Modeling with Real-Time Informative Feedback: Implementing and Evaluating a New Massive Open Online Course Component
Abstract 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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer Netherlands
2023
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| Online Access: | https://hdl.handle.net/1721.1/153008 |
| _version_ | 1826191052482019328 |
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| author | Wengrowicz, Niva Lavi, Rea Kohen, Hanan Dori, Dov |
| author2 | Massachusetts Institute of Technology. School of Engineering |
| author_facet | Massachusetts Institute of Technology. School of Engineering Wengrowicz, Niva Lavi, Rea Kohen, Hanan Dori, Dov |
| author_sort | Wengrowicz, Niva |
| collection | MIT |
| description | Abstract
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. |
| first_indexed | 2024-09-23T08:49:43Z |
| format | Article |
| id | mit-1721.1/153008 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:49:43Z |
| publishDate | 2023 |
| publisher | Springer Netherlands |
| record_format | dspace |
| spelling | mit-1721.1/1530082024-01-23T19:39:21Z Modeling with Real-Time Informative Feedback: Implementing and Evaluating a New Massive Open Online Course Component Wengrowicz, Niva Lavi, Rea Kohen, Hanan Dori, Dov Massachusetts Institute of Technology. School of Engineering Abstract 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. 2023-11-20T19:40:16Z 2023-11-20T19:40:16Z 2022-12-22 2023-11-19T04:53:54Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/153008 Wengrowicz, Niva, Lavi, Rea, Kohen, Hanan and Dori, Dov. 2022. "Modeling with Real-Time Informative Feedback: Implementing and Evaluating a New Massive Open Online Course Component." en https://doi.org/10.1007/s10956-022-10019-8 Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. The Author(s), under exclusive licence to Springer Nature B.V. application/pdf Springer Netherlands Springer Netherlands |
| spellingShingle | 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 |
| url | https://hdl.handle.net/1721.1/153008 |
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