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Modeling with Real-Time Informative Feedback: Implementing and Evaluating a New Massive Open Online Course Component

As part of the design, development, and deployment of a massive open online course (MOOC) on model-based systems engineering, we introduced MORTIF—Modeling with Real-Time Informative Feedback, a new learning-by-doing feature that enables the learner to model, receive detailed feedback, and resubmit...

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Autores principales: Wengrowicz, Niva, Lavi, Rea, Kohen, Hanan, Dori, Dov
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9771771/
https://www.ncbi.nlm.nih.gov/pubmed/36573101
http://dx.doi.org/10.1007/s10956-022-10019-8
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author Wengrowicz, Niva
Lavi, Rea
Kohen, Hanan
Dori, Dov
author_facet Wengrowicz, Niva
Lavi, Rea
Kohen, Hanan
Dori, Dov
author_sort Wengrowicz, Niva
collection PubMed
description As part of the design, development, and deployment of a massive open online course (MOOC) on model-based systems engineering, we introduced MORTIF—Modeling with Real-Time Informative Feedback, a new learning-by-doing feature that enables the learner to model, receive detailed feedback, and resubmit improved solutions. We examined the pedagogical usability of MORTIF by investigating characteristics of participants working with it, and their perceived contribution, preferred question type, and learning style. The research included 295 participants and applied the mixed-methods approach, using MOOC server data and online questionnaires. Analyzing 12,095 submissions, we found increasing frequency of using the model resubmitting option. Students ranked MORTIF as the highest of six question types in terms of preference and perceived contribution level. Nine learning style categories were identified and classified based on students’ verbal explanations regarding their preference of MORTIF over the other question types. MORTIF has been effective in promoting meaningful learning, supporting our hypothesis that the combination of active learning with real-time informative feedback is a learning mode that students eagerly embrace and benefit from. The benefits we identified for using MORTIF include active learning, provision of meaningful immediate feedback to the learner, the option to use the feedback on the spot and resubmitting an improved model, and its suitability for a variety of learning styles.
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spelling pubmed-97717712022-12-22 Modeling with Real-Time Informative Feedback: Implementing and Evaluating a New Massive Open Online Course Component Wengrowicz, Niva Lavi, Rea Kohen, Hanan Dori, Dov J Sci Educ Technol Article As part of the design, development, and deployment of a massive open online course (MOOC) on model-based systems engineering, we introduced MORTIF—Modeling with Real-Time Informative Feedback, a new learning-by-doing feature that enables the learner to model, receive detailed feedback, and resubmit improved solutions. We examined the pedagogical usability of MORTIF by investigating characteristics of participants working with it, and their perceived contribution, preferred question type, and learning style. The research included 295 participants and applied the mixed-methods approach, using MOOC server data and online questionnaires. Analyzing 12,095 submissions, we found increasing frequency of using the model resubmitting option. Students ranked MORTIF as the highest of six question types in terms of preference and perceived contribution level. Nine learning style categories were identified and classified based on students’ verbal explanations regarding their preference of MORTIF over the other question types. MORTIF has been effective in promoting meaningful learning, supporting our hypothesis that the combination of active learning with real-time informative feedback is a learning mode that students eagerly embrace and benefit from. The benefits we identified for using MORTIF include active learning, provision of meaningful immediate feedback to the learner, the option to use the feedback on the spot and resubmitting an improved model, and its suitability for a variety of learning styles. Springer Netherlands 2022-12-22 /pmc/articles/PMC9771771/ /pubmed/36573101 http://dx.doi.org/10.1007/s10956-022-10019-8 Text en © The Author(s), under exclusive licence to Springer Nature B.V. 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Wengrowicz, Niva
Lavi, Rea
Kohen, Hanan
Dori, Dov
Modeling with Real-Time Informative Feedback: Implementing and Evaluating a New Massive Open Online Course Component
title Modeling with Real-Time Informative Feedback: Implementing and Evaluating a New Massive Open Online Course Component
title_full Modeling with Real-Time Informative Feedback: Implementing and Evaluating a New Massive Open Online Course Component
title_fullStr Modeling with Real-Time Informative Feedback: Implementing and Evaluating a New Massive Open Online Course Component
title_full_unstemmed Modeling with Real-Time Informative Feedback: Implementing and Evaluating a New Massive Open Online Course Component
title_short Modeling with Real-Time Informative Feedback: Implementing and Evaluating a New Massive Open Online Course Component
title_sort modeling with real-time informative feedback: implementing and evaluating a new massive open online course component
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9771771/
https://www.ncbi.nlm.nih.gov/pubmed/36573101
http://dx.doi.org/10.1007/s10956-022-10019-8
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