Choosing Among Computational Software Tools to Enhance Learning in Introductory Business Statistics

Johnson, Marina; Berenson, Mark L.

doi:10.1111/dsji.12186

Cited by 4 publications

(7 citation statements)

References 30 publications

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“…The choice of computational software tools is essential to the success of an IBA course (Johnson & Berenson, 2019; Johnson et al., 2020; Rienzo & Chen, 2018). The Guidelines for Assessment and Instructions in Statistics Education College Report identifies the use of computational software as one of six recommendations to enhance the pedagogical delivery of statistics courses (Carver et al., 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

“…From the software tools surveyed in Johnson and Berenson (2019), Microsoft Excel supports topics covered in most introductory statistics courses. Excel has become one of the most used software packages for IBA and statistics courses (Haskin & Krehbiel, 2012; Warner & Meehan, 2001), and many IBA textbooks provide resources for using Excel (Camm et al., 2020; Evans, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Undoubtedly, teaching binary logistic regression requires computational software. From the software tools reviewed in Johnson and Berenson (2019), we decided to use Microsoft Excel because it has proven to be an effective teaching tool in IBA courses (Mamonov et al., 2015; Warner & Meehan, 2001), important for the employability of business graduates (Rienzo & Chen, 2018), and essential for entry‐level jobs in business analytics (Cegielski & Jones‐Farmer, 2016). However, Excel does not have an in‐built tool that allows students to run logistic regression.…”

Section: Introductionmentioning

confidence: 99%

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Teaching binary logistic regression modeling in an introductory business analytics course

Hoang

Watson

2022

Decision Sci J Innov Edu

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There is an increasing demand to introduce Introductory Business Analytics (IBA) courses into undergraduate business education. Many real-world business contexts require predictive analytics to understand the determinants of a dichotomous outcome; hence, IBA courses should include binary logistic regression analysis. This article provides our reflective discussions on the design of learning activities and assessments to assist business students in learning binary logistic regression in an IBA course. Data on student engagement and learning outcomes are used to shed light on the impacts of teaching logistic regression on student learning and experience. Notably, students opt to focus their assessment work more on logistic regression than on multiple regression analysis, showing the potential attraction of students toward binary logistic regression analysis. We also observed several challenges, mainly related to the use of Excel, that require special attention from instructors.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Teaching binary logistic regression modeling in an introductory business analytics course

Hoang

Watson

2022

Decision Sci J Innov Edu

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“…For example, the life sciences workforce should include a diverse and flexible class of workers who have skills in analytics and machine learning (Arboleda, 2018). Key stakeholders in the life sciences field emphasize the need to hire workers with hybrid skills (e.g., software development, statistics, and life sciences) and state their concerns that the widening skills gap will hinder market growth (Arboleda, 2018;Johnson & Berenson, 2019). BioNJ (2018) reports that there is substantial demand among life sciences employers for data scientists and engineers to support clinical trial optimization and drug development.…”

Section: Skill Sets Needed For the Future Workforce: New Jersey As A Case Studymentioning

confidence: 99%

Impact of Big Data and Artificial Intelligence on Industry: Developing a Workforce Roadmap for a Data Driven Economy

Johnson

Jain

Brennan-Tonetta

et al. 2021

Glob J Flex Syst Manag

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Big Data and Artificial Intelligence (BD&AI) have become so pervasive, and the opportunities they present so transformative, that they are viewed as essential for competitive growth. Since the number of firms adopting BD&AI technologies is growing exponentially, the demand for BD&AI practitioners is also growing at a rapid rate. However, several studies indicate that there is a BD&AI talent shortage and skills gap between labor market requirements and expertise available in the current workforce. This talent shortage and skills gap are now recognized as a crucial impediment in leveraging BD&AI for economic growth at the local, national, and global levels. This research aims to identify BD&AI workforce trends, gaps, and opportunities by using bibliometric analysis and extracting insights from job posting data. The study team first conducted bibliometric research and built word cooccurrence diagrams using BD&AI related articles published in high-impact journals to determine technological changes impacting various industry domains. The team then collected job postings data and summarized the skill sets required to be competitive in industries driven by BD&AI. Finally, the study team evaluated the curricula of BD&AI programs at various colleges and universities educating the future workforce and conducted a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis to bridge the gaps between industry needs and academic training. This multi-step research framework forecasts oncoming technological changes in various industry clusters, workforce skills that are and will be needed, and provides recommendations for a workforce development roadmap so that businesses can gain a competitive advantage through the use of BD&AI.

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“…Articles authored by Gros et al (2011), Marjanovic (2012, Yang and Liu (2013), Bolloju (2014), Gupta and Raja (2015), and Yap and Drye (2018) focus on improving BA education as a whole. Other authors, such as Levine and Stephan (2011), Anderson and Williams (2019), Pinder, 2013aPinder, , 2013b, and Johnson and Berenson (2019), study the impact of using either software platforms or systematic data analysis frameworks. The primary suggestions offered in these studies deal with making connections among various analytics methods or integrating programming tools and software applications when teaching analytics techniques.…”

Section: Related Literaturementioning

confidence: 99%

Teaching Bayesian and Markov methods in business analytics curricula: An integrated approach

Johnson

Misra

Berenson

2021

Decision Sci J Innov Edu

Self Cite

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In the era of artificial intelligence (AI), big data (BD), and digital transformation (DT), analytics students should gain the ability to solve business problems by integrating various methods. This teaching brief illustrates how two such methods-Bayesian analysis and Markov chains-can be combined to enhance student learning using the Analytics Project Life Cycle Management (APLCM) approach and a case study involving qualitative forecasting. The theoretical frameworks for combining Bayesian and Markov methods are developed, and a forecasting solution is implemented in both MS Excel and Python. Based on an assessment of student learning, applying this pedagogical approach helps students better use these disjoint methods and appreciate the value of integrating them. Although this teaching brief is designed and most appropriate for graduate students with previous BA courses, it can also be used in upper-level courses within an undergraduate BA curriculum. Finally, this teaching brief provides the instructors wishing to use this pedagogical approach in their appropriate courses with the necessary resources (i.e., case study, in-class example, and the MS Excel and Python templates).

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Choosing Among Computational Software Tools to Enhance Learning in Introductory Business Statistics

Cited by 4 publications

References 30 publications

Teaching binary logistic regression modeling in an introductory business analytics course

Teaching binary logistic regression modeling in an introductory business analytics course

Impact of Big Data and Artificial Intelligence on Industry: Developing a Workforce Roadmap for a Data Driven Economy

Teaching Bayesian and Markov methods in business analytics curricula: An integrated approach

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