The number of online resources available for teaching and learning in higher education has been growing enormously during the last decade. A recent development is the emergence of Massive Open Online Courses (MOOCs) and of Open Educational Resources (OER). The result is a huge number of videos that are available on line. Can these videos enrich learning? As a pilot study we added sixteen videos to an existing introductory course in Human-Computer Interaction. This course is mandatory in the Bachelor programs Computer Science and Information Science (second year). Watching the videos was optional for the students. The videos originated for the most part from the MOOC Human-Computer Interaction, produced by Stanford University. We offered this course to a pilot group of eight students. The educational context was problem-based learning in distance education. The videos were welcomed by all of the students and were found to be useful in their learning process. The students watched the videos intensively and appreciated them very well. A main reason for the students to be positive about the videos was that they liked to alternate reading texts and watching videos.

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 most common course delivery model is based on teacher (knowledge provider) - student (knowledge receiver) relationship. The most visible symptom of this situation is over-reliance on textbook’s tutorials. This traditional model of delivery reduces teacher flexibility, causes lack of interest among students, and often makes classes boring. Especially this is visible when teaching Computer Literacy courses. Instead, authors of this paper suggest a new active model which is based on MS Office simulation. The proposed model was discussed within the framework of three activities: guided software simulation, instructor-led activities, and self-directed learning activities. The model proposed in the paper of active teaching based on software simulation was proven as more effective than traditional.

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: This article examines how learning activities draw on resources in the work context to learn. Background The background is that if knowledge no longer is seen mainly as objects, but processes, how then to understand boundary objects? Our field study of learning activities reveals the use of pictures, documents and emotions for learning in the geographically distributed Norwegian Labor Inspection Authority Methodology: The study is a qualitative study consisting of interview data, observation data, and documents. Contribution: Contribute to practice based theorizing. Findings: Three ideal types of representing practices have been identified, i.e., ‘Visualizing’, ‘Documenting’ and ‘Testing’. All three are combined with storytelling, sensing, reflections and sensemaking, which point at the importance of processes in learning. The article also add insights about how emotions can be an important resource for boundary spanning – and sensemaking – by creating the capability of reflecting upon and integrating different knowledge areas in the in- practice context. Recommendations for Practitioners: Look for boundary objects within your field to promote online learning. Recommendation for Researchers: Study boundary objects in work context to understand learning. Impact on Society Role of objects in human learning. Future Research: Focus on how emotions can be used for online learning.

Aim/Purpose: Medical images are very sensitive data that can be transferred to medical laboratories, professionals, and specialist for referral cases or consultation. Strict security measures must be utilized to keep these data secured in computer networks when transferred to another party. On a daily basis, unauthorized users derive ways to gain access to sensitive patient medical information. Background: One of the best ways to which medical image could be kept secured is through the use of quantum cryptography Methodology : Applying the principles of quantum mechanics to cryptography has led to a remarkable new dimension in secured network communication infrastructure. This enables two legitimate users to produce a shared secret random bit string, which can be used as a key in cryptographic applications, such as message encryption and authentication. Contribution: This paper can make it possible for the healthcare and medical professions to construct cryptographic communication systems to keep patients’ transferred data safe and secured. Findings: This work has been able to provide a way for two authorized users who are in different locations to securely establish a secret network key and to detect if eavesdropping (a fraudulent or disruption in the network) has occurred Recommendations for Practitioners: This security mechanism is recommended for healthcare providers and practitioners to ensure the privacy of patients’ medical information. Recommendation for Researchers: This paper opens a new chapter in secured medical records Impact on Society Quantum key distribution promises network security based on the fundamental laws of quantum mechanics by solving the problems of secret-key cryptography . Future Research: The use of post-quantum cryptography can be further researched.

Aim/Purpose: Although cassava is one of the crops that can be grown during the dry season in Northeastern Thailand, most farmers in the region do not know whether the crop can grow in their specific areas because the available agriculture planning guideline provides only a generic list of dry-season crops that can be grown in the whole region. The purpose of this research is to develop a predictive model that can be used to predict suitable areas for growing cassava in Northeastern Thailand during the dry season. Background: This paper develops a decision support system that can be used by farmers to assist them determine if cassava can be successfully grown in their specific areas. Methodology: This study uses satellite imagery and data on land characteristics to develop a machine learning model for predicting suitable areas for growing cassava in Thailand’s Nakhon-Phanom province. Contribution: This research contributes to the body of knowledge by developing a novel model for predicting suitable areas for growing cassava. Findings: This study identified elevation and Ferric Acrisols (Af) soil as the two most important features for predicting the best-suited areas for growing cassava in Nakhon-Phanom province, Thailand. The two-class boosted decision tree algorithm performs best when compared with other algorithms. The model achieved an accuracy of .886, and .746 F1-score. Recommendations for Practitioners: Farmers and agricultural extension agents will use the decision support system developed in this study to identify specific areas that are suitable for growing cassava in Nakhon-Phanom province, Thailand Recommendation for Researchers: To improve the predictive accuracy of the model developed in this study, more land and crop characteristics data should be incorporated during model development. The ground truth data for areas growing cassava should also be collected for a longer period to provide a more accurate sample of the areas that are suitable for cassava growing. Impact on Society: The use of machine learning for the development of new farming systems will enable farmers to produce more food throughout the year to feed the world’s growing population. Future Research: Further studies should be carried out to map other suitable areas for growing dry-season crops and to develop decision support systems for those crops.

Aim/Purpose: Blended learning can transform students experience and learning in higher education. Although the literature extensively explores benefits of blended learning, limited research exists to provide a detailed design principle for implementing instructional activities in blended courses and its usage as tool to influence learning outcomes for second language first year accounting learners. Background: The objective of this study is to find out how the learning experience of students was impacted and by designing and implementing blended learning and connectivity between online and face-to-face learning. This paper reviews the challenges and benefits of blended learning and highlights teachers’ and students’ perceptions on the impact of the connectivity of online and face-to-face activities on students’ learning. Methodology: Data was collected from students enrolled in the course using an open-ended questionnaire. There were 220 respondents, representing a response rate of 65%. Data was extracted from the online learning data and grade center. Teachers’ experiences and observations were also noted. The survey results were analyzed using content analysis. Contribution: Research focusing on blended learning design and implementation is limited, and there is no one size fits all when it comes to blended learning. Consequently, this paper contributes to the discussion by highlighting how second language, first-year accounting students benefit from blended learning and the connectivity between online and face-to-face activities. Increased flexibility for learners appears to be one of the most cited rationale for the combination of traditional with online instructional methods, however, this study evaluates blended learning as a tool for transforming the learning experience of second language, first year accounting students. Findings: Findings show that students benefit from blended learning, and connectivity between online and in-class activities allows students to exploit the advantages of both online and face-to-face learning. Students can see the relevance of what they are doing online and how that contributes to their in-class activities and, hence, are motivated to complete the activities. Recommendations for Practitioners: Educators should use a well-designed blended learning pathway to empower students to be in charge of their learning. Placing materials online creates more and better opportunities for engaging students in class. Institutional support is important when implementing blended learning. Recommendations for Researchers: There is a need for more studies on blended learning design and implementation. Future researchers may carry out more studies on how blended learning design affects student engagement and learning for second language learners in other courses. Impact on Society: A blended learning pathway would greatly benefit second language learners to learn better and empower them to be more independent as a self-directed learner who is able to utilize their time wisely. Community of practice is an excellent platform to encourage teaching teams to work together and create innovative teaching and assessment materials. Future Research: Future studies may carry out the study using other methods for example quantitative surveys and interviews to get a deeper understanding of both students and teachers’ perceptions and experiences.

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: Providing both formative and summative assessment that allows students to learn from their mistakes is difficult in large classes. This paper describes an automated assessment system suitable for courses with even 100 or more students. Background: Assessment is a vital part of any course of study. Ideally students should be given formative assessment with feedback during the course so students and tutors can identify weaknesses and focus on what needs improvement before summative assessment, which results in a grade. This paper describes and automated assessment system that lessens the burden of providing formative assessment in large classes. Methodology: We used Checkpoint, a web-based automated assessment system, to grade assignments in a number of different computer science courses. Contribution: The students come from diverse backgrounds, with a wide range of ages, previous qualifications and technical skills, and our approach allows the students to work at their own pace according to their individual needs, submitting their solutions as many times as they wish up to a deadline, using feedback provided by the system to help identify and correct their mistakes before trying again. Findings: Use of automated assessment allows us to achieve the goals of both summative and formative assessment: we allow students to learn from their mistakes without incurring a penalty, while at the same time awarding them a grade to validate their efforts. The students have an overwhelmingly positive view about our use of automated assessment, and their comments support our views on the assessment process. Recommendations for Practitioners: Because of the increasing number of students in today’s courses, we recommend using automated assessment wherever possible.

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: 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: The primary purpose of this study is to provide a cost-effective and artificial intelligence enabled security solution for IoT enabled healthcare ecosystem. It helps to implement, improve, and add new attributes to healthcare services. The paper aims to develop a method based on an artificial neural network technique to predict suspicious devices based on bandwidth usage. Background: COVID has made it mandatory to make medical services available online to every remote place. However, services in the healthcare ecosystem require fast, uninterrupted facilities while securing the data flowing through them. The solution in this paper addresses both the security and uninterrupted services issue. This paper proposes a neural network based solution to detect and disable suspicious devices without interrupting critical and life-saving services. Methodology: This paper is an advancement on our previous research, where we performed manual knowledge-based intrusion detection. In this research, all the experiments were executed in the healthcare domain. The mobility pattern of the devices was divided into six parts, and each one is assigned a dedicated slice. The security module regularly monitored all the clients connected to slices, and machine learning was used to detect and disable the problematic or suspicious devices. We have used MATLAB’s neural network to train the dataset and automatically detect and disable suspicious devices. The different network architectures and different training algorithms (Levenberg–Marquardt and Bayesian Framework) in MATLAB software have attempted to achieve more precise values with different properties. Five iterations of training were executed and compared to get the best result of R=99971. We configured the application to handle the four most applicable use cases. We also performed an experimental application simulation for the assessment and validation of predictions. Contribution: This paper provides a security solution for the IoT enabled healthcare system. The architectures discussed suggest an end-to-end solution on the sliced network. Efficient use of artificial neural networks detects and block suspicious devices. Moreover, the solution can be modified, configured and deployed in many other ecosystems like home automation. Findings: This simulation is a subset of the more extensive simulation previously performed on the sliced network to enhance its security. This paper trained the data using a neural network to make the application intelligent and robust. This enhancement helps detect suspicious devices and isolate them before any harm is caused on the network. The solution works both for an intrusion detection and prevention system by detecting and blocking them from using network resources. The result concludes that using multiple hidden layers and a non-linear transfer function, logsig improved the learning and results. Recommendations for Practitioners: Everything from offices, schools, colleges, and e-consultation is currently happening remotely. It has caused extensive pressure on the network where the data flowing through it has increased multifold. Therefore, it becomes our joint responsibility to provide a cost-effective and sustainable security solution for IoT enabled healthcare services. Practitioners can efficiently use this affordable solution compared to the expensive security options available in the commercial market and deploy it over a sliced network. The solution can be implemented by NGOs and federal governments to provide secure and affordable healthcare monitoring services to patients in remote locations. Recommendation for Researchers: Research can take this solution to the next level by integrating artificial intelligence into all the modules. They can augment this solution by making it compatible with the federal government’s data privacy laws. Authentication and encryption modules can be integrated to enhance it further. Impact on Society: COVID has given massive exposure to the healthcare sector since last year. With everything online, data security and privacy is the next most significant concern. This research can be of great support to those working for the security of health care services. This paper provides “Security as a Solution”, which can enhance the security of an otherwise less secure ecosystem. The healthcare use cases discussed in this paper address the most common security issues in the IoT enabled healthcare ecosystem. Future Research: We can enhance this application by including data privacy modules like authentication and authorisation, data encryption and help to abide by the federal privacy laws. In addition, machine learning and artificial intelligence can be extended to other modules of this application. Moreover, this experiment can be easily applicable to many other domains like e-homes, e-offices and many others. For example, e-homes can have devices like kitchen equipment, rooms, dining, cars, bicycles, and smartwatches. Therefore, one can use this application to monitor these devices and detect any suspicious activity.

Aim/Purpose: 5G and IoT are two path-breaking technologies, and they are like wall and climbers, where IoT as a climber is growing tremendously, taking the support of 5G as a wall. The main challenge that emerges here is to secure the ecosystem created by the collaboration of 5G and IoT, which consists of a network, users, endpoints, devices, and data. Other than underlying and hereditary security issues, they bring many Zero-day vulnerabilities, which always pose a risk. This paper proposes a security solution using network slicing, where each slice serves customers with different problems. Background: 5G and IoT are a combination of technology that will enhance the user experience and add many security issues to existing ones like DDoS, DoS. This paper aims to solve some of these problems by using network slicing and implementing an Intrusion Detection System to identify and isolate the compromised resources. Methodology: This paper proposes a 5G-IoT architecture using network slicing. Research here is an advancement to our previous implementation, a Python-based software divided into five different modules. This paper’s amplification includes induction of security using pattern matching intrusion detection methods and conducting tests in five different scenarios, with 1000 up to 5000 devices in different security modes. This enhancement in security helps differentiate and isolate attacks on IoT endpoints, base stations, and slices. Contribution: Network slicing is a known security technique; we have used it as a platform and developed a solution to host IoT devices with peculiar requirements and enhance their security by identifying intruders. This paper gives a different solution for implementing security while using slicing technology. Findings: The study entails and simulates how the IoT ecosystem can be variedly deployed on 5G networks using network slicing for different types of IoT devices and users. Simulation done in this research proves that the suggested architecture can be successfully implemented on IoT users with peculiar requirements in a network slicing environment. Recommendations for Practitioners: Practitioners can implement this solution in any live or production IoT environment to enhance security. This solution helps them get a cost-effective method for deploying IoT devices on a 5G network, which would otherwise have been an expensive technology to implement. Recommendation for Researchers: Researchers can enhance the simulations by amplifying the different types of IoT devices on varied hardware. They can even perform the simulation on a real network to unearth the actual impact. Impact on Society: This research provides an affordable and modest solution for securing the IoT ecosystem on a 5G network using network slicing technology, which will eventually benefit society as an end-user. This research can be of great assistance to all those working towards implementing security in IoT ecosystems. Future Research: All the configuration and slicing resources allocation done in this research was performed manually; it can be automated to improve accuracy and results. Our future direction will include machine learning techniques to make this application and intrusion detection more intelligent and advanced. This simulation can be combined and performed with smart network devices to obtain more varied results. A proof-of-concept system can be implemented on a real 5G network to amplify the concept further.

Aim/Purpose: The aim of this study was to identify the critical predictors affecting project desertion in Blockchain projects. Background: Blockchain is one of the innovations that disrupt a broad range of industries and has attracted the interest of software developers. However, despite being an open-source software (OSS) project, the maintenance of the project ultimately relies on small core developers, and it is still uncertain whether the technology will continue to attract a sufficient number of developers. Methodology: The study utilized a systematic literature review (SLR) and an expert review method. The SLR identified 21 primary studies related to project desertion published in Scopus databases from the year 2010 to 2020. Then, Blockchain experts were asked to rank the importance of the identified predictors of project desertion in Blockchain. Contribution: A theoretical framework was constructed based on Social Cognitive Theory (SCT) constructs; personal, behavior, and environmental predictors and related theories. Findings: The findings indicate that the 12 predictors affecting Blockchain project desertion identified through SLR were important and significant. Recommendations for Practitioners: The framework proposed in this paper can be used by the Blockchain development community as a basis to identify developers who might have the tendency to abandon a Blockchain project. Recommendation for Researchers: The results show that some predictors, such as code testing tasks, contributed code decoupling, system integration and expert heterogeneity that are not covered in the existing developer turnover models can be integrated into future research efforts. Impact on Society: This study highlights how an individual’s design choices could determine the success or failure of IS projects. It could direct Blockchain crypto-currency investors and cyber-security managers to pay attention to the developer’s behavior while ensuring secure investments, especially for crypto-currencies projects. Future Research: Future research may employ additional methods, such as a meta-analysis, to provide a comprehensive picture of the main predictors that can predict project desertion in Blockchain.

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: 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 research aims to design a website-based group decision support system (DSS) user interface to support an integrated and sustainable waste management plan in Jagatera. The main focus of this research is to design a group DSS to help Jagatera prioritize several waste alternatives to be managed so that Jagatera can make the right decisions to serve the community. Background: The Indonesian government and various stakeholders are trying to solve the waste problem. Jagatera, as a waste recycling company, plays a role as a stakeholder in managing waste. In 2024, Jagatera plans to accept all waste types, which impacts the possibility of increasing waste management costs. If Jagatera does not have a waste management plan, this will impact reducing waste management services in the community. To solve this problem, the group DSS assists Jagatera in prioritizing waste based on aspects of waste management cost. Methodology: Jagatera, an Indonesian waste recycling company, is implementing a group DSS using the soft system methodology (SSM) method. The SSM process involves seven stages, including problem identification, problem explanation using rich pictures, system design, conceptual model design, real-life comparison, changes, and improvement steps. The final result is a prototype user interface design addressing the relationship between actors and the group DSS. The analytical hierarchy process (AHP) method prioritized waste based on management costs. This research obtained primary data from interviews with Jagatera management, a literature review regarding the group DSS, and questionnaires to determine the type of waste and evaluate user interface design. Contribution: This research focuses on determining waste handling priorities based on their management. It contributes the DSS, which uses a decision-making approach based on management groups developed using the SSM and AHP methods focused on waste management decisions. It also contributes to the availability of a user interface design from the DSS group that explains the interactions between actors. The implications of the availability of DSS groups in waste recycling companies can help management understand waste prioritization problems in a structured manner, increase decision-making efficiency, and impact better-quality waste management. Combining qualitative approaches from SSM to comprehend issues from different actor perspectives and AHP to assist quantitative methods in prioritizing decisions can yield theoretical implications when using the SSM and AHP methods together. Findings: This research produces a website-based group DSS user interface design that can facilitate decision-making using AHP techniques. The user interface design from the DSS group was developed using the SSM approach to identify complex problems at waste recycling companies in Indonesia. This study also evaluated the group DSS user interface design, which resulted in a score of 91.67%. This value means that the user interface design has met user expectations, which include functional, appearance, and comfort needs. These results also show that group DSS can enhance waste recycling companies’ decision-making process. The results of the AHP technique using all waste process information show that furniture waste, according to the CEO, is given more priority, and textile waste, according to the Managing Director. Group DSS developed using the AHP method allows user actors to provide decisions based on their perspectives and authority. Recommendations for Practitioners: This research shows that the availability of a group DSS is one of the digital transformation efforts that waste recycling companies can carry out to support the determination of a sustainable waste management plan. Managers benefit from DSS groups by providing a digital decision-making process to determine which types of waste should be prioritized based on management costs. Timely and complete information in the group DSS is helpful in the decision-making process and increases organizational knowledge based on the chosen strategy. Recommendation for Researchers: Developing a group DSS for waste recycling companies can encourage strategic decision-making processes. This research integrates SSM and AHP to support a comprehensive group DSS because SSM encourages a deeper and more detailed understanding of waste recycling companies with complex problems. At the same time, AHP provides a structured approach for recycling companies to make decisions. The group DSS that will be developed can be used to identify other more relevant criteria, such as environmental impact, waste management regulations, and technological capabilities. Apart from more varied criteria, the group DSS can be encouraged to provide various alternatives such as waste paper, metal, or glass. In addition to evaluating the group DSS’s user interface design, waste recycling companies need to consider training or support for users to increase system adoption. Impact on Society: The waste problem requires the role of various stakeholders, one of which is a waste recycling company. The availability of a group DSS design can guide waste recycling companies in providing efficient and effective services so that they can respond more quickly to the waste management needs of the community. The community also gets transparent information regarding their waste management. The impact of good group DSS is reducing the amount of waste in society. Future Research: Future research could identify various other types of waste used as alternatives in the decision-making process to illustrate the complexity of the prioritization process. Future research could also identify other criteria, such as environmental impact, social aspects of community involvement, or policy compliance. Future research could involve decision-makers from other parties, such as the government, who play an essential role in the waste industry.

Aim/Purpose: In this study, we developed a recommendation system model designed to support decision-makers in identifying consumers eligible for pay-later options via consensus-based decision-making. This approach was chosen due to the high and complex risks involved, such as delayed payments, challenges in reaching consumers, and issues of bad credit. Background: The “pay-later” option, which allows consumers to postpone payment for e-commerce purchases, offers convenience and flexibility but also introduces several challenges: (i) by enabling payment deferral, merchants face financial risks, including potential delays or defaults in payment, adversely affecting their cash flow and profitability; and (ii) this payment delay can also heighten the risk of fraud, including identity theft and unauthorized transactions. Methodology: This study initiated a risk analysis utilizing the ROAD process. Considering contemporary economic developments and advancements in neural networks, integrating these networks into risk assessment has become crucial. Consequently, model development involved the amalgamation of three deep learning methods – CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks), and LSTM (Long Short-Term Memory) – to address various risk alternatives and facilitate multi-stage decision-making recommendations. Contribution: Our primary contribution is threefold. First, our study identified potential consumers by prioritizing those with the smallest associated problem consequence values. Second, we achieved an optimal recall value using a candidate generator. Last, we categorized consumers to assess their eligibility for pay-later rights. Findings: The findings from this study indicate that our multi-stage recommendation model is effective in minimizing the risk associated with consumer debt repayment. This method of consumer selection empowers policymakers to make informed decisions regarding which consumers should be granted pay-later privileges. Recommendations for Practitioners: This recommendation system is proposed to several key parties involved in the development, implementation, and use of pay-later systems. These parties include E-commerce Executive Management for financial analysis and risk evaluation, the Risk Management Team to assess and manage risks related to users utilizing Pay-Later services, and Sales Managers to integrate Pay-Later services into sales strategies. Recommendation for Researchers: Advanced fraud detection mechanisms were implemented to prevent unauthorized transactions effectively. The goal was to cultivate user confidence in the safety of their financial data by ensuring secure payment processing. Impact on Society: Ensuring consumers understand the terms and conditions of pay-later arrangements, including interest rates, repayment schedules, and potential fees, is crucial. Providing clear and transparent information, along with educating consumers about their financial responsibilities, helps prevent misunderstandings and disputes. Future Research: Our future development plans involve the ongoing assessment of the system’s performance to enhance prediction accuracy. This includes updating models and criteria based on feedback and changes in economic or market conditions. Upholding compliance with security and data privacy regulations necessitates the implementation of protective measures to safeguard consumer information. The implementation of such a system requires careful consideration to ensure fairness and adherence to legal standards. Additionally, it is important to acknowledge that algorithms and models may evolve over time through the incorporation of additional data and continuous evaluations.

Aim/Purpose: This study examined the use of Social Media Applications (SMAs) for accessing health-related information within a heterogeneous population in Jordan. The objective of this study was therefore threefold: (i) to investigate the usage of SMAs, including WhatsApp, Twitter, YouTube, Snapchat, Instagram, and Facebook, for accessing health-related information; (ii) to examine potential variations in the use of SMAs based on demographic and behavioral characteristics; and (iii) to identify the factors that can predict the use of SMAs. Background: There has been limited focus on investigating the behavior of laypeople in Jordan when it comes to seeking health information from SMAs. Methodology: A cross-sectional study was conducted among the general population in Jordan using an online questionnaire administered to 207 users. A purposive sampling technique was employed, wherein all the participants actively sought online health information. Descriptive statistics, t-tests, and regression analyses were utilized to analyze the collected data. Contribution: This study adds to the existing body of research on health information seeking from SMAs in developing countries, with a specific focus on Jordan. Moreover, laypeople, often disregarded by researchers and health information providers, are the most vulnerable individuals who warrant greater attention. Findings: The findings indicated that individuals often utilized YouTube as a platform to acquire health-related information, whereas their usage of Facebook for this purpose was less frequent. Participants rarely utilized Instagram and WhatsApp to obtain health information, while Twitter and Snapchat were very seldom used for this purpose. The variable of sex demonstrated a notable positive correlation with the utilization of YouTube and Twitter for the purpose of finding health-related information. Conversely, the variable of nationality exhibited a substantial positive correlation with the utilization of Facebook, Instagram, and Twitter. Consulting medical professionals regarding information obtained from the Internet was a strong indicator of using Instagram to search for health-related information. Recommendations for Practitioners: Based on the empirical results, this study provides feasible recommendations for the government, healthcare providers, and developers of SMAs. Recommendation for Researchers: Researchers should conduct separate investigations for each application specifically pertaining to the acquisition of health-related information. Additionally, it is advisable to investigate additional variables that may serve as predictors for the utilization of SMAs. Impact on Society: The objective of this study is to enhance the inclination of the general public in Jordan to utilize SMAs for health-related information while also maximizing the societal benefits of these applications. Future Research: Additional research is required to examine social media’s usability (regarding ease of use) and utility (comparing advantages to risks) in facilitating effective positive change and impact in healthcare.

Aim/Purpose: This study investigates the drivers of user satisfaction in e-government services and its influence on continued use intention and citizen trust in government. It employs the integration of the Technology Acceptance Model (TAM) and the Information System Success Model (ISSM). Background: Electronic government, transforming citizen-state interactions, has gained momentum worldwide, including in India, where the aim is to leverage technology to improve citizen services, streamline administration, and engage the public. While prior research has explored factors influencing citizen satisfaction with e-government services globally, this area of study has been relatively unexplored in India, particularly in the post-COVID era. Challenges to widespread e-government adoption in India include a large and diverse population, limited digital infrastructure in rural areas, low digital literacy, and weak data protection regulations. Additionally, global declines in citizen trust, attributed to economic concerns, corruption, and information disclosures, further complicate the scenario. This study seeks to investigate the influence of various factors on user satisfaction and continuance usage of e-government services in India. It also aims to understand how these services contribute to building citizens’ trust in government. Methodology: The data were collected by utilizing survey items on drivers of e-government services, user satisfaction, citizen trust, and continuance use intention derived from existing literature on information systems and e-government. Responses from 501 Indian participants, collected using an online questionnaire, were analyzed using PLS-SEM. Contribution: This study makes a dual contribution to the e-government domain. First, it introduces a comprehensive research model that examines factors influencing users’ satisfaction and continuance intention with e-government services. The proposed model integrates the TAM and ISSM. Combining these models allows for a comprehensive examination of e-government satisfaction and continued intention. By analyzing the impact of user satisfaction on continuance intention and citizen trust through an integrated model, researchers and practitioners gain insights into the complex dynamics involved. Second, the study uncovers the effects of residential status on user satisfaction, trust, and continuance intention regarding e-government services. Findings reveal disparities in the influence of system and service quality on user satisfaction across different user segments. Researchers and policymakers should consider these insights when designing e-government services to ensure user satisfaction, continuance intention, and the building of citizen trust. Findings: The findings indicate that the quality of information, service, system, and perceived usefulness play important roles in user satisfaction with e-government services. All hypothesized paths were significant, except for perceived ease of use. Furthermore, the study highlights that user satisfaction significantly impacts citizen trust and continuance use intention. Recommendations for Practitioners: The findings suggest that government authorities should focus on delivering accurate, comprehensive, and timely information in a secure, glitch-free, and user-friendly digital environment. Implementing an interactive and accessible interface, ensuring compatibility across devices, and implementing swift query resolution mechanisms collectively contribute to improving users’ satisfaction. Conducting awareness and training initiatives, providing 24×7 access to online tutorials, helpdesks, technical support, clear FAQs, and integrating AI-driven customer service support can further ensure a seamless user experience. Government institutions should leverage social influence, community engagement, and social media campaigns to enhance user trust. Promotional campaigns, incentive programs, endorsements, and user testimonials should be used to improve users’ satisfaction and continuance intention. Recommendation for Researchers: An integrated model combining TAM and ISSM offers a robust approach for thoroughly analyzing the diverse factors influencing user satisfaction and continuance intention in the evolving digitalization landscape of e-government services. This expansion, aligning with ISSM’s perspective, enhances the literature by demonstrating how user satisfaction impacts continuance usage intention and citizen trust in e-government services in India and other emerging economies. Impact on Society: Examining the factors influencing user satisfaction and continuance intention in e-government services and their subsequent impact on citizen trust carries significant societal implications. The findings can contribute to the establishment of transparent and accountable governance practices, fostering a stronger connection between governments and their citizens. Future Research: There are several promising avenues to explore to enhance future research. Expanding the scope by incorporating a larger sample size could enable a more thorough analysis. Alternatively, delving into the performance of specific e-government services would offer greater precision, considering that this study treats e-government services generically. Additionally, incorporating in-depth interviews and longitudinal studies would yield a more comprehensive understanding of the dynamic evolution of digitalization.

In the context of asthma severity prediction, this study delves into the feature importance of various symptoms and demographic attributes. Leveraging a comprehensive dataset encompassing symptom occurrences across varying severity levels, this investigation employs visualisation techniques, such as stacked bar plots, to illustrate the distribution of symptomatology within different severity categories. Additionally, correlation coefficient analysis is applied to quantify the relationships between individual attributes and severity levels. Moreover, the study harnesses the power of random forest and the Gini importance methodology, essential tools in feature importance analytics, to discern the most influential predictors in asthma severity prediction. The experimental results bring to light compelling associations between certain symptoms, notably 'runny-nose' and 'nasal-congestion', and specific severity levels, elucidating their potential significance as pivotal predictive indicators. Conversely, demographic factors, encompassing age groups and gender, exhibit comparatively weaker correlations with symptomatology. These findings underscore the pivotal role of individual symptoms in characterising asthma severity, reinforcing the potential for feature importance analysis to enhance predictive models in the realm of asthma management and bioinformatics.

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.