost of the projects and supports effective decision making. Findings: It has been found more important to study and understand the nature of data before proceeding into mining. The automation of text classification process is required, with the increasing amount of data and need for accuracy. Another interesting research opportunity lies in building intricate text data models with deep learning systems. It has the ability to execute complex Natural Language Processing (NLP) tasks with semantic requirements. Recommendations for Practitioners: Frame analysis, deception detection, narrative science where data expresses a story, healthcare applications to diagnose illnesses and conversation analysis are some of the recommendations suggested for practitioners. Recommendation for Researchers: Developing simpler algorithms in terms of coding and implementation, better approaches for knowledge distillation, multilingual text refining, domain knowledge integration, subjectivity detection, and contrastive viewpoint summarization are some of the areas that could be explored by researchers. Impact on Society: Text classification forms the base of data analytics and acts as the engine behind knowledge discovery. It supports state-of-the-art decision making, for example, predicting an event before it actually occurs, classifying a transaction as ‘Fraudulent’ etc. The results of this study could be used for developing applications dedicated to assisting decision making processes. These informed decisions will help to optimize resources and maximize benefits to the mankind. Future Research: In the future, better methods for parameter optimization will be identified by selecting better parameters that reflects effective knowledge discovery. The role of streaming data processing is still rarely explored when it comes to text classification.

Aim/Purpose: The clustering techniques are normally considered to determine the significant and meaningful subclasses purposed in datasets. It is an unsupervised type of Machine Learning (ML) where the objective is to form groups from objects based on their similarity and used to determine the implicit relationships between the different features of the data. Cluster Analysis is considered a significant problem area in data exploration when dealing with arbitrary shape problems in different datasets. Clustering on large data sets has the following challenges: (1) clusters with arbitrary shapes; (2) less knowledge discovery process to decide the possible input features; (3) scalability for large data sizes. Density-based clustering has been known as a dominant method for determining the arbitrary-shape clusters. Background: Existing density-based clustering methods commonly cited in the literature have been examined in terms of their behavior with data sets that contain nested clusters of varying density. The existing methods are not enough or ideal for such data sets, because they typically partition the data into clusters that cannot be nested. Methodology: A density-based approach on traditional center-based clustering is introduced that assigns a weight to each cluster. The weights are then utilized in calculating the distances from data vectors to centroids by multiplying the distance by the centroid weight. Contribution: In this paper, we have examined different density-based clustering methods for data sets with nested clusters of varying density. Two such data sets were used to evaluate some of the commonly cited algorithms found in the literature. Nested clusters were found to be challenging for the existing algorithms. In utmost cases, the targeted algorithms either did not detect the largest clusters or simply divided large clusters into non-overlapping regions. But, it may be possible to detect all clusters by doing multiple runs of the algorithm with different inputs and then combining the results. This work considered three challenges of clustering methods. Findings: As a result, a center with a low weight will attract objects from further away than a centroid with higher weight. This allows dense clusters inside larger clusters to be recognized. The methods are tested experimentally using the K-means, DBSCAN, TURN*, and IDCUP algorithms. The experimental results with different data sets showed that IDCUP is more robust and produces better clusters than DBSCAN, TURN*, and K-means. Finally, we compare K-means, DBSCAN, TURN*, and to deal with arbitrary shapes problems at different datasets. IDCUP shows better scalability compared to TURN*. Future Research: As future recommendations of this research, we are concerned with the exploration of further available challenges of the knowledge discovery process in clustering along with complex data sets with more time. A hybrid approach based on density-based and model-based clustering algorithms needs to compare to achieve maximum performance accuracy and avoid the arbitrary shapes related problems including optimization. It is anticipated that the comparable kind of the future suggested process will attain improved performance with analogous precision in identification of clustering shapes.

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.

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%.

Alzheimer's disease (AD), a leading cause of dementia and mortality, presents a growing concern due to its irreversible progression and the rising costs of care. Early detection is crucial for managing AD, which begins with memory deterioration caused by the damage to neurons involved in cognitive functions. Although incurable, treatments can manage its symptoms. This study introduces a hybrid AlexNet+ResNet-50 model for AD diagnosis, utilising a pre-trained convolutional neural network (CNN) through transfer learning to analyse MRI scans. This method classifies MRI images into Alzheimer's disease (AD), moderate cognitive impairment (MCI), and normal control (NC), enhancing model efficiency without starting from scratch. Incorporating transfer learning allows for refining the CNN to categorise these conditions accurately. Our previous work also explored atlas-based segmentation combined with a U-Net model for segmentation, further supporting our findings. The hybrid model demonstrates superior performance, achieving 94.21% accuracy in identifying AD cases, indicating its potential as a highly effective tool for early AD diagnosis and contributing to efforts in managing the disease's impact.

Aim/Purpose: This study took place in a school with a “paperless classroom” policy. In this school, handwriting and reading on paper were restricted. The purpose of this study was to gain insights from the teachers teaching in a paperless classroom and to learn about the benefits and challenges of teaching and learning in such an environment. Background: In recent years, many schools are moving towards a “paperless classroom” policy, in which teachers and students use computers (or other devices such as tablet PCs) as an alternative to notebooks and textbooks to exchange information and assignments electronically both in and out of class. This study took place in a school with a “paperless classroom” policy. In this school, handwriting and reading on paper were uncommon. Methodology: This qualitative study involved semi-structured interviews with 12 teachers teaching in a paperless school. The research questions dealt with the instruc-tional model developed, the various ways in which the teachers incorporated the technology in their classrooms, and the challenges and difficulties they encountered. Contribution: This study provides important advice to the way teachers have to work in paperless classrooms. Findings: It pointed out the contribution to students in three ways: preparing students for the future; efficiency of learning; empowerment of students. The teachers presented a variety of innovative methods of using the laptops in class and described a very similar structure of the lesson. The teachers described the difficulties involved in conducting a paperless classroom instruction and emphasized that despite the efficiency of the computer and its ability to support the teaching process, they used technology critically. The findings also indicate that some teachers were concerned that the transition from the regular classroom to a paperless one may negatively impact students’ reading and writing skills. Recommendations for Practitioners: Teaching in a paperless school is challenging. On the one hand, going paperless contributes to active and adaptive learning, efficiency, and the acquisition of 21st-century skills or, as they described their main goal, to prepare students for the future. On the other hand, computers in class cause problems such as distraction and disciplinary issues, information overload, and disorganized information as well as technological concerns. Impact on Society: Teachers in the paperless school develop a solid rationale relying on ideas for teaching and learning in a paperless environment, and use varied technologies and develop innovative pedagogies. They are aware of the challenges of this environment and concerned about the disadvantages of using the technology. Thus they develop a realistic and critical view of the paperless classroom. Future Research: Future studies investigating the teachers’ voice as well as the pupils’ aspect could help guide schools in preparing teachers for the paperless classroom.

Aim/Purpose: This preface presents the papers included in the ninth issue of the Interdisciplinary Journal of e-Skills and Lifelong Learning (IJELL) special series of selected Chais Conference best papers. Background: The Chais Conference for the Study of Innovation and Learning Technologies: Learning in the Technological Era, is organized by the Research Center for Innovation in Learning Technologies, The Open University of Israel. The 12th Chais Conference was held at The Open University of Israel, Raanana, Israel, on February 14-15, 2017. Each year, selected papers of the Chais conference are expanded and published in IJELL. Methodology: A qualitative conceptual analysis of the themes and insights of the papers included in the ninth selection of IJELL special series of selected Chais Conference best papers. Contribution: The presentation of the papers of this selection emphasizes their novelty, as well as their main implications, describes current research issues, and chronicles the main themes within the discourse of learning technologies research, as reflected at the Chais 2017 conference. Findings: Contemporary research goes ‘beyond the walls of the classroom’ and investigates systemic and pedagogical aspects of integrating learning technologies in education on a large scale. Recommendation for Researchers: Researchers are encouraged to investigate broad aspects of seizing the opportunities and overcoming the challenges of integrating innovative technologies in education. Impact on Society: Effective application of learning technologies has a major potential to improve the well-being of individuals and societies. Future Research: The conceptual analysis of contemporary main themes of innovative learning technologies may provide researchers with novel directions for future research on various aspects of the effective utilization of learning technologies.

Aim/Purpose: This review paper aims to unveil some underlying machine-learning classification algorithms used for political election predictions and how stack ensembles have been explored. Additionally, it examines the types of datasets available to researchers and presents the results they have achieved. Background: Predicting the outcomes of presidential elections has always been a significant aspect of political systems in numerous countries. Analysts and researchers examining political elections rely on existing datasets from various sources, including tweets, Facebook posts, and so forth to forecast future elections. However, these data sources often struggle to establish a direct correlation between voters and their voting patterns, primarily due to the manual nature of the voting process. Numerous factors influence election outcomes, including ethnicity, voter incentives, and campaign messages. The voting patterns of successors in regions of countries remain uncertain, and the reasons behind such patterns remain ambiguous. Methodology: The study examined a collection of articles obtained from Google Scholar, through search, focusing on the use of ensemble classifiers and machine learning classifiers and their application in predicting political elections through machine learning algorithms. Some specific keywords for the search include “ensemble classifier,” “political election prediction,” and “machine learning”, “stack ensemble”. Contribution: The study provides a broad and deep review of political election predictions through the use of machine learning algorithms and summarizes the major source of the dataset in the said analysis. Findings: Single classifiers have featured greatly in political election predictions, though ensemble classifiers have been used and have proven potent use in the said field is rather low. Recommendation for Researchers: The efficacy of stack classification algorithms can play a significant role in machine learning classification when modelled tactfully and is efficient in handling labelled datasets. however, runtime becomes a hindrance when the dataset grows larger with the increased number of base classifiers forming the stack. Future Research: There is the need to ensure a more comprehensive analysis, alternative data sources rather than depending largely on tweets, and explore ensemble machine learning classifiers in predicting political elections. Also, ensemble classification algorithms have indeed demonstrated superior performance when carefully chosen and combined.

'What's going on?' Within the context of our critically-informed teaching practice, we see moments of deep learning and reflexivity in classroom discussions and assessments. Yet, these moments of criticality are interspersed with surface learning and reflection. We draw on dichotomous, linear developmental, and messy explanations of learning processes to empirically explore the learning journeys of 20 international Chinese and 42 domestic New Zealand students. We find contradictions within our own data, and between our findings and the extant literature. We conclude that expressions of surface learning and reflection are considerably more complex than they first appear. Moreover, developing critical reflexivity is a far more subtle, messy, and emotional experience than previously understood. We present the theoretical and pedagogical significance of these findings when we consider the implications for the learning process and the practice of management education.

Beside making organizations look like their peers through the adoption of similar attributes (which we call alignment), this paper highlights the fact that conformity also enables organizations to stand out by exhibiting highly salient attributes key to their field or industry (which we call conventionality). Building on the conformity and status literatures, and using the case of major U.S. symphony orchestras and the changes in their concert programing between 1879 and 1969, we hypothesize and find that middle-status organizations are more aligned, and middle-status individual leaders make more conventional choices than their low- and high-status peers. In addition, the extent to which middle-status leaders adopt conventional programming is moderated by the status of the organization and by its level of alignment. This paper offers a novel theory and operationalization of organizational conformity, and contributes to the literature on status effects, and more broadly to the understanding of the key issues of distinctiveness and conformity.

Improving equity in data science, edited by Colby Tofel-Grehl and Emmanuel Schanzer, is a thought-provoking exploration of how data science education can be transformed to foster equity, especially within K-16 classrooms. The editors advocate for redefining

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