Improving the Use of Academic Assessment Data to Inform Course Design

Benjamin Matthew Cagle

[excerpt from] Executive Summary At Q College (a pseudonym), this dissertation-in-practice addressed a persistent challenge: the inconsistent use of course-level academic assessment data to inform strategic decisions about course design and instructional improvement. Although the institution has collected assessment data to evaluate course learning outcomes (CLOs), these data have not been consistently integrated into decision-making processes to drive meaningful enhancements in teaching and learning. This inconsistency has compromised the institution’s ability to verify that instructional practices improve student learning, has undermined compliance with state mandates, and has posed risks to the college’s accreditation status following a Met With Concerns rating from the Higher Learning Commission (HLC) during the 2021 midcycle review. The following overarching research question guided this study: How can the college achieve the desired performance in faculty use of course-level academic assessment data? Four research subquestions (RQs) structured the inquiry: RQ1: What is the desired performance in faculty use of course-level academic assessment data? RQ2: What factors influence how faculty members use course-level assessment data to inform decisions about course design? RQ3: What factors influence how faculty members use course-level assessment data to change course design? RQ4: How can the course-level academic assessment process be supported to improve faculty use of assessment data? This study integrated four complementary performance improvement models. The human performance technology (HPT) model provided the overarching structured methodology for diagnosing performance gaps, designing targeted interventions, and evaluating outcomes (Van Tiem et al., 2012). Rummler and Brache’s (2013) three levels of performance framework enabled multidimensional analysis across organizational, process, and performer levels, identifying the systemic challenges that impeded effective assessment data use. Kotter’s (2014) accelerate (XLR8) model informed the strategic planning and change management required to map the college’s shared governance structure, specifically analyzing how distributed leadership roles and iterative improvement mechanisms would support the intervention’s institutionalization. Stufflebeam’s (2003) context, input, process, and product (CIPP) model structured the evaluation across formative, summative, and confirmative phases. Using action research, the study involved collecting data across 2021–2025 and included document analysis of institutional artifacts (i.e., strategic plan, academic assessment manual, assessment report rubric, and HLC [2026] policy book), an online questionnaire administered to 35 faculty members measuring perceived task difficulty, semistructured interviews with 12 stakeholders representing all three performance levels, and analysis of seven Assessment Report Review Team (ARRT) reports. The performance analysis proceeded through four interconnected phases: organizational analysis to establish the desired state, environmental analysis to capture the actual state, gap analysis to identify discrepancies, and root cause analysis to determine underlying causal factors. ...

Improving the Use of Academic Assessment Data to Inform Course Design

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Improving the Use of Academic Assessment Data to Inform Course Design

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