Title

Scalable N-Body Event Prediction

Keywords

Graduate courses

Abstract

Storytelling may be a powerful instructional approach for engaging learners and facilitating e-learning. However, relatively little is known about how to apply story within the context of systematic instructional design processes and claims for the effectiveness of storytelling in training and education have been primarily anecdotal and descriptive in nature, with little to no empirical data to support related claims. In this article, we describe the design, development and testing of two online courses that applied an innovative, storytelling approach to instructional design, including Level I and Level II training evaluation data gathered from over 100 educators who completed the two courses over a nine month period. Descriptions of the systematic process illustrate how a needs assessment, task analysis, and a unique StoryLearn™ method were applied to design and develop the two courses. Level I and II training evaluation data are analyzed and reported for the field-test. Results indicate that (a) learners’ perceived levels of attention, relevance, confidence, satisfaction (ARCS), and overall motivation were higher for the two online courses than for two prior online courses that applied more conventional online course designs, (b) learners’ perceived levels of ARCS, and overall motivation remained high throughout the two online courses that applied the storytelling approach, (c) factors, such as age, gender, technology proficiency, and educational level, had no effect on learners’ reported levels of ARCS, and overall motivation, (d) learners’ performance in both courses was consistent with expected performance rates in graduate courses, and (e) learner reported levels of ARCS, and overall motivation were unable to predict scores on course tests, assignments and activities. The findings suggest that storytelling may be a powerful approach for engaging learners and facilitating e-learning worth further investigation.

Publication Date

3-1-2012

Publication Title

Open Computer Science

Volume

2

Issue

2

Number of Pages

1-15

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.2478/s13537-012-0005-9

Socpus ID

85062385129 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85062385129

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