Modelling and Using Response Times in Online Courses
© 2019, UTS ePRESS. All rights reserved. Each time a learner in a self-paced online course seeks to answer an assessment question, it takes some time for the student to read the question and arrive at an answer to submit. If multiple attempts are allowed, and the first answer is incorrect, it takes...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Society for Learning Analytics Research
2021
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| Online Access: | https://hdl.handle.net/1721.1/134560 |
| _version_ | 1826212212419592192 |
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| author | Rushkin, Ilia Chuang, Isaac Tingley, Dustin |
| author2 | Massachusetts Institute of Technology. Department of Physics |
| author_facet | Massachusetts Institute of Technology. Department of Physics Rushkin, Ilia Chuang, Isaac Tingley, Dustin |
| author_sort | Rushkin, Ilia |
| collection | MIT |
| description | © 2019, UTS ePRESS. All rights reserved. Each time a learner in a self-paced online course seeks to answer an assessment question, it takes some time for the student to read the question and arrive at an answer to submit. If multiple attempts are allowed, and the first answer is incorrect, it takes some time to provide a second answer. Here we study the distribution of such “response times.” We find that the log-normal statistical model for such times, previously suggested in the literature, holds for online courses. Users who, according to this model, tend to take longer on submits are more likely to complete the course, have a higher level of engagement, and achieve a higher grade. This finding can be the basis for designing interventions in online courses, such as MOOCs, which would encourage “fast” users to slow down. |
| first_indexed | 2024-09-23T15:18:08Z |
| format | Article |
| id | mit-1721.1/134560 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:18:08Z |
| publishDate | 2021 |
| publisher | Society for Learning Analytics Research |
| record_format | dspace |
| spelling | mit-1721.1/1345602023-03-15T17:21:20Z Modelling and Using Response Times in Online Courses Rushkin, Ilia Chuang, Isaac Tingley, Dustin Massachusetts Institute of Technology. Department of Physics Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science © 2019, UTS ePRESS. All rights reserved. Each time a learner in a self-paced online course seeks to answer an assessment question, it takes some time for the student to read the question and arrive at an answer to submit. If multiple attempts are allowed, and the first answer is incorrect, it takes some time to provide a second answer. Here we study the distribution of such “response times.” We find that the log-normal statistical model for such times, previously suggested in the literature, holds for online courses. Users who, according to this model, tend to take longer on submits are more likely to complete the course, have a higher level of engagement, and achieve a higher grade. This finding can be the basis for designing interventions in online courses, such as MOOCs, which would encourage “fast” users to slow down. 2021-10-27T20:05:35Z 2021-10-27T20:05:35Z 2019 2018-05 2020-12-04T19:45:56Z Article http://purl.org/eprint/type/JournalArticle 1929-7750 https://hdl.handle.net/1721.1/134560 en 10.18608/JLA.2019.63.10 Journal of Learning Analytics Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/3.0/ application/pdf Society for Learning Analytics Research The Journal of Learning Analytics |
| spellingShingle | Rushkin, Ilia Chuang, Isaac Tingley, Dustin Modelling and Using Response Times in Online Courses |
| title | Modelling and Using Response Times in Online Courses |
| title_full | Modelling and Using Response Times in Online Courses |
| title_fullStr | Modelling and Using Response Times in Online Courses |
| title_full_unstemmed | Modelling and Using Response Times in Online Courses |
| title_short | Modelling and Using Response Times in Online Courses |
| title_sort | modelling and using response times in online courses |
| url | https://hdl.handle.net/1721.1/134560 |
| work_keys_str_mv | AT rushkinilia modellingandusingresponsetimesinonlinecourses AT chuangisaac modellingandusingresponsetimesinonlinecourses AT tingleydustin modellingandusingresponsetimesinonlinecourses |