Undergraduate courses such as mathematics, science, and computer programming require high levels of decision making, concentration, and cognitive demand. Researchers in the field of instructional design are interested in effective instructional strategies that can aid practitioners in teaching such abstract and complex skills.
One example of an instructional strategy that has proven effective in teaching these skills is cognitive apprenticeship (CA). While CA has been applied to courses such as mathematics and computer programming in face-to-face and blended learning environments, there is little evidence of the advantages of applying CA in a fully online computer programming course. Specifically, the introductory programming course, CS1, is the first contact that undergraduate computer science students have with their chosen major. Historically, drop-out rates for CS1 have been high and thus strategies for effective teaching of this course have served as an important topic in the research literature.
The goal was to design and validate internally an online CS1 course that incorporates CA strategies. A two-phase design and development research method was used to guide the construction and internal validation of a fully online CS1 course. Phase one resulted in the design and development of the course guide. An expert-review process using the Delphi technique was implemented in phase two to validate the design with regard to its effectiveness, efficiency, and appeal. Three rounds of review by the panel resulted in consensus.
Results from the expert-review confirmed the application of CA as an effective, efficient, and appealing instructional strategy to use when designing an online CS1 course. Future research should focus on external validation of the design by implementing the course to evaluate its effectiveness, efficiency, and appeal among stakeholders. In addition, it is hoped that the course guide can be used to help practitioners design and implement a fully online CS1 course that uses CA strategies.
| Identifer | oai:union.ndltd.org:nova.edu/oai:nsuworks.nova.edu:gscis_etd-1001 |
| Date | 21 August 2014 |
| Creators | Fernandez, Reinaldo |
| Publisher | NSUWorks |
| Source Sets | Nova Southeastern University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | CEC Theses and Dissertations |
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