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Teachers’ acceptance of technology as predictors of the use of Google Classroom analytical learning

International Journal of Evaluation and Research in Education (IJERE) · Vol. 12 No. 3 (2023) · hlm. 1383–1389

DOI
10.11591/ijere.v12i3.25021
ISSN
2620-5440, 2252-8822
Terbit
2023-09-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-nc/4.0

Abstrak

The upsurge in Malaysia’s Industrial Revolution 4.0 (4IR) is in line with the changes in the global education environment. One of the latest approaches in the education field is learning analytics (LA). The use of LA in Google Classroom (GC) as the learning platform that measures, collects, analyses data and reports information about students to help teachers improve teaching and learning (T&L). This study aimed to determine the factors of teachers’ technology acceptance as the predictor for use LA in GC among the teachers in secondary schools. It is done by observing 304 teachers in daily secondary schools in the Federal Territory of Kuala Lumpur. The research instrument used was online questionnaire using Google Form, which data were collected and analyses by using IBM SPSS Statistics Version 26.0. The findings from this study show that the use of GC, teachers’ perceived usefulness and behavioral intention were the predictors that influenced the performance of LA in GC. This study hoped to provide a description and contribute a required alternative for the Ministry of Education (MOE) Malaysia and the schools in designing an effective strategy that can help teachers monitor the suitability of using LA in GC during teaching and learning.

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34 penulis · 78 tautan kepenulisan bersama · 35 lintas organisasi

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  • Universiti Putra Malaysia
  • Sultan Idris Education University
  • Tun Hussein Onn University of Malaysia
  • Universiti Teknologi MARA
  • National University of Malaysia
  • Tunku Abdul Rahman University of Management and Technology
  • University of Technology Malaysia
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Rekan penulisOrganisasiArtikel bersama
Rohani Ahmad TarmiziUniversiti Putra Malaysia2
Farzaneh SaadatiUniversiti Putra Malaysia1
Riyan HidayatUniversiti Putra Malaysia1
Wan Nurazlina Wan Mohd NoorSultan Idris Education University1
Nurihan NasirSultan Idris Education University1
Norliza GhazaliUniversiti Putra Malaysia1
Siti Syuhada Abu HanifahUniversiti Putra Malaysia1
Rosfuzah RoslanTun Hussein Onn University of Malaysia1
Othman TalibUniversiti Putra Malaysia1
Teh Chin HoongNational University of Malaysia1
Ku Soh TingUniversiti Putra Malaysia1
Maslina ZolkepliUniversiti Putra Malaysia1
Chen Chuei YeeUniversiti Putra Malaysia1
Mausooma MohamedAfiliasi tidak diketahui1
Maizura YasinUniversiti Putra Malaysia1
Nur Raihan Che NawiUniversiti Putra Malaysia1
Fazilah Binti RazaliUniversiti Putra Malaysia1
Tajularipin SulaimanUniversiti Putra Malaysia1
Noraziela Abdul MajidTunku Abdul Rahman University of Management and Technology1
Nurzatulshima KamarudinUniversiti Putra Malaysia1
Hosseinali GholamiUniversiti Putra Malaysia1
Aida Suraya Md. YunusUniversiti Putra Malaysia1
Danakorn Nincarean Eh PhonUniversiti Malaysia Pahang Al-Sultan Abdullah1
Noor Hidayah Che LahSultan Idris Education University1
Mohamad Syakir Zulpadhli Mat SenuSultan Idris Education University1
Nurul Farhana JumaatUniversity of Technology Malaysia1
Suhaizal HashimTun Hussein Onn University of Malaysia1
Shamsulariffin SamsudinUniversiti Putra Malaysia1
Haslinda HusainUniversiti Putra Malaysia1
Mohamad Firdaus AhmadUniversiti Teknologi MARA1
Nur Syazwani Zulaikha SafwanUniversiti Teknologi MARA1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijere.iaescore.com/index.php/IJERE/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Teachers’ acceptance of technology as predictors of the use of Google Classroom analytical learning</dc:title>
	<dc:creator>Mohd Fasiah, Ellie Esterina</dc:creator>
	<dc:creator>Mohd Ayub, Ahmad Fauzi</dc:creator>
	<dc:creator>Zulkifli, Nurul Nadwa</dc:creator>
	<dc:subject xml:lang="en-US">Behavioral intention; Google Classroom; Learning analytics; Perceived ease of use; Perceived usefulness</dc:subject>
	<dc:description xml:lang="en-US">The upsurge in Malaysia’s Industrial Revolution 4.0 (4IR) is in line with the changes in the global education environment. One of the latest approaches in the education field is learning analytics (LA). The use of LA in Google Classroom (GC) as the learning platform that measures, collects, analyses data and reports information about students to help teachers improve teaching and learning (T&amp;amp;L). This study aimed to determine the factors of teachers’ technology acceptance as the predictor for use LA in GC among the teachers in secondary schools. It is done by observing 304 teachers in daily secondary schools in the Federal Territory of Kuala Lumpur. The research instrument used was online questionnaire using Google Form, which data were collected and analyses by using IBM SPSS Statistics Version 26.0. The findings from this study show that the use of GC, teachers’ perceived usefulness and behavioral intention were the predictors that influenced the performance of LA in GC. This study hoped to provide a description and contribute a required alternative for the Ministry of Education (MOE) Malaysia and the schools in designing an effective strategy that can help teachers monitor the suitability of using LA in GC during teaching and learning.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2023-09-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://ijere.iaescore.com/index.php/IJERE/article/view/25021</dc:identifier>
	<dc:identifier>10.11591/ijere.v12i3.25021</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Evaluation and Research in Education (IJERE); Vol 12, No 3: September 2023; 1383-1389</dc:source>
	<dc:source>2620-5440</dc:source>
	<dc:source>2252-8822</dc:source>
	<dc:source>10.11591/ijere.v12i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijere.iaescore.com/index.php/IJERE/article/view/25021/13638</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-nc/4.0</dc:rights>
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