Using Machine Learning Algorithms to Predict People’s Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach
Mobile learning has become an essential instruction platform in many schools, colleges, universities, and various other educational institutions across the globe, as a result of the COVID-19 pandemic crisis. The resulting severe, pandemic-related circumstances have disrupted physical and face-to-fac...
Uloženo v:
| Vydáno v: | JMIR medical education Ročník 7; číslo 1; s. e24032 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Canada
JMIR Publications
04.02.2021
|
| Témata: | |
| ISSN: | 2369-3762, 2369-3762 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Mobile learning has become an essential instruction platform in many schools, colleges, universities, and various other educational institutions across the globe, as a result of the COVID-19 pandemic crisis. The resulting severe, pandemic-related circumstances have disrupted physical and face-to-face contact teaching practices, thereby requiring many students to actively use mobile technologies for learning. Mobile learning technologies offer viable web-based teaching and learning platforms that are accessible to teachers and learners worldwide.
This study investigated the use of mobile learning platforms for instruction purposes in United Arab Emirates higher education institutions.
An extended technology acceptance model and theory of planned behavior model were proposed to analyze university students' adoption of mobile learning platforms for accessing course materials, searching the web for information related to their disciplines, sharing knowledge, and submitting assignments during the COVID-19 pandemic. We collected a total of 1880 questionnaires from different universities in the United Arab Emirates. Partial least squares-structural equation modeling and machine learning algorithms were used to assess the research model, which was based on the data gathered from a student survey.
Based on our results, each hypothesized relationship within the research model was supported by our data analysis results. It should also be noted that the J48 classifier (89.37% accuracy) typically performed better than the other classifiers when it came to the prediction of the dependent variable.
Our study revealed that teaching and learning could considerably benefit from adopting remote learning systems as educational tools during the COVID-19 pandemic. However, the value of such systems could be lessened because of the emotions that students experience, including a fear of poor grades, stress resulting from family circumstances, and sadness resulting from a loss of friends. Accordingly, these issues can only be resolved by evaluating the emotions of students during the pandemic. |
|---|---|
| AbstractList | BackgroundMobile learning has become an essential instruction platform in many schools, colleges, universities, and various other educational institutions across the globe, as a result of the COVID-19 pandemic crisis. The resulting severe, pandemic-related circumstances have disrupted physical and face-to-face contact teaching practices, thereby requiring many students to actively use mobile technologies for learning. Mobile learning technologies offer viable web-based teaching and learning platforms that are accessible to teachers and learners worldwide. ObjectiveThis study investigated the use of mobile learning platforms for instruction purposes in United Arab Emirates higher education institutions. MethodsAn extended technology acceptance model and theory of planned behavior model were proposed to analyze university students’ adoption of mobile learning platforms for accessing course materials, searching the web for information related to their disciplines, sharing knowledge, and submitting assignments during the COVID-19 pandemic. We collected a total of 1880 questionnaires from different universities in the United Arab Emirates. Partial least squares-structural equation modeling and machine learning algorithms were used to assess the research model, which was based on the data gathered from a student survey. ResultsBased on our results, each hypothesized relationship within the research model was supported by our data analysis results. It should also be noted that the J48 classifier (89.37% accuracy) typically performed better than the other classifiers when it came to the prediction of the dependent variable. ConclusionsOur study revealed that teaching and learning could considerably benefit from adopting remote learning systems as educational tools during the COVID-19 pandemic. However, the value of such systems could be lessened because of the emotions that students experience, including a fear of poor grades, stress resulting from family circumstances, and sadness resulting from a loss of friends. Accordingly, these issues can only be resolved by evaluating the emotions of students during the pandemic. Background: Mobile learning has become an essential instruction platform in many schools, colleges, universities, and various other educational institutions across the globe, as a result of the COVID-19 pandemic crisis. The resulting severe, pandemic-related circumstances have disrupted physical and face-to-face contact teaching practices, thereby requiring many students to actively use mobile technologies for learning. Mobile learning technologies offer viable web-based teaching and learning platforms that are accessible to teachers and learners worldwide. Objective: This study investigated the use of mobile learning platforms for instruction purposes in United Arab Emirates higher education institutions. Methods: An extended technology acceptance model and theory of planned behavior model were proposed to analyze university students’ adoption of mobile learning platforms for accessing course materials, searching the web for information related to their disciplines, sharing knowledge, and submitting assignments during the COVID-19 pandemic. We collected a total of 1880 questionnaires from different universities in the United Arab Emirates. Partial least squares-structural equation modeling and machine learning algorithms were used to assess the research model, which was based on the data gathered from a student survey. Results: Based on our results, each hypothesized relationship within the research model was supported by our data analysis results. It should also be noted that the J48 classifier (89.37% accuracy) typically performed better than the other classifiers when it came to the prediction of the dependent variable. Conclusions: Our study revealed that teaching and learning could considerably benefit from adopting remote learning systems as educational tools during the COVID-19 pandemic. However, the value of such systems could be lessened because of the emotions that students experience, including a fear of poor grades, stress resulting from family circumstances, and sadness resulting from a loss of friends. Accordingly, these issues can only be resolved by evaluating the emotions of students during the pandemic. Mobile learning has become an essential instruction platform in many schools, colleges, universities, and various other educational institutions across the globe, as a result of the COVID-19 pandemic crisis. The resulting severe, pandemic-related circumstances have disrupted physical and face-to-face contact teaching practices, thereby requiring many students to actively use mobile technologies for learning. Mobile learning technologies offer viable web-based teaching and learning platforms that are accessible to teachers and learners worldwide. This study investigated the use of mobile learning platforms for instruction purposes in United Arab Emirates higher education institutions. An extended technology acceptance model and theory of planned behavior model were proposed to analyze university students' adoption of mobile learning platforms for accessing course materials, searching the web for information related to their disciplines, sharing knowledge, and submitting assignments during the COVID-19 pandemic. We collected a total of 1880 questionnaires from different universities in the United Arab Emirates. Partial least squares-structural equation modeling and machine learning algorithms were used to assess the research model, which was based on the data gathered from a student survey. Based on our results, each hypothesized relationship within the research model was supported by our data analysis results. It should also be noted that the J48 classifier (89.37% accuracy) typically performed better than the other classifiers when it came to the prediction of the dependent variable. Our study revealed that teaching and learning could considerably benefit from adopting remote learning systems as educational tools during the COVID-19 pandemic. However, the value of such systems could be lessened because of the emotions that students experience, including a fear of poor grades, stress resulting from family circumstances, and sadness resulting from a loss of friends. Accordingly, these issues can only be resolved by evaluating the emotions of students during the pandemic. Mobile learning has become an essential instruction platform in many schools, colleges, universities, and various other educational institutions across the globe, as a result of the COVID-19 pandemic crisis. The resulting severe, pandemic-related circumstances have disrupted physical and face-to-face contact teaching practices, thereby requiring many students to actively use mobile technologies for learning. Mobile learning technologies offer viable web-based teaching and learning platforms that are accessible to teachers and learners worldwide.BACKGROUNDMobile learning has become an essential instruction platform in many schools, colleges, universities, and various other educational institutions across the globe, as a result of the COVID-19 pandemic crisis. The resulting severe, pandemic-related circumstances have disrupted physical and face-to-face contact teaching practices, thereby requiring many students to actively use mobile technologies for learning. Mobile learning technologies offer viable web-based teaching and learning platforms that are accessible to teachers and learners worldwide.This study investigated the use of mobile learning platforms for instruction purposes in United Arab Emirates higher education institutions.OBJECTIVEThis study investigated the use of mobile learning platforms for instruction purposes in United Arab Emirates higher education institutions.An extended technology acceptance model and theory of planned behavior model were proposed to analyze university students' adoption of mobile learning platforms for accessing course materials, searching the web for information related to their disciplines, sharing knowledge, and submitting assignments during the COVID-19 pandemic. We collected a total of 1880 questionnaires from different universities in the United Arab Emirates. Partial least squares-structural equation modeling and machine learning algorithms were used to assess the research model, which was based on the data gathered from a student survey.METHODSAn extended technology acceptance model and theory of planned behavior model were proposed to analyze university students' adoption of mobile learning platforms for accessing course materials, searching the web for information related to their disciplines, sharing knowledge, and submitting assignments during the COVID-19 pandemic. We collected a total of 1880 questionnaires from different universities in the United Arab Emirates. Partial least squares-structural equation modeling and machine learning algorithms were used to assess the research model, which was based on the data gathered from a student survey.Based on our results, each hypothesized relationship within the research model was supported by our data analysis results. It should also be noted that the J48 classifier (89.37% accuracy) typically performed better than the other classifiers when it came to the prediction of the dependent variable.RESULTSBased on our results, each hypothesized relationship within the research model was supported by our data analysis results. It should also be noted that the J48 classifier (89.37% accuracy) typically performed better than the other classifiers when it came to the prediction of the dependent variable.Our study revealed that teaching and learning could considerably benefit from adopting remote learning systems as educational tools during the COVID-19 pandemic. However, the value of such systems could be lessened because of the emotions that students experience, including a fear of poor grades, stress resulting from family circumstances, and sadness resulting from a loss of friends. Accordingly, these issues can only be resolved by evaluating the emotions of students during the pandemic.CONCLUSIONSOur study revealed that teaching and learning could considerably benefit from adopting remote learning systems as educational tools during the COVID-19 pandemic. However, the value of such systems could be lessened because of the emotions that students experience, including a fear of poor grades, stress resulting from family circumstances, and sadness resulting from a loss of friends. Accordingly, these issues can only be resolved by evaluating the emotions of students during the pandemic. |
| Author | Alshurideh, Muhammad Salloum, Said Al Ali, Amel Al Kurdi, Barween Akour, Iman |
| AuthorAffiliation | 3 Department of Marketing The University of Jordan Amman Jordan 4 Department of Business Administration Faculty of Economics and Administrative Sciences The Hashemite University Zarqa Jordan 1 Information Systems Department University of Sharjah Sharjah United Arab Emirates 2 Department of Management University of Sharjah Sharjah United Arab Emirates 5 Research Institute of Sciences & Engineering University of Sharjah Sharjah United Arab Emirates |
| AuthorAffiliation_xml | – name: 2 Department of Management University of Sharjah Sharjah United Arab Emirates – name: 1 Information Systems Department University of Sharjah Sharjah United Arab Emirates – name: 3 Department of Marketing The University of Jordan Amman Jordan – name: 4 Department of Business Administration Faculty of Economics and Administrative Sciences The Hashemite University Zarqa Jordan – name: 5 Research Institute of Sciences & Engineering University of Sharjah Sharjah United Arab Emirates |
| Author_xml | – sequence: 1 givenname: Iman orcidid: 0000-0002-6914-2213 surname: Akour fullname: Akour, Iman – sequence: 2 givenname: Muhammad orcidid: 0000-0002-7336-381X surname: Alshurideh fullname: Alshurideh, Muhammad – sequence: 3 givenname: Barween orcidid: 0000-0002-0825-4617 surname: Al Kurdi fullname: Al Kurdi, Barween – sequence: 4 givenname: Amel orcidid: 0000-0002-2129-0188 surname: Al Ali fullname: Al Ali, Amel – sequence: 5 givenname: Said orcidid: 0000-0002-6073-3981 surname: Salloum fullname: Salloum, Said |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33444154$$D View this record in MEDLINE/PubMed |
| BookMark | eNptkt2KEzEUx4OsuGvtK8iACIJU8zUziRfC0vWj0GV7Yb0NmcxJmzJNajIjeOdr6OP5JKbbdekWrxLO-fHLyeH_FJ354AGhMcFvKJHVW8oxo4_QBWWVnLC6omdH93M0TmmDMSY1p7iUT9A5Y5xzUvIL9HuZnF8V19qsnYdiDjr6feGyW4Xo-vU2FX0oFhFaZ_piAWHXwZ-fv1Ix8z343gW_7y8TFNehcd2RYdHp3oaYBVdD3Bf6NRTTm6-zqwmRxUL7FrbOvPvP07tdDLn4DD22ukswvjtHaPnxw5fp58n85tNsejmfmJKyftJq1jRNDbylVmCQAjSBuqmlMKUwuiRVpYngdcXyRprSYlvLljWcWqIBGGEjNDt426A3ahfdVscfKminbgshrpSOvTMdKG4xloQxIi3jgllhW8ZL1jAshWZZO0LvD67d0GyhNXlFUXcPpA873q3VKnxXAgtCa5EFr-4EMXwbIPVq65KBrtMewpAU5bUopRCkzuiLE3QThujzqhQtcU1LUUmZqefHE92P8i8CGXh5AEwMKUWw9wjBap8udZuuzL0-4Yzr9T4B-SOuO6H_An4xzzM |
| CitedBy_id | crossref_primary_10_3390_su15054400 crossref_primary_10_3390_data6050049 crossref_primary_10_3390_su15043507 crossref_primary_10_1155_2022_6149995 crossref_primary_10_1080_15567036_2023_2231898 crossref_primary_10_1007_s40692_022_00239_7 crossref_primary_10_7717_peerj_cs_986 crossref_primary_10_1080_23311975_2023_2242985 crossref_primary_10_3390_app12157733 crossref_primary_10_20525_ijrbs_v11i8_2144 crossref_primary_10_2196_42047 crossref_primary_10_1080_23311886_2024_2356721 crossref_primary_10_1016_j_chb_2025_108567 crossref_primary_10_3390_en16134911 crossref_primary_10_1007_s00521_021_06376_x crossref_primary_10_1155_2022_2779909 crossref_primary_10_1007_s42044_023_00158_5 crossref_primary_10_1016_j_iswa_2023_200197 crossref_primary_10_1007_s10639_022_10942_8 crossref_primary_10_1038_s41598_025_08147_3 crossref_primary_10_1080_10494820_2024_2444532 crossref_primary_10_3390_su141811389 crossref_primary_10_3390_w15193487 crossref_primary_10_1155_hbe2_1518987 crossref_primary_10_3389_fpsyg_2022_1050532 crossref_primary_10_7821_naer_2023_7_1287 crossref_primary_10_3390_informatics8020032 crossref_primary_10_3390_su142114006 crossref_primary_10_1080_02681102_2024_2423286 crossref_primary_10_1109_ACCESS_2021_3097753 crossref_primary_10_1155_2022_2353835 crossref_primary_10_1016_j_spc_2021_10_001 crossref_primary_10_24857_rgsa_v18n9_146 crossref_primary_10_3390_su15031819 crossref_primary_10_1142_S0129156424400196 crossref_primary_10_1177_21582440241288735 crossref_primary_10_1007_s10639_022_11173_7 crossref_primary_10_1007_s10639_024_12477_6 crossref_primary_10_1371_journal_pone_0308248 crossref_primary_10_1016_j_heliyon_2023_e19193 crossref_primary_10_1007_s11517_023_02890_3 crossref_primary_10_1016_j_imu_2022_100913 crossref_primary_10_1007_s41870_023_01655_3 crossref_primary_10_3233_MAS_220405 crossref_primary_10_1080_17501229_2025_2490110 crossref_primary_10_2196_48254 crossref_primary_10_3390_informatics8020024 crossref_primary_10_1080_14703297_2023_2239203 crossref_primary_10_3389_fpsyg_2022_915087 |
| Cites_doi | 10.1007/s11747-014-0403-8 10.1111/j.1559-1816.1992.tb00945.x 10.1007/978-3-030-31129-2_37 10.1109/ICMT.2011.6001903 10.1111/j.1540-5915.2008.00192.x 10.1016/j.chb.2014.05.024 10.1016/j.techsoc.2019.101212 10.11591/ijece.v10i6.pp6484-6496 10.9790/487X-2202030116 10.24251/hicss.2020.403 10.1007/978-3-030-44289-7_5 10.2307/4132314 10.1145/2669711.2669961 10.1080/07421222.2014.995564 10.14742/ajet.796 10.1007/s10639-020-10367-1 10.2307/2065853 10.4103/SHB.SHB_11_20 10.1111/bjet.12169 10.1287/mnsc.46.2.186.11926 10.1016/j.compedu.2012.04.015 10.1016/j.evalprogplan.2011.11.007 10.1016/j.ijinfomgt.2020.102144 10.1007/s11423-019-09695-y 10.1007/s11469-020-00270-8 10.1016/j.compedu.2020.103857 10.1108/02652320210446724 10.1016/0749-5978(91)90020-t 10.1111/j.1469-0691.2009.02947.x 10.13140/RG.2.2.17355.54565 10.35575/rvucn.n61a1 10.1016/j.sbspro.2016.02.071 10.1111/j.1467-8535.2011.01229.x 10.1016/j.chb.2018.09.005 10.1007/s10639-019-10094-2 10.1007/978-3-030-44289-7_9 10.1177/002224378101800104 10.1080/09593969.2019.1667854 10.1109/SCORED.2017.8305387 10.1109/access.2019.2930411 10.2307/249008 10.1016/j.psychres.2020.112958 10.1016/j.invent.2018.05.002 10.1109/ICMT.2011.6001924 10.1109/ACCESS.2019.2956349 10.1080/10494821003714632 10.1177/0735633116688320 10.2307/25750691 10.3844/jssp.2014.51.62 10.3991/ijet.v13i06.8275 10.1007/s10664-015-9388-2 10.3390/educsci11010006 10.1177/0020764020935488 10.1109/ICCSE.2018.8468849 10.1016/j.eswa.2010.03.015 10.1016/j.chb.2014.03.052 10.1016/j.dsx.2020.05.008 10.2307/25148690 10.1057/palgrave.ejis.3000717 10.1371/journal.pone.0227270 10.1016/j.chb.2019.106227 10.31234/osf.io/xmpk4 10.1080/10494820.2020.1830121 10.3991/ijim.v14i02.11115 10.2190/EC.49.1.e 10.1007/978-3-319-99010-1_59 |
| ContentType | Journal Article |
| Copyright | Iman Akour, Muhammad Alshurideh, Barween Al Kurdi, Amel Al Ali, Said Salloum. Originally published in JMIR Medical Education (http://mededu.jmir.org), 04.02.2021. 2021. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Iman Akour, Muhammad Alshurideh, Barween Al Kurdi, Amel Al Ali, Said Salloum. Originally published in JMIR Medical Education (http://mededu.jmir.org), 04.02.2021. 2021 |
| Copyright_xml | – notice: Iman Akour, Muhammad Alshurideh, Barween Al Kurdi, Amel Al Ali, Said Salloum. Originally published in JMIR Medical Education (http://mededu.jmir.org), 04.02.2021. – notice: 2021. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: Iman Akour, Muhammad Alshurideh, Barween Al Kurdi, Amel Al Ali, Said Salloum. Originally published in JMIR Medical Education (http://mededu.jmir.org), 04.02.2021. 2021 |
| DBID | AAYXX CITATION NPM 3V. 7X7 7XB 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU COVID DWQXO FYUFA GHDGH K9. M0S PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI 7X8 5PM DOA |
| DOI | 10.2196/24032 |
| DatabaseName | CrossRef PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College Coronavirus Research Database ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) Health & Medical Collection (Alumni Edition) ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Health & Medical Complete (Alumni) Coronavirus Research Database ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest One Health & Nursing ProQuest Hospital Collection (Alumni) ProQuest Central ProQuest Health & Medical Complete ProQuest Health & Medical Research Collection Health Research Premium Collection ProQuest One Academic UKI Edition Health and Medicine Complete (Alumni Edition) ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database PubMed MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Open Access Full Text url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: ProQuest Publicly Available Content url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2369-3762 |
| ExternalDocumentID | oai_doaj_org_article_4f00913319f3483f8fd3453b3098a39d PMC8081278 33444154 10_2196_24032 |
| Genre | Journal Article |
| GroupedDBID | 7X7 8FI 8FJ AAFWJ AAYXX ABUWG ADBBV AFFHD AFKRA AFPKN ALMA_UNASSIGNED_HOLDINGS AOIJS BCNDV BENPR CCPQU CITATION FYUFA GROUPED_DOAJ HMCUK HYE KQ8 M48 M~E OK1 PGMZT PHGZM PHGZT PIMPY RPM UKHRP ALIPV NPM 3V. 7XB 8FK AZQEC COVID DWQXO K9. PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c523t-da3bbb7e4d2f80e98ea1e7b798c58ca5166a184763236b5f0f79d3b42f1aee313 |
| IEDL.DBID | 7X7 |
| ISICitedReferencesCount | 59 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000848666000007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2369-3762 |
| IngestDate | Fri Oct 03 12:43:54 EDT 2025 Tue Nov 04 01:54:48 EST 2025 Sun Aug 24 03:36:49 EDT 2025 Tue Oct 07 07:07:56 EDT 2025 Thu Apr 03 06:58:29 EDT 2025 Tue Nov 18 22:21:14 EST 2025 Sat Nov 29 03:45:56 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | COVID-19 theory of planned behavior prediction behavior machine learning pandemic technology acceptance model intent fear mobile learning online learning |
| Language | English |
| License | Iman Akour, Muhammad Alshurideh, Barween Al Kurdi, Amel Al Ali, Said Salloum. Originally published in JMIR Medical Education (http://mededu.jmir.org), 04.02.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Education, is properly cited. The complete bibliographic information, a link to the original publication on http://mededu.jmir.org/, as well as this copyright and license information must be included. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c523t-da3bbb7e4d2f80e98ea1e7b798c58ca5166a184763236b5f0f79d3b42f1aee313 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-6914-2213 0000-0002-7336-381X 0000-0002-2129-0188 0000-0002-0825-4617 0000-0002-6073-3981 |
| OpenAccessLink | https://www.proquest.com/docview/2507258699?pq-origsite=%requestingapplication% |
| PMID | 33444154 |
| PQID | 2507258699 |
| PQPubID | 4997112 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_4f00913319f3483f8fd3453b3098a39d pubmedcentral_primary_oai_pubmedcentral_nih_gov_8081278 proquest_miscellaneous_2478598817 proquest_journals_2507258699 pubmed_primary_33444154 crossref_primary_10_2196_24032 crossref_citationtrail_10_2196_24032 |
| PublicationCentury | 2000 |
| PublicationDate | 20210204 |
| PublicationDateYYYYMMDD | 2021-02-04 |
| PublicationDate_xml | – month: 2 year: 2021 text: 20210204 day: 4 |
| PublicationDecade | 2020 |
| PublicationPlace | Canada |
| PublicationPlace_xml | – name: Canada – name: Toronto – name: Toronto, Canada |
| PublicationTitle | JMIR medical education |
| PublicationTitleAlternate | JMIR Med Educ |
| PublicationYear | 2021 |
| Publisher | JMIR Publications |
| Publisher_xml | – name: JMIR Publications |
| References | ref13 ref12 ref56 ref15 ref59 ref14 ref58 Hair, JF (ref72) 2009 ref53 ref52 ref11 ref55 ref10 ref54 ref17 ref19 ref18 Alomari, KM (ref79) 2019; 35 ref51 Makttoofa, N (ref34) 2020; 11 ref50 Mtebe, JS (ref57) 2014; 10 ref46 ref45 ref48 ref47 ref41 ref44 ref43 ref49 Ringle, CM (ref67) 2016 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref81 ref40 El-Gayar, OF (ref82) 2011; 14 ref83 ref80 ref35 ref37 ref36 ref31 ref74 ref33 ref77 ref32 ref76 ref2 ref1 Nchunge, DM (ref30) 2012; 2 ref38 Hair Jr, JF (ref70) 2016 Liu, SH (ref75) 2005; 4 Barclay, DW (ref68) 1995; 2 Ajzen, I (ref42) 1985 ref71 Nunnally, JC (ref66) 1994 ref73 ref24 ref23 ref26 ref25 ref69 ref20 ref64 ref63 ref22 ref21 ref65 ref28 ref27 ref29 Almaiah, MA (ref16) 2018; 96 Appavoo, P (ref39) 2019; 1014 ref60 ref62 ref61 Frank, E (ref78) 2009 |
| References_xml | – ident: ref73 doi: 10.1007/s11747-014-0403-8 – ident: ref74 doi: 10.1111/j.1559-1816.1992.tb00945.x – ident: ref14 doi: 10.1007/978-3-030-31129-2_37 – ident: ref13 doi: 10.1109/ICMT.2011.6001903 – ident: ref45 doi: 10.1111/j.1540-5915.2008.00192.x – ident: ref29 doi: 10.1016/j.chb.2014.05.024 – ident: ref32 doi: 10.1016/j.techsoc.2019.101212 – ident: ref3 doi: 10.11591/ijece.v10i6.pp6484-6496 – year: 2016 ident: ref70 publication-title: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) – volume: 35 start-page: 1368 issue: 19 year: 2019 ident: ref79 publication-title: Opcion – ident: ref7 doi: 10.9790/487X-2202030116 – ident: ref31 doi: 10.24251/hicss.2020.403 – volume: 4 start-page: H8 issue: 2 year: 2005 ident: ref75 publication-title: E-Learning – volume: 2 start-page: 17 issue: 10 year: 2012 ident: ref30 publication-title: Int J Humanit Soc Sci – ident: ref77 doi: 10.1007/978-3-030-44289-7_5 – ident: ref28 doi: 10.2307/4132314 – volume: 1014 start-page: 355 year: 2019 ident: ref39 publication-title: Frontiers in Intelligent Computing: Theory and Applications – ident: ref48 doi: 10.1145/2669711.2669961 – ident: ref21 doi: 10.1080/07421222.2014.995564 – ident: ref53 doi: 10.14742/ajet.796 – year: 2016 ident: ref67 publication-title: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Second Edition – ident: ref1 doi: 10.1007/s10639-020-10367-1 – ident: ref51 doi: 10.2307/2065853 – ident: ref5 doi: 10.4103/SHB.SHB_11_20 – ident: ref61 doi: 10.1111/bjet.12169 – ident: ref54 doi: 10.1287/mnsc.46.2.186.11926 – ident: ref50 doi: 10.1016/j.compedu.2012.04.015 – year: 2009 ident: ref78 publication-title: Data Mining and Knowledge Discovery Handbook – ident: ref19 doi: 10.1016/j.evalprogplan.2011.11.007 – volume: 2 start-page: 285 issue: 2 year: 1995 ident: ref68 publication-title: Technology Studies – ident: ref27 doi: 10.1016/j.ijinfomgt.2020.102144 – ident: ref55 doi: 10.1007/s11423-019-09695-y – ident: ref4 doi: 10.1007/s11469-020-00270-8 – ident: ref24 doi: 10.1016/j.compedu.2020.103857 – ident: ref46 doi: 10.1108/02652320210446724 – ident: ref62 doi: 10.1016/0749-5978(91)90020-t – ident: ref6 doi: 10.1111/j.1469-0691.2009.02947.x – ident: ref59 doi: 10.13140/RG.2.2.17355.54565 – ident: ref22 doi: 10.35575/rvucn.n61a1 – year: 1994 ident: ref66 publication-title: Psychometric theory (3rd ed.) – ident: ref10 doi: 10.1016/j.sbspro.2016.02.071 – ident: ref58 doi: 10.1111/j.1467-8535.2011.01229.x – ident: ref69 doi: 10.1016/j.chb.2018.09.005 – ident: ref49 doi: 10.1007/s10639-019-10094-2 – ident: ref76 doi: 10.1007/978-3-030-44289-7_9 – ident: ref71 doi: 10.1177/002224378101800104 – ident: ref33 doi: 10.1080/09593969.2019.1667854 – volume: 11 start-page: 73 issue: 9 year: 2020 ident: ref34 publication-title: International Journal of Innovation, Creativity and Change – ident: ref9 doi: 10.1109/SCORED.2017.8305387 – ident: ref18 doi: 10.1109/access.2019.2930411 – ident: ref41 doi: 10.2307/249008 – ident: ref83 doi: 10.1016/j.psychres.2020.112958 – ident: ref8 doi: 10.1016/j.invent.2018.05.002 – ident: ref12 doi: 10.1109/ICMT.2011.6001924 – ident: ref17 doi: 10.1109/ACCESS.2019.2956349 – ident: ref44 doi: 10.1080/10494821003714632 – volume: 10 start-page: 4 issue: 3 year: 2014 ident: ref57 publication-title: International Journal of Education and Development using Information and Communication Technology – year: 2009 ident: ref72 publication-title: Multivariate Data Analysis (7th Edition) – ident: ref52 doi: 10.1177/0735633116688320 – ident: ref36 doi: 10.2307/25750691 – ident: ref47 doi: 10.3844/jssp.2014.51.62 – volume: 96 start-page: 1 issue: 17 year: 2018 ident: ref16 publication-title: Journal of Theoretical and Applied Information Technology – ident: ref43 doi: 10.3991/ijet.v13i06.8275 – ident: ref38 – ident: ref20 doi: 10.1007/s10664-015-9388-2 – ident: ref2 doi: 10.3390/educsci11010006 – ident: ref26 doi: 10.1177/0020764020935488 – ident: ref11 doi: 10.1109/ICCSE.2018.8468849 – year: 1985 ident: ref42 publication-title: Action Control – ident: ref63 doi: 10.1016/j.eswa.2010.03.015 – ident: ref64 doi: 10.1016/j.chb.2014.03.052 – ident: ref81 doi: 10.1016/j.dsx.2020.05.008 – ident: ref37 doi: 10.2307/25148690 – ident: ref35 doi: 10.1057/palgrave.ejis.3000717 – ident: ref25 doi: 10.1371/journal.pone.0227270 – ident: ref40 doi: 10.1016/j.chb.2019.106227 – ident: ref60 doi: 10.31234/osf.io/xmpk4 – ident: ref23 doi: 10.1080/10494820.2020.1830121 – ident: ref56 – volume: 14 start-page: 58 issue: 2 year: 2011 ident: ref82 publication-title: Educational Technology & Society – ident: ref15 doi: 10.3991/ijim.v14i02.11115 – ident: ref65 doi: 10.2190/EC.49.1.e – ident: ref80 doi: 10.1007/978-3-319-99010-1_59 |
| SSID | ssj0001742059 |
| Score | 2.5239727 |
| Snippet | Mobile learning has become an essential instruction platform in many schools, colleges, universities, and various other educational institutions across the... Background: Mobile learning has become an essential instruction platform in many schools, colleges, universities, and various other educational institutions... BackgroundMobile learning has become an essential instruction platform in many schools, colleges, universities, and various other educational institutions... |
| SourceID | doaj pubmedcentral proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | e24032 |
| SubjectTerms | Algorithms Anxiety Coronaviruses COVID-19 Distance learning Emotions Fear & phobias Higher education Machine learning Medical research Mobile commerce Original Paper Pandemics Students Teachers Technology Acceptance Model Technology adoption |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NbtQwELZQhRASQiD-AqUyUq9RN7YTj7mVlgoOLTlQ1FtkJ3a70japNilnXgMejydhxkmX3aoSFw7JIWMljmc8mbG_fMPYbt4AGOWLtFaNSlWBJ6uMTX0QAqNzdMsuxGIT-uQEzs5MuVbqizBhIz3wOHB7KsyIuhItJUgFMkBopMqlkzMDVpqGvO9Mm7VkKq6uYMaHgcMD9oiwzmhle8Q7JzY-PpGj_67A8jY-cu2Dc_SEPZ4iRb4_9vApu-fbZ-xX3OLnxxEC6fnEjnrO9xfnHab5F5c9HzpeLmn7ZeBlhIf__vGz5xGqTkog-Wnv-XHn0CH8vUO5sAMFsD0_jH8ucowM-cGXb58P08zwktaaL-f1-zsePbGSP2enRx-_HnxKp_IKaY3Z55A2VjrntFeNCDDzBrzNvHbaQJ1DbfOsKCzmf-iAhCxcHmZBm0Y6JUJmvZeZfMG22q71rxhHh--1FQ6VVKvCOSsFQMjwUMGCzxO2ezPuVT1xj1MJjEWFOQipp4rqSdjOqtnVSLZxu8EHUtpKSNzY8QJaTDVZTPUvi0nY9o3Kq2nC9hVGglrkUBiTsHcrMU412j-xre-usY3SkBuATCfs5Wghq55IqSgzVQnTG7az0dVNSTu_iHTeVPtEaHj9P97tDXsoCHRDsHK1zbaG5bV_y-7X34d5v9yJc-QPYysZWg priority: 102 providerName: Directory of Open Access Journals |
| Title | Using Machine Learning Algorithms to Predict People’s Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/33444154 https://www.proquest.com/docview/2507258699 https://www.proquest.com/docview/2478598817 https://pubmed.ncbi.nlm.nih.gov/PMC8081278 https://doaj.org/article/4f00913319f3483f8fd3453b3098a39d |
| Volume | 7 |
| WOSCitedRecordID | wos000848666000007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Open Access Full Text customDbUrl: eissn: 2369-3762 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001742059 issn: 2369-3762 databaseCode: DOA dateStart: 20150101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2369-3762 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001742059 issn: 2369-3762 databaseCode: M~E dateStart: 20150101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2369-3762 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001742059 issn: 2369-3762 databaseCode: BENPR dateStart: 20150101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Health & Medical Collection customDbUrl: eissn: 2369-3762 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001742059 issn: 2369-3762 databaseCode: 7X7 dateStart: 20150101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content customDbUrl: eissn: 2369-3762 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001742059 issn: 2369-3762 databaseCode: PIMPY dateStart: 20150101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1fb9MwED_BhhAS4o9gEBiVkfYarbGd2OYF7a_YQ0uEGCpPkZ3YXaWuGU3GM18DPh6fBJ-btnSaeOEhlhJbiaU7n-_Ov_wOYC-tpFTcZnHJKx7zzDeaKx1bR6n3zr1ZNi4UmxDDoRyNVN4l3JoOVrm0icFQV3WJOfJ9v1ULmspMqfdX32KsGoWnq10JjbuwjWWzUc_FSKxzLD7u8-7DfXiIiGeva_vIPkc3tqDA1H-be3kTJfnXtnP6-H8n_AQedQ4nOVhoyFO4Y2fP4FdACpBBQFJa0pGsjsnBdOxf0V5cNqStST7HU5yW5AFl_vvHz4YExDvKEvvPG0sGtfF2Zf2GfKpb9IMbchx-gCTewSRHH7-cHceJIjmmrC8n5btbPt2Rmz-H89OTz0cf4q5KQ1z6ILaNK82MMcLyijrZt0panVhhhJJlKkudJlmmfRjp7RhlmUld3wlVMcOpS7S1LGE7sDWrZ_YlEL9vWKGpSZQreWaMZlRKl_iLOy1tGsHeUnBF2VGYYyWNaeFDGZRvEeQbQW817GrB2XFzwCFKfdWJFNvhQT0fF92KLbjrI2eqN1GOccmcdBXjKTOsr6Rmqopgdyn3olv3TbEWegRvV91-xeIxjJ7Z-tqP4UKmSspERPBioWKrmTDGMcDlEYgN5duY6mbPbHIRWMGxhAoV8tW_p_UaHlBE5SDunO_CVju_tm_gXvm9nTTzXlg-oZU92D48GeafeiFL4e_ys0H-9Q-twi35 |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3dbtMwFLbGQDAJ8SN-FhjDSOMyWmM7sY2E0FiZVm0tvdhQ7zI7sbtKXTOaDMQdrwEPwUPxJBy7SUunibtdcJFcxFZiOZ8_n2N_PgehrTgXQjKThBnLWcgSuCkmVWgsIWCdAy1r65NN8F5PDAayv4J-NWdhnKyy4URP1HmRuTXybZiqOYlFIuW788-hyxrldlebFBozWByYb1_BZSvfdtrwf18TsvfhaHc_rLMKhBk4XVWYK6q15oblxIqWkcKoyHDNpchikak4ShIFbg-MO0ITHduW5TKnmhEbKWNoROG9N9BN4HHunD0-4Is1HfAzwVy5je46hTVge9tFuyNLU57PDHCVOXtZlfnXNLd3_3_roAfoXm1Q453ZCHiIVszkEfrplRC465WiBtdBZId4ZzyEJlenZyWuCtyful2qCve9iv739x8l9op-h1VXflwa3C008ObiDf2xqpydX-K2P-CJwYDGux8_ddphJHHfLcmfjbI3V3y6Dt7-GB1fS3c8QauTYmLWEYZ50XBFdCRtxhKtFSVC2AguZpUwcYC2GqCkWR2i3WUKGafgqjk8pR5PAdqcVzufxSS5XOG9Q9m80IUQ9w-K6TCtGSlltuViwgIFW8oEtcLmlMVU05YUiso8QBsNztKa18p0AbIAvZoXAyO5bSY1McUF1GFcxFKIiAfo6QzS85ZQypwDzwLEl8C-1NTlksno1Ec9dyliCBfP_t2sl-jO_lH3MD3s9A6eozXiFEhOY8820Go1vTAv0K3sSzUqp5t-6GJ0ct1D4Q_pEYah |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3dbtMwFD4aHZqQED_iLzCGkcZl1MZ2EhsJobFSUY2WXDA0roKd2F2lrhlNBuKO14BH4XF4Eo7TtKXTxN0uuEgubMuxnO8cn2N_PgdgN8yFkNxEfsZz7vMIX4pL5RtLKVrnqJa1rZNNxMOhODqSyQb8WtyFcbTKhU6sFXVeZG6PvI1LdUxDEUnZtg0tIun2Xp5-9l0GKXfSukinMYfIgfn2Fd238kW_i__6GaW91-_33_hNhgE_Qwes8nPFtNax4Tm1omOkMCowsY6lyEKRqTCIIoUuEMogZZEObcfGMmeaUxsoY1jAsN8rsIkmOact2Ez6g-TjaocHC9F42YLrjm-NSG-72Hd0bQGs8wRcZNye52j-tej1bv7P03ULbjSmNtmby8Zt2DDTO_Cz5kiQQc0hNaQJLzsie5MRDrk6PilJVZBk5s6vKpLU_Prf33-UpOb6OxS7-sPSkEGhUaOuekgmqnIeQEm69dVPgqY12X_3od_1A0kSt1l_Ms6eX_DpJqz7XTi8lOm4B61pMTUPgOCKaWJFdSBtxiOtFaNC2AAfbpUwoQe7C9CkWRO83eUQmaToxDlspTW2PNhZNjudRys53-CVQ9yy0gUXrwuK2ShtdFXKbcdFi0XlbBkXzAqbMx4yzTpSKCZzD7YXmEsbjVemK8B58HRZjbrKHUCpqSnOsA2PRSiFCGIP7s_hvRwJY9y59tyDeA34a0Ndr5mOj-t46C55DI3Fw38P6wlsoQSkb_vDg0dwjTpqkiPf821oVbMz8xiuZl-qcTnbaeSYwKfLloU_uSCQ8A |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Using+Machine+Learning+Algorithms+to+Predict+People%27s+Intention+to+Use+Mobile+Learning+Platforms+During+the+COVID-19+Pandemic%3A+Machine+Learning+Approach&rft.jtitle=JMIR+medical+education&rft.au=Akour%2C+Iman&rft.au=Alshurideh%2C+Muhammad&rft.au=Al+Kurdi%2C+Barween&rft.au=Al+Ali%2C+Amel&rft.date=2021-02-04&rft.issn=2369-3762&rft.eissn=2369-3762&rft.volume=7&rft.issue=1&rft.spage=e24032&rft_id=info:doi/10.2196%2F24032&rft_id=info%3Apmid%2F33444154&rft.externalDocID=33444154 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2369-3762&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2369-3762&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2369-3762&client=summon |