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Abstract

Objectives

The purpose of this study is to measure how the implementation of an online, preclinical hybrid curriculum impacts dental student clinic readiness, the outcomes of grades, critical thinking skills, and student and faculty perceptions respectively.

Methods

This longitudinal comparative and descriptive study used objective data and subjective (survey) data for 4 dental class cohorts. Groups A and B experienced a traditional lecture‐based curriculum, while Groups C and D experienced a hybrid curriculum that was lecture‐free and implemented active learning. The Health Sciences Reasoning Test (HSRT), an objective assessment, was used to measure students’ critical thinking skills.

Results

Dental student outcomes have either remained steady or improved with the transition to a new hybrid curriculum. According to the student and faculty survey results, the hybrid curriculum promoted student learning, independence, critical thinking, initiative and self‐motivation, and clinic practice readiness. Group C (N = 68) Total Online Platform mean scores demonstrated a significant and moderately strong correlation with the preclinical course mean grades (r = 0.68, P = 0.00). Group D HSRT (n = 63) for Attempt 1 (end of year 1) and Attempt 2 (end of Year 2) paired T test resulted in HSRT Overall (mean difference = −2.27, SD = 7.21, t = −2.5, P = 0.02) for the second preclinical year.

Conclusion

The hybrid curricular approach afforded many benefits. Faculty took an active role in imparting knowledge when compared to the lecture hall. Having students immersed in continual assessment through an online adaptive platform and active learning promoted self‐motivation, deeper learning, applied knowledge, and discouraged superficial memorization.

Abstract

This article describes a cooperative research partnership among a large public university, a for-profit private institution and their common adaptive learning platform provider. The focus of this work explored adaptive analytics that uses data the investigators describe as metaphorical “digital learning dust” produced by the platform as a matter of course. The information configured itself into acquired knowledge, growth, baseline status and engagement. Two complimentary models evolved. The first, in the public university, captured end-of-course data for predicting success. The second approach, in the private university, formed the basis of a dynamic real-time data analytic algorithm. In both cases the variables that best predicted students at risk (effective use of time and revision attempts) were deemed teachable skills that can improve with intervention.

Abstract

Clustering of educational data allows similar students to be grouped, in either crisp or fuzzy sets, based on their similarities. Standard approaches are well suited to identifying common student behaviors; however, by design, they put much less emphasis on less common behaviors or outliers. The approach presented in this paper employs fuzzing clustering in the identification of these outlier behaviors. The algorithm is an iterative one, where clustering is applied, outliers identified, the data restricted to the outliers, and the process repeated. This approach produces a clustering that is crisp between each iteration and fuzzy within. It arose as a consequence of trying to cluster student progress trajectories in an adaptive learning platform. Included are results from applying the repeated fuzzy clustering algorithm to data from multiple courses and semesters at the University of Central Florida, (N=5,044).