Big data management is a reality for an increasing number of organizations in many areas and represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial to facilitate the management of big data in any kind of organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management can be supported by these three dimensions: technology, people and processes. Hence, this article discusses these dimensions: the technological dimension that is related to storage, analytics and visualization of big data; the human aspects of big data; and, in addition, the process management dimension that involves in a technological and business approach the aspects of big data management.

An algorithm learning tool was developed for an introductory computer science class in a specialized science and technology high school in Japan. The tool presents lessons and simple visualizations that aim to facilitate teaching and learning of fundamental algorithms. Written tests and an evaluation questionnaire were designed and implemented along with the learning tool among the participants. The tool’s effect on the learning performance of the students was examined. The differences of the two types of visualizations offered by the tool, one with more input and control options and the other with fewer options, were analyzed. Based on the evaluation questionnaire, the scales with which the tool can be assessed according to its usability and pedagogical effectiveness were identified. After using the algorithm learning tool there was an increase in the posttest scores of the students, and those who used the visualization with more input and control options had higher scores compared to those who used the one with limited options. The learning objectives used to evaluate the tool correlated with the test performance of the students. Properties comprised of learning objectives, algorithm visualization characteristics, and interface assessment are proposed to be incorporated in evaluating an algorithm learning tool for novice learners.

Internship courses, unlike others, are multi-pronged because they require coordination at different levels. Typically, a faculty member coordinates the communication and implementation at each level to achieve the desired outcomes. We call the position that this faculty holds the “internship coordinator”. For the work of the internship coordinator to be successful, he/she may need to synchronize the work of the internship with all parties involved. Failure to coordinate at one level or another may affect the work of other parties involved in completing the internship for the students. This paper explains the experience of an internship program at the computer science department (COSC) at Indiana University Indiana University of Pennsylvania (IUP). We focus on the work of the internship coordinator for this program and his work to communicate and coordinate to successfully implement the internship experience for the students. We first discuss the theoretical foundation that led to the development of internship programs in academia and then elaborate on the multiple levels of the role of the internship coordinator in completing the internship experience for the students.

This paper presents a validation research of seven Digital Learning Domains (DLDs) and sixty-five performance statements (PSs) as perceived by students with experience in learning via ICT. The preliminary findings suggest a statistical firmness of the inventory. The seven DLDs identified are Social Responsibility, Team-based Learning, Information Research and Retrieval, Information Management, Information Validation, Processing and Presentation of Information, and Digital Integrity. The 65 PSs will enable a teacher to identify the level of competency the learner has in each DLD, thus identifying students’ strengths and weaknesses that must be addressed in order to facilitate learning in the current era. As can be concluded from the findings, most of the participants evaluate themselves as digitally literate with regard to the basic information research and retrieval skills, validation and information management. But when it comes to PSs that require complex decision making or higher order thinking strategies, it seems that a large number of participants lack these skills. Also, social responsibility and digital integrity domains are perceived as known by the participants but not very well taken in terms of pro-active action to enforce appropriate digital behavior, or avoiding illegally obtained music or movies.

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: Assess the affordances and constraints of SimReal+ in teacher education Background There is a huge interest in visualizations in mathematics education, but there is little empirical support for their use in educational settings Methodology: Single case study with 22 participants from one class in teacher education. Quantitative and qualitative methods to collect students’ responses to a survey questionnaire and open-ended questions Contribution: The paper contributes to the understanding of affordances and constraints of visualization tools in mathematics education Findings: The visualization tool SimReal+ has potential for learning mathematics in teacher education, but the user interface should be improved to make it more usable for different users. Teachers need to consider technological and pedagogical affordances of SimReal+ at the student, classroom, and mathematics subject level Recommendations for Practitioners: Address technological and pedagogical affordances of SimReal+ Recommendation for Researchers: Improve the design of SimReal+ to make it technologically and pedagogically more usable Impact on Society: Understand the affordances and constraints of visualization tools in education Future Research: Implement a next cycle of experimentation with SimReal+ in teacher education to ensure more validity and reliability

Aim/Purpose: The uptake of university by regional students has been problematic for various reasons. This paper discusses a program, initiated by a South Australian regional university campus, aimed at attracting regional students into higher education. Background: A qualitative descriptive approach to study was used to determine the value of the program on participating students and school staff. Year 10 students from Roxby Downs, Port Augusta and Port Lincoln high schools were invited to participate in a two-day regionally-focussed school-university engagement program that linked students with the university campus and local employers. Methodology: A survey was administered to determine the impact of the program. Perceptions about the program by school staff were gathered using a modified One-Minute Harvard questionnaire. While 38 Year 10 students and 5 school staff members participated, 37 students and 3 staff evaluated the program. Findings: The findings revealed that the majority of the students would like to attend university, but financial and social issues were important barriers. The students learned about the regional university, what it can offer in terms of programs and support, and the employment prospect following university. The school staff benefited by developing a closer relationship with students and becoming better informed about the regional university. Recommendation for Practitioners: One way by which university uptake may be increased is to provide similar immersion programs featuring engagement with employers, our recommendation to other regional universities. In increasing the levels of education, individuals, communities and the society in general are benefited.

Aim/Purpose: The primary objective of this research is to build an enhanced framework for Applied and Computational Math. This framework allows a variety of applied math concepts to be organized into a meaningful whole. Background: The framework can help students grasp new mathematical applications by comparing them to a common reference model. Methodology: In this research, we measure the most frequent words used in a sample of Math and Computer Science books. We combine these words with those obtained in an earlier study, from which we constructed our original Computational Math scale. Contribution: The enhanced framework improves the Computational Math scale by integrating selected concepts from the field of Data Science. Findings: The resulting enhanced framework better explains how abstract mathematical models and algorithms are tied to real world applications and computer implementations. Future Research: We want to empirically test our enhanced Applied and Computational Math framework in a classroom setting. Our goal is to measure how effective the use of this framework is in improving students’ understanding of newly introduced Math concepts.

Aim/Purpose: This paper investigates the changing paradigms for technical skills that are needed by Data Engineers in 2018. Background: A decade ago, data engineers needed technical skills for Relational Database Management Systems (RDBMS), such as Oracle and Microsoft SQL Server. With the advent of Hadoop and NoSQL Databases in recent years, Data Engineers require new skills to support the large distributed datastores (Big Data) that currently exist. Job demand for Data Scientists and Data Engineers has increased over the last five years. Methodology: This research methodology leveraged the Pig programming language that used MapReduce software located on the Amazon Web Services (AWS) Cloud. Data was collected from 100 Indeed.com job advertisements during July of 2017 and then was uploaded to the AWS Cloud. Using MapReduce, phrases/words were counted and then sorted. The sorted phrase / word counts were then leveraged to create the list of the 20 top skills needed by a Data Engineer based on the job advertisements. This list was compared to the 20 top skills for a Data Engineer presented by Stitch that surveyed 6,500 Data Engineers in 2016. Contribution: This paper presents a list of the 20 top technical skills required by a Data Engineer.

Aim/Purpose: The aim of this project was to explore a method to enable an updated under-standing of digital literacy to be implemented in curricula in an environment of an existing, but outdated, understanding of digital literacy. . Background: The changing healthcare environment increasingly emphasizes the importance of digital literacy skills; therefore academics in the optometry discipline at Deakin University sought to better understand where digital literacy skills were taught in their program, and whether delivery was implicit or explicit. Methodology: This case study describes a systematic review of the optometric curriculum to first identify where and what digital literacy skills are currently being addressed in the curriculum, identify the gaps, and develop a strategy to address the gaps. Contribution: The main outcome of this work is the development of a spiraling curriculum to support the development of digital literacy skills required in later units of the program and for clinical practice post-graduation. Findings: Although the definition of digital literacy may be outdated, the digital literacy capabilities being addressed in the curriculum had grown as digital technology use by staff and students had expanded. This, together with the realization that students were not as digitally capable as expected, indicated that teaching digital literacy skills needed to be made overt throughout the curriculum. Recommendations for Practitioners: The process developed through this case study provides a strong foundation for course teams, curriculum developers and educational designers to efficiently analyze digital literacy expectations in existing, accredited health-related curricula and improve the curricula by more overtly embedding digital literacy teaching into it. Impact on Society: Graduates of the amended program of study are expected to be better prepared to undertake their future careers in a digitally enhanced and disrupted environment. Future Research: The framework will be used to explore digital literacy teaching practices in other disciplines. A systematic evaluation will be undertaken to identify the benefits and short comings of using the framework. The elements that make up the new definition of digital literacy need to be better articulated to allow curriculum developers to be better informed as to how to interpret the framework in their context.

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: Board of Directors seek to use their big data as a competitive advantage. Still, scholars note the complexities of corporate governance in practice related to information security risk management (ISRM) effectiveness. Background: While the interest in ISRM and its relationship to organizational success has grown, the scholarly literature is unclear about the effects of Chief Technology Officers (CTOs) leadership styles, the alignment of the governance of big data, and ISRM effectiveness in organizations in the West-ern United States. Methodology: The research method selected for this study was a quantitative, correlational research design. Data from 139 participant survey responses from Chief Technology Officers (CTOs) in the Western United States were analyzed using 3 regression models to test for mediation following Baron and Kenny’s methodology. Contribution: Previous scholarship has established the importance of leadership styles, big data governance, and ISRM effectiveness, but not in a combined understanding of the relationship between all three variables. The researchers’ primary objective was to contribute valuable knowledge to the practical field of computer science by empirically validating the relationships between the CTOs leadership styles, the alignment of the governance of big data, and ISRM effectiveness. Findings: The results of the first regression model between CTOs leadership styles and ISRM effectiveness were statistically significant. The second regression model results between CTOs leadership styles and the alignment of the governance of big data were not statistically significant. The results of the third regression model between CTOs leadership styles, the alignment of the governance of big data, and ISRM effectiveness were statistically significant. The alignment of the governance of big data was a significant predictor in the model. At the same time, the predictive strength of all 3 CTOs leadership styles was diminished between the first regression model and the third regression model. The regression models indicated that the alignment of the governance of big data was a partial mediator of the relationship between CTOs leadership styles and ISRM effectiveness. Recommendations for Practitioners: With big data growing at an exponential rate, this research may be useful in helping other practitioners think about how to test mediation with other interconnected variables related to the alignment of the governance of big data. Overall, the alignment of governance of big data being a partial mediator of the relationship between CTOs leadership styles and ISRM effectiveness suggests the significant role that the alignment of the governance of big data plays within an organization. Recommendations for Researchers: While this exact study has not been previously conducted with these three variables with CTOs in the Western United States, overall, these results are in agreement with the literature that information security governance does not significantly mediate the relationship between IT leadership styles and ISRM. However, some of the overall findings did vary from the literature, including the predictive relationship between transactional leadership and ISRM effectiveness. With the finding of partial mediation indicated in this study, this also suggests that the alignment of the governance of big data provides a partial intervention between CTOs leadership styles and ISRM effectiveness. Impact on Society: Big data breaches are increasing year after year, exposing sensitive information that can lead to harm to citizens. This study supports the broader scholarly consensus that to achieve ISRM effectiveness, better alignment of governance policies is essential. This research highlights the importance of higher-level governance as it relates to ISRM effectiveness, implying that ineffective governance could negatively impact both leadership and ISRM effectiveness, which could potentially cause reputational harm. Future Research: This study raised questions about CTO leadership styles, the specific governance structures involved related to the alignment of big data and ISRM effectiveness. While the research around these variables independently is mature, there is an overall lack of mediation studies as it relates to the impact of the alignment of the governance of big data. With the lack of alignment around a universal framework, evolving frameworks could be tested in future research to see if similar results are obtained.

Aim/Purpose. The problem statement in the proposed study focuses on that, despite the growing recognition that teenagers need to undergo security awareness training, little is known about the impacts security training experts believe implementing a mandatory gamified security awareness training curriculum in public middle schools will have on the long-term security behavior of students in Texas. Background. This study was guided by the research question: What are the impacts security training experts believe implementing a mandatory gamified security aware-ness training curriculum in public middle schools will have on the long-term security behaviors of students in Texas? The study gathers opinions from experts on the impacts of security awareness training on students. Methodology. Our research used semi-structured interviews with twelve experts chosen through the use of purposive sampling. The population for the study consisted of experts in the fields of security awareness training for and teaching middle school-aged children. Candidates were recruited through the Cyber-Texas Foundation and snowball sampling techniques. Contribution. The research contributed to the body of knowledge by using interviews to explore the impacts of security awareness training on middle school students based on the opinions and views of the teachers and instructors who work with middle school students. Findings. The findings of this study demonstrate that middle school is an ideal time to provide cybersecurity training and will impact student behaviors by making them more conscious of cyber threats and preparing them to be more tech-savvy professionals. The research also showed that well-designed cybersecurity games with real-world application combined with traditional teaching techniques can help students develop positive habits. The research also suggests that teachers possess the skills to teach cybersecurity classes and the classes can be integrated into the current school day without the need for any significant changes to existing daily schedules. Recommendations for Practitioners. A well-design gamification-based curriculum implemented in Texas Middle Schools, combined with traditional teaching techniques and repeated over an extended time period, will impact students’ behaviors by making them more able to recognize and respond to cyber risks and will transform them into more secure and tech-savvy members of society. Recommendations for Researchers. The research shows middle school instructors and technology experts believe the implementation of a security awareness training program in middle schools is both possible and practical, while also beneficial to the students. The recommendation is to encourage researchers to explore ways to build curricula and games capable of appealing to students and implementing the instruction into school programs. Impact on Society. Demonstrating that training provided in middle school will make lasting impacts and improvements to student behaviors benefits children and their families in the short-term and workplaces in the long-term. The development of a more security-conscious workforce can reduce the significant number of data breaches and cyber attacks resulting from the poor security habits of companies’ users. Future Research. Future research that will add significant value to the body of knowledge includes testing the effectiveness of habit-shaping games to determine whether existing long-term games maintain student interest. Qualitative studies could interview parents of teenagers using habit-shaping games to determine the effectiveness of the applications. Another qualitative study could interview teachers to determine how teachers’ ages affect their comfort level teaching technology classes. Both studies could provide valuable insights into how to implement security awareness training in schools.

Aim/Purpose. The study aimed to examine the remote and face-to-face experience of pedagogical training in kindergarten after the third COVID-19 closure in Israel. Background. The outbreak of the COVID-19 epidemic in 2020 changed the training system, and preservice teachers were required to have their practical experience in the kindergartens both remotely and face-to-face. They had to adapt to the new requirements of teacher training programs and receive professional coaching and support from the pedagogical instructor remotely. Methodology. The sample comprised 26 early childhood preservice teachers, who received academic training that includes proficiency in digital technology. The data were collected through feedback that they wrote themselves during the training period and analyzed in the interpretive approach. Contribution. The contribution of the present study is that it examines the pedagogical coaching from the perspective of preservice teachers in a kindergarten during the COVID-19 epidemic, which forced a transition from face-to-face to remote pedagogical training, then back to face-to-face pedagogical instruction. To the best of my knowledge, no such study has been carried out to date, which makes it unique. Findings. The main findings indicate the dissatisfaction of most preservice kindergarten teachers with the remote pedagogical training (about 85%) at the physical, emotional, technological, and pedagogical levels, and the satisfaction of most preservice kindergarten teachers with face-to-face pedagogical training (about 92%) at the physical, emotional, and pedagogical levels. The main conclusion is that technology is a potential barrier in training, and that preservice kindergarten teachers need a pedagogical instructor present at a professional face-to-face meeting. Recommendations for Practitioners. The findings of the study show how important in-person learning and engagement is for everyone especially for Preservice teachers’ and may be helpful for pedagogical coaching teams. Recommendations for Researchers. Preservice teachers’ awareness of the pedagogical coaching experiences could persuade the coaching teams to avoid potential difficulties, increase emotional support, and refine the use of technology to make it a closer substitute for frontal communication. Impact on Society. Face-to-face training based on interpersonal relationship, allows to develop better during the training period.