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Background: In the area of education research, it is well-known that studies of a defi ned question are seldom replicated. Furthermore, e-learning resources with evidence-based content in dentistry have received relatively little attention from researchers.
The Context and Purpose of the Study: The aim of this clinical study was to evaluate how dentistry students from two consecutive cohorts in their fi rst clinical semester rate a long-standing evidencebased dentistry (EbD) resource in an e-learning environment using a questionnaire of 43 specifi c items on 1) general questions regarding user-friendliness and acceptability, as well as 2) specifi c questions on content and functional range (A), handling and technical aspects (B), and didactics and educational value (C) based on a Likert scale from 0 = ‘strongly disagree’ to 3 = ‘strongly agree’, and how this compares to a primary study in which the resource was addressed as a novelty. The data were analyzed statistically using a one-way ANOVA followed by a Kruskal-Wallis multiple-comparison Z-test.
Results: A response rate of 100% was achieved. The majority of the users thought the topic of EbD to be important. The e-learning resource was rated with a score of 2.40 ± 0.66 (on a Likert scale from 1-6 where 1 = "very good" and 6 = "insuffi cient"). 86.15% of the students stated that they consider the resource benefi cial for their study in clinical simulation and in patient treatment courses. The results averaged for A: 1.92 (±0.57; median: 1.928), B: 1.48 (±0.60), and C: 2.27 (±0.67). The obtained results in the replication study showed no statistical signifi cant differences to the primary study.
Conclusions: The e-learning resource with dentistry vignettes cases and learning components on evidence-based principles was consistently rated positively by the students. Owing to their agreement with the data of the primary study, the results of the present study point to the remarkable validity of the method of evaluation. This should be addressed in future studies with larger cohorts.
We propose and create a new data model for learning specific environments and learning analytics applications. This is motivated from the experience in the Fiber Bundle Data Model used for large - time and space dependent - data. Our proposed data model integrates file or stream-based data structures from capturing devices more easily. Learning analytics algorithms are added directly to the data, and formulation of queries and analytics is done in Python. It is designed to improve collaboration in the field of learning analytics. We leverage a hierarchical data structure, where varying data is located near the leaves. Abstract data types are identified in four distinct pathways, which allow storing most diverse data sources. We compare different implementations regarding its memory footprint and performance. Our tests indicate that LeAn Bundles can be smaller than a naïve xAPI export. The benchmarks show that the performance is comparable to a MongoDB, while having the benefit of being portable and extensible.