IDEAS home Printed from https://ideas.repec.org/a/inm/orited/v21y2020i1p41-56.html
   My bibliography  Save this article

STRATA: A Spreadsheet Tool for Multidimensional Analysis of Operations Research/Management Science Assessment Test Data

Author

Listed:
  • Susan W. Palocsay

    (Department of Computer Information Systems and Business Analytics, College of Business, James Madison University, Harrisonburg, Virginia 22801)

  • Scott P. Stevens

    (Department of Computer Information Systems and Business Analytics, College of Business, James Madison University, Harrisonburg, Virginia 22801)

  • Luis J. Novoa

    (Department of Computer Information Systems and Business Analytics, College of Business, James Madison University, Harrisonburg, Virginia 22801)

Abstract

Accrediting organizations in higher education have shifted emphasis from indirect measures such as student course evaluations, focus-group interviews, and employer surveys to direct measurement of student performance using appropriate assessment instruments. In response, we developed a spreadsheet tool for reports and analysis of test assessments (STRATA) to document and evaluate quantitative data from test questions linked to learning objectives in an introductory undergraduate management science (MS) course. STRATA automatically generates a variety of tables and charts that allow us to examine our students’ achievement at three different levels: individual question item, MS topic (subsets of items), and total score. It also tracks test results over time and produces summary reports. In this paper, we discuss STRATA’s features, its usefulness in meeting accreditation requirements, and how it can provide faculty with valuable feedback in ways that encourage meaningful conversations about course content and pedagogy.

Suggested Citation

  • Susan W. Palocsay & Scott P. Stevens & Luis J. Novoa, 2020. "STRATA: A Spreadsheet Tool for Multidimensional Analysis of Operations Research/Management Science Assessment Test Data," INFORMS Transactions on Education, INFORMS, vol. 21(1), pages 41-56, September.
    • Handle: RePEc:inm:orited:v:21:y:2020:i:1:p:41-56
    DOI: 10.1287/ited.2019.0229
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/ited.2019.0229
    Download Restriction: no
    File URL: https://libkey.io/10.1287/ited.2019.0229?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Matthew J. Liberatore & Wenhong Luo, 2010. "The Analytics Movement: Implications for Operations Research," Interfaces, INFORMS, vol. 40(4), pages 313-324, August.
    2. Scott P. Stevens & Susan W. Palocsay, 2004. "A Translation Approach To Teaching Linear Program Formulation," INFORMS Transactions on Education, INFORMS, vol. 4(3), pages 38-54, May.
    3. Beate Klingenberg, 2012. "Teaching Note ---Learning Outcome Assessment Using an Integrative Assignment on Location Decision Making," INFORMS Transactions on Education, INFORMS, vol. 12(3), pages 140-146, May.
    4. Scott P. Stevens & Susan W. Palocsay, 2012. "Identifying Addressable Impediments to Student Learning in an Introductory Statistics Course," INFORMS Transactions on Education, INFORMS, vol. 12(3), pages 124-139, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Scott P. Stevens & Susan W. Palocsay & Luis J. Novoa, 2023. "Practical Guidance for Writing Multiple-Choice Test Questions in Introductory Analytics Courses," INFORMS Transactions on Education, INFORMS, vol. 24(1), pages 51-69, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tom Pape, 2020. "Prioritising data items for business analytics: Framework and application to human resources," Papers 2012.13813, arXiv.org.
    2. Scott P. Stevens & Susan W. Palocsay, 2017. "Teaching Use of Binary Variables in Integer Linear Programs: Formulating Logical Conditions," INFORMS Transactions on Education, INFORMS, vol. 18(1), pages 28-36, September.
    3. Mortenson, Michael J. & Doherty, Neil F. & Robinson, Stewart, 2015. "Operational research from Taylorism to Terabytes: A research agenda for the analytics age," European Journal of Operational Research, Elsevier, vol. 241(3), pages 583-595.
    4. Isabelle Piot-Lepetit & Joseph Nzongang, 2021. "Business analytics for managing performance of microfinance Institutions: A flexible management of the implementation process," Post-Print hal-03209188, HAL.
    5. Julie Ann Stuart Williams & Randall Reid & Kristine Gallamore & Maxwell Rankin, 2018. "Introducing Troubleshooting for Model Formulation, Spreadsheet Development, and Memo Communication with Feedforward," INFORMS Transactions on Education, INFORMS, vol. 18(2), pages 102-115, January.
    6. Durrel Ramrathan & Mabutho Sibanda, 2014. "Impact of Analytics in Financial Decision Making: Evidence from a Case Study Approach," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 10(5), pages 124-135, October.
    7. Elitzur, Ramy, 2020. "Data analytics effects in major league baseball," Omega, Elsevier, vol. 90(C).
    8. Isabelle Piot-Lepetit & Joseph Nzongang, 2021. "Business Analytics for Managing Performance of Microfinance Institutions: A Flexible Management of the Implementation Process," Sustainability, MDPI, vol. 13(9), pages 1-22, April.
    9. Mantin, Benny & Rubin, Eran, 2018. "Price volatility and market performance measures: The case of revenue managed goods," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 120(C), pages 35-50.
    10. Simonofski, Anthony & Handekyn, Phebe & Vandennieuwenborg, Celien & Wautelet, Yves & Snoeck, Monique, 2023. "Smart mobility projects: Towards the formalization of a policy-making lifecycle," Land Use Policy, Elsevier, vol. 125(C).
    11. Hindle, Giles A. & Vidgen, Richard, 2018. "Developing a business analytics methodology: A case study in the foodbank sector," European Journal of Operational Research, Elsevier, vol. 268(3), pages 836-851.
    12. Benjamin T. Hazen & Joseph B. Skipper & Christopher A. Boone & Raymond R. Hill, 2018. "Back in business: operations research in support of big data analytics for operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 201-211, November.
    13. Käki, Anssi & Kemppainen, Katariina & Liesiö, Juuso, 2019. "What to do when decision-makers deviate from model recommendations? Empirical evidence from hydropower industry," European Journal of Operational Research, Elsevier, vol. 278(3), pages 869-882.
    14. Duan, Yanqing & Cao, Guangming & Edwards, John S., 2020. "Understanding the impact of business analytics on innovation," European Journal of Operational Research, Elsevier, vol. 281(3), pages 673-686.
    15. Michael F. Gorman, 2017. "Interfaces Editor’s Statement," Interfaces, INFORMS, vol. 47(1), pages 1-3, February.
    16. Vishwesh Singbal & Gajendra K. Adil & Rakesh Venkitasubramony, 2020. "Case Article—Forward-Reserve Storage Design: A Case of Carriage Repair Workshop, Indian Railways," INFORMS Transactions on Education, INFORMS, vol. 21(1), pages 30-34, September.
    17. repec:ers:journl:v:xxiv:y:2021:i:special2:p:127-140 is not listed on IDEAS
    18. Matthew Liberatore & Wenhong Luo, 2013. "ASP, The Art and Science of Practice: A Comparison of Technical and Soft Skill Requirements for Analytics and OR Professionals," Interfaces, INFORMS, vol. 43(2), pages 194-197, April.
    19. Erland Hejn Nielsen & Steen Nielsen, 2020. "Preparing students for careers using business analytics and data-driven decision making," Economics Working Papers 2020-08, Department of Economics and Business Economics, Aarhus University.
    20. Conboy, Kieran & Mikalef, Patrick & Dennehy, Denis & Krogstie, John, 2020. "Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda," European Journal of Operational Research, Elsevier, vol. 281(3), pages 656-672.
    21. Johnson, Michael P. & Midgley, Gerald & Chichirau, George, 2018. "Emerging trends and new frontiers in community operational research," European Journal of Operational Research, Elsevier, vol. 268(3), pages 1178-1191.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orited:v:21:y:2020:i:1:p:41-56. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.