Abstract: Learning Management Systems (LMS) generate substantial volumes of unstructured behavioral data, commonly referred to as “dark data,” that hold considerable promise for enhancing instructional design. Despite their availability, these data often remain underutilized by faculty in higher education. This quantitative study examined the extent to which perceived usefulness, perceived ease of use, and institutional readiness predict the integration of LMS dark data into instructional practices. Survey data were collected from 150 faculty members and analyzed using Pearson correlation, multiple linear regression, and moderated regression analysis. Pearson correlations indicated weak, non-significant relationships between the predictors and dark data integration: [r = –.107] for perceived usefulness, [r = –.029] for perceived ease of use, and [r = –.040] for institutional readiness. A multiple linear regression model also failed to reach statistical significance, [F(3, 146) = 0.62, p = .602, R² = .013], suggesting that these variables did not meaningfully explain variation in dark data use. To explore whether institutional readiness influenced these relationships, two moderated regression models were tested. Results revealed no significant interaction effects for perceived usefulness × institutional readiness, [β = –0.087, p = .420], or perceived ease of use × institutional readiness, [β = 0.095, p = .477]. These findings challenge the predictive adequacy of the Technology Acceptance Model (TAM) in the context of emerging learning analytics practices and suggest that faculty engagement with dark data may depend more on factors not captured by traditional models. The study underscores the potential importance of digital self-efficacy, behavioral intention, ethical clarity, and departmental data culture in shaping instructional use of LMS-generated behavioral data. Implications for institutional policy and instructional design strategy are discussed.
Keywords: dark data, healthcare professionals, data utilization, technology acceptance, perceived value, readiness, data literacy, unstructured data.
Title: Unrealized Potential: Faculty Perceptions and the Limited Integration of LMS Dark Data in Instructional Design
Author: Dr. David Augustine Bull
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Vol. 13, Issue 3, July 2025 - September 2025
Page No: 136-154
Research Publish Journals
Website: www.researchpublish.com
Published Date: 06-August-2025
