Using Continuous Improvement in Online Program Design: DMAIC as a Tool for Assurance of Learning Assessments

Abstract

The integration of technology into education has forced radical innovations to traditional instructional delivery models. Given its prevalence, a thorough understanding of pedagogical best practices associated with the design and implementation of such programs is critical. Furthermore, the need for an institutional commitment to assessment and a framework to objectively measure student performance against a program's learning goals and objectives is also necessary. The impetus behind this study is to suggest that applying the principles of continuous improvement (CI) to pedagogical assessments can foster institutional commitment and enhance the process by which universities assess student performance. This study applies CI to an Assurance of Learning assessment of two courses in an entirely online supply chain management program by implementing DMAIC as a tool to improve program-level outcomes. The results show how the course content and program can be continuously improved to enhance student learning and the related outcome measures.

Continual Improvement of Online and Blended Teaching Using Relative Proximity Theory

“…Carnovale et al (2016) looked at continuous improvement to AoL measures, dealing with the technical service aspect of the content of the courses. Carnovale et al (2016) looked at continuous improvement to AoL measures, dealing with the technical service aspect of the content of the courses.…”

“…Swart's (2017) book on continual improvement of flipped learning includes a case study that uses the RPT model as a quality measure. Carnovale et al (2016) apply the DMAIC cycle (a variant of the PDSA) to AoL. The focus of these works is either theoretical (continuous improvement should be used) or technical (course content or AoL).…”

“…The latter theme continues with research such as Smith and Kellogg (2015) developing a predictive model for whether group or individual work produces greater perceived learning among online students and Whitaker, Ireland, and New (2015) using Adaptive Structuration Theory to identify three factors (technological tools and course design, internal and external environment, and learning styles and teaching modes) that influence the successful introduction of Massive Open Online Courses. Year 2016 saw the publication of a special issue of Decision Sciences Journal of Innovative Education dealing with critical success factors in online education, where Eom and Ashill (2016) looked again at determinants of student satisfaction, Callister and Love (2016) compared online versus face-to-face courses for skills-based courses, Carnovale et al (2016) used the DMAIC framework to look at continuous improvement relative to AoL criteria, and Li et al (2016) considered whether students were satisfied with their online experiences. The latter article included the interesting statement that individual learning characteristics are mostly insignificant and that research is needed on learning design changes to enhance the experiences of the students.…”

“…This gets us back to the issue of service quality, technical and functional. Carnovale et al (2016) looked at continuous improvement to AoL measures, dealing with the technical service aspect of the content of the courses. This work extends that idea to the functional service aspect of student satisfaction with how the courses are taught.…”

“…These are certainly not the only two possible measures of product quality for higher education, but as teachers, they are the measures that only we can directly affect. Carnovale, Allen, Pullman, and Wong (2016) illustrated a continuous improvement process centered on AoL using the existing AoL process at their institution to repeatedly nudge their courses to do a better job on teaching content. Continual improvement of student satisfaction, however, has received much less attention, and a service provider can ignore customer satisfaction only when the customer has little choice.…”