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: Executive doctoral programs in higher education are under-researched. Scholars, administers, and students should be aware of all common delivery methods for higher education graduate programs. Background This paper provides a review and analysis of executive doctoral higher education programs in the United States. Methodology: Executive higher education doctoral programs analyzed utilizing a qualitative demographic market-based analysis approach. Contribution: This review of executive higher education doctoral programs provides one of the first investigations of this segment of the higher education degree market. Findings: There are twelve programs in the United States offering executive higher education degrees, though there are less aggressively marketed programs described as executive-style higher education doctoral programs that could serve students with similar needs. Recommendations for Practitioners: Successful executive higher education doctoral programs require faculty that have both theoretical knowledge and practical experience in higher education. As appropriate, these programs should include tenure-line, clinical-track, and adjunct faculty who have cabinet level experience in higher education. Recommendation for Researchers: Researchers should begin to investigate more closely the small but growing population of executive doctoral degree programs in higher education. Impact on Society: Institutions willing to offer executive degrees in higher education will provide training specifically for those faculty who are one step from an executive position within the higher education sector. Society will be impacted by having someone that is trained in the area who also has real world experience. Future Research: Case studies of students enrolled in executive higher education programs and research documenting university-employer goals for these programs would enhance our understanding of this branch of the higher education degree market.

Aim/Purpose: To understand readiness of students for learning in online environments across different age groups. Background: Online learners today are diverse in age due to increasing adult/mature students who continue their higher education while they are working. Understanding the influence of the learners’ age on their online learning experience is limited. Methodology: A survey methodology approach was followed. A sample of one thousand nine hundred and twenty surveys were used. Correlation analysis was performed. Contribution: The study contributes by adding to the limited body of knowledge in this area and adds to the dimensions of the Online Learning Readiness Survey additional dimensions such as usefulness, tendency, anxiety, and attitudes. Findings: Older students have more confidence than younger ones in computer proficiency and learning skills. They are more motivated, show better attitudes and are less anxious. Recommendations for Practitioners: Practitioners should consider preferences that allow students to configure the learning approach to their age. These preferences should be tied to the dimensions of the online learning readiness survey (OLRS). Recommendations for Researchers: More empirical research is required using OLRS for online learning environments. OLRS factors are strong and can predict student readiness and performance. These are opportunities for artificial intelligence in the support of technology-mediated tools for learning.

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 aim of this study is to propose a teaching approach based on AI-based chatbot agents and to determine whether the use of this approach increases the students’ motivation. Background. Today, chatbots are an integral part of students’ lives where they are used in various contexts. Therefore, we are interested in incorporating these tools into our teaching process in order to profit from their benefits, assist and guide students while working with to prevent issues such as plagiarism and mainly to boost students’ motivation. Methodology. Using the proposed approach, new chatbot based learning activities were de-signed in three different courses for computer science engineering students. A mixed-method experimental study was conducted to evaluate students’ impression and satisfaction. Survey results of the students (N=58) who participated in the experiment (experimental group) were compared to the results of the students from the control group (N=60). Contribution. Trending AI conversational agents can be engaged in daily teaching activities as a learning assistant and coach to boost students motivation and skills development. Findings. Our study focuses on the impact of chatbots on student’s motivation. The study aimed to analyze the benefits and drawbacks associated with these conversational chatbots. Our findings revealed the significant role that chatbots can play in enhancing student motivation and improving teaching practices.

Aim/Purpose. Business schools need to design, develop and deliver courses that are relevant to business problem-solving. Current pedagogies do not often provide the insight – or experience – necessary to close the gap between theory and practice. Background. The paper describes an initiative to design, develop and deliver courses in business-technology problem-solving that thoroughly immerses students in the actual world of business. Methodology. The methodology included case-based analysis where actual cases where selected to model problem-solving scenarios. Contribution. Several courses are developed that immerse students into actual problem-solving experiences. Findings The courses will be delivered to business students to assess the impact of immersive/experiential learning. Recommendations for Practitioners. Additional courses should be informed by actual cases; the commitment to relevance should be expanded. Recommendations for Researchers. Ongoing research to measure the impact of immersive/experiential learning is recommended. Impact on Society. Business schools should rethink the content of their courses and the pedagogies that have dominated business schools for many decades. Future Research. Additional research will include more courses and additional immersive/experiential pedagogies.