2026 ASEE Annual Conference & Exposition

This full-paper abstract describes the development and implementation of a Direct assessment system for an ABET Software Engineering program. This assessment process has been designed to support two levels of continuous improvement: For guiding student formation (focused on 1st and 2nd year courses) and for formative program assessment (focused on 3rd and 4th year courses). Data pertaining to student performance at these two levels are reviewed at the end of every semester. Courses required of the program contribute direct assessment data at the end of the semester, typically two ‘key assignments’ which are assessed against relevant Performance Indicators.

Program assessment is centered around the assessment of performance indicators that the department has developed for assessing the program’s Student Learning Outcomes (SLOs). Currently, there are 30 performance indicators (PIs) selected (and continue to be revised) to provide a reasonably comprehensive operational definition for each of the program student learning outcomes. The goal of the PIs are to provide assessable touch points wherein aspects of one (or more SLOs) can be seen in student work.

Success of the system is based on a reuseable 5-valued metric that can be used with each PI, and the deployment of PI-centered questions using the Outcomes mechanism available in Canvas. From this, easy-to-use rubrics are imported into each course, and faculty select 1-3 assignments to contribute to the program assessment. Data analysis then isolates all students by major, and from the major the expectation-level of the course, so that faculty can leverage broad and detailed information to support continuous improvement of the program and of the assessment system.

The paper will present the process, data analysis and overview of findings from three semester-cycles of evaluation, and the analysis of how it supports continuous improvement of a software engineering program.