Information Technology (IT) high school learners are constantly struggling to cope with the challenges of succeeding in the subject. IT teachers, therefore, need to be empowered to utilize appropriate teaching–learning strategies to improve IT learners’ success in the subject. By promoting critical thinking skills, IT learners have the opportunity to achieve greater success in the most difficult part of the curriculum, which is programming. Participating IT teachers received once-off face-to-face professional development where some teachers received professional development in critical thinking strategies while other IT teachers received professional development in critical thinking strategies infused into pair programming. To determine how teachers experience these suggested strategies, teachers participated in initial interviews as well as follow-up interviews after they had implemented the suggested strategies. From the interviews, it became evident that teachers felt that their learners benefited from the strategies. Teachers in the pair programming infusing critical thinking strategies focused more on the pair programming implementation than on the totality of pair programming infused with critical thinking. Although teachers were initially willing to change their ways, they were not always willing to implement new teaching–learning strategies.

Many degree courses at technical universities include the subject of control systems engineering. As an addition to conventional approaches Fuzzy Control can be used to easily find control solutions for systems, even if they include nonlinearities. To support further educational training, models which represent a technical system to be controlled are required. These models have to represent the system in a transparent and easy cognizable manner. Furthermore, a programming tool is required that supports an easy Fuzzy Control development process, including the option to verify the results and tune the system behavior. In order to support the development process a graphical user interface is needed to display the fuzzy terms under real time conditions, especially with a debug system and trace functionality. The experiences with such a programming tool, the Fuzzy Control Design Tool (FHFCE Tool), and four fuzzy teaching models will be presented in this paper. The methodical and didactical objective in the utilization of these teaching models is to develop solution strategies using Computational Intelligence (CI) applications for Fuzzy Controllers in order to analyze different algorithms of inference or defuzzyfication and to verify and tune those systems efficiently.

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.

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: The objective of this study was to concept test a new instructional aid called Virtual Pathology Learning Resource (VPLR), which was used as a vehicle to communicate information and enhance teaching and learning of basic sciences (Anatomy, Physiology, and Pathology) to allied health science students at a South Australian university. Background: Pathology was traditionally taught using potted specimens to review disease manifestations independently. However, this approach was found inadequate and ineffective. VPLR is a new teaching platform comprising of digitised human normal and human pathology specimens (histology, histopathology), patient case studies, short answer and critical thinking questions, and self-assessment quizzes. Using authentic learning theory as an educational pedagogy, this learning resource was developed to enhance the teaching and learning of Pathology. Methodology: Cross-sectional study design was used. A survey, given at the end of the course, gathered qualitative and quantitative data concerning the perceptions and experiences of the students about VPLR and its components. The online tool SurveyMonkey was utilised so that students could respond anonymously to a web link that displayed the questionnaire. The perceived impact on students was assessed using an 18-item questionnaire seeking agreement or disagreement with statements about VPLR, multiple choice and open-ended questions querying the best things about VPLR, benefits to be derived, and areas for improvement. Descriptive and frequency analyses were performed. Contribution: The VPLR approach involved rich learning situations, contextualised content, and facilitated greater understanding of disease concepts and problems. Findings: In a sample of 103 Medical Radiation students, 42% of students (N=43) responded to the post-intervention survey. The majority of students reported highly positive effects for each component of the VPLR. The overall results indicated that this tool was a promising strategy in teaching Pathology as it assisted students’ gaining knowledge of the science, facilitated connections between sciences, and allowed students to make better links with professional practice and skills. Recommendations for Practitioners: As students found VPLR to be beneficial, it is recommended that the same approach is applied for the teaching of Pathology to other health science students, such as Nursing. Other universities might consider adopting the innovation for their courses. Recommendation for Researchers: Applying VPLR to teaching other allied health science students will be undertaken next. The innovation will be appropriate for other health science students with particular emphasis on case-based or problem-based learning and combined with clinical experiences. Impact on Society: In reshaping the way of teaching a science course, students are benefited with greater depth of understanding of content and increase motivation to study. These are important to keep students engaged and ready for practice. VPLR may impact on education and technology trends so that exploration and possibilities of initiatives are ongoing to help students become successful learners. Other impacts are the new forms of learning discovered, the renewed focus on group work and collaboration, and maximising the use of technology in innovation. Future Research: Future directions of this research would be to conduct a follow-up of this cohort of students to determine whether the impacts of the innovation were durable, meaning the change in perceptions and behaviour is sustained over time.

Aim/Purpose: The need for Higher Education Institutions (HEIs) to maximize the use of their intellectual property and strategic resources for research and teaching has become ever more evident in recent years. Furthermore, little attention is paid in developing an enabling system that will facilitate knowledge transfer in the Research-Teaching Nexus (RTN). Hence, this study assesses the current state of practice in knowledge management of the nexus in higher education in Oman. It also explores the context of how Knowledge Management System (KMS) for the nexus can be designed and utilized by HEIs and challenges them to rethink their traditional approaches in managing their knowledge as-sets to boost individual and organizational learning. Background: This study provides a Knowledge Management-based framework and design of a knowledge management system that support the academic community towards the improvement of the nexus. This study sets out ideas from various academic and professional experts on how academic stakeholders in the higher education can improve and promote knowledge transfer and make better use of its knowledge and research assets for teaching and learning. It stressed the importance of having the knowledge assets or resources that can easily be pooled, accessed, and made available to its intended stakeholders. Methodology: Data were gathered from 29 out of 49 institutional quality audit reports of all HEIs in Oman. The panel comments were coded and analysed to extract valuable insights regarding the management of knowledge assets in research. Additionally, data were gathered from the institutional accreditation outcomes page of the same website. Manifest and latent content analyses were used in reporting the findings of the panel. Contribution: The study will contribute to a greater understanding and acceptance of Knowledge Management (KM) in higher education and extended the body of knowledge concerning knowledge management for the RTN. Findings: The reports revealed a very limited practice of the nexus in terms of people and culture, structure ad processes, and computing and web technologies. A few staff are involved in RTN work, there is an uneven understanding of the RTN among staff, limited joint research between staff and students are some of the reasons for this. Significantly, there is no explicit research framework or policy for the RTN, and systems and/or mechanisms are limited. Further-more, the reports did not account any use of computing and web technologies for the nexus. These limitations can lead to students with less academic, research, and graduate skills. Hence, this study presents a feature design of a KMS that incorporates various RTN best practices, as informed by the reports and literature. The design will allow the staff to utilize the research assets in the classroom, at the same time, engages students in research and scholarly under-takings. Recommendations for Practitioners: All HEIs must have a innovative system that integrates a formal agenda and approach, and set initiatives, strategies, policies, and procedures for knowledge management in utilizing research assets for teaching and learning. It must be designed so that RTN practices remain up-to-date, relevant, and responsive to the needs of the stakeholders, as well as, address academic accreditation challenges. Recommendation for Researchers: Researchers can evaluate the knowledge management of RTN practices of other HEIs outside of Oman to effectively recommend the proper course of action for teaching and learning improvement. Impact on Society: This study will redefine the role and contribution of HEIs, which are key players in advancing a knowledge economy. HEIs are expected to be powerhouses where academic knowledge is discovered, created, disseminated, shared, and re-invented. They must be able to fully grasp the value of managing knowledge to be able to effect positive and purposeful change to the community. Future Research: Future work should include staff and student surveys that examine the knowledge management need of the learning organization to better inform the design of a KMS for the RTN. Thereafter, future research can test the stage to test the effectiveness of the conceptual design.

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

Aim/Purpose: 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 authors provide different technology applications useful in online instruction in addition to providing effective strategies for use in a virtual environment. Background: Last year, educators were forced to move their instruction online almost overnight. Many were not prepared to teach effectively in a virtual environment. Contribution: This paper serves as a resource to educators who are unfamiliar with teaching online as well as for those who would like to enhance their current practice. Recommendations for Practitioners: Be flexible when teaching in a virtual environment. Remain open to using new and unfamiliar technologies. Be consistent in providing feedback to students and communicate frequently with them. Impact on Society: The abrupt transition for educators, as well as for most workplaces, to an exclusively online environment in response to COVID has long-lasting effects in how business as usual will be conducted. Being proficient and comfortable in navigating a virtual environment is essential. Future Research: As we continue to work virtually, ongoing research that informs our practice is critical for remaining effective educators. Additionally, it is important to remain knowledgeable about current and new technologies available to us. Keywords online instruction, technology applications, strategies

Health Data collection is one of the major components of public health systems. Decision makers, policy makers, and medical service providers need accurate and timely data in order to improve the quality of health services. The rapid growth and use of mobile technologies has exerted pressure on the demand for mobile-based data collection solutions to bridge the information gaps in the health sector. We propose a prototype using open source data collection frameworks to test its feasibility in improving the vaccination data collection in Kenya. KenVACS, the proposed prototype, offers ways of collecting vaccination data through mobile phones and visualizes the collected data in a web application; the system also sends reminder short messages service (SMS) to remind parents on the date of the next vaccination. Early evaluation demonstrates the benefits of such a system in supporting and improving vaccination of children. Finally, we conducted a qualitative study to assess challenges in remote health data collection and evaluated usability and functionality of KenVACS.

Aim/Purpose: The aim of this study is to examine empirically the effects of certain key Critical Success Factors (CSFs) for the implementation of Enterprise Resource Planning (ERP) Systems on the comprehensive achievement of the crucial roles of Computer-Based Information Systems (CBISs) Background: The effects of the CSFSs were examined in the higher education sector in the Kingdom of Saudi Arabia (KSA) using a case study of the ERP adoption in Prince Sattam Bin Abdulaziz University. Methodology: A theoretical model was proposed based on the literature written on the CSFs and the roles of CBISs in business. The model encompasses six key CSFs and their associations with the realization of the crucial roles of CBISs. To test the proposed model, a questionnaire was developed by considering the most frequently used measurements items in the ERP’s literature. The data were collect-ed from 219 key stakeholders. Contribution: This study acts as one of the few empirical studies in assessing the effects of the important CSFs for ERP implementation upon its successful implementation. Its outcomes provide more insights and clarifications about the effects of six key CSFs on the comprehensive achievement of the crucial CBIS’s roles. Particularly, the uniqueness of this study lies in addressing the effects of these CSFs on the achievement of the vital CBIS’s roles collectively rather than the achievement of each role individually. Moreover, the study examined these effects in the higher education environment, which is characterized by its own special business processes and services. Findings: The results reveal that the six key CSFs have a positive relationship with the comprehensive achievement of the crucial roles of CBISs. These findings are consistent with many previous studies on the effects of the CSFs on the realization of the expected benefits of the enterprise systems. Recommendations for Practitioners: The managers and other key stakeholders should carefully manage the vital aspects of the CSFs in order to realize the promised ERP’s benefits, including the CBIS’s roles. Future Research: Additional empirical examinations are needed to investigate the effects of the rest of the CSFs on realizing the roles of information systems.