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The corona virus (COVID-19) caused serious havoc on society impacting all industries resulting in major changes in business practices, operations, and policy. In the case of academia, at a moment's notice, all universities that used face-to-face instruction were forced to move to an online format and make serious modification to successful courses. This paper documents a successful certification course structure and reports the experience and results of moving that course in Excel comprehension (certification) from traditional face to face to online. In addition, this paper provides a discussion into the strategies used for remote testing and teaching. Overall, the COVID-19 sections were not as successful as previous traditional sections. This paper concludes with lessons learned that can be helpful for future remote or online course deliveries.

Computer programmers in the U.S. labor force are facing a shortage. Focusing on recruiting females has the potential to address this shortage. Computing is a male dominated field which provides an opportunity to recruit the other 50% of the population, females, to fill the open positions. This work studies gender differences in computer programming based on an Hour of Code tutorial. Following a pre- and post-test design, this work demonstrates that males have significantly more previous exposure to computer programming and are significantly more interested in pursuing computer programming. Results also indicate that females do equally as well or better in programming comprehension. In one comprehension question following the tutorial, women significantly outperformed men demonstrating that women may have a higher aptitude for computer programming; however, they are underrepresented in the job market. Based on our results, we suggest that more should be done in early formative years to attract females into computer programming to aid in filling the gap of the projected employment market.

This study investigates the delivery of the "Hour of Code" tutorials to college students. The college students who participated in this study were surveyed about their opinion of the Hour of Code. First, the students' comments were discussed. Next, a content analysis of the offered tutorials highlights their reliance on visual programming in stylized languages with continual feedback in gaming contexts. Difficulties in delivery stem in part from the poor organization of tutorials from Code.org which makes it difficult to locate suitable tutorials. Based on the analysis of the students' comments and the content analysis of the "Hour of Code" tutorials, the authors suggest that a deeper alignment of marketing, teaching organizations, and content providers would help sustain the type of initiative exemplified by the Hour of Code.

Currently, there are more technology related jobs then there are graduates in supply. The need to understand user acceptance of computing degrees is the first step in increasing enrollment in computing fields. Additionally, valid measurement scales for predicting user acceptance of Information Technology degree programs are required. The majority of existing research regarding methods for increasing enrollment focus on subjective measures that are often invalid or invalidated. This research study adapts a well-known, validated and established user acceptance of information technology model (TAM) developed by Davis in 1989. The TAM model was adapted to understand factors for the acceptance of information technology and was based on the long standing Theory of Reasoned Action from behavioral psychology. This work adapts TAM to explore factors that influence parents' decision to recommend Information Technology as a Major to their children. Since parents have a high degree of influence over the major selection of their children, determining factors for recommending IT as a major can assist IT programs in improved marketing to increase enrollment. In this work, we hypothesize that perceived usefulness (PU) and perceived ease of use (PEoU) will impact a parent's likelihood of recommending IT as a major to their children. Results revealed parent's perception of the perceived usefulness of IT (PU) affected their willingness to recommend IT as a major to their children; conversely, parents were not concerned with the ease of use of IT (PEoU). Implications include improved marketing of IT programs to parents by focusing on the usefulness of IT as a discipline.

Phishing and spam have been a cybersecurity threat with the majority of breaches resulting from these types of social engineering attacks. Therefore, detection has been a long‐standing challenge for both academic and industry researcher. New and innovative approaches are required to keep up with the growing sophistication of threat actors. One such illumination which has vast potential are large language models (LLM). LLM emerged and already demonstrated their potential to transform society and provide new and innovative approaches to solve well‐established challenges. Phishing and spam have caused financial hardships and lost time and resources to email users all over the world and frequently serve as an entry point for ransomware threat actors. While detection approaches exist, especially heuristic‐based approaches, LLMs offer the potential to venture into a new unexplored area for understanding and solving this challenge. LLMs have rapidly altered the landscape from business, consumers, and throughout academia and demonstrate transformational potential to profoundly impact the society. Based on this, applying these new and innovative approaches to email detection is a rational next step in academic research. In this work, we present IPSDM, an improved phishing spam detection model based on fine‐tuning the BERT family of models to specifically detect phishing and spam emails. We demonstrate our fine‐tuned version, IPSDM, is able to better classify emails in both unbalanced and balanced datasets. Moreover, IPSDM consistently outperforms the baseline models in terms of classification accuracy, precision, recall, and F1‐score, while concurrently mitigating overfitting concerns. [ABSTRACT FROM AUTHOR]

Teaching programming and mobile application development concepts can be challenging for instructors; however, teaching an interdisciplinary class with varied skill levels amplifies this challenge. To encompass a broad range of students, many instructors have sought to improve their lessons and methods by experimenting with group/team programming. However, these studies focused on the instructor's usage of the method and not the students' perceptions of the method. This study was conducted to understand students' perceptions regarding the effectiveness of the student's group/team experience and learning outcomes when developing a mobile application. Results were favorable towards using group work for mobile application development learning, productivity, enjoyment and confidence of quality.

Enterprise Resource Planning and Customer Relationship Management are becoming important topics at the university level, and are increasingly receiving course-level attention in the curriculum. In fact, the Information Systems Body of Knowledge specifically identifies Enterprise Architecture as an Information Systems-specific knowledge area. The revised Information Systems Curriculum Guide from 2010, sponsored by the Association of Information Systems and the Association of Computing Machinery, suggest Enterprise Architecture as a required course with Enterprise Systems and Business Process Management as suggested electives. Implementing the aforementioned courses into the curriculum poses challenges such as providing necessary resources, overcoming institutional constraints, and the lack of hardware architecture for advanced systems such as Microsoft Dynamics AX. This work addresses three critical issues. First, we provide a suggested technical architecture built upon the Windows Server family and Dynamics AX which may be used to implement ERP and CRM, based on Dynamics AX, into the classroom. Second, we demonstrate connectivity between an installation of Dynamics AX 2012 R3 and CRM 2011 in the cloud using the Microsoft Connector for Dynamics. Finally, we suggest a sample scenario and case for implementing ERP and CRM concepts into the university curriculum.

Computer programming is challenging to teach and difficult for students to learn. Instructors have searched for ways to improve student learning in programming courses. In an attempt to foster hands-on learning and to increase student learning outcomes in a programming course, the authors conducted an exploratory study to examine student created screencasts and their impact on students' performance regarding specific learning outcomes in a hands-on programming course. This study was conducted over four semesters when an instructor taught two sections of the course per semester; one section generated self created student screencasts in-class and the other section did not. The subjects were undergraduate business students enrolled in an upper level applications/programming course at a university in Pennsylvania State System of Higher Education system. The experimental method was used to compare the differences in graded classroom activities, theory assessments, lab assessments, and final exam scores between the classes. Results showed that students who created screencasts while following along with the instructors step by step programming instructions as well as created screencast while independently working significantly (p<0.05) performed more successful on theory assessments, lab assessments, and the final exam scores verses those students that did not.

Cloud Computing and Big Data continue to be disruptive forces in computing and has introduced new threats and vulnerabilities to our networks. The paper seeks to demonstrate how an end-to-end network intrusion detection system can be built, trained, and deployed using Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP). We determined the performance of these tools by building a network intrusion detection system (NIDS) and evaluating the performance of each based on precision, accuracy, F1 Score, recall, user experience, cost and computation time for training and predicting the model. Overall, all three platforms performed greater than 90% accuracy with Google Vertex AI having the highest accuracy using the decision tree and Microsoft Azure performing the best based on accuracy, precision, and computation time. [ABSTRACT FROM AUTHOR]

The focus of this study was to assess the skill requirements for data analytics positions and to understand data analysis employment expectations for new graduates. Furthermore, this work seeks to highlight issues relevant to curriculum management in university degree programs. 786 job postings were analyzed for domain-related, soft skills, as well as degree requirements. Soft skills, often referred to as people skills, comprised the largest part of the results (11 of the top 21 skills). Results revealed the most frequent soft skills were related to communication and teams or teamwork. The most frequent domain skills were related to visualization, data cleaning, data extraction and programming. Implications for curriculum based on results are discussed, and suggestions for future research are provided. [ABSTRACT FROM AUTHOR]

Generative Artificial Intelligence (AI) model is a revolutionary type of AI capable of producing high quality images based on textual inputs. These models utilize natural language processing (NLP) techniques and computer vision to understand and interpret the textual descriptions and then generate images that align with the given descriptions. This study evaluates four prominent text-to-image generative models- DALL-E, Google Imagen, Stable Diffusion, and GROK AI emphasizing on the text-to-image diffusion models. Using a comprehensive evaluation approach, we employ three mathematical formulas the Fréchet Inception Distance (FID), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR) to assess image quality and realism across datasets collected from these AI platforms. Additionally, human evaluations are conducted to compare the perceptual impact of AI-generated images with mathematical metrics. Our findings highlight the varying degrees of perceived realism among different image generative With AI models, DALL-E and Imagen generally being perceived as more realistic than Stable Diffusion and GROK. As examined, human evaluation is the current gold standard in text-to-image evaluation; however, mathematical based metrics also have promise and value. Our findings contribute to the advancement of text-to-image synthesis and our results provide support for FID as the gold standard evaluation method as it most closely represented human evaluation. [ABSTRACT FROM AUTHOR]

Increases in digitalization have made it possible to track and measure every click, every payment, every message, and almost everyone's daily thoughts. Companies are extremely interested in the robustness of this data, specifically regarding understanding the sentiment of consumers. Yet the amount of information being produced and processed is quite staggering causing information overload. As such, companies tend to fall into analysis paralysis which can result in missing important insights that could help their business. The goal of this study is to analyze and categorize the top posts on multiple hacking subreddits to determine the most discussed topics and to examine the sentiment of these posts expressed by the users. We began by scraping data, specifically the title, ID, score, comments, and URL for each top post from multiple hacking subreddit communities. We then used the Natural Language Toolkit (NLTK) to perform the data preprocessing techniques for an effective analytic process and bias-free results. The results of the testing allowed us to filter through the posts and determine whether sentiment was positive, negative, or neutral. In the case of the hacking subreddits, many of the posts were of a neutral opinion. This study aims to provide a contribution by utilizing Natural Language Processing methods Topic Modeling such as Term Frequency Inverse Document Frequency, Latent Semantic Analysis (LSA) algorithm, and Sentiment Analysis to gather and synthesize cybersecurity data. [ABSTRACT FROM AUTHOR]

The practice of including online discussion posts to traditional courses is increasing. Online discussions allow for active learning to occur as students express their ideas and respond to others. The time and thought provided by online discussion posts allows students to utilize higher level cognitive skills. Web-based assessments are another technology tool that instructors are including in their courses. This study examined the impact of online discussion posts on achievement of web-based assessments for an upper level undergraduate business and technology writing intensive course. Using a treatment group and a control group, student achievement scores for the online assessments were measured. Results indicate that assessed grades of the treatment groups were higher than the control group, however statistical significance was mixed among the web assessments. The results further illustrate the need for additional research into online discussions applied to web-based assessments.

Facial Expression analysis (FEA) is a process that involves recognition and understanding of human emotions based on facial cues. While FEA has potential applications in various field, this can also be misused, leading to the spread of misinformation through deepfake technology. This research aims to evaluate the effectiveness of facial expressions in distinguishing between deepfake and genuine videos, addressing the gap in how well FEA can identify manipulated contents. To address this issue, a research experiment was conducted to gain an insight into how people react towards deepfake and authentic contents. Respondents were shown videos and an analysis was conducted on participant's facial expressions as well as assessing their knowledge of deepfake detection. A survey was designed to test their confidence with the level of deepfake and authentic video identification, trust, security, and attitude towards them. Facial expressions were analyzed using Noldus FaceReader 7 to detect and classify 7 facial expressions (such as happy, sad, neutral, angry, surprised, disgusted, and other). The study findings indicate that FaceReader analysis discerns a statistically significant difference in emotional responses between real and deepfake videos, while participants reported a higher percentage of neutrality (70% vs. 62.5%) in real videos compared to deepfakes. [ABSTRACT FROM AUTHOR]

The Hour of Code is a one-hour introduction to computer science organized by Code.org, a non-profit dedicated to expanding participation in computer science. This study investigated the impact of the Hour of Code on students' attitudes towards computer programming and their knowledge of programming. A sample of undergraduate students from two universities was selected to participate. Participants completed an Hour of Code tutorial as part of an undergraduate course. An electronic questionnaire was implemented in a pre-survey and post-survey format to gauge the change in student attitudes toward programming and their programming ability. The findings indicated the positive impact of the Hour of Code tutorial on students' attitude toward programming. However, the students' programming skills did not significantly change. The authors suggest that a deeper alignment of marketing, teaching, and content would help sustain the type of initiative exemplified by the Hour of Code.

Spreadsheets have long played a key role in business decisions and operations. The use and adoption of data analytics has substantially increased over the last few years and amplified this role. Spreadsheets are often a first tool for data analytics as such applications provide ease of calculation with basic statistics and chart development. For much of the last two decades universities have provided training in Microsoft Excel because that was what companies used and demanded. Since mid-2020, there has been an increase in use of Google Sheets causing some faculty to believe that MS Excel should be replaced. Faculty should always be aware of current and future employer demands and ensure programs meet the expectations of both employers and recent graduates. This study reviews business job postings seeking employees with two or fewer years of work experience between 2019 and 2021 and examines demand for spreadsheet application experience. Results overwhelmingly indicate that Microsoft Excel still is the most required spreadsheet application by employers. Faculty should pause before changing MS Excel training or removing certifications. [ABSTRACT FROM AUTHOR]

As a result of the health pandemic, the United States (U.S.) has experienced a labor shortage and a decrease in higher education retention and enrollment which has many educational institutions in a crisis mode as they rapidly search for guaranteed sustainable long-term student enrollment. Numerous research studies have explored students' viewpoints through surveys or focus groups to investigate their preferences regarding online or in-person courses. However, there has not been a research study that has examined the post pandemic registration process of student enrollment data over a traditional scheduling period to determine their unbiased preference in modality offerings. More specifically, when students are given a choice to enroll in an online or face-to-face course, which modality do they independently choose? Student enrollment data collected from a publicly accessible website starting from the first day for priority students and continuing throughout the entire registration period for business and technology courses. Results revealed a significant difference between student enrollment for online versus face-to-face courses. [ABSTRACT FROM AUTHOR]

Natural language Processing is a discipline under artificial intelligence that involves interaction between human language and computer systems. It involves analyzing and representation of natural language, the ability to comprehend both text and spoken words. Natural language processing has evolved to the extent of having the ability to give useful responses to human beings. Large language models have been making landmark advances with new more efficient algorithms and improved hardware and processing power. Models like Google's BERT power predictive text in search predictions. Recently, companies have been training models on billions of parameters, a task that was not feasible just a few short years ago. OpenAI is the market leader in this technology; however, competitors have emerged. This research project aims to investigate perceptions of OpenAI versus an emerging competitor, AI21 on the ability to answer questions and predict text. We developed two web applications that allowed users to key in any questions or text in the textbox, the web application will then answer the user query as a response. The web applications were embedded with Jurassic-1 language model API and the GPT-3 language model APIs. Subjects asked the AI systems questions and rated their perceptions of the results. Furthermore, we investigate perceived privacy of AI systems via a post survey. [ABSTRACT FROM AUTHOR]

Most people interact with websites expecting them to perform quick results and provide quick responses to their requests and many do not realize the performance is due to server side or backend programming. There are several types of backend web framework/scripting technologies. Programmers and developers often debate over which is the technologies is the better solution. Most debates are based on various dimensions such as performance, scalability, and architecture. The most common factor for settling the debate or choosing the most appropriate frameworks tends to be the performance dimension. This study assesses the performance of both Node.js and PHP by implementing well-known algorithms of binary, bubble, and quick sort along with Heap’s algorithm for permutations. These algorithms were selected for their increasing time complexities which allows us to observe the performance differences between the backend framework/scripting. By comparing the performance of these two backend scripting technologies, one can gain a better understanding of the circumstances when migrating from PHP to Node.js would be beneficial. Our results showed that a significant difference occurs in the performance of PHP and Node.js and specifically, Node.js outperformed PHP in terms of latency and other performance metrics. This study provides valuable information for software engineers, developers, and managers who are seeking the best framework for their web applications. [ABSTRACT FROM AUTHOR]