An Excel Solver Exercise to Introduce Nonlinear Regression

Business students taking business analytics courses that have significant predictive modeling components, such as marketing research, data mining, forecasting, and advanced financial modeling, are introduced to nonlinear regression using application software that is a “black box” to the students. Thus, although correct models are estimated, students often do not obtain a thorough understanding of the nonlinear estimation process. The exercise presented in this article was created to demonstrate to students the need for nonlinear regression estimation—rather than using linear transformations and Ordinary Least Squares (OLS) and subsequently demonstrate the nonlinear optimization process to estimate nonlinear regression models. Using the spreadsheet exercise, students can see effects on the fit of the model by changing the model parameters as they change the values of the decision variables. After applying the spreadsheet to further exercises, students have expressed a deep understanding of the linear regression software. This exercise is innovative because the active learning exercise requires the students to make the logical connections between the structure of the model, the model's parameters, and the objective function.     

Active Learning through Modeling: Introduction to Software Development in the Business Curriculum*

Modern software practices call for the active involvement of business people in the software process. Therefore, programming has become an indispensable part of the information systems component of the core curriculum at business schools. In this paper, we present a model‐based approach to teaching introduction to programming to general business students. The theoretical underpinnings of the new approach are metaphor, abstraction, modeling, Bloom's classification of cognitive skills, and active learning. We employ models to introduce the basic programming constructs and their semantics. To this end, we use statecharts to model object's state and the environment model of evaluation as a virtual machine interpreting the programs written in JavaScript. The adoption of this approach helps learners build a sound mental model of the notion of computation process. Scholastic performance, student evaluations, our experiential observations, and a multiple regression statistical test prove that the proposed ideas improve the course significantly.     

Investigating the role of computer‐supported annotation in problem‐solving‐based teaching: An empirical study of a Scratch programming pedagogy

For more than 2 years, Scratch programming has been taught in Taiwanese elementary schools. However, past studies have shown that it is difficult to find appropriate learning methods or tools to boost students’ Scratch programming performance. This inability to readily identify tutoring tools has become one of the primary challenges addressed in Scratch programming studies. To cope with this problem, we propose an innovative approach, which combines an Annotation‐based Scratch Programming (ASP) tool with the problem‐solving‐based teaching approach in Scratch programming pedagogy. The ASP tool was developed to enable students to create, review and share Scratch programming and homework annotations. In a quasi‐experimental study, we have evaluated Scratch programming pedagogy at a North Taiwanese elementary school to investigate the effects of instructional‐tools‐supported programming instructional modes on Scratch programming performance. The experimental results show that students who received ASP tool support in conjunction with a problem‐solving‐based teaching approach performed significantly better than the other groups. Based on our findings, the innovative approach was believed to play an important role in improving the learning patterns of younger pupils. Therefore, we suggest that teachers consider incorporating the innovative method into their teaching environments in order to boost students’ learning achievements in the area of Scratch programming and the subsequent learning process.     

Helping Business Students Acquire the Skills Needed for a Career in Analytics: A Comprehensive Industry Assessment of Entry‐Level Requirements

Fact‐based decision making is changing job functions within organizations more than any other technology. Analytics, once the purview of the data scientist, is now spread throughout organizations. No longer is there a single job title, job function, or set of required skills and credentials for an analytics career. Companies have moved away from seeking applicants with a specific degree to now recruiting analytics talent based on required skill sets. For more than a decade, business schools have been developing new programs in analytics in response to industry's needs. However, in developing meaningful career‐ready professionals, business programs must understand the skills required across different analytics job functions. In this article, the authors present a comprehensive assessment of the skills sought by employers when considering a candidate for an entry‐level analytics position. The authors describe the demand for various types of analytics professionals, identify the job titles and functions with the most significant demand, and then draw a comparison of the job requirements of hard skills, soft skills, software skills, and credentials between three of the most sought‐after analytics areas: data science, data analytics, and business analytics. The authors conclude by providing faculty and administrators with recommendations on how to adapt their courses and programs to provide students with the fundamental preparation necessary for careers in data science, data analytics, and business analytics.     

Bridging the gap between supply chain management practice and curriculum: A location analytics exercise

This teaching brief explores how and why location analytics should be taught in business schools using three objectives. First, an explanation is provided for the importance of including location analytics in the standard business school curriculum—especially in the field of supply chain management. Second, a lack of GIS-based location analytics methods in business school curricula is demonstrated. Third, a three-part location analytics exercise is introduced to contribute to the supply chain management curriculum. The proposed exercise utilizes the output of a GIS-based location analytics software (namely, Esri's ArcGIS Online) as the input for a location set covering problem that can be solved using an integer programming solver. The exercise can also be used as a stand-alone example of GIS in a supply chain management course. This teaching brief aims to (1) develop a new method to use in teaching location analytics in supply chain management and analytics courses and (2) bridge an important gap between supply chain management practice and curriculum.     

Increasing Student Interest and Engagement with Business Cases by Turning Them into Consulting Exercises

How can you get undergraduate students more interested and engaged with business cases? Instead of using the traditional case teaching format, turn the case into a consulting exercise. Give students only the managerial dilemma and some starting information, form them into consulting teams, and then have each team requests additional case information by submitting a restricted number of questions. Through successive rounds, teams will gather enough information and data to analyze the situation and develop a set of recommendations. This approach transforms case‐based learning from a passive analysis of case details to an active process of discovery through smart questioning.     

Utilizing a Simulation Exercise to Illustrate Critical Inventory Management Concepts

Most undergraduate business students simply do not appreciate the elegant mathematical beauty of inventory models. So how does an instructor capture students’ interest and keep them engaged in the learning process when teaching inventory management concepts? This paper describes a competitive and energizing in‐class simulation game that introduces students to basic inventory management concepts. This hands‐on, active‐learning exercise presents students with a simulated single‐product inventory environment that requires them to make ordering decisions in an attempt to minimize the total relevant inventory costs in the face of variable demand.     

Foundations of dynamic learning analytics: Using university student data to increase retention

With digitisation and the rise of e‐learning have come a range of computational tools and approaches that have allowed educators to better support the learners' experience in schools, colleges and universities. The move away from traditional paper‐based course materials, registration, admissions and support services to the mobile, always‐on and always accessible data has driven demand for information and generated new forms of data observable through consumption behaviours. These changes have led to a plethora of data sets that store learning content and track user behaviours. Most recently, new data analytics approaches are creating new ways of understanding trends and behaviours in students that can be used to improve learning design, strengthen student retention, provide early warning signals concerning individual students and help to personalise the learner's experience. This paper proposes a foundational learning analytics model (LAM) for higher education that focuses on the dynamic interaction of stakeholders with their data supported by visual analytics, such as self‐organising maps, to generate conversations, shared inquiry and solution‐seeking. The model can be applied for other educational institutions interested in using learning analytics processes to support personalised learning and support services. Further work is testing its efficacy in increasing student retention rates.     

Using Spreadsheet Modeling to Teach Exchange Curves (Optimal Policy Curves) in Inventory Management

Inventory management is widely researched and the topic is taught in business programs across the spectrum of operations and supply chain management. However, the concepts are notoriously difficult for students to practice once they finish school and become managers responsible for inventory control. This article explains the structure and details of an inventory management class module designed to bridge the gap between learning inventory management theory and applying the learning to practice. Through an active learning exercise, groups of logistics management graduate students in two sections of an introductory inventory management class (in‐residence and online) are taught the exchange curve concept, introduced to its use in practice, and given a tool for implementing this learning in a variety of practical scenarios. The exercise is valuable as it is well suited for students with a limited math background because it allows the exchange curve concept to be taught and demonstrated without presenting complicated mathematics. A three‐tiered assessment of the exercise reveals its effectiveness in meeting the goals of providing an engaging and interesting use of learning time and giving the students an in‐depth practical understanding of the exchange curve concept. The exercise is designed to be equally useful for application in an undergraduate class.     

Empowering learners with personalised learning approaches? Agency,equity and transparency in the context of learning analytics

Abstract

The emergence of personalised data technologies such as learning analytics is framed as a solution to manage the needs of higher education student populations that are growing ever more diverse and larger in size. However, the current approach to learning analytics presents tensions between increasing student agency in making learning-related decisions and ‘datafying’ students in the process of collecting, analysing and interpreting data. This article presents a study that explores staff and student experience of agency, equity and transparency in existing data practices and expectations towards learning analytics in a UK university. The results show a number of intertwined factors that have contributed to the tensions between enhancing a learner’s control of their studies and, at the same time, diminishing their autonomy as an active agent in the process of learning analytics. This article argues that learner empowerment should not be automatically assumed to have taken place as part of the adoption of learning analytics. Instead, the interwoven power relationships in a complex educational system and the interactions between humans and machines need to be taken into consideration when presenting learning analytics as an equitable process to enhance student agency and educational equity.     

Examining Undergraduate Students’ Attitudes toward Business Statistics in the United States and China

The rapid growth of analytics is bringing more attention to quantitative core curriculum requirements in undergraduate business programs. Statistical knowledge and skills are unequivocally recognized as essential cornerstone of business analytics. Furthermore, educational research has shown that academic performance in statistics classes is related to the attitudes that students bring to the course. This article assesses the reliability and validity of the Survey of Attitudes toward Statistics (SATS) in measuring noncognitive dimensions of attitudes among undergraduate business students. Sample data from U.S. and Chinese introductory business statistics classes were collected and analyzed to learn more about this aspect of student engagement across business schools located in countries with substantially different levels of success in international mathematics achievement testing, as well as differing cultural and educational practices. Results show that the six‐factor model structure of the SATS provides a good fit in both populations, with students entering business statistics holding only slightly positive attitudes toward the subject. Significant distinctions between four of the six attitude components were identified. Implications of measuring and improving these attitudes are discussed. Business statistics instructors are encouraged to use the survey as a standardized instrument to measure effects of interventions and make evidence‐based pedagogical decisions.     

Anatomical knowledge gain through a clay‐modeling exercise compared to live and video observations

Clay modeling is increasingly used as a teaching method other than dissection. The haptic experience during clay modeling is supposed to correspond to the learning effect of manipulations during exercises in the dissection room involving tissues and organs. We questioned this assumption in two pretest–post‐test experiments. In these experiments, the learning effects of clay modeling were compared to either live observations (Experiment I) or video observations (Experiment II) of the clay‐modeling exercise. The effects of learning were measured with multiple choice questions, extended matching questions, and recognition of structures on illustrations of cross‐sections. Analysis of covariance with pretest scores as the covariate was used to elaborate the results. Experiment I showed a significantly higher post‐test score for the observers, whereas Experiment II showed a significantly higher post‐test score for the clay modelers. This study shows that (1) students who perform clay‐modeling exercises show less gain in anatomical knowledge than students who attentively observe the same exercise being carried out and (2) performing a clay‐modeling exercise is better in anatomical knowledge gain compared to the study of a video of the recorded exercise. The most important learning effect seems to be the engagement in the exercise, focusing attention and stimulating time on task. Anat Sci Educ 7: 420–429. © 2014 American Association of Anatomists.     

Perceived Role of Multimedia Instructional Materials on Multicriteria Technology and Engineering Decisions*

Studies have shown that it is difficult for people to deal with multicriteria decision‐making situations. Information technology tools such as decision‐support systems and expert systems have been developed in order to help them in such situations. Another tool that has been identified as helping managers understand complex engineering decision‐making situations is multimedia instructional materials. This research investigates the perceptions of business versus engineering students on improvement of their higher‐level cognitive skills when they participated in a multimedia‐based case study that used an expert system to model a complex engineering and technical problem. Two groups of students, business and engineering, participated in an experiment, where they analyzed the case study and made their recommendations. Two questionnaires measured their perceptions on the improvements achieved on different constructs. A structural equations model was developed in order to test the research hypotheses with business students being the experimental group and the engineering students as a control group. The major findings are no significant relationship between student major and higher‐order cognitive skills improvement, business students perceived more higher‐order skills improvement compared to engineering students, both groups perceived an improvement in learning‐driven factor, and business students valued learning‐driven factors more compared to engineering students. These results show that multimedia instructional materials were perceived to be very useful in making multicriteria engineering and technical decisions.     

Identifying Cognitive Processing Events during Clicker Exercises and Exploring Their Relationship with Student Learning

The use of clickers in the classroom has been linked to student learning. However, studies that examine the effects of clickers on learning often conceptualize a clicker exercise as a single, homogeneous cognitive processing (CP) event. We offer a conceptualization of sequenced cognitive activities that unfold during a clicker exercise. An inductive approach is adopted to identify three CP events that occur during a clicker exercise: an investigation event, a confirmation event, and a ratification event. We then use data collected from a sample of 187 students in an undergraduate business capstone course to explore the relationships between the three CP events and student learning. Contrary to expectations, not all CP events contribute positively to student learning. We discuss the implications of these findings and offer directions for future research that advance the understanding of clicker‐enhanced student learning. These insights afford instructors new options for optimizing the contributions of clicker exercises to student learning in their courses.     

Active Learning and Teaching through Digital Technology and Live Performance: ‘Choreographic Thinking’ as Art Practice in the Tertiary Sector

This article is an interdisciplinary reflective response to an intensive studio learning and teaching experience involving artists, academics and postgraduate students. The authors of this article teach, research and practise in coding, digital design, dance, and virtual and live performance. As lecturers and students we reflect upon and propose future approaches to art practice in tertiary education informed by live performance, performance capture and studio‐based responses to digital and virtual platforms. We reflect on an innovative contribution to the field of research–teaching nexus as informed by digital and virtual data capture identifying the key element of immediacy in live performance and choreographic improvisation with systems. We reflect on practice‐based inquiry via the Choreographic Coding Lab (CCL) model – a dialogical negotiation between capture technology and interdisciplinary artists in industry and academia. How can we encourage potential studio inquiry as an adapted model in tertiary learning and teaching? Our interdisciplinary voices, presented as authors’ reflections, provide suggestions for future studio‐based, active learning contexts.     

BOPPPS模型在Java程序设计课程中的应用

为提高Java程序设计课程教学质量,促进自主学习、培养创新型高技能应用人才,针对Java程序设计课程教学目标及存在的问题,依据BOPPPS模型理论,分析该模型在Java程序设计中的运用。对BOPPPS模型每个理论以1~2个实例作为支撑,分析并改善运用效果,将BOPPPS理论模型恰当融入Java程序设计课程实际教学中。BOPPPS模型对改善Java程序设计课程教学效果、提高学习效率、培养创新应用型人才起到了积极作用。     

A Demonstration of Regression False Positive Selection in Data Mining

Business analytics courses, such as marketing research, data mining, forecasting, and advanced financial modeling, have substantial predictive modeling components. The predictive modeling in these courses requires students to estimate and test many linear regressions. As a result, false positive variable selection (type I errors) is nearly certain to occur. This article describes an in‐class demonstration that shows the frequency and impact of false positives on data mining regression‐based predictive modeling. In this demonstration, 500 randomly generated independent (X) variables are individually regressed against a single, randomly generated (Y) variable, and the resulting 500 p‐values are sorted and examined. This experiment is repeated and the distribution of the number of variables significant at the 5% level resulting from this simulation is presented and discussed. The demonstration provides a tangible example in which students see the reality and risks of incorrectly inferring statistical significance of independent regression variables. Students have expressed a deeper understanding and appreciation of the risks of type I errors through this demonstration. This demonstration is innovative because the scale of the simulation allows the students to experience the near certainty that the correlations shown in the results are truly random.     

Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course

In this article, we present an experiential perspective on how a big data analytics course was designed and delivered to students at a major Midwestern university. In reference to the MSIS 2006 Model Curriculum, we designed this course as a level 2 course, with prerequisites in databases, computer programming, statistics, and data mining. Students in the class were mostly seniors or at the graduate level, and had a strong technical and quantitative background. We include details of concepts covered in the course, as well as summaries of four major sample course assignments used. Some of the concepts covered include large‐scale data collection and management using the Hadoop ecosystem, stream mining, visual analytics, and social network analytics. Besides Hadoop, the course also introduced various IBM and Teradata big data tools. We show how the course modules align with the intended learning goals and course objectives. A post‐course survey indicated that the structure and organization of the course helped students clearly and concisely assimilate the course content.     

Operationalizing Design Thinking in Business Intelligence and Analytics Projects

Design Thinking has been applied successfully in many fields; however, in Information Systems research most early studies focus on applying the specific toolsets to developing product and system designs to solve strategic, managerial, and operational problems. There is little research on how Design Thinking can be embedded in the learning processes in design‐oriented IS research and enabled in the context of business intelligence (BI) and business analytics (BA). How can Design Thinking be embedded in the learning processes and enabled in the context of BI/BA projects in the classroom environment, especially in the proof of concept stage? A practical view on integrating the mindset and toolset of the Design Thinking approach and in the learning process as a case based in‐class experience is presented along with a guideline for coaching the Design Thinking team and the lessons learned from each stage of Design Thinking. The results of this study show that Design Thinking practices can be enlisted to help students frame their creations and that these practices have a valuable contribution as alternatives when designing curricula for teaching and learning.