Abstract This paper introduces a competitive, interactive classroom game designed to teach predictive analytics by simulating the end‐to‐end process of building machine learning models. The game is conducted in two rounds engaging students in tasks such as variable selection, model building, parameter calibration, and performance evaluation, all within the context of optimizing a fundraising campaign. Played in undergraduate and MBA programs, the game emphasizes hands‐on learning, teamwork, and decision‐making under time constraints. Analysis of model performance across two rounds showed that 74.3% of student teams improved their results after the debriefing session, demonstrating the game's effectiveness in reinforcing predictive modeling concepts through iterative feedback. Results from an anonymous survey (n = 78) demonstrated high levels of engagement and learning outcomes, with 93.6% of students recommending the game for future use. Key findings include a mean score of 2.10 (on a scale of –3 to +3) for enhancing the learning experience through competition, 1.94 for increasing interest in analytics careers, and 1.90 for encouraging deeper critical thinking compared to traditional exercises. These outcomes highlight the game's effectiveness in bridging theoretical concepts with real‐world applications, encouraging critical thinking, and preparing students for analytics‐focused careers.
Abstract This article presents a role‐playing activity designed to address a common challenge in analytical courses, where students rely on software outputs without fully understanding the underlying analytical concepts. The activity highlights the continued significance of human analysts in interpreting results, guiding analysis, and leveraging domain expertise, skills that persist as essential even with advancements in AI. The exercise simulates the software maintenance stage, requiring students to switch between user and developer roles to identify and fix software bugs. This structure promotes both technical proficiency and soft skill development, particularly in communication and collaborative problem‐solving. Student feedback, including qualitative reflections and quantitative survey results, demonstrated significant learning gains, with over 80% reporting improvements in analytical reasoning, debugging skills, and communication abilities. The activity aligns with Education 4.0 principles by promoting interdisciplinary collaboration, adaptive learning, and experiential engagement. The article concludes with practicl recommendations for integrating such activities into analytical coursework, emphasizing the importance of clear initial guidance and reflective debriefing to maximize learning outcomes.
Abstract The rapid expansion of online MBA programs has improved flexibility and accessibility but continues to face challenges related to limited personalization, reduced interactivity, and summative feedback. To mitigate these issues, we introduce FLEX (Feedback‐Linked Education for Excellence), an AI‐powered educational platform designed to enhance learning efficiency through dynamic, iterative feedback and active engagement. Developed using R Shiny and integrated with OpenAI's API, FLEX provides a modular, user‐friendly scaffolding tool that supports reflective learning, active learning, personalized learning, and formative feedback. Utilizing the Apple Transportation Case, a logistics‐focused assignment, FLEX guided MBA students at a large US university through critical decision‐making tasks while offering tailored feedback. Results indicated significant improvements in reflective learning, active engagement, personalized learning and formative feedback. This article also discusses FLEX's potential applications across disciplines and its implications for the future of online education.
Abstract The growing demand for data literacy across disciplines has intensified the need for instructional approaches that balance technical rigor with accessibility, engagement, and real‐world relevance. In data science education, code‐centric instruction can support technical depth, but it may also divert novice learners’ attention toward syntax and debugging at the expense of problem formulation, interpretation, and decision reasoning. At the same time, the rise of generative AI has changed this landscape by reducing some syntax‐related barriers while increasing the importance of validation, critique, and responsible workflow design. This paper positions low‐code/no‐code (LC/NC) analytics environments as one component of a broader hybrid pedagogy for teaching analytics and data science. Drawing on cognitive load theory, constructivist learning, and prior classroom and practitioner literature, the paper develops a conceptual roadmap for using visual workflow tools, GenAI supports, and selective coding in complementary ways. Rather than arguing that LC/NC platforms replace programming, the paper proposes that they can serve as effective scaffolds for helping diverse learners engage earlier with analytical reasoning, collaboration, and end‐to‐end workflow thinking. The paper concludes with practical implications for educators seeking to design more inclusive and pedagogically coherent data science curricula.
Prompt engineering has emerged as a critical skill for optimizing interactions with Large Language Models (LLMs) across diverse applications. Despite its growing influence in the commercial sector, this discipline remains underrepresented in academic curricula, particularly within business schools. To address this gap, we present a comprehensive syllabus for an introductory prompt engineering course, tailored specifically for future digital product designers and managers. This course equips students with the skills to harness LLMs effectively, transforming them into valuable tools for problem-solving and innovation. By detailing the course's motivation, objectives, key assignments, and expected outcomes, this article demonstrates how integrating prompt engineering education can prepare business students to navigate and lead in an AI-driven marketplace, ultimately bridging the divide between academic learning and industry demands.
The Burrito Optimization Game is a fun, free, and interactive online game that introduces learners to basic concepts behind mathematical optimization (MO) in the fields of operations research (OR), business analytics (BA), and supply chain management (SCM). In the game, players drag and drop burrito trucks onto a city map to serve customers and maximize the burrito company's profits. The game presents a classic MO challenge called a facility location problem. This article discusses an experiment to explore the effectiveness of the game as an aid for teaching MO in a classroom environment. Results show that the game effectively builds understanding and confidence in MO, while also reinforcing prior knowledge when paired with instructor-guided discussion.
This study employs the Fuzzy Analytical Hierarchy Process (FAHP) to assess stakeholder preferences regarding MBA program attributes and delivery modes, specifically online versus on-site, in the post-COVID-19 context of Nepal. Data were collected from three core stakeholder groups: Academic Bodies (faculty and administrators), Business Sector professionals (employers and HR managers), and Decision-Makers (students, alumni, applicants, and parents). Results reveal distinct value orientations: Academic Bodies prioritized curricular frameworks and instructional quality, showing a slight preference for online delivery; the Business Sector emphasized adaptive learning and job readiness, favoring on-site programs; Decision-Makers focused on employability and cost, also preferring on-site formats. The findings provide a structured, comparative view of stakeholder priorities and delivery preferences, offering evidence-based guidance for reforming MBA programs. This study demonstrates the utility of FAHP in triangulating stakeholder input and enhancing policy decisions in management education within developing contexts.
Accurate demand forecasting is critical for optimizing supply chains, yet traditional statistical models struggle with complex, nonlinear demand patterns. Recurrent Neural Networks (RNNs) offer a powerful alternative by capturing sequential dependencies and adapting to shifting trends. However, RNNs steep learning curve presents challenges for business students with limited technical backgrounds and instructors with varying technical expertise. This study responds to such challenges by developing a structured instructional framework that bridges the gap between traditional forecasting and RNN-based techniques in business education. Through a scaffolded learning approach with comprehensive implementation support, students progressively transition from statistical models to hands-on RNN implementation using Python. Pre-module and post-module assessments demonstrate significant gains in conceptual understanding, technical proficiency, and the ability to apply RNN-driven forecasts in decision-making contexts. A high level of student engagement further reinforces the effectiveness of this approach. This study contributes to the literature by advancing RNN education beyond purely technical considerations through the development of a structured instructional framework accessible to business students with limited programming experience and instructors across diverse institutional contexts. By establishing an adaptable educational model, this study facilitates the integration of RNN-driven forecasting into business curricula, enhancing students' ability to apply machine learning techniques in real-world contexts.
Traditional lecture-based teaching methods can fall short of preparing students for the complexities of modern workplaces. With the arrival of generative AI (GenAI) in both workplaces and academia, faculty must choose whether and how to introduce students to the powers of artificial intelligence. This teaching brief explores the implementation of a series of scaffolded lessons using GenAI in a quantitative, undergraduate operations management course. Leveraging Fink's theory of significant learning and Kuh's High Impact Practices (HIPs), the initiative integrates real-world applications of artificial intelligence to enhance student learning and career readiness. This brief explains how the teaching innovation allowed students to compare the practical value of Excel versus ChatGPT in conducting statistical analysis. Industry experts and a site visit provided insights into the use of generative AI in business contexts. Results indicate that these course enhancements elevate student interest in operations management and business analytics and offer a model for future improvements in business education.
This study leverages the digital sticky note feature in Zoom's whiteboard to provide students with opportunities to gain a better understanding of supply chain management through engagement and hands-on experience in a synchronous online class. The activity was implemented within an online graduate course in Operations and Supply Chain Management at the College of Business Administration at a public university. By using digital sticky notes to brainstorm and exchange ideas with their teammates, students designed supply chains for selected products. The activity provided students with the opportunity to interact with their peers, instructors, and course content, promoting high levels of engagement and enhancing comprehension of the supply chain concept. Survey results indicate that participants had a positive learning experience and an enhanced understanding of supply chain management.
This teaching brief highlights innovative adaptations of established pedagogical techniques to equip students with critical skills for success in Industry 4.0. More specifically, this brief focuses on two courses: an undergraduate Operations and Supply Chain Management course and an MBA Supply Chain Management course, both designed to foster critical thinking, adaptability, and analytical skills aligned with the principles of Education 4.0. The MBA course is detailed, showcasing a dynamic structure that blends flipped classroom sessions, collaborative group activities, industry news discussions, real-time simulations like the Beer Game, and guest speaker engagements to deepen industry awareness and refine professional skills. The undergraduate course is presented with a brief overview, emphasizing an interconnected approach where students progress through foundational topics using iterative learning and culminate their experience with a competitive simulation. These approaches demonstrate how thoughtful integration of well-established methods with tailored innovations can support student learning in a technology-driven, globalized world. In this way, this teaching brief extends the current literature on deliberate practice in business education by offering a more comprehensive framework that integrates technical, strategic, and interpersonal competencies tailored to the demands of the modern supply chain landscape. In doing so, this pedagogical approach bridges the gap between academic instruction and industry practice, equipping students with the hard and soft skills essential for thriving in digital, automated, and interconnected supply chain environments.
As educators, we seek engaging ways to demonstrate the crucial importance of developing appropriate management (HR) practices when undertaking international expansion. In this exercise, teams are asked to formulate and justify human resource/talent management policy modifications that contribute to a competitive advantage for a hypothetical North American niche grocery chain (Friendly & Fresh Foods) planning expansion into China. The exercise's unique value is its focus on the interconnectedness between business strategy, talent management, and culture in helping to “win” the local challenge when entering an unfamiliar territory where global leaders may need to adapt both competitive strategy and cultural practices to achieve business entry success. Recommendations are honed after consideration of their assigned home country's business model (differentiation or cost leadership) and the host country's culture. The choice of China as the expansion country serves to magnify the “distances” that must be examined when expanding internationally, in this case when a North American operation decides to expand its operations to China. The exercise's theoretical foundation is informed by international business, global human resource management, and cultural research. The exercise's effectiveness is well supported by graduate students who have experienced the exercise and by faculty evaluations of team presentations.
Supply chain disruptions can lead to shortages. To manage this risk, companies often adopt a customer segmentation strategy to prioritize customers when confirming orders. To protect the limited supply for important customers, strategic segments are created, where some segments are given priority at the expense of other segments. In enterprise resource planning (ERP) software, this process is behind the scenes and automated, meaning that students do not have the opportunity to grasp the logic and process involved. To extend student knowledge in this area, we developed an exercise through which students can learn the underlying rationale behind the customer segmentation strategy. In the exercise, students prioritize orders from current and new customers based on the customer segment. The results of a survey on this exercise provided evidence of student learning. Modeling the process in spreadsheets helps students gain knowledge and skills in automated backorder processing in ERP systems.
Preparing for tomorrow's supply chain leaders today involves an understanding of how students perceive the educational value of classroom learning approaches. To better understand how to prepare our students for the Industry 4.0 era, we surveyed current and former students on their perceptions of pedagogical methods that contributed to their learning and preparation to enter the workforce as supply chain management professionals. Results from former students, who are working in the supply chain management field, indicate that real-world teaching methods have a higher impact on learning than methods that are not as real-world. However, both groups rated guest speakers as the most effective learning method and lectures as the least effective. We offer an exploratory study motivated by students’ informal post-discussion of learning via Zoom during COVID-19. This article offers insights into how teachers should deliver supply chain content based on the student's perception of learning, an approach that will prepare students for supply chain management careers in the Industry 4.0 era.
In recent years, automated warehouses have become increasingly important to meet the rising demand of supply chain operations. Despite their growing relevance to industry, these systems remain largely underrepresented in academic settings, which contributes to a significant gap in student knowledge and preparedness. Traditional educational approaches often fail to equip future professionals with the practical skills required by modern logistics systems. While the learning factory paradigm partially addresses this gap, it typically places limited emphasis on logistics processes. To bridge this divide between theory and industrial practice, a hands-on learning experience was conducted in a logistics-focused learning factory involving Bachelor's and Master's engineering students. A structured questionnaire was administered to evaluate students’ perceptions of automated warehouses, and statistical methods were employed to analyze both the short- and medium-term impacts of the experience. Findings revealed a strong interest among students in industrial logistics, despite limited prior exposure to automation technologies. Consistent with previous research, the hands-on approach was particularly effective for Master's students, highlighting its potential as a valuable educational tool in logistics engineering.
Flying a paper airplane as a standalone activity is hardly a novel instructional practice. However, in a typical university business analytics course where most work is on a computer, such a tactile experience is innovative. Aiming to build a paper airplane as an artifact, measure and record its characteristics, and report results provides an engaging opportunity for students to simulate a supply chain work experience. The paper airplane competition described in this brief provides instructors freedom within a framework to customize the practice of data acquisition for management decision-making. The illustration of key concepts in operations and supply chain management leads students to content and experiences consistent with components of the Education 4.0 framework. Course data include high self-reported levels of engagement; thus, we recommend using the paper airplane activity to demonstrate the business analytics process from data acquisition through visualization.
This teaching brief presents a new experiential exercise for teaching statistical process control (SPC) within a quality management course. This novel approach involves a semester-long personal quality control project, where students apply SPC methodology to their daily habits, using data collected from their smart devices. Surveys conducted at the end of the semester show that students felt more confident using quality control concepts both personally and professionally. The survey results also suggest that students believe the exercise helped them to better understand their own daily habits and aided their ability to apply quality management methodologies through greater engagement with the concepts. This innovative teaching method both enhances students' theoretical understanding and prepares them for effective problem-solving in industry settings.
Design thinking and design-oriented information systems share commonalities in applying specific toolsets to develop product and system designs that address strategic, managerial, and operational problems. How can design thinking be embedded as an innovative and creative learning process to facilitate decision-making in business intelligence and analytics within a classroom environment, particularly during the proof-of-concept stage? This study aims to expand these implications both academically and practically by presenting a technological, data-oriented design process for integrating design thinking into business intelligence (BI) and business analytics (BA) curriculums. The proposed data-oriented design approach highlights five areas that serve as the building blocks of the BI and BA strategy: problem, data, analytics, technology, and user spaces. The case study of a retail supermarket provides guidelines on how alternative designs that emerge during the problem formulation stage of the design thinking approach are transformed into prototypes in the proof-of-concept stage and are subsequently tested and implemented to demonstrate their proof-of-value and proof-of-use in the retail industry. This study also outlines six key learning experiences-categorized as objectives, assessment, space, activities, artifacts, and culture-for teachers, students, IS scholars, CIOs, CDOs, and other top management to create a design-oriented organizational structure.
Microsoft Excel and Access are widely used in introductory courses designed to teach the fundamentals of spreadsheet and relational database modeling skills in various disciplines. To achieve the targeted learning outcomes, instructors of these courses strive to provide students with guided, frequent, and incrementally fruitful practice with the software. Instructors often can facilitate such practice using the actual software or by employing a virtually simulated version of the software integrated into a learning management system. This article presents the results of a study that compared the effectiveness of using actual versus simulated Excel and Access environments as alternative or complementary instructional platforms in an introductory undergraduate course in information systems. The underlying study distinguished between instructor-led guidance using the actual software and autonomous self-paced instructions provided in the simulated environment while using multiple objective and subjective learning assessment tools. The mixed impacts of these scenarios promote a hybrid learning approach that uses both strategies to most effectively enhance students' competency in Excel and Access.
Compared to more complex personality assessments, Wired That Way by Marita Littauer, presents four personality types that students find easy to understand and internalize: Popular Sanguine, Powerful Choleric, Perfect Melancholy, and Peaceful Phlegmatic. Students' awareness of their own and their peers' classification in this comprehensive personality plan can be useful in the administration of group projects and other classroom activities. The understanding and application of the innate strengths and weaknesses of each personality type in a group setting provides tools and language that enable students to navigate challenges. This Teaching Brief presents the benefits and process of incorporating Wired That Way into the administration of group projects. Recommendations are based on analyzing the outcomes of a group project over 6 years for 402 students in 125 groups in 18 sections of an upper-division undergraduate Business Administration course in Tourism and Hospitality Management. Analysis results demonstrate that the personality makeup of individuals in a group can influence project component grades, most notably how the personalities in each group influence creativity, group cohesion, and overall project performance.
