Teaching during a pandemic has compelled educators to transform traditional strategies towards more innovative solutions. These innovative solutions use a variety of educational technologies, and often, shift delivery modalities to an online or blended approach to learning. A key strategy in online teaching is the development of quality e-learning modules based on the core tenets of e-learning. E-learning modules aim to enhance knowledge, performance, and retention through interactive and engaging strategies. While the value of a quality e-learning module is well-supported in the literature, there are limited resources available for developers to assess if the module adheres to the core tenets of e-learning. The University of Nebraska Medical Center created a scorecard (Nebraska E-Learning Scorecard, NEscore) based on established core tenets for e-learning that was both useable and reliable in evaluating quality e-learning modules. To determine the usability and reliability of NEscore, we conducted a pilot study and six experts and six novice participants evaluated five e-learning modules utilizing NEscore. Reliability was calculated with Cronbach’s alpha and intra-class correlation coefficients. We also gathered data on demographic information and the perceived satisfaction of participants in using the NEscore. The findings showed strong internal consistency among scores with overall high reliability, and high consistency among participants, showing no significant difference between the two groups of experts and novices. Overall, participants were satisfied with the usability of NEscore. The NEscore offers institutions an established set of criteria to evaluate existing e-learning modules and also serves as a guide for the development of new e-learning modules.

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Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Table 1-NEScoreCard.docx (17 kB)
Table summary of Research Questions, Data Sources, Data Analysis, & Results