2026 ASEE Annual Conference & Exposition

Integrating Canvas and Power BI for Evidence-Based Assessment of ABET Student Outcomes in a Mechanical Engineering Program

Presented at Mechanical Engineering (MECH) Session 11: Assessment, Accreditation, and Evaluation

This paper presents a systematic approach to program-level assessment in a Mechanical Engineering program that integrates Canvas-based data collection, standardized rubrics, and Microsoft Power BI visualizations to evaluate ABET Student Outcomes. Program outcomes are defined within Canvas, aligned with ABET criteria, and mapped to assessment tasks across the curriculum, while standardized rubrics embedded in assignments ensure consistent evaluation of student performance and enable automated aggregation of assessment data. The paper details the Canvas assessment process, including how outcomes are linked to coursework to streamline data collection and management. Assessment data are exported and organized for analysis in Microsoft Power BI, where dynamic dashboards illustrate outcome achievement levels, performance trends, and cohort comparisons, providing insights for program improvement and evidence-based decision-making. This integrated approach reduces the manual burden of data handling, enhances the reliability and transparency of assessment results, and promotes continuous program improvement. By combining a learning management system with advanced visualization tools, this approach transforms program assessment and offers a replicable model for other engineering programs seeking to improve the efficiency, effectiveness, and impact of ABET-related assessment processes.

Authors
  1. Dr. Kevin Wanklyn Kansas State University [biography]
  2. Frederick W Burrack Kansas State University [biography]
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