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To study the impact of Google Classroom as a platform of learning and collaboration at the teacher education level

  • Published: 01 August 2020
  • Volume 26 , pages 843–857, ( 2021 )

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  • Adit Gupta   ORCID: orcid.org/0000-0003-0018-608X 1 &
  • Pooja Pathania 1  

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The purpose of this study was to assess the impact of Google Classroom Platform of learning at the teacher education level. Web-Based Learning Environment Inventory (WEBLEI) (Chang and Fisher 1998 , 2003 ) and Google Classroom Evaluation Survey was used in this study. The sample of 60 students consisting of both males and females was collected from one college of education in Jammu city, where teaching-learning process was being conducted using the Google Classroom setup. Data analysis revealed that students could access the learning activities easily, they could communicate with other students in their subject electronically, they could decide when they wanted to learn, and they could work at their own pace. Results also showed that the students could regularly access online resources and they had the autonomy to ask their tutor what they did not understand. Students experienced a sense of satisfaction and achievement and they felt at ease in working collaboratively with other students. The students were also happy to print lectures and exercise materials from resources uploaded by their teachers. Responses to the Google Classroom Evaluation survey showed that the teachers were able to give better individual attention and students developed a group feeling in such a classroom setup. Students also felt that learning through the Google classroom was not boring and it was not a waste of time. They found it to be an effective medium of studying.

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Data Availability

The data and material for the said research is available with the office of our department. The scholar has provided the data with the college which has been collected as part of the Masters Dissertation.

Abbreviations

Web Based Learning Environment Inventory.

World Wide Web.

Mobile Enhanced Learning Environment Inventory.

Technology Rich Outcomes Focussed Learning Environment Inventory.

Masters in Education.

Master of Arts.

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Acknowledgements

The authors wish to acknowledge the MIER College of Education for providing the facilities to the scholar for conducting this research.

The funding has been done by the Authors from their own funds and no support has been provided by any external agency.

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Gupta, A., Pathania, P. To study the impact of Google Classroom as a platform of learning and collaboration at the teacher education level. Educ Inf Technol 26 , 843–857 (2021). https://doi.org/10.1007/s10639-020-10294-1

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Received : 21 June 2020

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Published : 01 August 2020

Issue Date : January 2021

DOI : https://doi.org/10.1007/s10639-020-10294-1

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GOOGLE CLASSROOM

Saving teachers time supporting their students with new feedback tools., user testing.

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This conceptual project (unaffiliated with Google) focuses on Google Classroom, a free learning management tool with over 150 million users as of 2020. Classroom's goal is to simplify teaching and learning so teachers have more time to focus on helping students.

I was excited to design a new feature for this platform due to my past experience in education. I've used Google Classroom myself and have seen first hand how it is a powerful product for both teachers and students.

The scope of this project was to add a new feature (later identified as a feedback tool) to better support teachers.

UX researcher,  UX/UI designer

Sole designer

Figma, Whimsical, Maze

The problem

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Teachers continue to face increased scrutiny on student outcomes, increasing responsibilities, and less time to do it all.

Teachers need a streamlined and time-saving way to better support students.

case study about google classroom

Looking for a specific part of my process?

Click a section to go directly to that step.

creative-thinking 2.png

Understanding the problem

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Designing solutions

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Testing solutions

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Revising & hi-fi screens

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Reflecting on the process

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It was clear from the beginning of this project that teachers needed a way to save time and better support students. However, I needed to identify the best tool for accomplishing that goal. I focused my research on discovering how teachers currently use online learning management systems (LMS) and what struggles they face day to day.

Research goals

Determine for what reasons teachers typically use Classroom (or other LMS)

Identify frustrations with current products​​

Research methods

Competitive analysis, user interviews.

I began with secondary research on the learning management system (LMS) industry and conducted a competitive analysis of the current most popular products. My goal with this step was to identify gaps in features where there could be opportunities for product improvement.

Based on the research and my own assumptions from my experience in the field, I developed several provisional personas of potential users.

Competitive Analysis

I used my provisional personas as a guide for recruiting participants for the user interviews. Participants shared their experiences with using learning management systems and the daily obstacles they face with time management and supporting students.

RESULTS: Top user goals

Collaborate with colleagues

Maximize limited planning time

Maximize instructional time

Provide helpful feedback to students

Provide a "1-stop-shop" for students to engage with class resources

RESULTS: Top user frustrations

Feedback is tedious with multiple clicks

Lack of feedback customization

Students can submit blank work which wastes time for teacher to check (and student "To do list" is inaccurate)

From the interviews, I identified areas for potential time-saving features. I sent out a survey to a wider group of participants that spanned all grade levels to learn which tasks teachers prioritize. I asked participants to rank the importance of each task, and then I averaged the scores.

Based on the results, it seemed new feedback tools would be the most beneficial. Several participants mentioned some feedback tools they wish existed.

"Rank the tasks from 5 (most important) to 1 (least important)."

Giving feedback to students, 4.75  avg. rating, communication with families, 3.20  avg. rating, collaboration with colleagues, 2.59  avg. rating, analyzing assessment data, 2.58  avg. rating, providing opportunities to display student work, 1.92  avg. rating, user feedback, "we have a clunky amount of feedback to give in different places that we have to figure out. i wish it was more intuitive.", "i've found google classroom (especially with hybrid learning now) is very helpful. i do not think it is highly efficient & effective for teacher tasks.", "it's a matter of time that all teachers are facing. to prep, give feedback, communicate, grade, and everything else we do.", empathizing with users.

I had now heard users describe their current joys and frustrations with learning management systems and was able to identify giving feedback as an area that needed improvement. I wanted to synthesize the data into quick-reference visuals in the form of a persona and empathy map. This helped me to really get in the heads of the users.

User Persona

At this point, I was confident in my understanding of the users and their needs. It was time to begin planning the structure of where the new feedback tools would live in the existing Google Classroom interface.

I created several deliverables to do this (not all are highlighted in this case study).

A task flow of giving and returning feedback to a student

A user flow of giving and returning feedback to a student

Multiple sketches of each screen

Mid-fidelity wireframes

The vertical portion of the user flow is the existing flow when accessing Classroom. The amended flow with the new tools begins once teachers have accessed an individual's assignment.

User flow

Based on user comments from my research, I decided to include the following tools in the new feedback feature:

Comment tagging

Ability to write on/draw on student's document

Feedback in the form of images, audio, and file upload

Favorites collection for easy access to frequently used feedback

Assignment status selection to eliminate confusion around missing work

I created several sketches of potential designs for these tools, but narrowed them down to 2 sketches to show to users for feedback. Version A featured a panel on the right side with all available tools visible. Version B featured the same panel, but with all tools hidden until clicking the + button.

IMG_0513.jpg

A/B testing

I reached out to my initial interview participants for informal A/B testing. I asked each participant to look at the 2 sketches and give feedback on which would be easier to use and why.

Preferred by all users

Preferred by no users, "everything is right there and i don't have to click multiple times.", "the amount of clicks to access the tool i need seems tedious.".

Based on user feedback, I chose the layout of version A.  I also made small adjustments while creating the wireframes to make the layout more clean since the digitized version looked different than in a sketch.

GC Wireframes

Usability testing

I turned the mid-fidelity wireframes into high-fidelity screens using the existing Google Classroom branding and UI styles. Since I was only adding to an existing product with a defined style, I chose to create the hi-fi screens and prototype before conducting user testing.

I used Maze for user testing and asked users to complete several tasks using the new feedback tools. 

At the end of each task, I asked users to rate the ease of the process to accomplish that task. I then averaged the responses to give each task a score.

"Rate the process of _______ from 5 (easy) to 1 (difficult)."

Adding a tagged comment, 4.5  avg. rating, using the mark-up tools, 3.3  avg. rating, adding an image from favorites, "the drop down choice of 'assignment needs revision' is very helpful, especially if you have a large number of students.", "the first task with the comment tagging was the most useful because it easily draws attention.", "i love how easy it is to add images and change the status to 'needs revision'.", frustrations, "it took me a minute to figure out how to mark-up the document with lines and arrows.", "at first just finding where the new buttons were located was challenging. once i figured that out, it was easy.".

Overall, the new features seemed to meet users' needs. Users rated tagging comments, using the favorites collection, and changing the assignment status very highly.

However, it was apparent that the mark-up tools needed to be adjusted since users only rated the ease of use at 3.3 out of 5. Users also had a low success rate at completing that task during the Maze testing.

Revising & finalizing hi-fi screens

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The user testing highlighted the mark-up tools as an area of the design that needed to be revised to provide the best experience for users. Since I had already created hi-fi screens, I made the iterations to those screens.

Mark-up tools

I removed the existing Google Docs toolbar from the top of the assignment since users likely would not need those features anymore with the new mark-up tools. I moved the mark-up tools to this space for greater clarity, visibility, and less of a learning curve for established users.

I also made a small change to the CTA button to better meet the Material Design guidelines for dropdowns and buttons. I turned the "return" button into the primary CTA since the end goal for users is to return feedback to students. 

GC Version 1

New iteration

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High fidelity screens & prototype

case study about google classroom

Existing Google Classroom product (same screens)

case study about google classroom

Check out the comment tagging and mark-up tools.

case study about google classroom

Project summary & reflection

The goal of this project was to create a time-saving way for teachers to better support their students. I accomplished this by designing a new feedback center that gives teachers more feedback options in one easy to access place . Overall, users who tested the design did find that the new feedback center helped them save time and better support students . At the end of testing, I asked one overall question to users:

Rate how effective the new features are in helping you save time while providing better feedback to students from 5 (very effective) to 1 (not effective). 

4.3  avg. rating.

During the project, I did run into several challenges and recognized changes I would make for next time. Due to time constraints, I chose the feedback features based on several comments made during user interviews and on my own experience in the field. If I had more time, I would have done a second round of research/interviews to confirm these were the most needed tools. I could have also done a dot voting activity with users on the wireframes to see which tools they found helpful or not helpful.

I also found that during user testing, many users had issues with the prototype itself and needing to click specific areas or not realizing some things would not be functional yet. I know for next time to make directions more clear and to design the prototype in a way that offers more flexibility with clicks. 

Test the revised mark-up design

Conduct user testing interviews to supplement the Maze testing to hear verbal feedback and observe users' thought processes

Conduct additional research/testing to determine the usefulness of each new feedback tool or if there are others that could be added in the future

View another case study

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Factors influencing graduate students’ behavioral intention to use Google Classroom: Case study-mixed methods research

Mohialdeen alotumi.

1 Department of English, Faculty of Languages, Sana’a University, P.O. Box 14317, Sana’a, Yemen

2 Sana’a, Yemen

Blended learning combines face-to-face instruction and online learning experiences. It capitalizes on online learning management systems, one of which is Google Classroom (GC). Nevertheless, empirical investigations have mirrored literature gaps in understanding how the GC platform affects students’ behavioral intention to harness it for web-based learning. Therefore, this case study applied a modified version of the extended unified theory of acceptance and use of technology (UTAUT2) as a theoretical underpinning to examine factors influencing graduate students’ behavioral intention to utilize the GC platform. Employing mixed methods explanatory sequential design, the study first analyzed survey data from 23 EFL graduate students implementing partial least squares structural equation modeling (PLS-SEM). Subsequently, it conducted a qualitative stage carrying out semi-structured interviews for data collection and thematic analysis for its evaluation. The study through PLS-SEM results revealed that the most crucial determinant of students’ behavioral intention toward the GC platform was habit, which hung on facilitating conditions and hedonic motivation. Besides, it evinced facilitating conditions as the most important performing interaction factor in determining graduate students’ behavioral intention. Nonetheless, it indicated that performance expectancy, effort expectancy, social influence, facilitating conditions, and hedonic motivation had no direct effect on behavioral intention. The follow-up qualitative findings explained that since the students mainly used the GC platform off-campus, the GC App on their smartphones and the interesting content on the GC platform sustained their habitual tendencies toward employing the GC platform. Accordingly, the study explicates implications and recommendations for theory, policy, and practice.

Introduction

Information and communication technology (ICT) has significantly affected various walks of life, including economics, politics, culture, arts, and education. In the latter, ICT has compensated for the deficits of traditional books and learning systems, requiring users to be technically and digitally literate. To that end, Learning Management Systems (LMSs) can come in handy. LMSs are web-based learning systems that allow educators to create, manage, and deliver course content (Dobre, 2015 ; Turnbull et al., 2019 ). LMSs can play a critical role in improving and supporting teaching and learning in today’s pervasive digital environment (Bereczki & Kárpáti, 2021 ; Müller & Mildenberger, 2021 ; Turnbull et al., 2020 ). For instance, in blended learning , which incorporates face-to-face and technology-assisted learning (Oliver & Trigwell, 2005 ; Sharma & Barrett, 2007 ), LMSs can render interactive tools, such as blogs, wikis, chat rooms, and discussion platforms, thereby enabling blended learning to foster constructivist approaches to learning.

Google Classroom (GC) is a free blended-learning LMS and one of the most widely used LMSs in tertiary education (Bahri et al., 2021 ). It allows teachers to focus on building meaningful pedagogical activities while offering instructions and electronic resources in a collaborative setting to improve and augment student learning (Kumar et al., 2020 ; Shana et al., 2021 ; Sujannah et al., 2020 ). Because of its simplicity and functionality, GC can be valuable in the learning process. For example, it enables more efficient communication and workflow. It also furnishes possibilities to establish paperless learning. Therefore, students may better organize their information and consume less paper in their education (Kumar et al., 2020 ). In this regard, relevant research has suggested that the GC platform can aid the teaching and learning process (Albashtawi & Al Bataineh, 2020 ; Dash, 2019 ; Heggart & Yoo, 2018 ; Sujannah et al., 2020 ). Furthermore, it is easy to use whenever needed (Oktaria & Rahmayadevi, 2021 ; Ruqia et al., 2021 ). It is also cost-effective and user-friendly (Kumar et al., 2020 ).

In Yemen, COVID-19 has pushed universities to adopt Google Suite, including the GC platform, as a cheaper LMS to facilitate the possibility of off-campus learning during the pandemic and to foster blended learning afterward. However, it is unclear how tertiary instructors and students, who have received no proper formal training, employ the GC platform. Furthermore, besides the under-investigation of e-learning in the Yemeni context (Alotumi, 2020 ; Shormani & AlSohbani, 2018 ), only a few studies have examined users’ perceptions of LMS incorporation (e.g., Aldowah et al., 2019 ; Aqlan et al., 2021 ; Ghazal et al., 2018 ). Moreover, no study has looked into user adoption of the GC platform in the Yemeni English-as-a-foreign-language (EFL) setting—as to the researcher’s best knowledge. In addition, integrating LMS in any educational context does not ensure its successful implementation. Accordingly, vetting users’ acceptance of the LMS in a given educational environment is critical for its effective application (Amadin et al., 2018 ; Kumar et al., 2020 ; Le et al., 2021 ; Rahmad et al., 2019 ; Salloum & Shaalan, 2019 ).

Therefore, applying a modified version of the extended unified theory of acceptance and use of technology (UTAUT2), this case study-mixed methods research addresses such a gap in the relevant literature by identifying factors that could facilitate the GC adoption in the EFL college graduate programs at Sana'a University. More specifically, the study attempts to answer the following questions:

  • Which factors determine Yemeni EFL college graduate students’ behavioral intention to use Google Classroom (GC) as part of their blended learning?
  • What are the important and performing factors in determining Yemeni EFL college graduate students’ behavioral intention to use the GC platform as part of their blended learning?

Literature review

Theoretical framework.

The relevant research on technology user adoption in higher education has utilized the unified theory of acceptance and use of technology (UTAUT) to predict students’ technology acceptance successfully (Al-Maroof et al., 2021 ; Anthony et al., 2020 ). UTAUT was developed by Venkatesh and Davis ( 2003 ) based on a thorough examination of the most common technology adoption models. It seeks to elucidate user intentions to accept technology and resulting usage behavior. According to Venkatesh and Davis ( 2003 ), there are six primary constructs in the original UTAUT model: Performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, and use behavior. However, with UTAUT’s extensive knowledge expansion, new constructs, namely, hedonic motivation, price value, and habit, were included in this model, reintroduced as UTAUT2 (Venkatesh et al., 2012 ) (see Fig.  1 ). UTAUT2 was found to explain 74% of behavioral intention (Venkatesh et al., 2016 ) and a robust model that accounts for behavioral intention and actual technology implementation (Abbad, 2021 ; Tamilmani et al., 2021 ).

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UTAUT2 Key Variables . Note . Key variables are visualized according to Venkatesh et al. ( 2012 )

Performance expectancy (PE) refers to the extent to which a person believes that utilizing a system would increase their work performance (Venkatesh and Davis, 2003 ). According to UTAUT2, PE directly affects an individual’s behavioral intention (Venkatesh et al., 2012 ). Recent relevant research has established that PE can be a significant predictor of college students’ sustained intention to use technology in blended learning, which is a recent phenomenon in tertiary education (Abbad, 2021 ; Chen et al., 2021 ; Kumar & Bervell, 2019 ; Salloum & Shaalan, 2019 ; Yunus et al., 2021 ). In this study, PE refers to EFL college graduate students’ self-reported expectations for utilizing the GC platform to enhance their learning performance, increase their knowledge and skills, and fulfill their individual learning needs. Determined by their preconceptions, students’ benchmarks for assessing the utility of the LMS platform are that it will improve their results and help them achieve their goals. Therefore, this study examines graduate students’ continuous intention to employ the GC platform as per their perceived PE.

Effort expectancy (EE) refers to the degree to which an individual believes the system is easy or difficult to use (Venkatesh and Davis, 2003 ). The UTAUT2 model includes the concept of effort expectancy, which is a critical predictor of technology acceptance (Venkatesh et al., 2012 ). EE can directly impact college students’ behavioral intention to continue using LMS (Abbad, 2021 ; Chen et al., 2021 ; Kumar & Bervell, 2019 ; Yunus et al., 2021 ). This study defines EE as EFL college graduate students’ self-reported degree to which they believe the GC platform will be easy to employ in their blended learning. Students’ perceptions that the LMS platform will be easy to apply in their blended learning determine their continued usage; hence, this study looks into graduate students’ sustained intentions to harness the GC platform based on their perceived EE.

Social influence (SI) is the level to which a person believes their important others (e.g., family and friends) think they should utilize the new system (Venkatesh and Davis, 2003 ). It can be conceived as the extent to which social circle influences LMS use, either positively or adversely (Bervell et al., 2021 ). This study defines SI as EFL college graduate students’ self-reported perceptions of the extent to which they are encouraged by teachers, classmates, family, and friends to use the GC platform. SI can affect users’ behavioral intention in various contexts (Kim & Lee, 2020 ; Lu et al., 2005 ; Salloum & Shaalan, 2019 ; Yunus et al., 2021 ). Consequently, this study investigates graduate students’ perceptions of SI and its connection to their ongoing intentions to use the GC platform in their blended learning.

Facilitating conditions (FC) denote the extent to which a person feels that a technological and organizational infrastructure exists to enable the utilization of the system (Venkatesh and Davis, 2003 ). Compared to the system’s usefulness, FC can significantly predict user behavioral intention (Liu et al., 2018 ; Salloum & Shaalan, 2019 ). According to (Khechine et al., 2020 ), empowering conditions are critical in reinforcing online learning engagement. The current research defines FC as EFL college graduate students’ self-reported perceptions of the degree to which they think they have the aiding means (e.g., resources, skills, and support) to employ the GC platform in their blended learning. Furthermore, FC can crucially predict use behavior when considering technological affordance in underdeveloped nations (Huang et al., 2020 ). Accordingly, this study examines students’ perceptions of FC and its relation to their continuing intentions to utilize the GC platform.

Hedonic motivation (HM) refers to the enjoyment or pleasure gained from using the system (Venkatesh et al., 2012 ). Recent relevant research germane to tertiary education (e.g., Arain et al., 2019 ; Moorthy et al., 2019 ; Sitar-Taut, 2021 ) demonstrated that HM could be a significant predictor of behavioral intention when it comes to technology implementation in higher education. This study characterizes HM as EFL college graduate students’ self-reported perceptions of the degree to which they believe they enjoy applying the GC platform in their blended learning. HM can also be a significant predictor of GC use within the context of college blended learning. (Amadin et al., 2018 ; Bervell et al., 2021 ; Kumar & Bervell, 2019 ). Consequently, the current research investigates graduate students’ perceptions of HM and its link to their persisting intentions to employ GC in their blended learning.

Price value (PV) is an individual’s cognitive trade-off between the perceived benefits from using the system and its monetary cost (Venkatesh et al., 2012 ). According to The UTAUT2 model, PV directly influences BI in utilizing technology (Venkatesh et al., 2012 ). Furthermore, PV can significantly predict college students’ BI to utilize technology (Moorthy et al., 2019 ). Since GC is a free LMS platform for student use, this study did not include it.

Habit (HA) is the degree to which an individual tends to perform behaviors using the system (Venkatesh et al., 2012 ). HA is a significant predictor of technology users’ behavioral intention in the UTAUT2 model (Venkatesh et al., 2012 ). In tertiary education, it can forecast students’ behavioral intention of technology utilization (Arain et al., 2019 ; Moorthy et al., 2019 ). Since HA can also directly impact college students’ behavioral intention of using GC (Bervell et al., 2021 ; Kumar & Bervell, 2019 ), this study looks into graduate students’ perceived HA and its connection to their sustained intentions to apply GC in their blended learning.

Behavioral intention (BI) refers to the willingness of users to try new technologies (Venkatesh and Davis, 2003 ). According to the UTAUT2 model, BI can be directly influenced by performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit (Venkatesh et al., 2012 ). Recent empirical research has substantiated UTAUT2 factors’ predictability of college students’ BI of LMS (Abbad, 2021 ; Chen et al., 2021 ; Kumar & Bervell, 2019 ; Yunus et al., 2021 ). This study defines BI as EFL college graduate students’ self-reported perceptions of the degree of their willingness in attempting to utilize the GC platform in their blended learning. Since BI can be critical in predicting use behavior (Venkatesh and Davis, 2003 ; Venkatesh et al., 2012 ), this study looks into students’ BI and its connection to their ongoing intentions to use the GC platform.

According to Venkatesh et al. ( 2016 ), UTAUT2 should be used as an underpinning model to hypothesize the relationships among proposed variables on technology user adoption. Moreover, as Dwivedi et al. ( 2019 ) pointed out, most related research employed only a subset of the UTAUT model and often dropped moderators. Therefore, this study adapted and applied a modified version of the UTAUT2 model put forth by Kumar and Bervell ( 2019 ). Figure  2 presents the hypothesized model, and Table  1 displays the suggested hypotheses.

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Hypothesized Model of the Study. Note .

Adapted from Venkatesh et al. ( 2012 ) and Kumar and Bervell ( 2019 )

The Hypotheses Proposed in the Study

Google classroom adoption

Google Classroom (GC) is a free web-based LMS from Google. It is popular among teachers (Moorhouse & Wong, 2022 ) because it comes complimentary as part of a Google account (Saidu & Al Mamun, 2022 ). It can also be simple to use for both teachers and students. Furthermore, since it combines Google Docs, Sheets, Slides, Calendar, and Gmail into a single platform, it can ease communication and collaboration (Delos Reyes et al., 2022 ; Kumar & Pande, 2021 ), boosting student engagement and facilitating collaborative work (Beaumont, 2018 ). When properly harnessed, the GC platform can help higher education institutions implement flexible learning, particularly in hard times such as COVID-19 (Zuniga-Tonio, 2021 ).

Nonetheless, few studies have investigated the adoption of the GC platform in various tertiary contexts utilizing the UTAUT2 model. For instance, Jakkaew and Hemrungrote ( 2017 ) applied UTAUT2 to examine factors shaping college students’ use of the GC platform as part of an introductory course at a Thai University. Having surveyed 3,315 college students with a 5-point Likert scale and conducted multiple Pearson’s correlations, they found that performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit significantly influenced students’ behavioral intention. They also reported that facilitating conditions and behavioral intention affected students’ GC use. Besides, they indicated that despite students acknowledging the GC platform was a useful and simple tool, they did not fully harness its features.

To assess university students’ behavioral intention of the GC platform for mobile learning, Kumar and Bervell ( 2019 ) employed a modified UTAUT2 that included six non-linear relationships as a theoretical underpinning. They used a purposive sampling technique and a 5-point Likert scale to gather data from 163 college students. Having applied Partial Least Squares Structural Equation Modeling (PLS-SEM), they found that hedonic motivation and habit had substantial non-linear correlations with the other components of the UTAUT2 model. In addition, they showed that habit, hedonic motivation, and performance expectancy played a significant role in students’ behavioral intention to accept Google Classroom. They also demonstrated that habit and hedonic motivation had positive and significant non-linear relationships with performance expectancy, effort expectancy, and Social Influence toward students’ behavioral intention of the GC use. Further, they revealed that habit was the strongest predictor of students’ behavioral intention.

Bervell et al. ( 2021 ) developed a model founded on UTAUT2 to investigate the link between facilitating conditions and latent variables toward students’ behavioral intention to use the GC platform. They applied mixed methods explanatory sequential design. Their quantitative phase scrutinized survey data from 163 college students with PLS-SEM. Afterward, they utilized open-ended questions to collect qualitative data, which was examined employing thematic analysis. Their PLS-SEM outcomes substantiated the hypothesized model confirming the significant predictive association of facilitating conditions with effort expectancy, hedonic motivation, habit, and social influence; however, it had an insignificant relationship with behavioral intention. Having masked the role of facilitating conditions, they found hedonic motivation and habit critical predictors of behavioral intention. Further, their qualitative results unveiled that habit and perceived control of GC use affected hedonic motivation.

Farah et al. ( 2021 ) employed the UTAUT2 model to examine factors affecting university students’ utilization of the GC platform in their learning process. They used an online survey to collect data from 261 college students in Indonesia. In their analysis of the data, they applied PLS-SEM. They found that effort expectancy, performance expectancy, social influence, facilitating conditions, trust of government (TG), and trust of the Internet (TI) significantly influenced students’ behavioral intention to harness the GC platform. Further, they reported that TG and TI affected students’ performance expectancy.

Methodology

Research design.

This research is a case study in nature. According to Blatter and Haverland ( 2012 ) and Yin ( 2018 ), a case study can be about an individual, organization, or activity determined by boundaries. Specifically, the investigation is an LMS case study since it focuses on Google Classroom (GC) as a platform for content delivery, sharing, and interaction as part of blended learning. A case study in LMS is research into single or numerous instances of complex observable phenomena with well-defined limits (Turnbull et al., 2021 ). It adopted a case study-explanatory sequential mixed methods design. Guetterman and Fetters ( 2018 ) explained that a case study-mixed methods research is a parent case study encompassing nested mixed methods for data collection and analysis. Within this design, the study applied explanatory sequential mixed methods (Creswell & Plano Clark, 2018 ) to address the inquiry questions on the case of the GC platform.

Accordingly, quantitative data was gathered and analyzed first, followed by qualitative data collection and analysis. That is, to explore EFL college graduate students’ perceptions on the factors that determine their behavioral intentions (BI) to use the GC platform, data were first collected through an online self-reported questionnaire and evaluated statistically. Then, to qualitatively explain and refine the outcomes in the quantitative phase of the research, individual semi-structured phone interviews were conducted and afterward dissected applying thematic analysis.

Participants and setting

This study encompassed an intact group of 23 graduate students (8 males; 15 females) purposely selected based on GC integration. They were enrolled in the research methods course of the MA-in-English program hosted by the English Department of the Faculty of Languages, Sana'a University, Yemen, and they were in the first semester of the academic year 2021–2022. Most participants were 29 or fewer years old. The research methods course lasted for 16 weeks. It employed the GC as an online platform to support the blended learning for the graduate students, who were given a short orientation about its features at the beginning of the course.

Instruments

Gc platform.

In this study, the course teacher set up the GC platform and then added their students using their Gmail addresses. The GC platform was employed as a tool for blended learning in the MA course of research methods. It was mainly used outside the classroom to share materials, communicate queries and feedback, and submit assignments.

Using Google Forms, an online questionnaire survey aimed at data collection was delivered to all enrolled graduate students at the end of their first semester of the academic year 2021–2022. The survey comprised two parts. The first part collected students’ demographic information on gender, age, and online learning experience with the GC platform (three items).

The second part was devoted to gathering data on the modified UTAUT2 factors, which were performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM), habit (HA), and behavioral intention (HI). It had 21 items adapted based on Jakkaew and Hemrungrote ( 2017 ) and Kumar and Bervell ( 2019 ) (see Appendix A ). The items were on a 5-point Likert scale, ranging from 1 =  strongly disagree to 5 =  strongly agree . Items 1–3 measured students’ perceived PE. Items 4–6 appraised their perceived EE. About their perceived SI, it was estimated using items 7–9. Concerning items 10–12, they were structured to assess students’ perceived FC, while items 13–15 evaluated their perceived HM. Concerning items 16–18, they quantified students’ perceived HA. Finally, items 19–21 gauged students’ perceived BI. Besides, the survey ended with a yes/no question about willingness for interview participation and a short-answer statement for a contact number. All items were mandatory except for the last one about rendering a phone number if a student agrees to an interview. A panel of experts validated the survey before its administration, and the reliability of its seven subdomains was high (Cronbach’s α  > 0.80).

Open-ended questions for interview

The purpose of the interview was to clarify and refine the statistical results (Creswell & Plano Clark, 2018 ). After collecting and analyzing students’ responses to the online survey, the researcher purposely selected participants from the willing respondents and conducted one-on-one, phone, semi-structured interviews as dictated by the survey findings. The guiding open-ended questions were focused on participants’ explanations of the different UTAUT2 factors. All interviewees expressed their thoughts in English and answered the questions honestly (see Appendix B for the semi-structured interview form).

Data collection and analysis procedures

This was done in two stages, following the procedures recommended by (Creswell & Plano Clark, 2018 ) (see Fig.  3 ). First, the quantitative data prioritized in this study were obtained and then analyzed. Second, the qualitative data were collected and dissected to explain the quantitative findings. The two stages were independent. According to Huang et al. ( 2020 ), combining quantitative and qualitative analyses can give a holistic perspective on technology-adoption factors.

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Explanatory Sequential Design of the Study (QUAN qual) . Note .

Adapted from Creswell and Plano Clark ( 2018 )

Quantitative stage procedures

In the first stage, after following all necessary relevant ethics by getting institutional permission, teacher’s cooperation, and students’ consent to take part in the study, a Google Forms link to the online survey was sent to the graduate students’ representatives through Telegram, with a response window of a week. The online Google Forms survey started with a section that informs the respondents about the purpose of the investigation and how the data would be utilized before initiating their responses. It also notified them that their replies were kept anonymous and confidential. Indeed, 23 participants answered the survey with a 100% response rate. The data was collected electronically using Google Drive. Later, it was downloaded as a comma-separated values file (CSV).

The study applied Partial Least Square Structural Equation Modeling (PLS-SEM) to evaluate the research model. It met the minimum sample size recommendation (i.e., 21 participants) with a statistical power of 80% (minimum R 2  = 0.50, p  = 0.05) to run PLS-SEM on six independent variables (Hair et al., 2017 ). Unlike other methods, PLS-SEM can handle small sample sizes and complex models effectively, and it is nonparametric, i.e., it makes no distributional assumptions (Cassel et al., 1999 ; Hair et al., 2017 ). Besides, with small sample sizes, it usually obtains high levels of statistical power (Hair et al., 2017 , 2019 ). To that end, the analysis employed SmartPLS 3 with bootstrapping at 5000 resamples to predict the relationships posed in the hypothetical model of the study.

Qualitative stage procedures

The follow-up qualitative stage aimed to furnish an in-depth explanation of the quantitative findings (i.e., PLS-SEM results) (Creswell & Plano Clark, 2018 ). According to Huang et al. ( 2020 ), such an approach is necessary to gain a holistic perspective and assess how the selected factors influence students’ adoption of the GC platform. A week after collecting and analyzing the survey data, the researcher communicated with the student representatives to contact the purposely chosen willing participants for interviews. Based on the saturation principle , the point at which no new themes are observed in the data (Ando et al., 2014 ; Guest et al., 2016 , 2020 ; Saunders et al., 2018 ), and using a semi-structured interview form, eight interviews were recorded and transcribed. Each interview was on the phone and lasted for 10 min. In its evaluation of the qualitative data, this research implemented thematic analysis procedures proposed by Braun and Clarke ( 2006 ), whereby themes emerged from collated codes. In addition, it applied the comparative method for themes saturation recommended by Constantinou et al. ( 2017 ). This qualitative analysis employed MAXQDA 20.2 following the procedures suggested by Kuckartz and Rädiker ( 2021 ).

Demographics

This case study collected and analyzed data from 23 EFL college graduate students. As showcased in Table ​ Table2, 2 , most participants were 25–29 years old, revealing they were younger adults. Most of them were females, pointing to the dominance of female students in the graduate program. Besides, the majority of the respondents had an online learning experience with the GC platform, indicating that most participants had experienced the GC platform before joining the MA program. Table ​ Table2 2 displays the number of students by age, gender, and online learning experience, as well as their percentages.

Demographic Aspects of the Study Participants (N  =  23)

Quantitative findings

Measurement model assessment.

Before carrying out the structural-model path analysis, the reflective measurement model was assessed based on convergent validity, composite reliability, and average variance extracted (AVE) (Hair et al., 2017 ). In this regard, the results of an initial PLS algorithm for confirmatory factor analysis (CFA) estimated the validity and reliability of the model. As Table  3 displays, all CFA outer loadings using PLS Algorithm were greater than the suggested value of 0.708. (Hair et al., 2017 , 2019 ). Besides, every composite reliability rating exceeded 0.80, indicating that the items employed to test each construct were internally reliable. The obtained AVE values ranged from 0.67 to 0.80, all of which were greater than the required threshold value of 0.5 (Hair et al., 2017 , 2019 ). As shown in Table  3 and reflected in Fig.  4 , the values for the measurement model indices suggest that the measurement model has attained internal consistency.

Internal Consistency Measures

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PLS Algorithm for CFA

Discriminate validity

Discriminant validity assesses how each construct inside the model differs from other variables in terms of what it measures (Hair et al., 2017 ). Using a strict Heterotrait-Monotrait Ratio (HTMT) criterion, this study verified each construct’s distinctiveness in the model. As shown in Table  4 , all HTMT values within the analyzed model were less than 0.90, as proposed by Henseler et al. ( 2015 ). An HTMT value greater than 0.90 indicates a lack of discriminant validity (Hair et al., 2017 , 2019 ).

HTMT Criterion Values

Multicollinearity

Diagnosing collinearity for a reflective model is vital in shunning type 1 and type 2 errors while assessing path significance (Hair et al., 2017 ). Therefore, this study adopted the criterion of variance inflation factor (VIF), proposed by Kock ( 2015 ), to evaluate multicollinearity in the measurement model. According to Hair et al. ( 2017 ), a VIF value should not be higher than 5. As presented in Table ​ Table5, 5 , all VIF values were below Kock’s ( 2015 ) strict criterion of 3.3, except for habit, which had a VIF value little above 3.3, signifying no critical collinearity levels (Hair et al., 2017 , 2019 ). However, it was retained since its outer weight was significant, as recommended by Hair et al. ( 2017 ). This result indicates the absence of multicollinearity issues in the model.

VIF Values for Multicollinearity Diagnosis

Structural model

Hair et al. ( 2017 ) and Hair et al. ( 2019 ) recommended analyzing structural model relationships, Coefficients of determination ( R 2 ), confidence intervals, effect size ( f 2 ), and model predictive relevance ( Q 2 ) for evaluating the structural model.

Path analysis

Table ​ Table6 6 shows the paths’ significant results and verified indicators based on bootstrapping of 5000 samples (Hair et al., 2017 ), while Fig.  5 shows the bootstrap image results.

Model Path Results

* p < .05; **p < .001

BI = behavioral intention, EE = effort expectancy, FC = facilitating conditions, HA = Habit, HM = hedonic motivation, PE = performance expectancy, SI = social influence

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Bootstrap Image for Path Analysis

As shown in Table ​ Table6, 6 , the determinant of students’ behavioral intention of Google Classroom (GC) use is habit (β = 0.99, p  < 0.05). The f 2 effect size further supports this finding. Habit on behavioral intention ( f 2 = 0.89) has the largest effect size. On the other hand, performance expectancy (β = 0.08, p  > 0.05), effort expectancy (β =—0.20, p  > 0.05), social influence (β =—0.56, p  > 0.05), facilitating conditions (β = 0.46, p  > 0.05), and hedonic motivation (β =—0.15, p  > 0.05) were insignificant in determining students’ behavioral intention to use the GC platform. However, hedonic motivation (β = 0.48, p  < 0.001) and facilitating conditions (β = 0.36, p  < 0.05) were found to determine habit which determined performance expectancy (β = 0.47, p  < 0.05)), effort expectancy (β = 0.81, p  < 0.001) and social influence (β = 0.73, p  < 0.001).

The coefficient of determination ( R 2 ) adds to the certainty of predicting exogenous variables from their endogenous equivalents. Table ​ Table7 7 shows the findings of the total variance explained by the various endogenous variable predictions. Accordingly, the model accounted for 72% of the variance in behavioral intention to use the GC platform. Hair et al. ( 2017 ) and Hair et al. ( 2019 ) suggested R 2 values of 0.25, 0.50, and 0.75, respectively, as weak, moderate, and substantial. Correspondingly, the total variance explained by the model is relatively substantial. The R 2 values for habit ( R 2  = 0.56), social influence ( R 2  = 0.46), and performance expectancy ( R 2  = 0.31) were closer to the moderate threshold value of prediction. Nonetheless, the variance explained in performance expectancy ( R 2  = 0.31) was relatively small since it was determined only by habit.

Variance Explained by the Model

All significant path associations exhibited a one-dimensional pattern representing their confidence intervals’ minimum and maximum values, indicating that they were not spurious. In terms of the magnitude of the forecasts, most f 2 effect sizes ranged from a low of 0.03 to a high of 0.89 (see Table ​ Table6), 6 ), pointing to small, medium, and large effect sizes as per the guiding threshold values of 0.02, 0.15, and 0.35 proposed by Cohen ( 1988 ).

As for the model predictive relevance ( Q 2 ), the values for each endogenous construct in the hypothesized model had a Q 2 value above 0.1 (see Table ​ Table8 8 and Fig.  6 ), demonstrating good model predictive relevance. According to Hair et al. ( 2017 ) and Hair et al. ( 2019 ), the Q 2 values above 0.0, 0.25, and 0.50 indicate, respectively, the model’s small, medium, and large predictive relevance.

Values of Predictive Relevance from the Model

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Results of Blindfolding

Regarding Important-Performance Map Analysis (IPMA) for behavioral intention toward Google Classroom utilization, Table ​ Table9 9 shows the results of the IPMA analysis, which are represented graphically in Fig.  7 . According to the IPMA results, the most important performing interaction factor in determining students’ behavioral intention toward Google Classroom was facilitating conditions (0.60: 60.61), followed by habit (0.46: 57.58).

IPMA Result for BI

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IPMA for Google Classroom Behavioral Intention

Qualitative findings

Following the guidelines by Creswell and Creswell ( 2018 ), the study utilized the qualitative approach to get insights into the insignificant quantitative findings. The quantitative analysis revealed five insignificant relationships. In other words, it showed insignificant associations between behavioral intention and five of the proposed variables, namely, performance expectancy, effort expectancy, facilitating conditions, hedonic motivation, and social influence (see Table ​ Table10 10 for a summary). Since the study aimed at examining graduate students’ behavioral intention to use Google Classroom (GC), the interview questions concentrated on how performance expectancy, effort expectancy, social influence, facilitating conditions, and hedonic motivation could influence their behavioral intentions of using the GC platform.

Summary of the Quantitative Findings on the Hypothesized Relationships

Table ​ Table11 11 exhibits themes and sample responses. According to the interview data, most interview participants had prior experience using Google Classroom (GC) and reflected their intentions of using the GC platform for blended learning in the future. Such findings suggest that students are comfortable using the GC platform and are willing to experience it in the future. Their intentions seem fueled by their habitual engagement of using the “Google Classroom App” to “respond and react frequently” due to “interesting materials in various forms” posted by teachers on the GC platform. One interviewee stated, “The more I interact on the Google Classroom App using my phone, the more frequent I use it,” another respondent stated, “interesting materials posted by the teacher makes us share and interact more and thus use Google Classroom App frequently.” Such feedback could explain the significant direct relationship between habit and behavioral intention. Further, it could account for why hedonic motivation and facilitating conditions affect habit toward behavior intention. In other words, their smartphones and the interesting content on the GC platform fuel their habitual formations toward the GC platform. This is evident when participants pointed out the GC platform “provides access to learning,” which is “is not always fun.” Nonetheless, if the content on the GC platform is interesting, they would “use the phone to visit it often to react, share, and download materials.”

Themes and Sample Responses from the Semi-Structured Interview

Regarding performance expectancy, almost all interviewees positively highlighted the GC platform’s usefulness to their learning. Their perceptions were reflected through frequent phrases such as “it’s helpful,” “it’s useful,” and “share materials.” However, they considered GC as a neutral delivery platform. They believed its utility relies on teachers’ use. This is evident in phrases such as “neutral tool,” “just a delivery platform,” and “usefulness depends on teachers.” Since most participants unanimously viewed the GC platform as a neutral tool that allows for communicating and sharing content and gives room for interaction, attributing its potential to teachers’ utilization. Such a finding could account for the lack of a significant direct relationship between performance expectancy and behavioral intention.

About effort expectancy, almost all participants genuinely believed that the GC platform is easy to use. Phrases such as “easy to use,” “simple app,” and “simple to navigate through” frequently appeared in the student responses. This finding suggests that students are comfortable operating the GC platform in their blended learning. However, when asked about the easiness of GC and their intentions to use it in the future, approximately all interviewees reflected that it is a simple app, like other phone apps they are utilizing, and it does not require any special training; therefore, it might not affect their intents of future use. This finding could elucidate the lack of a significant direct relationship between effort expectancy and behavioral intention.

Besides, the interview data indicated that respondents had no training on the GC platform and used it outside the classroom with their smartphones employing self-tutoring. They mirrored that they took the lead in mastering the platform through self-orientation. For example, one interviewee stated, “I trained myself on how to use Google Classroom by navigating through it and watching YouTube videos,” whereas another one pointed out, “the administration of the school where I teach told us about Google Classroom but provided no training. So, I browsed the Internet for video tutorials and trained myself after I downloaded its app.” Such replies could explain why facilitating conditions had an insignificant direct relationship with students’ behavioral intention. Besides, such feedback could also justify the lack of a significant direct relationship between social influence and behavioral intention. This is evident in what one respondent stated, “My phone is quite enough,” and another stressed, “I downloaded the Google Class App and led my way through self-orientation on its features.” However, it seems that social influence had a negative correlation with behavioral intention. In other words, some participants indicated they were pushed to use the platform in their work due to COVID-19. For example, a participant stated, “They were pushed to utilize Google Classroom at the school where I teach without any training.” another one responded, “I’d use it out of my experiential learning but not others’ views or obligation.” This conveys that others’ views of using the GC platform could be interpreted negatively and lead to a negative correlation with GC utilization.

This study set out to answer the following research questions: 1) Which factors determine Yemeni EFL college graduate students’ behavioral intention to use Google Classroom (GC) as part of their blended learning? 2) What are the important and performing factors in determining Yemeni EFL college graduate students’ behavioral intention to use the GC platform as part of their blended learning?

Regarding the first research question, the quantitative outcome of the study reveals that the determinant of Yemeni EFL college graduate students’ behavioral intention of Google Classroom (GC) use was mainly habit, which was also reported by Jakkaew and Hemrungrote ( 2017 ), Kumar and Bervell ( 2019 ) and Bervell et al. ( 2021 ). Besides, it indicates that hedonic motivation has no significant direct relationship with behavioral intention, which goes against the findings by Jakkaew and Hemrungrote ( 2017 ) and Kumar and Bervell ( 2019 ) that hedonic motivation significantly predicts behavioral intention of the GC platform.

However, the findings of this study evince that hedonic motivation affects behavioral intention through habit. In other words, hedonic motivation determines habit, which determines behavioral intention. As evidenced by interview replies, hedonic motivation is associated with the content posted by the teacher on the GC platform. Such interesting content fosters students’ habitual tendencies, promoting their positive cognitive orientations toward the GC platform. Because of extensive use, GC has become familiar and easy to operate (Bervell et al., 2021 ; Kumar & Bervell, 2019 ).

The significant relationships that habit has with hedonic motivation, performance expectancy, effort expectancy, and social influence could stipulate that graduate students’ habitual formations, propelled by the enjoyment of the posted content, drive the usefulness, ease, and others’ views about the GC platform. According to Kumar and Bervell ( 2019 ), the habit of using Google Classroom showed that the mobile learning platform provides students with positive and expected benefits. Correspondingly, once students started employing Google Classroom regularly, it was clear that they were benefiting from it.

Additionally, the study findings show that performance expectancy, effort expectancy, social influence, and facilitating conditions had insignificant direct effects on graduate students’ behavioral intention of the GC platform. Such a finding aligns with the insignificant predictive relationships of effort expectancy, social influence, and facilitation conditions on behavioral intention reported by Bervell et al. ( 2021 ) and Kumar and Bervell ( 2019 ). It also corresponds with the findings reported by Attuquayefio and Addo ( 2014 ), Birch and Irvine ( 2009 ), Khalid et al. ( 2021 ), Jairak et al. ( 2009 ), and Nicholas-Omoregbe et al. ( 2017 ) that performance expectancy had an insignificant relationship with students’ behavioral intention to adopt technology in higher education. Moreover, because the students were already familiar with the GC platform, the ease and others’ social influence had no direct effect on forming their intents to utilize it (Bervell et al., 2021 ). Besides, according to Maruping et al. ( 2017 ), social influence and facilitating conditions are related to external factors and are better forecasters of behavioral expectation than behavioral intention. Correspondingly, respondents voiced in the interviews that they did not receive the necessary support to adopt the GC platform because they used it mainly outside the classroom, and they got the needed support through self-tutoring.

Regarding the second research question, the study findings manifest that facilitating conditions are the most important and performing factor in determining Yemeni EFL college graduate students’ behavioral intention to use GC as part of their blended learning. This is reasonable because facilitating conditions and hedonic motivation determine habit, which in turn affects the intensity of behavioral intention. As a result, facilitating conditions mirrored in the interviews as self-control or regulation and hedonic motivation, driven out of the GC content and interaction, underpin the role of habit in understanding students’ behavioral intention toward using the GC platform. Such a finding is congruous with similar ones reported by Bervell et al. ( 2021 ), Gardner et al. ( 2020 ), and Kumar and Bervell ( 2019 ). Furthermore, this indirect association between facilitating conditions and behavioral intention supports Bervell et al.’s ( 2021 ) and Maruping et al.’s ( 2017 ) interpretation of facilitating conditions as external aspects rather than internal ones, essential in defining behavioral intention.

Implications

The findings of this study convey implications for theory and pedagogy. Theoretically, most of the non-linear interactions examined in this study were significant. They helped explain the intricate relationships between the numerous predictors of graduate students’ behavioral intentions toward using Google Classroom (GC). This corroborates previous research highlighting the importance of including non-linear correlations in models based on UTAUT2 that investigate Google Classroom. Besides, this study established a significant association between facilitating conditions and habit in modeling the predictors of the behavioral intention of using the GC platform. Such a novel finding implies that the non-linear relationship between facilitating conditions and habit should be included in studies of behavioral intentions toward using the GC platform or other LMS platforms.

Pedagogically, because habit formation is essential for accepting the GC platform in higher education, teachers should design the GC content and activities to encourage students to employ the GC platform enjoyably and regularly. On the other hand, habit relies on facilitating conditions and hedonic motivation. This implies that technical support (e.g., resources and troubleshooting) should be provided if the GC platform is used on campus. If used off-campus, teachers should orient their students to the main features of the GC platform. Furthermore, the content and tasks on the GC platform should be geared toward fostering enjoyable learning and participation. It is necessary to foster technology acceptance among students and help them establish habitual usage attitudes, as habit has a positive association with others’ influence. If students are encouraged to utilize the GC platform by providing the necessary support as well as engaging learning content and online engagement, they could form better habitual tendencies, which can have a positive impact on students’ perceptions of the ease of use and utility expectations toward this LMS platform.

Limitations and suggestions for further research

This research was limited to the Yemeni EFL college graduate students’ behavioral intention toward Google Classroom (GC) as a case study. It did not incorporate educators’ perceptions. Tertiary education professors’ utilization of GC is highly subjective since some instructors might prefer to use other LMS platforms. A future investigation could encompass teachers’ views or perhaps combine the perspectives of students and teachers. Moreover, the study included only graduate students from a single program at a particular university. It employed a small sample—albeit meeting the minimum sample requirement for conducting PLS-SEM on six independent variables—due to difficulty having a graduate program with bigger groups. It necessitates caution when making generalizations of the findings. Accordingly, further research could extend to other courses and encompass a large randomized sample to understand the phenomenon better. Besides, moderation of demographic factors (e.g., age, gender) has not been included in this research; therefore, a future study can look into the moderating effects of such variables while modeling predictors of behavioral intention toward the GC platform.

Using the modified UTAUT2 model, this case study-mixed methods research set out to investigate the determinants influencing behavioral intention to use Google Classroom (GC) as part of blended learning in a postgraduate program during the COVID-19 pandemic. Only one variable, habit, directly affected behavioral intention to utilize the GC platform. In contrast, the other five variables—performance expectancy, effort expectancy, social influence, facilitating conditions, and hedonic motivation—had no direct impact. However, facilitating conditions and hedonic motivation underlie habits toward utilizing the GC platform. The findings could aid university administrators and teachers in accommodating the contributors to the successful adoption of the GC platform in equivalent academic settings. This empirical mixed methods research adds to the expanding body of knowledge in the utilization of educational technology.

Appendix A. Screenshot of google classroom survey

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Appendix B. Semi-structured interview form

  • How did you come to know about Google Classroom for the first time?
  • How can Google Classroom be helpful to you during your blended learning?
  • How easy is Google Classroom for you to use?
  • Who encouraged you to use Google Classroom in your learning? Why?
  • Do you think you have sufficient support to use Google Classroom in your learning? Why/why not?
  • How do you find Google Classroom compared to traditional classrooms?

Declarations

The original online version of this article was revised due to blinded information.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Change history

A Correction to this paper has been published: 10.1007/s10639-022-11076-7

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International Journal of Research and Innovation in Social Science (IJRISS) | Volume IV, Issue IV, April 2020 | ISSN 2454–6186

Effectiveness of Google Classroom as a Digital Tool in Teaching and Learning: Students’ Perceptions

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 Iliyasu Hussaini 1* , Sawida Ibrahim 2 , Bashir Wali 3 , Ibrahim Libata 4 , Usman Musa 5 1 Universal Basic Education Commission, Abuja, Nigeria 2,3,4&5 Faculty of Education, Kebbi State University of Science and Technology, Aliero *Corresponding author

Abstract: – The aim of this study is to evaluate Students’ perceptions on the effectiveness of Google Classroom as a Digital tool in Teaching and Learning. The study was conducted through a Survey Research Design to investigate the Students’ Perceptions. The population of study consists of all UG II Undergraduate Students, Faculty of Education, Kebbi State University of Science and Technology Aliero (KSUSTA). Data analysis was conducted using Descriptive Statistics. The results of the study indicated that Google Classroom is effective in improving Students access and attentiveness towards learning, knowledge and skills gained through Google Classroom makes Students to be active learners, as a Digital Tool, it provides meaningful feedback to both Students and Parents. However, Poor network hinders students from effective utilization of Google Classroom; thus, submitting their work late. Therefore, teachers should integrate the conventional teaching with Google Classroom to improve Students’ Performance. Google Classroom should also be a form of assessing Students’ Assessment through online Assignments and Quizzes; hence making Students to participate actively in Educational Technology Classes. The University should also provide a standard network to enable Students join Google Classroom and submit their assignments on time.

Keywords: Students’ Perceptions, Digital Tool & Google classroom

I. INTRODUCTION

Google Classroom Google Classroom is a free application designed to assist students and teachers connect, work together, organize and create assignments, it enables learning to be paperless. As a Digital Tool, Google Classroom is accessible only to users with Google Apps for Education (GAFE). This is a free collaborative set of tools, these tools includes web tools like Google Docs, Google Drive, Gmail, and more. All users will GAFE account, have access to these web tools. Google Classroom can be used at any grade (basic, post basic and tertiary) levels, but this depends on the teachers’ and students’ competence (Bell, 2015).

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Widi Andewi , Dwi Puastuti , Tommy Hastomo; ELT blends: A case study of using Google classroom. AIP Conf. Proc. 4 December 2023; 2621 (1): 030006. https://doi.org/10.1063/5.0142458

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This study discovers a case study using Google Classroom to teach English to Informatic System students (N=50) in STMIK Pringsewu, Indonesia. This study aimed to uncover students’ perception of ELT Blends’ enlightening benefit and determine if Google Classroom improves writing ability. The instructors conducted 16 meetings in the Google Classroom during the second semester, including materials and exercises to explore the writing subject. This research exposes the effectiveness of Google Classroom as an educational platform for teaching English. The researcher utilized open-response questionnaires as a qualitative and quantitative research method consisting of pre-test and post-test to evaluate the students’ writing ability. The results show that there is a significant improvement for Group 1 who were being taught using Google Classroom. The mean score of the students in group 1 is higher than the students in group 2. It means that Google Classroom is an effective educational platform for teaching EFL at a higher education level. Google Classroom has the best features for applying virtual classrooms, such as Online Grading System, Virtual Discussion, Announcements, and Live Classes. This online platform facilitates the students’ best experience to learn EFL because of the accessibility, simplicity, real-time interaction, and engagement. The students can utilize Google Classroom as an educational environment to enhance learning EFL based on the questionnaire’s data collection. The STMIK Pringsewu students have better learning achievement in writing course based on the pre-test and post-test analysis.

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Classkick: Enabling real-time learning from anywhere

Classkick logo

About Classkick

Educators use Classkick to give their students activities and faster feedback from anywhere and anytime, on digital canvasses, worksheets, recordings and more. Students use Classkick to go at their own pace and get help right when they need it.

Tell us your challenge. We're here to help.

Classkick uses google cloud to help educators create on-demand learning experiences for students and provide real-time feedback from anywhere., google cloud results.

  • Enhances reach of platform to 170 million students and educators through Google for Education
  • Supports 15x growth in DAUs annually
  • Enables real-time teaching, student activities and assessments regardless of device in more than 120 countries
  • Maintains continuity of operations and education through Google Workspace and Google Cloud tools

Supports 8x growth in annual revenue user-based KPIs

As COVID-19 impacts schools around the world, more educators and administrators rely on technologies that bring classrooms into students’ homes. Many of these education technology (EdTech) tools have seen recent exponential growth due to the demand for digital classrooms. Classkick, a digital activities and formative assessment tool, allows educators to create assignments, monitor students’ progress, and provide real-time feedback entirely online.

“We built this tool in a pre-pandemic world, and it’s incredibly well-suited to how people need to teach and learn today,” says Andrew Rowland, Chief Executive Officer and co-founder of Classkick. “Educators already using Classkick transitioned quite seamlessly to a remote model, thanks to Classkick. They say Classkick is the only thing they’ve found that’s the same both in-class and remote.”

Andrew is an alumnus of Teach for America and saw just how much some school districts and students struggle to access educational resources. He baked this pursuit of improving equity into Classkick’s DNA, as the company strives to provide strong educational experiences for all students.

Classkick uses Google Cloud’s solutions like Firebase and operations tools to enable more asynchronous, or on-demand and self-paced, digital learning experiences outside of the classroom—giving students more flexibility with their distance learning.

Classkick helps educators create on-demand and open-ended learning experiences for students and provide real-time feedback from anywhere. First graders describe their thinking, fourth graders practice sheet music while listening to audio, middle schoolers edit each others' essays, tenth graders write and speak Mandarin, and twelfth graders do physics and calculus.

“Google Cloud enables our core value to address real problems in education. More than half of our employees are former educators, so we have a deep understanding of the challenges facing educators, students, parents, and administrators. Google Workspace helps us maintain real-time relationships with our customers.”

The company also partners with Google for Education to reach 170 million students and educators across the country through Google Workspace for Education . Since the pandemic began, most user-related KPIs have grown 10x+ for the company, including traffic and use.

Even before COVID-19, education was moving toward more digitally driven classroom experiences. With increased uncertainty about when students will return to schools full-time across the U.S., the pressure to establish richer digital teaching opportunities has increased dramatically.

“There’s so many existing great learning management systems and collaboration tools for students and educators, but we are working to fill a gap in the ‘last mile’ and enable educators to see all of their students’ work at scale,” says Andrew.

Classkick enables real-time and on-demand teaching while giving educators the power to help one student at a time without losing sight of other students. This fosters an environment for consistent, real-time interactions between educators, students, and even school districts.

“We believe it’s important that education technology decreases—not increases—the equity gap,” says Andrew. “Since Chromebooks are the most prevalent devices in classrooms today, we moved toward the web and made our solutions accessible on Chromebooks.”

Fostering real-time educational experiences

Classkick has long relied on Firebase Realtime Database to enable real-time interactions between educators and students regardless of location. Firebase provides zero latency in the product and allows for a distance learning experience similar to in-person classrooms. Google for Education integrations including Google Sign-In, Google Classroom API for rostering, and Share to Google Classroom provides a frictionless experience to users.

“A lot of educators who start using Classkick think it’s magic. The student writes on a screen, and the teacher sees all students’ work on her own screen as if everyone is in the same room together,” says Andrew. “The educators then talk directly to the student or write them a note, as if they’re writing on the same board in a classroom.”

A former teacher, Andrew is familiar with the challenges educators face when trying to build relationships with their students and learning how to encourage them. Classkick offers consistency for educators to help ensure students continually get the support they need, whether they are instructing in classrooms or remotely.

In addition, Classkick relies on Google Workspace solutions like Google Meet for seamless communication between the company and educators, allowing outstanding customer experience issues to be addressed immediately.

“Google Cloud enables our core value to address real problems in education,” says Andrew. “More than half of our employees are former educators, so we have a deep understanding of the challenges facing educators, students, parents, and administrators. Google Workspace helps us maintain real-time relationships with our customers.”

“Our strategy is to connect every student with their educational advocates. We accomplish this by providing frictionless experiences that keep students and educators in sync throughout the learning process.”

Providing equitable access to educational advocates

Like many other education technology companies, Classkick works to make its technologies as accessible as possible to improve access to everyone and provide robust learning experiences to every student.

Chromebooks and Chrome browsers provide the necessary tools to put Classkick and other technologies into the hands of students and educators across the U.S.

“Our strategy is to connect every student with their educational advocates. We accomplish this by providing frictionless experiences that keep students and educators in sync throughout the learning process,” says Andrew. “Our platform also helps to keep other stakeholders like parents and administrators, as well as peers, connected every step of the way.”

Moving to a digital classroom

Classkick uses Google Cloud’s operations suite that enables cloud infrastructure monitoring to analyze its use of technologies and reduce spending. This usage-monitoring approach enabled Classkick to make its services available to a larger audience when the pandemic closed schools.

Even when the pandemic ends, Classkick plans to continue growing its user base and build solutions that support students both inside the classroom and on-demand. Classkick’s existing customer relationships will be the key to informing these future developments and product directions.

“Educators across the U.S. have done a remarkable job of minimizing disruption to learning in these unprecedented times,” says Andrew. “We want to help them fulfill their teaching goals both during and after the pandemic. Teachers tell us Classkick is indispensable to their in-class and remote workflows in reaching students. We believe if we continue listening to their needs and improving the platform, we will achieve our vision that every student is happy and successful in their learning."

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Shareable class templates and classwork in Google Classroom are now generally available

What’s changing.

Shareable class templates and classwork

Who’s impacted 

Why you’d use it .

easily preview, select, and import high-quality classwork into their classes.

Additional details 

  • Student information, such as assignment submissions, comments, and grades, will not be visible when previewing a shared class. 
  • Imported class materials will be saved in draft mode for the selected class. 

Getting started 

  • Admins: There is no admin control for this feature, however, Admins should make sure the following is set up for end users: 
  • In order to share classes, educators must have a Google Workspace for Education Plus license assigned to them. 
  • To preview and import classwork from shared classes, educators must be verified teachers . 
  • End users: 
  • To share a class, click the “Share classwork” button on the Classwork page. 
  • After receiving a class link, open it in your browser. When previewing the shared class, select the classwork items you want to export to a class. 
  • Visit the Help Center to learn more about sharing class templates and classwork. 

Rollout 

  • Rapid Release and Scheduled Release domains : Gradual rollout (up to 15 days for feature visibility) starting on January 31, 2024 

Availability 

  • Available to Education Plus 

Resources 

  • Google Help: Share & preview classwork & class templates 
  • Google Help: Preview & export class templates & classwork

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Google Case Study Reveals Search Console Evolution Via APIs

Google case study shows how APIs can change what it means to use Search Console and evolve how it's accessed in the future

New Google case study shows how Search Console APIs allows data to be reviewed and manipulated within a CMS or a proprietary SEO dashboard. While the article is a case study, a call to action at the end of the article reveals how Google is using APIs to transform search console from a SaaS to a data stream that can be manipulated in the GUI of your choice.

Application Programming Interface (API)

API is a widely used technology that acts as a bridge between two applications that enables one to manipulate the other. It’s use is everywhere, particularly in WordPress where an API can allow a plugin to access and manipulate the website information contained in the database.

Wix Case Study

The collaboration between Google and Wix embedded Google’s Search Console APIs within the Wix dashboard, streamlining the SEO process for millions of Wix users globally.

Users benefit by gaining easy access to useful insights and functionalities of Google Search Console within the familiar Wix dashboard, keeping a unified experience within Wix without having to learn a different user interface.

Implementation and User Benefits

Wix’s integration strategy focused on leveraging Google APIs to enhance its own SEO tools that users are already familiar with. The process involved choosing and integrating specific Google functionalities that complement Wix’s user interface (dashboard UI), resulting in a more intuitive experience of Google’s search console features.

The case study reports that users who integrated search console APIs experienced an average increase in traffic of 15% over the course of one year.

Ecommerce sites experienced a 24% increase in Gross Product Value compared to Wix similar Wix ecommerce sites that did not use the search console API integrations.

According to the case study:

“So far, over 2 million Wix sites connected their Search Console account and submitted a sitemap to Google through the new integration. They also regularly used the new features, such as Site Inspection and Analytics Reports to troubleshoot indexing errors, fix them and get insights on resulting changes in performance. “

APIs Enables Evolution Of Search Console

The successful integration of Google’s APIs into Wix’s platform demonstrates the value of collaborations between Google and companies that offer content management systems, including webhosts that develop their own point and click web builders based on WordPress.

But another goal of the case study is to show how inhouse SEO tools and dashboards can integrate Google Search Console functionalities through the use of APIs.

It’s not until the end of the case study that Google discretely makes a call to action soliciting organizations to contact them through a web form or Twitter.

The article writes:

“If you’re a CMS and interested in collaborating with us, reach out using this form or through our social media.”

The call to action shows how the API is changing how Google’s search console data is accessed and pointing toward a trend where it’s less about signing in to search console to view data within Google’s user interface.

APIs already enable importing search console data into Screaming Frog to combine it with crawl data and of course there are WordPress plugins that can use it, too. The Wix case study shows a novel application that showcases the flexibility of how search console data can be used in the future beyond how it’s currently accessed.

Read Google’s Wix case study:

How Wix generated value for their users by integrating stats and functionality via Google APIs directly into the Wix UI

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    Specifically, the investigation is an LMS case study since it focuses on Google Classroom (GC) as a platform for content delivery, sharing, and interaction as part of blended learning. A case study in LMS is research into single or numerous instances of complex observable phenomena with well-defined limits (Turnbull et al., 2021). It adopted a ...

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    Curriculum leads and verified teachers can now share links to high-quality classes and class templates so other educators in their organization can preview and import classwork to an existing class or to a new class. This will provide educators with ideas for instructional design and enable them stay up-to-date with the best materials.

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  25. Google Case Study Reveals Search Console Evolution Via APIs

    According to the case study: "So far, over 2 million Wix sites connected their Search Console account and submitted a sitemap to Google through the new integration.