Identifying priority antecedents of educational data mining acceptance using importance-performance matrix analysis
Previous studies on educational data mining (EDM) acceptance were focused on antecedents that were adopted from various models and theories. However, the ways in which such antecedents became the most important tools for educational improvement have not been researched in detail. This study aims to...
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| Veröffentlicht in: | Education and information technologies 2019-03, Vol.24 (2), p.1741-1752 |
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| creator | Wook, Muslihah Ismail, Suhaila Yusop, Nurhafizah Moziyana Mohd Ahmad, Siti Rohaidah Ahmad, Arniyati |
| description | Previous studies on educational data mining (EDM) acceptance were focused on antecedents that were adopted from various models and theories. However, the ways in which such antecedents became the most important tools for educational improvement have not been researched in detail. This study aims to identify the priority antecedents of EDM acceptance, particularly among undergraduate students since they are the most affected by this technology. Therefore, six antecedents with 11 variables have been formulated based on positive and negative readiness acquired from the technology readiness index (TRI). Meanwhile, cognition, emotion, internal control belief, and external control belief were obtained from the technology acceptance model 3 (TAM3). The Importance-Performance Matrix Analysis (IPMA) was used to identify priority antecedents of EDM acceptance, which was run using the SmartPLS 3.0 software. The findings revealed that perceived usefulness (PU) is the most important antecedent, followed by perceived ease of use (PEOU), and optimism (OPT). This study contributes to the literature by offering new insights on the field of EDM and extending existing knowledge on how cognition, positive readiness, negative readiness, emotion, internal control belief, and external control belief were combined for identifying priority antecedents of EDM acceptance. |
| doi_str_mv | 10.1007/s10639-018-09853-4 |
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However, the ways in which such antecedents became the most important tools for educational improvement have not been researched in detail. This study aims to identify the priority antecedents of EDM acceptance, particularly among undergraduate students since they are the most affected by this technology. Therefore, six antecedents with 11 variables have been formulated based on positive and negative readiness acquired from the technology readiness index (TRI). Meanwhile, cognition, emotion, internal control belief, and external control belief were obtained from the technology acceptance model 3 (TAM3). The Importance-Performance Matrix Analysis (IPMA) was used to identify priority antecedents of EDM acceptance, which was run using the SmartPLS 3.0 software. The findings revealed that perceived usefulness (PU) is the most important antecedent, followed by perceived ease of use (PEOU), and optimism (OPT). This study contributes to the literature by offering new insights on the field of EDM and extending existing knowledge on how cognition, positive readiness, negative readiness, emotion, internal control belief, and external control belief were combined for identifying priority antecedents of EDM acceptance.</description><identifier>ISSN: 1360-2357</identifier><identifier>EISSN: 1573-7608</identifier><identifier>DOI: 10.1007/s10639-018-09853-4</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Analysis ; Beliefs ; Computer Appl. in Social and Behavioral Sciences ; Computer Science ; Computers and Education ; Data Analysis ; Data Collection ; Data mining ; Education ; Educational Improvement ; Educational Technology ; Emotional Response ; Information Systems Applications (incl.Internet) ; Instructional Improvement ; Knowledge ; Positive Attitudes ; Schemata (Cognition) ; Self Concept ; Self Control ; Student Attitudes ; Technological Literacy ; Technology Acceptance Model ; Undergraduate Students ; Usability ; User Interfaces and Human Computer Interaction</subject><ispartof>Education and information technologies, 2019-03, Vol.24 (2), p.1741-1752</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019</rights><rights>COPYRIGHT 2019 Springer</rights><rights>Education and Information Technologies is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-b5d1898bdf60ed118c1bcda5938819c20a666406e780ca6f2e27846220b761203</citedby><cites>FETCH-LOGICAL-c408t-b5d1898bdf60ed118c1bcda5938819c20a666406e780ca6f2e27846220b761203</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10639-018-09853-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10639-018-09853-4$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttp://eric.ed.gov/ERICWebPortal/detail?accno=EJ1209260$$DView record in ERIC$$Hfree_for_read</backlink></links><search><creatorcontrib>Wook, Muslihah</creatorcontrib><creatorcontrib>Ismail, Suhaila</creatorcontrib><creatorcontrib>Yusop, Nurhafizah Moziyana Mohd</creatorcontrib><creatorcontrib>Ahmad, Siti Rohaidah</creatorcontrib><creatorcontrib>Ahmad, Arniyati</creatorcontrib><title>Identifying priority antecedents of educational data mining acceptance using importance-performance matrix analysis</title><title>Education and information technologies</title><addtitle>Educ Inf Technol</addtitle><description>Previous studies on educational data mining (EDM) acceptance were focused on antecedents that were adopted from various models and theories. However, the ways in which such antecedents became the most important tools for educational improvement have not been researched in detail. This study aims to identify the priority antecedents of EDM acceptance, particularly among undergraduate students since they are the most affected by this technology. Therefore, six antecedents with 11 variables have been formulated based on positive and negative readiness acquired from the technology readiness index (TRI). Meanwhile, cognition, emotion, internal control belief, and external control belief were obtained from the technology acceptance model 3 (TAM3). The Importance-Performance Matrix Analysis (IPMA) was used to identify priority antecedents of EDM acceptance, which was run using the SmartPLS 3.0 software. The findings revealed that perceived usefulness (PU) is the most important antecedent, followed by perceived ease of use (PEOU), and optimism (OPT). 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Ismail, Suhaila ; Yusop, Nurhafizah Moziyana Mohd ; Ahmad, Siti Rohaidah ; Ahmad, Arniyati</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-b5d1898bdf60ed118c1bcda5938819c20a666406e780ca6f2e27846220b761203</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Analysis</topic><topic>Beliefs</topic><topic>Computer Appl. in Social and Behavioral Sciences</topic><topic>Computer Science</topic><topic>Computers and Education</topic><topic>Data Analysis</topic><topic>Data Collection</topic><topic>Data mining</topic><topic>Education</topic><topic>Educational Improvement</topic><topic>Educational Technology</topic><topic>Emotional Response</topic><topic>Information Systems Applications (incl.Internet)</topic><topic>Instructional Improvement</topic><topic>Knowledge</topic><topic>Positive Attitudes</topic><topic>Schemata (Cognition)</topic><topic>Self Concept</topic><topic>Self Control</topic><topic>Student Attitudes</topic><topic>Technological Literacy</topic><topic>Technology Acceptance Model</topic><topic>Undergraduate Students</topic><topic>Usability</topic><topic>User Interfaces and Human Computer Interaction</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wook, Muslihah</creatorcontrib><creatorcontrib>Ismail, Suhaila</creatorcontrib><creatorcontrib>Yusop, Nurhafizah Moziyana Mohd</creatorcontrib><creatorcontrib>Ahmad, Siti Rohaidah</creatorcontrib><creatorcontrib>Ahmad, Arniyati</creatorcontrib><collection>ERIC</collection><collection>ERIC (Ovid)</collection><collection>ERIC</collection><collection>ERIC</collection><collection>ERIC (Legacy Platform)</collection><collection>ERIC( SilverPlatter )</collection><collection>ERIC</collection><collection>ERIC PlusText (Legacy Platform)</collection><collection>Education Resources Information Center (ERIC)</collection><collection>ERIC</collection><collection>CrossRef</collection><collection>ProQuest Social Sciences Premium Collection【Remote access available】</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Education Database (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Social Science Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Education Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>ProQuest Education Journals</collection><collection>ProQuest research library</collection><collection>Research Library (Corporate)</collection><collection>ProQuest One Education</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><jtitle>Education and information technologies</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wook, Muslihah</au><au>Ismail, Suhaila</au><au>Yusop, Nurhafizah Moziyana Mohd</au><au>Ahmad, Siti Rohaidah</au><au>Ahmad, Arniyati</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><ericid>EJ1209260</ericid><atitle>Identifying priority antecedents of educational data mining acceptance using importance-performance matrix analysis</atitle><jtitle>Education and information technologies</jtitle><stitle>Educ Inf Technol</stitle><date>2019-03-16</date><risdate>2019</risdate><volume>24</volume><issue>2</issue><spage>1741</spage><epage>1752</epage><pages>1741-1752</pages><issn>1360-2357</issn><eissn>1573-7608</eissn><abstract>Previous studies on educational data mining (EDM) acceptance were focused on antecedents that were adopted from various models and theories. However, the ways in which such antecedents became the most important tools for educational improvement have not been researched in detail. This study aims to identify the priority antecedents of EDM acceptance, particularly among undergraduate students since they are the most affected by this technology. Therefore, six antecedents with 11 variables have been formulated based on positive and negative readiness acquired from the technology readiness index (TRI). Meanwhile, cognition, emotion, internal control belief, and external control belief were obtained from the technology acceptance model 3 (TAM3). The Importance-Performance Matrix Analysis (IPMA) was used to identify priority antecedents of EDM acceptance, which was run using the SmartPLS 3.0 software. The findings revealed that perceived usefulness (PU) is the most important antecedent, followed by perceived ease of use (PEOU), and optimism (OPT). This study contributes to the literature by offering new insights on the field of EDM and extending existing knowledge on how cognition, positive readiness, negative readiness, emotion, internal control belief, and external control belief were combined for identifying priority antecedents of EDM acceptance.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10639-018-09853-4</doi><tpages>12</tpages></addata></record> |
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| subjects | Analysis Beliefs Computer Appl. in Social and Behavioral Sciences Computer Science Computers and Education Data Analysis Data Collection Data mining Education Educational Improvement Educational Technology Emotional Response Information Systems Applications (incl.Internet) Instructional Improvement Knowledge Positive Attitudes Schemata (Cognition) Self Concept Self Control Student Attitudes Technological Literacy Technology Acceptance Model Undergraduate Students Usability User Interfaces and Human Computer Interaction |
| title | Identifying priority antecedents of educational data mining acceptance using importance-performance matrix analysis |
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