Research Article
Google Classroom's Quadratic Purposefulness and Moderation During Covid-19 Spread
Solomon Oluyinkaa, Daenos Richardb, Cusipag Narag Mariac Najim Ayodele Lasisi d
a Baliuag University & City College of Angeles, Philippines,
b City College of Angele,
cBaliuag University/ De La Salle Araneta University, Philippines;
d Augustine University, Lagos, Nigeria
a solomon467@gmail.com, b ragdaenos@yahoo.com, cmariacusipag@yahoo.com, dlasisiayodele@yahoo.com
Article History: Received: 11 January 2021; Revised: 12 February 2021; Accepted: 27 March 2021; Published
online: 10 May 2021
Abstract: During the COVID 19 epidemic in the Philippines, this research aims to look at the quadratic purposefulness and moderating effects of Google Classroom (GCR) as a learning management system in a few public colleges and universities. An online survey questionnaire was distributed to 1068 respondents from seven colleges and universities; however, only 926 were considered for the achieved model. Twelve of the fifteen hypotheses were supported at p < 0.01 or p < 0.05 levels of significance, including the following: H1: Intended attitude can affect intention to use GCR; H2: Intend attitude can be affected by assumed purposefulness. H3: There is a quadratic relationship between purposefulness and attitude. H4: GCR trial runs can influence perception of purposefulness in using technology, and H5: GCR trial runs may influence educational policy to LMS adherence among public HEIs in Region III. As a result, organizations should be prepared to recognize methods for fostering and inspiring student participation in Google Classroom, as well as recognizing the importance of learning skills. Students that are unable to properly use technologies due to a lack of access should be given more time. Since Google Classroom is considered a new platform in Philippine education, potential researchers will need to double check the results of this study and examine the influence of different moderator variables.
Index Terms: Purposefulness, Moderating Effects, Google Classroom Educational Policy/Advisory, Platform. 1. Introduction
The COVID-19 pandemic has had a major impact on the global education environment. More than 370 million students have missed school as a result of partial or extended school closures enforced by governments around the world. The epidemic showcased a number of topics surrounding educational access as well as broader socioeconomic concerns [1]. In the Philippines, Proclamation No. 929 was issued by President Rodrigo Duterte declaring the country to be under a state of calamity within a period of six (6) months starting March 15, 2020. This was followed by an Enhanced Community Quarantine (ECQ) in the whole Luzon Island from March 17 to April 13, 2020 and was later extended in different parts of the country depending on the number of people affected by the virus. The announcement was supported by Republic Act 11489 or the "Bayanihan to Heal as One Act."
Moreover, the Commission on Higher Education (CHED) issued COVID-19 advisories, series nos.1 to 6, to prevent the spread of corona virus in different institutions. On April 13, 2020, Advisory No. 6 states, “All CHED recognized private HEIs, regardless of accreditation status, are allowed to exercise flexibility in determining the extent of adjustments for their approved academic calendar” [2]. They were required to inform the CHED regional offices regarding the changes in their academic calendar. Furthermore, higher education institutions (HEIs) were given the option to use appropriate alternative learning platforms for their students such as electronic and non-electronic learning methods, simulations, self-directed learning activities, modules, case-based scenarios, and others.
Alternative learning platforms such as those recommended by CHED have already been used in many parts of the world. Over 80 percent of colleges and universities in advanced territories have been actively involved with the use of e-learning to promote the standard of education [3]. Realistically, the learning management system (LMS) is the suggested means for dealing with the conventional face-to-face way of education [1]. Several universities use open-source learning management systems [4], [5]. With students staying at home, the conventional face-to-face learning system is giving way to the alternative learning platforms suggested by CHED. Many private colleges and universities started the school year in August and since then, some have had the choice of using more than one learning platform from Canvas to the messenger or zoom and the Microsoft Teams. Google Meet and Google Classroom are becoming more popular at different schools in the country.
2. Literature Review
A. Acceptance of Google Classroom
The acceptance of Google Classroom is influenced by a variety of elements, with some not yet clearly explained in previous articles. Oluyinka et al [3] agree that distance learning brings numerous benefits over conventional learning. Google Classroom (GCR) is a reasonably free learning management program that started in 2014. It takes
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into account the achievement of specific objectives such as the simplification of student-teacher interaction and the convenience with which assignments are distributed and graded. It offers an opportunity to complete the process of teaching and learning. Its features are appropriate for both learners and teachers. It gives students the flow of information and builds up academic research.
Bayarmaa [6] found an effective strategy for training professionals and encouraging the long-term development of information and skills in a problem-based learning study (PBL). Their study dealt with PBL being introduced both online and face-to-face to analyze GCR and establish PBL for students. Findings proved the usability and evaluation of GCR which resulted in a proposed e-learning platform.
As defended by Washington [7], the awareness and complexities encountered in using GCR were explored by obtaining feedback from the views and opinions of users of the online system. Nevertheless, the use of modern technology in education (also known as e-learning) facilitates online and self-paced education through extensive use of approved ICT [8].
B. Technology-Organization-Environment Model
Technology-Organization-Environment (TOE) framework was developed by Tornatzky [9]. This system is concerned with the process by which a company adopts new ideas as a result of technical, operational, and environmental factors. The characteristics and availability of internal and external technology that is critical to the job are referred to as technology transformation. Platforms could be made up of a variety of devices and mechanisms. Structures, procedures, scale, and slack are all reflected in the organizational aspect. Systemic market structure, infrastructure, and legislation are all part of the environmental context. Nonetheless, the TOE model, adoption, and application discovered in this study are important. Tom et al. [10] investigated the challenges driving the adoption of e-learning for cloud technology using technical, organizational, and environmental theory. Results showed that decision-makers would recognize potential advantages, efficiency gains, increased competition, and assistance for providers to encourage the effective sharing and acceptance of cloud technology through e-learning. Similarly, Alajmi [11] utilized the TOE theory to determine the variables whether or not the institutions will accept Cloud-based e-learning (CBEL), the relative advantage, compatibility, and IT readiness that supported the study found related for revalidation. The utilization of TOE to justify e-learning acceptance was found relevant in several studies [12], [13].
However, studies such as [14, [15] clarified the reliability of the TOE model for understanding a cloud-based model for higher learning. Ansong [16] also used TOE as predictors of e-learning adoption. Similarly, TOE was used to examine the acceptance of e-learning in HEIs; ease of use and usefulness were found to be the most significant factors in the study 17].
A study [11] used TOE to determine the variables whether or not the institutions will accept Cloud-based e-learning, relative advantage, compatibility, and IT readiness that supported the study found related for revalidation. The utilization of TOE to justify e-learning acceptance was found relevant in several studies [13].
C. Technology-Acceptance Model
Various conceptual frameworks of Information System (IS) have been formulated to examine the intention to use technology. TAM is frequently alluded to as IS’s most significant and commonly used theory developed in 1989 by [19], ease of use perceived and usefulness remain the most significant factors. Nevertheless, a study [18] recognize perceived usefulness as one of the most influencing variables in innovation adoption. Fig (a) demonstrated the TAM adopted in this study.
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TAM-based research was conceived to identify factors influencing ICT acceptance among distance learners. Results revealed significant associations between perceived usefulness and ease of ICT use and attitude towards using technologies, between attitude towards ICT use, and intention to use ICT, including between the action to use ICT and its actual usage [20]. In addition, teachers' preparedness and infrastructure had a major effect on the subjective perception of e-learning's usefulness and ease of use [21], [3].
Nonetheless, embracing m-learning is highly dependent on the students’ personal attitude; thus, their study focused on the specific context in which student's willingness for m-learning is examined using TAM agreed that the readiness of learners is highly influenced the perceived ease of use (PEU) and perceived usefulness (PU) [22], [23]. Likewise, a study [24] used TAM to describe instructional practices, and technology acceptance; the study confirmed TAM’s practical value.
Further study aimed to understand the effect of teachers’ readiness to effectively implement TAM-based e- learning technology. Analyses indicated that the preparedness of teachers was relatively influential towards students’ knowledge and acceptance domains and moderately influences the cultural factors domain. Teachers preparedness and infrastructure had a major effect on the subjective perception of e-learning’s usefulness and ease of use. The two components had a significant impact on the direction of attitude on using technology [25]. This study also considers some elements of TOE and TAM to structure a dependable e-learning model.
D. Combination of TOE and TAM Model
Based on the previous studies, TAM and TOE were widely complemented in several pieces of research that focused on technology adoption at the organization level. Although, TAM’s substantial impact on socio-psychological parameters should not be neglected in all the cases [26]. However, some studies integrated the TOE and the TAM to support their comprehensive implementation plan for LMS [27], [28].
To our knowledge, no research study has been conducted in Philippine higher education institutions to support the usefulness of Google Classroom using a mixture of TAM and TOE theories to support generability of acceptance. The adoption of Google Classroom among students has been validated by several international studies [29], [30]; [31]; [32]. As a result, the current researchers attempted to carry out this investigation. Meanwhile the proposed hypothetical model for this study demonstrated Fig (b).
Fig(b): Hypothetical Research model.
None related research study has yet been done in the Philippines supporting the effectiveness of the different learning systems currently used in different higher learning institutions. Some foreign studies validated the acceptance of GCR among students [29], [30]. This study aims to explore the determinants that may have significant effects on the adoption of the Google Classroom. Thus, hypotheses were tested and developed.
E. Hypotheses Development
Accordingly, the impression of students about GCR may be influenced by the attitude of teachers [33], [34], [35]. In this study, it is assumed in the same way, that: H1: Attitude expected will possibly influence the intent to use GCR.
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GCR offers a valuable opportunity to facilitate blended learning and professional growth. The study results have indicated that value of technology affects GCR implementation. A study [35] agreed that distance edification should be supported with facilities and infrastructure that guarantee the quality of graduates through the government. Their study justified the significant usefulness of GCR to teachers and students' readiness. To test, the following hypotheses proposed: H2: Perceived purposefulness will possibly influence the attitude expected.H3: A quadratic connection exists between purposefulness and expected attitude.
Furthermore, another study [11] reported there is a connection between perceived purposeful, simplicity, compatibility, and IT readiness. Perceived purposefulness trialability, data privacy, and convenient e-learning seems to be the most supported elements of the study. A research work [33] found that usefulness, ease of use, access to technology, and competencies of teachers are factors influencing student adoption of information technology. The results were found consistent in several studies such as [34], 36] study on the moderating effects of system exposure indicated that the implemented program used has a moderating effect on the electronic-textbook adoption behavior of users. Considering these facts, it is thus hypothesized that:H4: Trial-runs of GCR will possibly influence perception on purposefulness in using it.H5: Trial runs of GCR will possibly influence educational policy on system acceptance. H6: Trial runs of GCR will possibly influence the perception on technical access.H7: Trial runs of GCR may have a moderating effect on the educational policy in relation to distance learning advisory of HEIs.H8: There is a quadratic relationship between trial runs of GCR and the perception of technical access in using it.
Meanwhile, a scholar [37] claimed that there is a stronger need for education systems to enhance teaching quality and actively contribute to educational goals beyond traditional classrooms, quality higher education is considered as a crucial factor to produce a competent professional to build a strong nation and the best way to get along with global competition [38], [39]. The study suggested that interactive instructional materials from GCR may be adopted to support the institutional educational objectives. Thus, this study assumes that:H9: Institutional willingness will possibly influence educational policy in relation to use of GCR among the HEIs. H10: Educational policy, in relation to distance education, will possibly influence the use of GCR directly. H11: Perceived purposefulness of GCR will possibly influence educational advisory in relation to distance learning.
In the study of [40], teachers' adoption and usage of LMS in teaching was reviewed The predictive model incorporates usefulness, ease of use, subjective norm, and innovativeness in the context of information technology support, knowledge, and technology access. Findings have shown that information use serves as a precursor for communication use. Ease of use is the strongest predictor in LMS acceptance, and technical access and support have a direct impact to use of information and a subjective standard, a study also [3] found that organizations should be willing to take responsibility for ensuring that the system is readily accessible to facilitate LMS delivery effectively. Thus, it is assumed that:H12: There is a link between perceived technical access and intent to use GCR among users in this study.H13: Perceived technical access will possibly influence the institutional willingness to use the GCR.
Studies such as [41], [42] examined the factors affecting the decision of college faculty members to continue using LMS. There is an increasing global need for universities to embrace e-learning. Essential independent factors relating to organizational, technical, and personal attributes were presented to address the research gap to provide a better framework that will explain the continuing purpose of professors to use LMS. Based on their research, the impacts of institutional will, technological and personal motivations significantly promote the continuing intention of faculty to use LMS. It is then hypothesized that:H14: Institutional willingness will possibly influence GCR usage directly.H15: There is a quadratic relationship between perceived technical access and institutional willingness towards GCR use.
3. Methodology
A validated researcher-made online survey questionnaire was developed primarily in the context of the reviewed TAM and TOE models. A 4-point Likert scale ranging from 1 to 4 was adopted with 4 as strongly disagree. The questionnaire comprises two segments: the first segment questions are related to respondents' personal demographics (age, gender, affiliation, and educational attainment). Pre-investigation with GRC prior to COVID-19 has been asked, as has experience with any LMS, with the second section questions concentrating on technology adoption [6]; organizational environment, and educational policy (TOE-GCR). Creation of this section was based on previous related studies such as LMS adoption, online learning, blended learning, and Google classroom [27], 33], [3], [8].
The online survey questionnaire, a linked type obtained via Google form, shared with the participants after approval from presidents and vice presidents of public. The survey was conducted from March to July 2020; respondents over the age of 18 agreed and voluntarily participated in this research. Nonetheless, a total of 1068 learners and educators via their formal and informal contacts. Hence, Table (a) indicated the demographic profile of the respondents.
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Categories Frequency Percent
Respondents Students 842 79% Teachers 226 21% Age 19-26 748 70% 27-32 171 16% 33-38 64 06% 39+++ 85 08% Gender Male 534 50% Female 534 50% Educational Qualification Undergraduate 842 79% Bachelors 118 11% Masters 065 06% PhD 043 04%
The research study was dominated by students with 79 percent and teachers with 21 percent. Considering the age groups, 70 percent were 19 to 26 years old and 16 percent were 27 to 32 years old. The gender displayed the same equal percentage. Moreover, analysis focuses on students and teachers would be at least at a ratio of 1 to 3 claimed by a study [43]. Furthermore, Table (b) illustrated internet connectivity and familiarity with LMS during the COVID-19 period.
Table (b): GCR and level of connectivity before and during the COVID-19 outbreak
Categories Frequenc y Percent LMS Familiarity Yes 160 15% No 908 85% GCR Account Yes 142 13% No 926 87% Level of Connectivity Unlimited Connectivity 128 12% Moderate Connectivity 342 32% Slow Connectivity 310 29% No Connectivity 288 27%
4. Analysis and Reports
The initial number of surveyed 1068 respondents includes public higher education institutions in Region III, Philippines. These include City College of Angeles (CCA), Bulacan State University (BULSU), Gordon College (GC), Bataan Peninsula State University (BPSU), Bulacan Agricultural State College (BASC), Pampanga State Agricultural University (PSAU) and Nueva Ecija University of Science and Technology (NEUST). Based on the 1068 respondents, 85 percent said they were not familiar with any form of LMS, 87 percent said they did not have an active GCR account, 12 percent reported unlimited internet connectivity, 32 percent with moderate internet connectivity and 27 percent indicated no connectivity. The findings supported the reasons why the students and teachers need to build a dependable GCR model.
A. Analytical Tools
This study is a quantitative study using SmarPls 3.0 version in order to get the desired results. Sarstedt et al. (2019) [44]) SmarPls 3 is computer statistical software that has built-in analytical characteristics to identify the quadratic and moderating effects of the model factors. Several studies such as [45], [46], [47]; [48] also claimed that SmarPls is dependable when structuring and validating a measurement model.
B. Instrument Reliability and Validity Analysis
A total of thirty-three measures were subjected to an internal consistency and reliability test with Cronbach coefficient justification. Details can be seen in Fig (c).
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Fig (c): Achieved Cronbach’s Coefficients
As shown in Figure 4, the value of the coefficient varies considerably from 0.705 to 0.975 for technology constructs that exceed the recommended value of 0.7 and achieved and found consistent with several studies like [49], [50]. AVE correlation above 0.5 and accomplished results provided in the Figure 4 and the cross loading of items considered illustrated in Table (c) of this study.
Fig (d): Achieved Average Variance Extracted (AVE Table (c): Cross-Loading Factors
A T T E D U PO GC R IN S T I M OD PU R P Qu a T ec Qu a Pu Qu a T T ec h A T ri a l ATT1 0.839 ATT2 0.867 ATT4 0.810 EP1 0.662 EP2 0.874 EP3 0.924 GCR1 0.916 GCR2 0.715 GCR5 0.947 GCR6 0.941 GCR7 0.863 IW1 0.964 IW2 0.939 IW4 0.784 PP1 0.873 PP2 0.755 PP3 0.789 PTA2 0.674 PTA3 0.826 PTA5 0.848 PURPOSEFUL* 10 PURPOTRIAL* 1.0 TECHNI ACE * 1.0 TRIAL* RIAL 1.0 TRU2 0.942 TRU3 0.861 TRU5 0.727
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A total of 926 samples may not justify the normal distribution of data, thus bootstrapping the initial of 5000 samples in the Smartpls3 subsample setting considered (Mikalef et al., 2020 [51]), p-value < 0.05 adopted (Sarstedt et al., 2019 [44]), and variance explained 0f 35-65% claimed (Fowler et al., 2013 [52]). Table (d) indicated path coefficients achieved.
Table (d): Path coefficients achieved. Hypothetical paths St-Error t stat p-value Supported H1 Attitude expected to Google classroom (GCR) 0.061 4.316 0.000 Yes H2 Perceived purposefulness to Attitude expected 0.139 3.788 0.000 Yes H3 Quadratic purposefulness to Attitude expected 0.096 2.786 0.005 Yes H4 Trial runs to Perceived purposefulness 0.066 11.099 0.000 Yes H5 Trial runs to Educational policy 0.144 2.802 0.005 Yes H6 Trial runs to Perceived technical access 0.044 20.882 0.000 Yes H7 Moderator trial runs to Educational policy 0.073 3.446 0.001 Yes H8 Quadratic trial runs to Perceived technical access 0.096 0.310 0.757 No H9 Institutional willingness to Educational policy 0.129 1.937 0.053 Yes H10 Educational policy to Google classroom (GCR) 0.079 9.130 0.000 Yes H11 Perceived purposefulness to Educational policy 0.123 6.778 0.000 Yes H12 Perceived technical access to Google Classroom 0.128 1.768 0.077 No H13 Perceived technical 0.140 5.642 0.000 Yes
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access to Institutional willingness H14 Institutional willingness to Google Classroom 0.062 2.324 0.020 Yes H15 Quadratic technical access to Institutional willingness 0.146 1.852 0.064 NoA total of 15 hypotheses were tested and reported, that H1: Attitude expected will possibly influence the intent to use GCR is supported (t =4.316; p<0.000); for H2: Perceived purposefulness will possibly influence the attitude expected is supported (t =3.788; p<0.000); and H3: Quadratic relationship exists between purposefulness and attitude expected is supported (t =2.786; p<0.005); this implies that perceived purposefulness of GCR will influence attitude to use and vice-versa.
Conversely, trial runs of GCR will possibly influence perception purposefulness in using technology as stated H4 is found highly supported (t =11.099; p<0.000); H5: Trial runs of the GCR will possibly influence educational policy also supported (t =2.802; p<0.000), and for H6: Trial runs of GCR will possibly influence the technical access perception is found most supported in this study (t =20.882; p<0.000); this denotes that educational policy/advisory should allow adoption of GCR trial run. Moreover, H7: Trial runs of GCR have moderating effects on the educational advisory supported with (t =3.446; p<0.001). However, H8: Quadratic trial run of GCR regressed on technical access not supported and rejected.
Institutional willingness regressed on educational policy (H9) found slightly supported (t =1.937, p<0.001). H10: Educational policy regressed on the use of GCR is directly highly supported with (t =9.130, p<0.001); H11: Perceived purposefulness regressed on educational policy is found statistically supported with (t =6.778, p<0.000). The relationship between perceived technical access and intent to use GCR (H12) denied (t =1.678, p<0.077).
As indicated in Table 2, perceived technical access will possibly affect institutional willingness to use the GCR (H13) is supported (t =5.642, p<0.000) and for H14: Institutional willingness will possibly influence GCR usage is moderately statistically supported (t =2.324, p<0.020). Finally, H15: There is a quadratic connection between technical access perception and institutional willingness towards GCR usage found not supported. However, variance explained of this study illustrated in Fig (e).
Fig (e): Demonstrated the variance explained of the model
The bootstrapped model had an R2 of 0.937 and a total variance of 0.937, indicating that the general model was 94 percent accurate. The educational advisory to use GCR explains 86 percent of the variance, technological access
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to institutional willingness explains 83 percent of the variance, expected attitude to use GCR explains 57 percent of the variance, and the perceived purposefulness of the expected attitude and educational advisory to use GCR explains 54 percent of the variance.
5. Discussion of Findings A. Discussion
The study described some relationships which assessed willingness to use Google Classroom. It examined the quadratic impact of purposefulness, perception of technological access, and the moderating effects of trial runs in line with the educational advisory during the outbreak of COVID 19 in the Philippines. Table 4 summarizes the overall quadratic and moderating effects and the indirect relationship between TAM and TOE models developed for public HEIs toward the support of distance education using GCR. Based on the findings, institutional willingness has a positive impact on the use of Google Classroom and has a total effect on educational policy/advisory. These findings justify the fact that GCR’s acceptance depends on the positive relationship and agreement between public higher education institutions and the consideration of the educational board on distance learning [37].
The outcomes of this study justified that perceived purposefulness has a significant and indirect impact on intention to use Google classroom. It supports a total effect on the educational advisory. The quadratic effect analysis indicates that an indirect total effect exists between purposefulness and use of Google classroom. It distinguishes the total effect of quadratic purposefulness on an attitude that would be expected to accept GCR use among the respondents. It has been noted that GCR is found to be useful and necessary during the COVID-19 pandemic. The said technology could serve as a point of reference to substantially contribute to conventional learning. The findings of this study are relevant even for preceding and future studies [6], [27], [33].
Analyzing the moderating effects of GCR trial runs in relation to distance learning advisory given by CHED during the COVID 19 outbreak is possibly the most interesting part of this study. The construct has a positive overall and indirect correlation with the use of Google classroom, educational policy, anticipated attitude, and institutional willingness. According to the statistical explanations for the target objectives, a trial run is considered the most significant factor in this analysis. Additionally, the GCR trial run indicated the absolute direct effect on perceived purposefulness and perception of technological access. Nevertheless, these findings also indicate that the trial runs play a mediating role in the policies and aims of the education board to allow GCR to be used by the public HEIs. With these results, the education board should consider the adoption of Google Classroom, which has been proved to be effective and thus accepted by students and teachers [11], [53].
Likewise, findings confirmed that perceived technical access had a strong direct impact on the institutional willingness and anticipated attitude of the respondents. This implies that public HEIs should support users with an internet connection and further improve technology acceptance. Several studies have found support for these [34].
The hypotheses rejected in this study are quadratic trial run on perceived technical access, quadratic technical access to institutional willingness, and perceived technical access on the use of GCR. Similarly, the use of technology regressed on technical support which was also rejected in some previous studies like [6], [38]. However, this study agreed to the extent that certain factors may not be supported but added to the justified 94 percent variance explained by the quadratic purposefulness and moderating effects of GCR trial run.
B. Practical Implications of the Findings
In this study, TAM and TOE are the two most relevant models adopted to formulate the hypotheses in the use of GCR as a friendly learning platform. In Region III, the learners and teachers are willing to try GCR before its complete acceptance. It would be of help to establish an educational policy about technical evaluation and the institutions’ readiness. GCR test runs would improve teaching and learning, recognizing the application related to adoption. All of these are expected to guide students to concentrate on the skills required to be developed and be effective in learning during and after the outbreak of COVID 19. This would help enrich the learning process by developing and engaging in a productive learning environment.
Effective adoption of GCR ensures its relevance among students and teachers. The trial runs play a mediating role in the educational policy and the willingness of the public HEIs to use GCR. This suggests that proactive measures must be taken for GCR to be technologically accessible. College and university management must promote and support the implementation of GCR to establish an academic learning atmosphere where both teachers and students encourage acceptance of the system.
6. Conclusion and Future Work 6.1 Conclusion and Recommendation
The objective of this study is to examine the quadratic purposefulness and moderating effects of Google Classroom in line with CHED's distance learning advisory during the COVID 19 pandemic. Public HEI students
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and teachers who have never been involved in using Google Classroom in Region III, Philippines, were considered respondents to this study. TAM was used to measure teachers' and learners' acceptance of Google classrooms for teaching and learning activities. Through this reference, TAM serves as the basis for the usefulness of modern innovation. Additionally, TAM indicates that when an innovation is introduced to students and faculty, many factors may influence their willingness to accept. However, most technology-related studies agree that TAM could be extended to include other technology models or factorial elements. Some studies suggested that TAM and TOE be merged to justify research investigations.
The present study adopted TAM and TOE models, and reviewed studies related to the acceptance of technology in education. A structural equation model with the specification of complete bootstrapped, regression, quadratic, and moderating effects built-in SmartPLS3 for analysis was used to achieve the objectives of the study. However, trial runs adopted as moderating factors were set back on perceived technical access, attitude, educational policy, institutional willingness, and perceived purposefulness towards GCR among the justified predictable constructs investigated based on 5000 bootstrapped settings samples. Similarly, the quadratic purposefulness was justified as regressed on the expected attitude towards GCR use. Thus, users of GCR should consider the relevance of technological competencies to be established, learned, and strengthened as practical skills at all levels of education. Furthermore, institutions should be prepared to identify strategies for promoting and inspiring student engagement and understanding the value of learning skills in the Google classroom. Students who are unable to have full access to technology should not be forced by educational institutions but encouraged to use and allocate more time due to a low level of connectivity, as indicated in research concerns.
Centered on the analysis methods of the quadratic purposefulness and moderating effects of GCR trial runs, specific recommendations must be tailored to achieve the aim of the study. Colleges and universities must be kept up-to-date on their learning materials, so students and faculty can consider the purposefulness of GCR for their learning. Additionally, further consideration must be given regarding the institutional willingness to use GCR, thereby promoting the development of effective, enjoyable, and meaningful educational tools beyond the necessary learning materials. Workshops and seminars need to be arranged for teachers to encourage the use of GCR for learning activities outside the classroom.
The generalizability of the findings can be verified by conducting another study and it is highly suggested. The survey on moderating effects is deemed exploratory as Google Classroom is considered a new platform in Philippine education. Future researchers may need to check the research findings and investigate the impact of various variables within moderators. The inclusion of similar moderators and models from other areas in the Philippines where infrastructure and services are insufficient can be considered; thus, comparative analysis of study results can be performed in various cultural contexts.
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