Open AccessStrengthening the coding skills of teachers in a low dropout Python MOOC
Author(s) -
Fotis Lazarinis,
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Anthi Karatrantou,
Chris Panagiotakopoulos,
Vassilis Daloukas,
Theodor Panagiotakopoulos,
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AUTHOR_ID,
AUTHOR_ID,
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Publication year - 2022
Publication title -
advances in mobile learning educational research
Language(s) - English
Resource type - Journals
ISSN - 2737-5676
DOI - 10.25082/amler.2022.01.003
Subject(s) - workload , coding (social sciences) , outreach , python (programming language) , computer science , mathematics education , multimedia , dropout (neural networks) , medical education , psychology , medicine , mathematics , statistics , political science , law , operating system , machine learning
In this paper, we present a structured approach to developing an outreach program aimed at improving the coding abilities of pre- and in-service teachers. The paper presents the design and development decisions made using the ADDIE model. External evaluators assessed the material's quality, confirmed the estimated workload, and examined the material's relevance to the educational goals. Learners’ active participation was encouraged through multiple quizzes, and learners were assisted in their learning activities by means of practical examples. Based on the number of people who actually logged into the course, a completion rate of 70.84 percent is achieved. The paper presents and discusses the findings of an evaluation conducted with the assistance of experienced teachers and course participants.