Indian Journal of Educational Technology Volume 4, Issue 2, July 2022 114 Research Paper Perception of the Key Stakeholders of Education on the Acceptance of Edutech Platforms in Teaching-Learning Process Dilip Suthar1 & Pankaj Parmeshwar Sharma2 1Junior Research Fellow, S D School of Commerce, Gujarat University, Navrangpura 2Assistant Professor (GES-II), Shri K. K. Shastri Government Commerce College, Khokhra Road, Maninagar, Affiliated to Gujarat University, Navrangpura Email: pankajkms731@gmail.com, sharmapankaj12011996@gmail.com Abstract EduTech applications have played a vital role in carrying out the learning activities during lockdown periods for the students and educationists. In the future; these platforms are going to change the learning approach for students and educators. This study examines the perception and acceptance of the students and teaching professionals towards the usage of the EduTech applications. The study-in-progress analysed the technology acceptance model (TAM) for work-related tasks with e-learning and used TAM as a basis for hypothesizing the effects of such variables on the use of e-learning as the application. The study concludes that attitude towards use and perceived ease of use significantly affects the intention to use teaching-learning applications. The study suggests that to capture the large market and satisfy participants, it is necessary for the existing and potential EduTech platforms to provide active experience and complete course content to the participants. Keywords: Education, Technology, Stakeholders, Acceptance, Platforms Introduction Teaching Styles have changed significantly from traditional methods to modern interactive methods over a period of time. The initial methods of providing education were through recitation and memorization, whereas the modern approach utilizes online and interactive methods. Stable progress of e-Learning in recent years (Mulder and Janssen, 2013) has also prompted universities and educators to use a variety of online learning techniques, such as Learning Management Systems, Internet-based technology for learning, Information, Communication, and Technology (ICT), and Social Network- based Learning or mobile learning (Liao et al., 2019; Eksail and Afari, 2020; Huang et al., 2020), to improve the effectiveness of traditional classroom instruction by assisting students in learning independently and developing problem- solving abilities. (Liu et al., 2010; Tian et al., 2014). But COVID-19 emerged at the end of December 2019 and this global pandemic has made a significant impact on higher education students’ learning because they were in the middle of semesters and the lockdown imposed on them,  forced them to change their learning techniques. Due to the pandemic initiated complete lockdown in countries, students were not able to learn on a face-to-face basis with their educators. To cope with this situation students and educators used computer- based or technology-aided methods (teaching-learning applications) for learning. Students and educators used different teaching-learning applications like Google classroom/meet, Unacademy, Byjus, YouTube Channels, Zoom, Jio Meet, etc. Despite the rise in the number of online learners, Online learning has always been associated with a number of dangers, including Indian Journal of Educational Technology Volume 4, Issue 2, July 2022 115 the absence of teachers, a lack of peer connection  compared to face- to-face learning, low motivation, poor time management, and a lack of individual learning abilities. (Cole et  al., 2004; Golladay et  al.2000; Hannay & Tracy, 2018; Kirtman, 2009; McKeachie, 2002; Nguyen, 2015; Ryan, 2001; Serwatka, 2003; Xu & Jaggars, 2013). Several researches comparing students’ perspectives of e-learning with traditional learning in terms of social presence, social interaction, and satisfaction,  discovered that e-learning is evaluated as lacking in social interaction, social presence, and effective coordinated communication., it provides several benefits including convenience and ease of time, an easy understanding of critical concepts and subjects and gives opportunities to work while learning (Cuthrell & Lyon, 2007). In this study, we investigated the perception and acceptance of teaching- learning applications by students and teaching professionals through the Technology acceptance model (TAM). Literature Review This study investigated the use, intention and acceptance of teaching-learning applications based on the theory of TAM, targeting students and educators in the Ahmedabad city. This section consists of TAM Model and online teaching- learning-related research work. The Technology Acceptance Model (TAM) created by Davis (1989) is one of the most generally utilized models to explain a potential user’s behavioural intentions towards using technological innovation. This model, with high reliability and validity as reported in Adams (1992), contained the constructs of perceived ease of use, perceived usefulness, attitudes towards using, and behavioural intention of use (1989). Using the Extended Technology Acceptability Model (ETAM), Prasetyo (2021) assessed student acceptance of an online learning platform during the COVID-19 pandemic. The results showed that PEU (perceived ease of use) had the highest impact on actual use (AU), followed by UI (user interface) and SQ (system quality). Iaman and Turki (2012) revealed that accessing course materials, looking for relevant information, sharing knowledge, and completing homework were significantly associated with students’ perceived usefulness of mobile learning. During the investigation of students’ use and acceptance of course websites, Selim (2003) discovered that there is a significant relationship between utilisation and ease of use when it comes to determining how frequently a website course is used. Using Google Meet’s media-assisted teaching style, Setyawan et al. (2020) examined how well students learned at home and found out that students taught using Google Meet media-assisted lectures had higher knowledge and learning outcomes than comparison groups. Khan et al. (2021) analyzed the perception of university students toward e-learning during the ongoing COVID-19 pandemic. It revealed students’ positive perception of e-learning and thus acceptance of this new learning system. Dorji (2021) studied teachers’ preferences for classroom and online teaching in Bhutanese primary and secondary schools. Quantitative data found that over 50 per cent of teachers favoured e-learning, whereas qualitative data revealed that teachers preferred classroom teaching over online education for reasons such as authenticity, comfort, and affordability. According to Gismalla et al. (2021), most medical students like e-learning. During COVID 19 shutdown, 64 per cent of students said E-learning was excellent. A substantial link was found between medical students’ opinions on starting E-learning and their level (Pre-clerkship and Clerkship). During Indian Journal of Educational Technology Volume 4, Issue 2, July 2022 116 the Covid-19 outbreak, Olayemi et al. (2021) assessed students’ readiness for online learning in Nigeria, The majority of respondents reported high levels of ICT skills and abilities required for online learning. Fear of high data costs, inadequate internet services, unstable power supplies, inaccessibility to online library resources, and limited computer access were reported barriers to effective online learning. Aggarwal (2020) assessed among all the other service providers and the competitors, Unacademy proves out to be the favourite among the people as the majority of the people are connected and have applied in Unacademy. Kim (2020) investigated the impact of zoom video lectures on learners’ English reading achievement in real time remote video education; the study’s findings revealed that zoom video lectures have a beneficial impact on learners’ English reading achievement. Therefore, researchers aimed to investigate the perception and acceptance level of teaching-learning applications by students and educators. The research questions were as follows: 1. Whether students and educators accepted online teaching-learning technology during the COVID-19 pandemic? 2. Which kinds of Edutech platforms are preferable? Research Methodology This paper investigates the acceptance of teaching-learning technology and what kind of perception students and professionals have regarding its use with the help of TAM. Our research method consisted of four parts. In the first part, we created the research framework; second, we handled research assumptions; third, we explained the research method and steps and fourth, we examined research objects and sampling methods. Research framework In this study, TAM is divided into four aspects: perceived usefulness, perceived ease to use, intention to use, and attitude toward using, as indicated in Chart 1. Chart-1: Empirical Model for Teaching-learning Technology Acceptance Methodology The main objective of this study is to identify the perception, acceptance, and attitude of the professionals, students, and other aspirants regarding the use of different teaching-learning applications. To meet the objective the relevant literature has been studied from Google scholar, emerald publication, sage publication, web of science, Research Gate, medley, and other authentic sources. The design of the study is a descriptive and survey method. The variables identified from the literature suggest ease of use, perceived usefulness, and attitude towards use as Indian Journal of Educational Technology Volume 4, Issue 2, July 2022 117 the independent variables that explain the dependent variable which is the intention to use the teaching-learning applications. From the literature, the research questions have been identified and some hypotheses are developed. The structured questionnaire has been framed to collect the data. The demographics, and the seven- point scale of perceived usefulness, perceived ease of use, attitude towards use, and intention to use are (Adams, Nelson, & Todd, 1992; Davis et al., 1989; Legris et al., 2003; Venkatesh & Davis, 2000) used for teaching-learning application acceptance. Therefore, 150 questionnaires are distributed, and 142 responses were recorded through the purposive sampling method from February to March 2021 in Ahmedabad city. Those 130 responses yielded valid responses that were used for analysis. The collected data has been analysed through SmartPLS 3 software to perform partial least square structural equation modelling. This study used some design, methods, literature, tools, and techniques that have certain limitations and the same applies to this study. Based on this, we propose the following hypotheses: H1- Perceived usefulness has a significant effect on the perceived ease to use. According to Davis (1989), perceived usefulness is the notion that using new technology will improve one’s professional performance. Multiple times, the favourable impact of this variable on the adoption of information technologies has been demonstrated empirically. (Davis, 1989; Davis et al., 1989; Igbaria, Liveri, & Margahh, 1995; Lederer et al., 2000; Ong, YaLui.) H2- Perceived ease to use has a significant effect on the intention to use. As previously said, perceived ease of use is defined as a person’s perception of how easy it will be to use new technology. (Davis, 1989). H3- Attitude towards using has a significant effect on the intention to use. The degree to which a user is interested in specific systems is known as attitude, and it has a direct impact on the user’s desire to use those systems in the future. (Bajaj & Nididumolu, 1998). H4- Attitude towards using has a significant effect on perceived usefulness. According to TAM, perceived usefulness has a direct impact on attitudes toward new technology use. The degree to which a user is interested in specific systems determines whether or not that user intends to utilize those systems in the future. (Bajaj & Nididumolu, 1998). Table-1 indicates the scale of perceived usefulness, perceived ease of use, attitude towards use, and intention to use the teaching-learning applications. Table-1: List of Variables Used in TAM Model SECTION - I Perceived Usefulness (USE) Efficient Learning on TLA USE1 Proper guidance & solution of queries on TLA USE2 I can teach/learn at any place or time on TLA USE3 SECTION - II Perceived Ease to use (ETU) Easy & Convenient for me to use TLA ETU1 Simple to Understand TLA ETU2 I can easily interact with students/teacher ETU3 Indian Journal of Educational Technology Volume 4, Issue 2, July 2022 118 SECTION - III Attitude toward use (ATU) Learning on TLA is fun ATI1 TLA provides a pleasant way to learn ATI2 I feel happy & satisfied using TLA ATI3 I like using TLA ATI4 SECTION - IV Intention to use (ITU) I am willing to use TLA ITU1 I am Planning to use TLA in future ITU2 I will recommend others to use TLA ITU3 Data analysis Chart-2: Research Model for Teaching-learning application acceptance Table-2: Demographics of Respondents Demographics Frequency Percentage Gender Male Female 52 78 40 60 Age Below 20 20 to 40 Above 40 18 108 04 14 83 03 Experience of TLA (Teaching-learning Application) Yes No 72 58 55 45 Indian Journal of Educational Technology Volume 4, Issue 2, July 2022 119 Status (Key Stakeholders of Education) School Student College Student Pursuing Professional Course Preparing for competitive exams Teacher/Professor Other 04 50 10 21 30 15 03 38 08 16 23 12 Table-3: Path Coefficient Attitude Ease of Use Intention to Use Usefulness Attitude 0.520 0.684 Ease of Use 0.276 Intention to Use Usefulness 0.728 Indirect Effect Attitude 0.497 0.137 Ease of Use Intention to Use Usefulness 0.201 Total Effect Attitude 0.497 0.657 0.684 Ease of Use 0.276 Intention to Use Usefulness 0.728 0.201 Table-3 indicates that usefulness has the strongest effect on ease of use (0.728). These two constructs explain 52.9 per cent of the variance of the endogenous construct ease of use (R2 = .0529). Then Attitude has a significant effect on the usefulness (0.684) and intention to use (0.520). Table-2 indicates that valid responses include 52 male (40 per cent) and 78 female (60 per cent) respondents. The Majority of participants were between 20 to 40 years, with 108 responses (80 per cent). Participants having experience in teaching-learning applications are 55 per cent while those not having experience are 45 per cent. The majority of the respondents are college students (38 per cent), and then comes the teachers/professors (23 per cent). Indian Journal of Educational Technology Volume 4, Issue 2, July 2022 120 Table-4: Outer Loadings Indicators Attitude Ease of Use Intention to Use Usefulness ATI1 0.648 ATI2 0.905 ATI3 0.920 ATI4 0.823 EOU2 0.890 EOU2 0.897 EOU3 0.859 ITU1 0.930 ITU2 0.945 ITU3 0.950 USF1 0.929 USF2 0.921 USF3 0.727 Table-4 indicates that all outer loadings of the reflective constructs EOU, ITU, and USF are above the threshold value of 0.70, which suggests sufficient levels of indicator reliability. Table-5: R Square R Square R Square Adjusted Ease of Use 0.529 0.520 Intention to Use 0.524 0.505 Usefulness 0.467 0.457 Table-5 presents the value of R-square for dependent variables. The usefulness variable is nearby 0.40 which indicates weak predictive accuracy of the model. Ease of use and Intention to use variables are above 0.50 which indicates moderate predictive accuracy of the model. The R square value of 0.25, 0.50, and 0.75 describes the substantial, moderate, and weak predictive accuracy of the model. Table-6: F Square Attitude Ease of Use Intention to Use Usefulness Attitude 0.351 0.877 Ease of Use 0.099 Intention to Use Usefulness 1.125 Indian Journal of Educational Technology Volume 4, Issue 2, July 2022 121 Table-6 indicates the effect size of the constructs. The constructs with F-square ≥ 0.02, F-square ≥ 0.15 and F-square ≥ 0.35 represent weak, moderate, and strong effects, respectively (Cohen, 1988). Table-7: Construct Reliability and Validity Cronbach’s Alpha rho_A Composite Reliability AVE Attitude 0.848 0.891 0.898 0.691 Ease of Use 0.859 0.873 0.913 0.778 Intention to Use 0.936 0.939 0.959 0.887 Usefulness 0.827 0.866 0.897 0.747 Table-7 evinces the construct reliability and validity through internal consistency and convergent validity of constructs. The internal consistency and validity of the construct are measured from Cronbach’s alpha, composite reliability, and AVE. The Cronbach’s alpha of all the constructs suggests an adequate level of internal consistency (Yusoff, 2012). The composite reliability of all the constructs exceeds 0.07 (Hair et al., 2014) which is also adequate. The average variance extracted is more than 0.50 for all the constructs that indicate satisfactory convergent validity of constructs (Hair et al., 2010) Table-8: Discriminant Validity - (Fornell-Larcker Criterion) Attitude Ease of Use Intention to Use Usefulness Attitude 0.831 Ease of Use 0.618 0.882 Intention to Use 0.691 0.597 0.942 Usefulness 0.684 0.728 0.756 0.864 Discriminant Validity - (Cross Loadings) ATI1 0.648 0.325 0.341 0.356 ATI2 0.905 0.581 0.628 0.553 ATI3 0.920 0.606 0.690 0.682 ATI4 0.823 0.488 0.566 0.618 ETU1 0.531 0.890 0.497 0.547 ETU2 0.556 0.897 0.502 0.562 ETU3 0.544 0.859 0.567 0.774 FIU1 0.674 0.562 0.930 0.735 FIU2 0.596 0.550 0.945 0.689 FIU3 0.677 0.575 0.950 0.711 USE1 0.707 0.662 0.805 0.929 USE2 0.663 0.647 0.625 0.921 USE3 0.345 0.582 0.499 0.727 Indian Journal of Educational Technology Volume 4, Issue 2, July 2022 122 Discriminant Validity - (Heterotrait-Monotrait Ratio) Attitude Ease of Use 0.705 Intention to Use 0.751 0.658 Usefulness 0.775 0.846 0.864 Table-8 presents the discriminant validity of all the constructs using the Fornell Larcker criteria, cross- loadings, and Heterotrait-Monotrait Ratio and these all are adequate and satisfactory according to thresholds (Hensler et al., 2009) (Chin, 1998) (Hair et al., 2010) (Hulland, 1999). As per the Fornell Larcker Criterion the square roots of the AVEs for the reflective constructs Attitude (0.831), Ease of use(0.882), Intention to use (0.942) and Usefulness (0.864) are all higher than the correlations of these constructs with other latent variables in the path model, thus indicating all constructs are valid measures of unique concepts. This table also shows the loadings and cross-loadings for every indicator. The indicator ATI3 has the highest value for the loading with its corresponding construct ATI (0.920), while all cross- loadings with other constructs are considerably lower and the same approach is followed for other indicators also. The HTMT ratio indicates all values below 0.90, therefore the discriminant validity has been established between constructs. (Hensler et al., 2015). Table-9: VIF – Variance Inflation factor – Collinearity Statistics (Outer VIF Values) VIF ATI1 1.798 ATI2 3.482 ATI3 3.335 ATI4 2.196 ETU1 3.093 ETU2 3.179 ETU3 1.658 FIU1 3.385 FIU2 4.701 FIU3 4.754 USE1 3.309 USE2 3.228 USE3 1.397 Table-9 presents the variance inflation factor that is VIF, which shows the multicollinearity that states the correlation of variables with other predictors. We conclude, therefore, that collinearity does not reach critical levels in any of the formative constructs and is not an issue for the estimation of the PLS path model. Indian Journal of Educational Technology Volume 4, Issue 2, July 2022 123 Table-10: Model Fit (Fit Summary) Saturated Model Estimated Model SRMR 0.090 0.108 d_ULS 0.729 1.059 d_G 0.652 0.721 Chi-Square 175.683 181.431 NFI 0.732 0.723 Model Fit (rms Theta) 0.276 Table-11: Bootstrapping Path Coefficients (Mean, STDEV, T-Values, P- Values) Original Sample Sample Mean Standard Deviation T statistics P values ATI ITU 0.520 0.520 0.132 3.953 0.000 ATI USE 0.684 0.701 0.077 8.848 0.000 ETU ITU 0.276 0.276 0.122 2.256 0.024 USE ETU 0.728 0.743 0.062 11.655 0.000 Table-12: Path Coefficient (Confidence Intervals Bias Corrected) Original Sample Sample Mean Bias 2.5% 97.5% ATI ITU 0.520 0.520 -0.000 0.271 0.771 ATI USE 0.684 0.701 0.017 0.536 0.833 ETU ITU 0.276 0.276 0.000 0.037 0.502 USE ETU 0.728 0.743 0.015 0.603 0.846 Tables-11 & 12 show the mean, STDEV, P values, confidence intervals, and confidence intervals bias-corrected. Assuming a 2.5 per cent significance level, we find that all relationships in the structural model are significant. Table-13: Goodness of Fit Stress and Fit Measures Normalized Raw Stress .18129 Stress-I .42578g Stress-II .89766g S-Stress .36900h Dispersion Accounted For (D.A.F.) .81871 Tucker’s Coefficient of Congruence .90483 PROXSCAL minimizes Normalized Raw Stress. g. Optimal scaling factor = 1.221. h. Optimal scaling factor = .847. Indian Journal of Educational Technology Volume 4, Issue 2, July 2022 124 Chart-3: Scree Plot Diagram The data has been converted into proximities and a Scree plot (Chart 3) is prepared to know the number of dimensions that can accurately present the data. As the scree plot diagram shows that it can have two dimensions that present DAF (Table 20: Dispersion Accounted For) 0.8187. This means that these two identified dimensions can present 82 per cent of data if interpreted accurately. If three dimensions are presented then it becomes ambiguous to interpret the results therefore two- dimensional analysis has been selected. Table-14: Final Coordinates Final Coordinates Dimension 1 2 Jio Meet -.309 .237 Zoom -.247 .741 Google Meet -.675 .025 YouTube .124 -.109 Unacademy .333 -.569 Byju .773 -.326 Indian Journal of Educational Technology Volume 4, Issue 2, July 2022 125 Chart-4: Common Space - Perceptual Map of Teaching-Learning participants for the preference for Teaching-Learning Platforms From the study of the goodness of fit table and the chart of a scree plot diagram, two-dimensional analysis has been selected. A common plot and Final Coordinates for two dimensions are prepared. Which are presented in Table 14: Final Coordinates and Chart 4: Perceptual Map of participants for the preference for teaching-learning platforms. Considering the values of different teaching-learning platforms, dimension one is named Active Experience, and dimension number two is Complete Course Content. The data indicates that while preferring teaching- learning platforms the two attributes or dimensions which are considered by users are dimension 1 Active Experience and dimensions 2 Complete Course Content. Managerial Implications The EduTech service providers shall focus on building and creating a positive attitude of the key stakeholders of education towards the platforms by offering ease of use and comfort to them. This will have a greater impact on the intention to use the EduTeh Platforms for the stakeholders. The existing and potential teaching-learning service provider platforms shall offer active experience and complete course content to the stakeholders of education to capture a larger market and satisfy them. Findings and Conclusion The main aim of the study is to know the perception and acceptance of EduTech applications among students, educators, and others. The study made a preliminary analysis of the reliability and discriminant validity of our model’s measurement scale using Cronbach’s Alpha, rho_A, Composite reliability, Fornell-larcker criterion, Indian Journal of Educational Technology Volume 4, Issue 2, July 2022 126 and heterotrait-monotrait ratio, etc. Research models satisfy all the criteria of these parameters. The finding illustrates that the ease to use and attitude toward using have a significant direct influence on the intention to use teaching-learning applications. Attitude towards using has a significant influence on the usefulness and usefulness has a significant indirect influence on the intention to use teaching-learning applications. Teaching-learning applications developers can focus more on the ease-of-use criteria of applications and attitude has a major effect directly and indirectly on the intention to use the applications, so EduTech platforms have to give the users that kind of comfort that switch their attitude towards traditional methods of learning to modern online sources of teaching and learning. During COVID-19 Pandemic, these platforms play a prominent role in continuing the study without classrooms and after the lockdown period, now people are comfortable with these online platforms and the opportunity is here, those platforms create more user- friendly, easy to use, and provide quality knowledge, it can acquire major online market. The perceptual map presents that on the Active Experience dimension the Byju is least preferable and the Google Meet is highly preferable by the teaching-learning participants. On the second dimension i.e., Complete Course Content the Zoom is least preferable and the Unacademy is highly preferable by the users. From the above data, it can be concluded that the teaching-learning participants prefer platforms that offer active experience and complete course content. Thus, to capture the large market and satisfy participants, the existing and potential EduTech or teaching-learning platforms must provide active experience and complete course content to the participants. During the present era, it is important to use information and communication technology (ICT) technologies to support e-learning in education. E-learning has been defined as learning and teaching facilitated online through network technologies with no barriers of time and place (NGai, Poon, & Chan, 2007). E-learning environments reduce the cost of provision and therefore increase revenues for academic institutions (Ho & Dzeng, 2010). 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