High-Fidelity Digitized Assessment Of Heat Transfer Fundamentals Using A Tiered Delivery Strategy

Abstract

Computer-Based Assessment (CBA) approaches are sought to support increasing enrollments within Engineering curricula. The potential benefits of CBA include user-authenticated, consistent, and fair testing, while facilitating auto-grading and statistical analysis of assessment results. Ideally, CBA could increase the frequency and value of formative feedback within Engineering courses, relative to conventional paper-based exams which are prevalent. However, since multiple-choice question formats are inherently restrictive, an open challenge facing CBA is how to fully assess skills within Engineering disciplines. Specific assessment needs include free-form design approaches, abstract concepts, analytical formulas, plots/graphs, problem-solving skills, and soft skills. These require innovations for realization within the quiz delivery capabilities of today's Learning Management Systems (LMSs). There are also logistical challenges to enabling partial credit, solution composability/traceability including handwritten work, and assessment of creative design aspects. Herein, a layered solution towards these objectives called the STEM-Optimal Digitized Assessment Strategy (SODAS) is developed. SODAS has been successfully integrated within a high-enrollment Mechanical Engineering Heat Transfer course at a large state university, and its method and results are described in detail. A range of issues to consider in the design and delivery of CBAs within undergraduate Engineering courses are addressed, which are discussed along with sample assessment formats validated through student use. Additionally, schedules of key responsibilities for instructors are provided to increase the likelihood of successful assessment delivery, along with lessons learned.

Publication Date

6-23-2018

Publication Title

ASEE Annual Conference and Exposition, Conference Proceedings

Volume

2018-June

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

85051218195 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85051218195

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