In recent years, technology has enabled Universities and Colleges to offer web-based courses, in which, teachers (or experts) design, curate and upload all course material required to teach the course online so that students can learn at their own pace, time and location. This research proposes a tutoring framework called Example Recommendation System (ERS) that is based on example-based learning (EBL) instructional method. ERS focuses on students devoting their time and cognitive capacity to studying worked-out examples so that they can enhance their learning and apply it to graded tasks assigned to them. ERS uses regular expression analysis to extract basic learning units (LU) (e.g. scanf is a LU in C programming) from all task solutions and worked-out examples and represents this knowledge in vector space. Then, these vectors are mined to generate a customized list of worked-out examples for each assigned task. The prime contribution of ERS’s extraction module is its extendibility to new domains without requiring highly trained experts. Besides extendibility, ERS extracts LUs with 81% correctness for the domain of “Programming in C” and 95% for domain of “Programming in Miranda”. ERS’s data mining model used for customization has 93% accuracy and 88% f score. ERS’s educational impact is also evident from experiments that show that students score an average of 89% in tasks for which they use ERS’s recommended worked-out examples, as opposed to an average of 73% for those tasks that students attempt without ERS’s assistance.
| Published in | International Journal on Data Science and Technology (Volume 8, Issue 1) |
| DOI | 10.11648/j.ijdst.20220801.14 |
| Page(s) | 22-35 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2022. Published by Science Publishing Group |
Customized Learning, ITS, Domain Model, Tasks and Examples, Knowledge Extraction, Regular Expressions, K-nearest Neighbors
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APA Style
Ritu Chaturvedi, Christie I. Ezeife. (2022). Customized Learning in Online Tutoring Systems by Mining Learning Units from Tasks and Examples. International Journal on Data Science and Technology, 8(1), 22-35. https://doi.org/10.11648/j.ijdst.20220801.14
ACS Style
Ritu Chaturvedi; Christie I. Ezeife. Customized Learning in Online Tutoring Systems by Mining Learning Units from Tasks and Examples. Int. J. Data Sci. Technol. 2022, 8(1), 22-35. doi: 10.11648/j.ijdst.20220801.14
AMA Style
Ritu Chaturvedi, Christie I. Ezeife. Customized Learning in Online Tutoring Systems by Mining Learning Units from Tasks and Examples. Int J Data Sci Technol. 2022;8(1):22-35. doi: 10.11648/j.ijdst.20220801.14
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Download@article{10.11648/j.ijdst.20220801.14,
author = {Ritu Chaturvedi and Christie I. Ezeife},
title = {Customized Learning in Online Tutoring Systems by Mining Learning Units from Tasks and Examples},
journal = {International Journal on Data Science and Technology},
volume = {8},
number = {1},
pages = {22-35},
doi = {10.11648/j.ijdst.20220801.14},
url = {https://doi.org/10.11648/j.ijdst.20220801.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20220801.14},
abstract = {In recent years, technology has enabled Universities and Colleges to offer web-based courses, in which, teachers (or experts) design, curate and upload all course material required to teach the course online so that students can learn at their own pace, time and location. This research proposes a tutoring framework called Example Recommendation System (ERS) that is based on example-based learning (EBL) instructional method. ERS focuses on students devoting their time and cognitive capacity to studying worked-out examples so that they can enhance their learning and apply it to graded tasks assigned to them. ERS uses regular expression analysis to extract basic learning units (LU) (e.g. scanf is a LU in C programming) from all task solutions and worked-out examples and represents this knowledge in vector space. Then, these vectors are mined to generate a customized list of worked-out examples for each assigned task. The prime contribution of ERS’s extraction module is its extendibility to new domains without requiring highly trained experts. Besides extendibility, ERS extracts LUs with 81% correctness for the domain of “Programming in C” and 95% for domain of “Programming in Miranda”. ERS’s data mining model used for customization has 93% accuracy and 88% f score. ERS’s educational impact is also evident from experiments that show that students score an average of 89% in tasks for which they use ERS’s recommended worked-out examples, as opposed to an average of 73% for those tasks that students attempt without ERS’s assistance.},
year = {2022}
}
TY - JOUR T1 - Customized Learning in Online Tutoring Systems by Mining Learning Units from Tasks and Examples AU - Ritu Chaturvedi AU - Christie I. Ezeife Y1 - 2022/03/30 PY - 2022 N1 - https://doi.org/10.11648/j.ijdst.20220801.14 DO - 10.11648/j.ijdst.20220801.14 T2 - International Journal on Data Science and Technology JF - International Journal on Data Science and Technology JO - International Journal on Data Science and Technology SP - 22 EP - 35 PB - Science Publishing Group SN - 2472-2235 UR - https://doi.org/10.11648/j.ijdst.20220801.14 AB - In recent years, technology has enabled Universities and Colleges to offer web-based courses, in which, teachers (or experts) design, curate and upload all course material required to teach the course online so that students can learn at their own pace, time and location. This research proposes a tutoring framework called Example Recommendation System (ERS) that is based on example-based learning (EBL) instructional method. ERS focuses on students devoting their time and cognitive capacity to studying worked-out examples so that they can enhance their learning and apply it to graded tasks assigned to them. ERS uses regular expression analysis to extract basic learning units (LU) (e.g. scanf is a LU in C programming) from all task solutions and worked-out examples and represents this knowledge in vector space. Then, these vectors are mined to generate a customized list of worked-out examples for each assigned task. The prime contribution of ERS’s extraction module is its extendibility to new domains without requiring highly trained experts. Besides extendibility, ERS extracts LUs with 81% correctness for the domain of “Programming in C” and 95% for domain of “Programming in Miranda”. ERS’s data mining model used for customization has 93% accuracy and 88% f score. ERS’s educational impact is also evident from experiments that show that students score an average of 89% in tasks for which they use ERS’s recommended worked-out examples, as opposed to an average of 73% for those tasks that students attempt without ERS’s assistance. VL - 8 IS - 1 ER -
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