Assistive Learning Intelligence Navigator (ALIN) Dataset: Predicting Test Results from Learning Data | Journal of Data Science and Intelligent Systems

<!DOCTYPE html> <html lang="en" xml:lang="en"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> Assistive Learning Intelligence Navigator (ALIN) Dataset: Predicting Test Results from Learning Data | Journal of Data Science and Intelligent Systems </title> <link rel="icon" href="https://ojs.bonviewpress.com/public/journals/7/favicon_en_US.png"> <meta name="generator" content="Open Journal Systems 3.4.0.7"> <meta name="gs_meta_revision" content="1.1"/> <meta name="citation_journal_title" content="Journal of Data Science and Intelligent Systems"/> <meta name="citation_journal_abbrev" content="JDSIS"/> <meta name="citation_issn" content="2972-3841"/> <meta name="citation_author" content="Guijia He"/> <meta name="citation_author_institution" content="Zhejiang Laboratory, China"/> <meta name="citation_author" content="Chengwei Huang"/> <meta name="citation_author_institution" content="Zhejiang Laboratory, China"/> <meta name="citation_author" content="Steven Yang"/> <meta name="citation_author_institution" content="ALIN.ai, Singapore"/> <meta name="citation_author" content="Kelvin Lwin"/> <meta name="citation_author_institution" content="ALIN.ai, Singapore"/> <meta name="citation_author" content="Eng Lieh Ouh"/> <meta name="citation_author_institution" content="Singapore Management University, Singapore"/> <meta name="citation_author" content="Ran Ju"/> <meta name="citation_author_institution" content="Zhejiang Laboratory, China"/> <meta name="citation_author" content="Xiaoming Zhu"/> <meta name="citation_author_institution" content="Zhejiang Laboratory, China"/> <meta name="citation_title" content="Assistive Learning Intelligence Navigator (ALIN) Dataset: Predicting Test Results from Learning Data"/> <meta name="citation_language" content="en"/> <meta name="citation_date" content="2023/12/26"/> <meta name="citation_doi" content="10.47852/bonviewJDSIS32021707"/> <meta name="citation_abstract_html_url" content="https://ojs.bonviewpress.com/index.php/jdsis/article/view/1707"/> <meta name="citation_abstract" xml:lang="en" content="Data mining techniques have garnered significant attention within the realm of education. In this paper, we present two public available datasets for adaptive learning and studied predicting algorithms for learning results. First, we present a student dataset characterized by its size and distinctive attributes. This dataset encompasses various task-related topics interconnected through a learning pathway, thereby enabling researchers to delve into the data from novel perspectives. Moreover, it encompasses extensive longitudinal student behavioral data, a rarity that adds substantial value. Spanning the years from 2010 to 2021, our dataset comprises a cohort of 7933 students, 64,344 test scores, and 183,390 behavior records, solidifying its status as a valuable resource for educational research. Second, we proposed methods for predicting the testing results with and without practice tests. Novel learning features are constructed and various machine learning algorithms are compared. Finally, in our experiments, we achieved precision rate of 0.703 and recall rate of 0.734 in the prediction of students’ test outcomes based on behavioral learning data. The robustness of our dataset makes it well-suited for examining the connection between student behavior and academic performance, developing tailored learning recommendations, and exploring diverse research avenues. Received: 8 September 2023 | Revised: 27 November 2023 | Accepted: 25 December 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in [Google] at https://sites.google.com/site/assistmentsdata/home/2009-2010-assistment-data, in [Github] at https://github.com/AdaptiveLearning2022/DataSetALIN2022, and in [IEEE Dataport] at https://ieee-dataport.org/documents/alin-open-dataset-math-adaptive-learning."/> <meta name="citation_keywords" xml:lang="en" content="academic performance"/> <meta name="citation_keywords" xml:lang="en" content="progress prediction"/> <meta name="citation_keywords" xml:lang="en" content="score prediction"/> <meta name="citation_keywords" xml:lang="en" content="learning behavior"/> <meta name="citation_keywords" xml:lang="en" content="learning dataset"/> <meta name="citation_keywords" xml:lang="en" content="educational data mining"/> <meta name="citation_pdf_url" content="https://ojs.bonviewpress.com/index.php/jdsis/article/download/1707/744"/> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="Guijia He"/> <meta name="DC.Creator.PersonalName" content="Chengwei Huang"/> <meta name="DC.Creator.PersonalName" content="Steven Yang"/> <meta name="DC.Creator.PersonalName" content="Kelvin Lwin"/> <meta name="DC.Creator.PersonalName" content="Eng Lieh Ouh"/> <meta name="DC.Creator.PersonalName" content="Ran Ju"/> <meta name="DC.Creator.PersonalName" content="Xiaoming Zhu"/> <meta name="DC.Date.created" scheme="ISO8601" content="2023-12-26"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2023-09-08"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2022-12-09"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2024-11-05"/> <meta name="DC.Description" xml:lang="en" content="Data mining techniques have garnered significant attention within the realm of education. In this paper, we present two public available datasets for adaptive learning and studied predicting algorithms for learning results. First, we present a student dataset characterized by its size and distinctive attributes. This dataset encompasses various task-related topics interconnected through a learning pathway, thereby enabling researchers to delve into the data from novel perspectives. Moreover, it encompasses extensive longitudinal student behavioral data, a rarity that adds substantial value. Spanning the years from 2010 to 2021, our dataset comprises a cohort of 7933 students, 64,344 test scores, and 183,390 behavior records, solidifying its status as a valuable resource for educational research. Second, we proposed methods for predicting the testing results with and without practice tests. Novel learning features are constructed and various machine learning algorithms are compared. Finally, in our experiments, we achieved precision rate of 0.703 and recall rate of 0.734 in the prediction of students’ test outcomes based on behavioral learning data. The robustness of our dataset makes it well-suited for examining the connection between student behavior and academic performance, developing tailored learning recommendations, and exploring diverse research avenues. Received: 8 September 2023 | Revised: 27 November 2023 | Accepted: 25 December 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in [Google] at https://sites.google.com/site/assistmentsdata/home/2009-2010-assistment-data, in [Github] at https://github.com/AdaptiveLearning2022/DataSetALIN2022, and in [IEEE Dataport] at https://ieee-dataport.org/documents/alin-open-dataset-math-adaptive-learning."/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="1707"/> <meta name="DC.Identifier.DOI" content="10.47852/bonviewJDSIS32021707"/> <meta name="DC.Identifier.URI" content="https://ojs.bonviewpress.com/index.php/jdsis/article/view/1707"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2023 Authors"/> <meta name="DC.Rights" content="https://creativecommons.org/licenses/by/4.0/"/> <meta name="DC.Source" content="Journal of Data Science and Intelligent Systems"/> <meta name="DC.Source.ISSN" content="2972-3841"/> <meta 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d="M256,128c0,70.7-57.3,128-128,128C57.3,256,0,198.7,0,128C0,57.3,57.3,0,128,0C198.7,0,256,57.3,256,128z"/> <g> <path class="st1" d="M86.3,186.2H70.9V79.1h15.4v48.4V186.2z"/> <path class="st1" d="M108.9,79.1h41.6c39.6,0,57,28.3,57,53.6c0,27.5-21.5,53.6-56.8,53.6h-41.8V79.1z M124.3,172.4h24.5 c34.9,0,42.9-26.5,42.9-39.7c0-21.5-13.7-39.7-43.7-39.7h-23.7V172.4z"/> <path class="st1" d="M88.7,56.8c0,5.5-4.5,10.1-10.1,10.1c-5.6,0-10.1-4.6-10.1-10.1c0-5.6,4.5-10.1,10.1-10.1 C84.2,46.7,88.7,51.3,88.7,56.8z"/> </g> </svg> <a href="https://orcid.org/0009-0000-4129-1877" target="_blank"> https://orcid.org/0009-0000-4129-1877 </a> </span> </li> <li> <span class="name"> Chengwei Huang </span> <span class="affiliation"> Zhejiang Laboratory, China </span> <span class="orcid"> <svg class="orcid_icon" viewBox="0 0 256 256" aria-hidden="true"> <style type="text/css"> .st0{fill:#A6CE39;} .st1{fill:#FFFFFF;} </style> <path class="st0" d="M256,128c0,70.7-57.3,128-128,128C57.3,256,0,198.7,0,128C0,57.3,57.3,0,128,0C198.7,0,256,57.3,256,128z"/> <g> <path class="st1" d="M86.3,186.2H70.9V79.1h15.4v48.4V186.2z"/> <path class="st1" d="M108.9,79.1h41.6c39.6,0,57,28.3,57,53.6c0,27.5-21.5,53.6-56.8,53.6h-41.8V79.1z M124.3,172.4h24.5 c34.9,0,42.9-26.5,42.9-39.7c0-21.5-13.7-39.7-43.7-39.7h-23.7V172.4z"/> <path class="st1" d="M88.7,56.8c0,5.5-4.5,10.1-10.1,10.1c-5.6,0-10.1-4.6-10.1-10.1c0-5.6,4.5-10.1,10.1-10.1 C84.2,46.7,88.7,51.3,88.7,56.8z"/> </g> </svg> <a href="https://orcid.org/0000-0001-9060-6361" target="_blank"> https://orcid.org/0000-0001-9060-6361 </a> </span> </li> <li> <span class="name"> Steven Yang </span> <span class="affiliation"> ALIN.ai, Singapore </span> </li> <li> <span class="name"> Kelvin Lwin </span> <span class="affiliation"> ALIN.ai, Singapore </span> </li> <li> <span class="name"> Eng Lieh Ouh </span> <span class="affiliation"> Singapore Management University, Singapore </span> </li> <li> <span class="name"> Ran Ju </span> <span class="affiliation"> Zhejiang Laboratory, China </span> </li> <li> <span class="name"> Xiaoming Zhu </span> <span class="affiliation"> Zhejiang Laboratory, China </span> <span class="orcid"> <svg class="orcid_icon" viewBox="0 0 256 256" aria-hidden="true"> <style type="text/css"> .st0{fill:#A6CE39;} .st1{fill:#FFFFFF;} </style> <path class="st0" d="M256,128c0,70.7-57.3,128-128,128C57.3,256,0,198.7,0,128C0,57.3,57.3,0,128,0C198.7,0,256,57.3,256,128z"/> <g> <path class="st1" d="M86.3,186.2H70.9V79.1h15.4v48.4V186.2z"/> <path class="st1" d="M108.9,79.1h41.6c39.6,0,57,28.3,57,53.6c0,27.5-21.5,53.6-56.8,53.6h-41.8V79.1z M124.3,172.4h24.5 c34.9,0,42.9-26.5,42.9-39.7c0-21.5-13.7-39.7-43.7-39.7h-23.7V172.4z"/> <path class="st1" d="M88.7,56.8c0,5.5-4.5,10.1-10.1,10.1c-5.6,0-10.1-4.6-10.1-10.1c0-5.6,4.5-10.1,10.1-10.1 C84.2,46.7,88.7,51.3,88.7,56.8z"/> </g> </svg> <a href="https://orcid.org/0000-0002-3020-0615" target="_blank"> https://orcid.org/0000-0002-3020-0615 </a> </span> </li> </ul> </section> <section class="item doi"> <h2 class="label"> DOI: </h2> <span class="value"> <a href="https://doi.org/10.47852/bonviewJDSIS32021707"> https://doi.org/10.47852/bonviewJDSIS32021707 </a> </span> </section> <section class="item keywords"> <h2 class="label"> Keywords: </h2> <span class="value"> academic performance, progress prediction, score prediction, learning behavior, learning dataset, educational data mining </span> </section> <section class="item abstract"> <h2 class="label">Abstract</h2> <p>Data mining techniques have garnered significant attention within the realm of education. In this paper, we present two public available datasets for adaptive learning and studied predicting algorithms for learning results. First, we present a student dataset characterized by its size and distinctive attributes. This dataset encompasses various task-related topics interconnected through a learning pathway, thereby enabling researchers to delve into the data from novel perspectives. Moreover, it encompasses extensive longitudinal student behavioral data, a rarity that adds substantial value. Spanning the years from 2010 to 2021, our dataset comprises a cohort of 7933 students, 64,344 test scores, and 183,390 behavior records, solidifying its status as a valuable resource for educational research. Second, we proposed methods for predicting the testing results with and without practice tests. Novel learning features are constructed and various machine learning algorithms are compared. Finally, in our experiments, we achieved precision rate of 0.703 and recall rate of 0.734 in the prediction of students’ test outcomes based on behavioral learning data. The robustness of our dataset makes it well-suited for examining the connection between student behavior and academic performance, developing tailored learning recommendations, and exploring diverse research avenues.</p> <p> </p> <p><strong>Received: </strong>8 September 2023 <strong>| Revised: </strong>27 November 2023 <strong>| Accepted:</strong> 25 December 2023</p> <p> </p> <p><strong>Conflicts of Interest</strong></p> <p>The authors declare that they have no conflicts of interest to this work.</p> <p> </p> <p><strong>Data Availability Statement</strong></p> <p>The data that support the findings of this study are openly available in [Google] at <a href="https://sites.google.com/site/assistmentsdata/home/2009-2010-assistment-data">https://sites.google.com/site/assistmentsdata/home/2009-2010-assistment-data</a>, in [Github] at <a href="https://github.com/AdaptiveLearning2022/DataSetALIN2022">https://github.com/AdaptiveLearning2022/DataSetALIN2022</a>, and in [IEEE Dataport] at <a href="https://ieee-dataport.org/documents/alin-open-dataset-math-adaptive-learning">https://ieee-dataport.org/documents/alin-open-dataset-math-adaptive-learning</a>.</p> </section> <br /><div class="separator"></div><div class="item abstract" id="trendmd-suggestions"></div><script defer src='//js.trendmd.com/trendmd.min.js' data-trendmdconfig='{"website_id":"89269", "element":"#trendmd-suggestions"}'></script> </div> <div class="entry_details"> <div class="item cover_image"> <div class="sub_item"> <a href="https://ojs.bonviewpress.com/index.php/jdsis/issue/view/onlinefirst"> <img src="https://ojs.bonviewpress.com/public/journals/7/cover_issue_29_en_US.png" alt=""> </a> </div> </div> <div class="item galleys"> <h2 class="pkp_screen_reader"> Downloads </h2> <ul class="value galleys_links"> <li> <a class="obj_galley_link pdf" href="https://ojs.bonviewpress.com/index.php/jdsis/article/view/1707/744"> PDF </a> </li> </ul> </div> <div class="item published"> <section class="sub_item"> <h2 class="label"> Published </h2> <div class="value"> <span>2023-12-26</span> </div> </section> </div> <div class="item issue"> <section class="sub_item"> <h2 class="label"> Issue </h2> <div class="value"> <a class="title" href="https://ojs.bonviewpress.com/index.php/jdsis/issue/view/onlinefirst"> Online First </a> </div> </section> <section class="sub_item"> <h2 class="label"> Section </h2> <div class="value"> Research Articles </div> </section> </div> <div class="item copyright"> <h2 class="label"> License </h2> <p>Copyright (c) 2023 Authors</p> <a rel="license" href="https://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" src="//i.creativecommons.org/l/by/4.0/88x31.png" /></a><p>This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> </div> <div class="item citation"> <section class="sub_item citation_display"> <h2 class="label"> How to Cite </h2> <div class="value"> <div id="citationOutput" role="region" aria-live="polite"> <div class="csl-bib-body"> <div class="csl-entry">He, G., Huang, C., Yang, S., Lwin, K., Ouh, E. L., Ju, R., & Zhu, X. (2023). 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Assistive Learning Intelligence Navigator (ALIN) Dataset: Predicting Test Results from Learning Data | Journal of Data Science and Intelligent Systems