The Impact of Google Classroom-assisted Collaborative Learning Approach on Economics Students' Attitudes
Noornadiah Md Sari1, Khoo Yin Yin1*, Zainizam Zakariya1
1 Faculty of Management and Economics, Sultan Idris University of Education, Tanjong Malim, Malaysia
*Corresponding Author: [email protected] Accepted: 15 November 2021 | Published: 1 December 2021
DOI:https://doi.org/10.55057/ijares.2021.3.4.3
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Abstract: The education recovery plan in Malaysia was opened in stages according to four phases. The Government launched the State Digital Network plan (Jalinan Digital Negara, JENDELA) 2020-2022 by focusing on learning needs at home as one of the national agendas.
This situation presented a challenge for students, especially those who would face major examinations such as the Malaysian High School Certificate (Sijil Tinggi Pelajaran Malaysia, STPM) because the usual learning approach was face-to-face. The Google Classroom application was one of the preferred learning management systems in Malaysia. The application offered easy communication and discussion, making it easier for students to carry out collaborative learning. However, to what extent does the collaborative learning approach assisted by Google Classroom impact the attitudes of economics students. This study aims to test the impact of Google Classroom-assisted collaborative learning on the attitudes of economics students. This quasi-experimental study uses pre-test and post-test instruments to collect data on 207 Form Six economics students through cluster random sampling.
Descriptive (frequency, percentage, and mean) and inferential (ANOVA) analyses were performed after data were collected and coded. The results showed that the experimental group which was exposed to the collaborative approach (GCDK) showed a better attitude towards economics learning than the group of students who were not exposed to the collaborative approach (GCTK and KPK). It is recommended for future researchers to further expand the study in various locations that offered economics courses at the pre-university level. The results impacted the teachers, administrators, and policymakers to preparedly face the learning environment after Covid-19 in the future.
Keywords: Google Classroom, student attitudes, collaborative learning, economics education, digital education, post-Covid-19
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1. Introduction
The Covid-19 pandemic has affected many institutions, impacting nearly 1.725 billion children in over 95% of countries worldwide (Smith, 2021). The pandemic has also affected the education sector in Malaysia. When the Covid-19 pandemic hit the country, the Malaysian Ministry of Education (MOE) ordered the educational institutions to physically shut the operations down and implement online learning (KPM, 2020a). In Malaysia, 10,220 schools had to be physically closed, affecting 4,987,401 students nationwide (KPM, 2020b). This aims to curb the spread of Covid-19 among students and ensure student safety. This situation poses a challenge to students, especially those who will face big exams such as the Malaysian Higher
School Certificate. A face-to-face learning approach had been a standard before the Covid-19 pandemic struck. To overcome the existing constraints, the education systems undergo several phases of transition to adapt to the current situation.
The United Nations International Children's Emergency Fund (UNICEF) has recommended the digital learning approach be continued as one of the steps in the education recovery plan in the post-Covid-19 phase (UNICEF, 2021). The recovery plan for the education sector in Malaysia is done in stages according to phases, namely phase 1, phase 2, phase 3, and phase 4 as shown in Figure 1 (National Recovery Council, Majlis Pemulihan Negara, 2021).
Researchers believed that online learning will become the new norm in the post-Covid-19 learning environment (Adedoyin & Soykan, 2020; Sim et al., 2021; Zinn, 2021).
Figure 1: Education Sector Recovery Phase in Malaysia (National Recovery Council, 2021)
Malaysian educators are increasingly accepting the digital learning environment as part of the new norms. The statistical report of the Department of Statistics Malaysia (2021) found that there were a sharp increase in the use of the internet (6.0%), computers (3.5%), and mobile phones (0.5%) in 2020 compared to 2017 due to the e-learning needs. The research of Ahmad Alif et al. (2021) and Sim et al. (2021) found acceptance of online learning among students in Malaysia. Practical and usability factors of online learning are identified as contributors to their acceptance (Albashtawi & Al Bataineh, 2020; Md Yunus et al., 2021). In this regard, the government has launched the National Digital Network (JENDELA) plan from 2020 to 2022 (MCMC, 2020). One of the main goals of the plan is to support a learning environment at home.
The plan is designed to upgrade the nation's internet services with wider coverage. This measure is in line with the community's need for good and quality internet today (Taib et al., 2021). Therefore, digital learning is not foreign in today's Malaysian learning environment.
The prevailing situation illustrates that there is a crucial need for teachers and students to master ICT literacy.
In this regard, educators in schools need to adapt to the new learning approaches by implementing online learning. Among the online learning mediums of choice in Malaysia is the Google Classroom application. Figure 2 shows the distribution of Google Classroom users and Malaysia is the second highest out of 57 countries involved (Google Trends, 2021).
Figure 2: User Distribution of Google Classroom Application (Google Trends, 2021)
The Google Classroom app offers various advantages such as being easy to use, time-saving, flexible, free of charge, and mobile-friendly compared to other apps (Iftakhar, 2018; Muttaqin
& Hasan, 2020). The initial users are satisfied and recommend its continuing use in the future (Khalil, 2018; Quigley & Herro, 2016; Ventayen et al., 2018). Additionally, this application also provides communication and discussion solutions such as online discussions, forums, comments, short messages, and e-mail to facilitate students to implement collaborative learning. Student attitude can turn this platform into a source of distraction that diverted them from effective learning and academic achievement (Ansong-Gyimah, 2020; Dontre, 2020).
Past research often tested the effectiveness of this application on the attitude at the tertiary level, but not much is done among school students (Fauzi et al., 2021; Francom et al., 2020;
Tusino et al., 2021).
It is common for economics teachers to adapt conventional teaching methods such as lectures (Azieyana & Andin, 2018; Beckers & Watts, 2001; Calimeris, 2018; Ford & Leclerc, 2000;
Ongeri, 2017). Jalani and Sern (2015) assert that this method only applies to the basic knowledge level of the subject principles and concepts. This teacher-centered one-way approach makes the learning environment boring, non-interactive, and uncomfortable (Wan et al., 2017; Xu, 2018). As a result, students are passive, only memorize theories, lose focus, have no motivation to study economics subjects, and do not fully understand the content of learning (Baharin & Yusop, 2011; Ramlee et al., 2020; Salemi, 2002). This passive learning environment does not stimulate knowledge formation, and it violates Vygotsky’s (1978) constructivist learning recommendation that knowledge formation occurs when students actively interact.
Researchers found that negative attitude also affected student achievement in economics subjects (Ananthan, 2016; Anusia, 2015; Karstensson & Vedder, 1974; Lawson, 1994;
Norshahida, 2015; Walstad, 1987). Attitude factors have a close relationship to self-efficacy and the responsibility to learn, understand, and complete assigned tasks (Laging & Vobkamp, 2016; Susskind, 2005). Negative attitudes do not motivate individuals to increase their understanding of economics (Ayers, 2019). This is because students’ negative attitudes towards economics literacy can be observed explicitly through the behaviors they exhibited. Negative students are often seen as lacking in enthusiasm, not seriously answering tests, doing revisions, having initial preparation, and doing other work (Grimes & Nelson, 1998; Lopes et al., 2015;
O’Neill, 2001; Skagerlunda et al., 2018). At first, the student's negative attitude towards economics will consider the subject as foreign, difficult to understand, and boring, which causes them to behave negatively such as frequent skipping classes, not paying attention in class, not doing homework, and so on. As a result, students show less performance on learning outcomes.
The first way for teachers to change negative behaviors towards economics is by changing students’ attitudes. Therefore, the attitude of students must be developed to change student behavior. Researchers have suggested that a positive learning environment such as a collaborative learning approach can shape students' positive attitudes (Adu & Galloway, 2015;
Davadas & Lay, 2018, 2020). Therefore, in addition to delivering lessons effectively, forming students’ positive attitudes should be focused on. Past collaborative learning research has often examined its impact on academic achievement (Shimuzu et al., 2021). Based on the research gap, researchers are motivated to conduct this study to test the effect of Google Classroom- assisted collaborative learning on students' attitudes in the current learning environment.
2. Methodology
A quasi-experimental study was conducted in three schools in the state of Melaka, Malaysia.
The participating respondents consisted of 207 Form Six economics students (semester 1) who were selected through cluster random sampling. Each group of students was exposed to different learning approaches namely; (a) Google Classroom-assisted learning with collaborative approach (GCDK) (63 people); (b) Google Classroom-assisted learning without collaborative approach (GCTK) (63 people); (c) conventional learning approach (KPK) (83 people). Researchers used a student attitude questionnaire instrument (eight items) with a five- point Likert scale of 1 (strongly disagree) to 5 (strongly agree). The Cronbach’s Alpha reliability value of this instrument was 0.92. According to Hopkins (1998), a reliability value of 0.90 was good and acceptable. Therefore, the reliability of this instrument was high and suitable for use in real studies. First, the researcher applied for permission from the Department of Education Policy Planning and Research, Melaka State Education Department, school principals and attain students’ participation consent.
Figure 3 showed the flow chart of the quasi-experimental study. Pre-tests were administered for about 10 min before the interventions were conducted. Participating students received initial briefings and training for a week before the interventions were conducted. Each student was given and used the same economics learning materials for 12 weeks. GCDK group students were divided into several smaller groups consisting of 4 to 6 people using Google Classroom learning medium collaboratively, GCTK group students used Google Classroom-assisted economics learning without collaboration and KPK group learned with the existing teacher. At the end of the 12th week, post-tests were administered to obtain information after students were exposed to the treatment. Pre-test and post-test data were analyzed using descriptive analysis (frequency, percentage, and mean) and inferential analysis (ANOVA) to measure attitude differences based on the learning approach conducted on each group.
Figure 3: Quasi-experimental flow chart
3. Findings
207 respondents were involved in this study, consisting of 70.5% female and 29.5% male.
According to the demographics by location of residence, there were students from rural (23.2%), suburban (18.4%), and urban (58.5%). The majority of students owned at least one to two devices (67.6%), 18.4% owned three to four, and 14% owned more than four. In addition, the demographics based on user experience found that 49.3% had more than seven years, 43.5%
had four to seven years and 7.2% had less than three years of experience. Table 1 summarized the profile distribution of the respondents.
Table 1: Demographic Profile of Study Respondents (N = 207)
Details Characteristics Frequency Percentage
Gender
Female 146 70.5
Male 61 29.5
Location of Residence
Rural 48 23.2
Suburban 38 18.4
Urban 121 58.5
Number of devices
No 0 0
1 to 2 140 67.6
3 to 4 38 18.4
More than 4 29 14.0
Usage Experience Period
Less than 3 years 15 7.2
4 to 7 years 90 43.5
More than 7 years 102 49.3
Pre-intervention and post-intervention data mean scores comparison was conducted on the attitude variables based on the questionnaire responses received from the study respondents.
Table 2 summarized the student attitude variables responses based on the learning approaches
12 week GCDK Group
(n=63)
GCTK Group (n=63)
KPK Group (n=81)
Pre-test
Post-test Google Classroom
with collaborative learning approach
Google Classroom without collaborative learning approach
Conventional learning approach
20 minutes
Preliminary Briefing
20 minutes 1 week
of GCDK, GCTK, and KPK. Referring to the information in Table 2, the GCDK group reported the highest post-mean score (GCDK = 4.3968) on item D2 (I feel a loss if I don’t attend economics class). This is because the collaborative Google Classroom-assisted learning approach effectively encourages students to make initial preparations before an economics topic was taught. Consequently, the students are ready to get further explanations to strengthen understanding during the classroom learning sessions. Whereas the GCTK and KPK groups reported the highest mean score value (GCTK = 4.2698 and KPK = 3.9259) on item D1 (I try to complete the assignment within the allotted time). This was because the students in these groups needed further explanation from teachers and classmates to complete the assignment.
Meanwhile, the GCDK treatment group showed the lowest post-mean score (GCDK = 3.3257) on item D7 (I am involved in an economics program). This was probably because students needed time to understand and be familiar with the application to master the skills first before engaging in organized activities. Similarly, the GCDK and KPK groups showed the lowest post-mean score on item D8 (I voluntarily answered the teacher’s question in the class (raised my hands)) (GCTK = 2.6984 and KPK = 2.7407). In general, the mean scores of the treatment group (GCDK and GCTK) and control (KPK) recorded an increase in the post-mean score compared to the pre-mean score.
Table 2: Responses of Study Respondents on Attitude Variables Based on the Learning Approaches Learning approaches GCDK (N=63) GCTK(N=63) KPK(N=81)
Statement Pre-
Mean
Post- Mean
Pre- Mean
Post- Mean
Pre- Mean
Post- Mean D1 I try to complete the assignment within
the allotted time
3.7619 4.3651 3.7937 4.2698 3.8519 3.9259 D2 I feel a loss if I don’t attend economics
class
3.5873 4.3968 3.9206 3.9524 3.6296 3.7778 D3 I felt enthusiastic when my economics
teacher was teaching
3.5714 4.0476 3.7143 3.8571 3.4938 3.5556 D4 I reviewed the economics topics taught by
the teachers at home
3.5397 3.8254 3.6508 3.9206 3.4568 3.5802 D5 Economics is an important subject to
learn in daily life
3.6349 4.1429 3.7778 3.8413 3.4938 3.7037 D6 I made initial preparations before class
started
3.1905 3.5238 2.8095 3.2381 2.8519 2.9753 D7 I am involved in the economics programs 3.0952 3.2857 2.7302 3.0476 2.6667 2.8148 D8 I volunteer to answer the teacher's
questions in class (raise my hand)
3.0635 3.4127 2.5714 2.6984 2.6173 2.7407
Next, a significant comparison of the student attitudes variable among the treatment groups (GCDK and GCTK) and the control group (KPK) in the pre-intervention and post-intervention through descriptive tests of mean scores and standard deviations was conducted. The recorded attitude variable mean scores according to the learning approach at the pre-experimental stage were GCDK = 3.4306, GCTK = 3.3710, and KPK = 3.2577. Meanwhile, at the post- experimental stage, each group recorded a mean score value of GCDK = 3.8750, GCTK = 3.6032 and KPK = 3.3843. In comparison, the treatment and control groups showed an increase in the mean score at the post-experimental stage of 0.4444 (post-GCDK ‒ pre-GCDK = 3.8750- 3.4306 = 0.4444) for the GCDK group, 0.2322 (post-GCTK ‒ pre-GCTK = 3.6032‒3.3710 = 0.2322) for the GCTK group, while 0.1266 (post-KPK ‒ pre-KPK = 3.3843‒3.2577 = 0.1266) for the KPK control group. Table 3 summarizes the mean scores and standard deviations report on attitudes based on the learning approach.
Table 3: The Results of Students’ Mean Scores and Standard Deviations for Attitude Variable in the Pre- and Post-Experiments Based on the Learning Approaches
Variables Learning Approaches
N Mean SD
Student attitudes (pre-experiment)
GCDK 63 3.4306 0.51626
GCTK 63 3.3710 0.74459
KPK 81 3.2577 0.61376
Student attitudes (post-experiment)
GCDK 63 3.8750 0.45625
GCTK 63 3.6032 0.67110
KPK 81 3.3843 0.61959
Next, an ANOVA test was performed to identify the students' attitudes differences in the pre- experiment and post-experiment according to the learning approaches. Table 4 showed that the significant value at the pre-experimental stage was F (2, 204) = 1.411, p = 0.246 (p> 0.05), Eta Squared = 0.0136. Whereas, the significant value at the post-experimental stage was F (2, 204)
= 12.169, p = 0 .000 (p <0.05), Eta Squared = 0.1066. These findings proved that there were significant differences in GCDK and GCTK learning approaches compared to KPK on students' attitudes. The effect size analysis of student attitude variables in the pre-experimental reported an Eta Squared value of 0.040, which was small (0.01> Eta Squared <0.6) (Cohen, 1988). Meanwhile, the Eta Squared value at the post-experiment was 0.1066, indicating a moderate effect size (0.06> Eta Squared <0.14) (Cohen, 1988). These findings proved that there was a significant moderate effect size on attitude variables between the treatment group and the control group post-experiment. Table 4 reported the ANOVA test results on students’
attitudes in the pre-experiment and post-experiment according to the learning approaches.
Table 4: The results of Students’ Attitudes ANOVA Test in the Pre-Experiment and Post-Experiment according to the Learning Approaches
Variables DK Mean F Sig. Eta
Squared Student
attitudes (pre- experiment)
Between groups 2 0.560 1.411 0.246 0.0136
Within groups 204 0.397
Student attitudes (post- experiment)
Between groups 2 4.268 12.169 0.000 0.1066
Within groups 204 0.351
*Sig. at the .05 level
Furthermore, an advanced Post Hoc Tukey ANOVA test was conducted to identify the comparison of mean score values between the learning approaches that had been tested on students ’attitudes in detail. Table 5 reported the analysis results of the Post Hoc Tukey ANOVA test. The results of the paired comparison showed that on the whole, there was no significant difference in students' attitudes for the pre-experimental treatment group (GCDK and GCTK) and control group (KPK) (p> 0.05). Meanwhile, the post-experimental stage reported a significant difference (p <0.05) between GCDK with GCTK and KPK. Whereas, there was no significant difference between GCTK and KPK. This means that there were significant differences in student attitude between the groups of GCDK, GCTK, and KPK in the pre and post experiments.
Table 5: The results of Tukey HSD Post Hoc Test Analysis of Attitude Differences Based on Learning Approaches
Variables Learning Approaches Mean Differences Sig.
Student attitudes (pre-experiment)
GCDK GCTK 0.05952 0.857
KPK 0.17284 0.234
GCTK GCDK -0.05952 0.857
KPK 0.11332 0.534
KPK GCDK -0.17284 0.234
GCTK -0.11332 0.534
Student attitudes (post-experiment)
GCDK GCTK 0.27183* 0.029
KPK 0.49074* 0.000
GCTK GCDK -0.27183* 0.029
KPK 0.21892 0.073
KPK GCDK -0.49074* 0.000
GCTK -0.21892 0.073
* Sig. at the .05 level
4. Discussion
ANOVA analysis was used to prove whether the interventions given in this study affected the dependent variable. The analysis found there were significant differences (p <0.05) on the attitude variable based on the learning approaches tested.
These results are supported by past research such as Akay and Gumusoglu (2020), Yilmaz and Karaoglan Yilma (2020), Aburezeq (2019), Amedu and Gudi (2017), Magen-Nagar and Shonfeld (2017), Whitman Cobb (2016), Farah (2015) and Kuuk and Arslan (2020), which compared the effects of online collaborative learning on attitude variable, and showed there are significant differences between the experimental group and the conventional approach.
However, the experimental research by Basarmak and Mahiroglu (2016), Nam (2016), and Al- Rawahi and Al-Mekhlafi (2015) shows that there is no difference in attitude aspect based on the given learning approach. Nevertheless, researchers are aware that individual attitudes change according to the situation and the time (Glasman & Albarracín, 2006). If students receive exposure over a longer time, their attitudes will also change based on current assessments.
The current generation of students is exposed to digital learning platforms. The surveys reported that students' attitudes towards the use of learning management systems are positive (Alghamdi, 2018, Alshorman & Bawaneh, 2018; Taghizadeh & Hajhosseini, 2020; Zhang et al., 2018). Students who are exposed to active learning methods showed more positive attitudes than students who did not (Lee & Osman, 2021; Magen-Nagar & Shonfeld, 2017; Tibi, 2018).
Students who had a positive attitude towards online collaborative learning activities showed a higher frequency of accessing the internet (Farah, 2015). Students' experience in using digital learning platforms makes them more creative and dynamic in learning (Ansari & Khan, 2020).
A local study by Kabilan et al. (2010) found that this learning approach successfully formed a positive attitude among 74% of students. Past researchers have also suggested that an active learning approach be implemented among generation Z (Mulyani et al., 2021; Murillo- Zamorano et al., 2019; Ngussa et al., 2021; Nilavu, 2019). In this study, economics students of GCDK and GCTK groups have a positive attitude because they have received exposure to the tested learning approach compared to the attitude of the KPK group. Furthermore, the
respondents came from a generation that is exposed to the use of mobile devices and technological equipment in daily life.
Moreover, in the new learning environment, accessibility and usability factors affect students
’attitudes (Ashrafi et al., 2020; Davis, 1989; Unal & Uzun, 2020) and learning satisfaction (Olson & Brown, 2018). Accessibility refers to the student's expectation of an application that does not involve complex technical aspects, while usability refers to the expectation of benefits obtained from the use (Aryadoust et al., 2016) and online discussion facilities (Lee, 2013).
Wengrowicz et al. (2018) added that students’ existing attitudes towards interactive learning are a significant predictor for satisfaction. Learning satisfaction, in turn, strongly impact the attitude in the context of collaborative learning (Magen-Nagar & Shonfeld, 2017). Positive attitudes consequently become the main catalyst of students’ inclination to practice collaboration (Kuo et al., 2017; Weinberger & Shonfeld, 2018). Based on the learning approach tested, GCDK and GCTK groups have a positive attitude toward practicing collaborative learning because they feel satisfaction in economics learning and the students' initial expectations (accessibility and usability aspects) have been met compared to the KPK group.
Researcher Dı´ez-Palomar et al. (2020) mentioned that social elements have an influence on students’ attitudes. In the context of online collaborative learning, student attitudes have a relationship with group awareness (Chatterjee & Correia, 2019). Group learning and collaborative activities can strengthen relationships between friends (Adams, 2020; Ädel, 2011), build group trust (Tseng, 2019) and students get to know each other better (Yilmaz, 2017). Group success demands teamwork where each member has to work together and perform their respective responsibilities. Therefore, each member has the responsibility to contribute to the work of the group. The 2015 PISA (Program for International Student Assessment) analysis measures attitudes toward collaborative learning based on the benefits of teamwork (Li et al., 2021). Awareness of individual responsibility towards the group causes students to be more careful in their actions so as not to negatively impact other groupmates.
The formation of good work culture in a group can foster a positive attitude to enhance the quality of work and contribute to the success of the group.
A meta-analysis by Tutal and Yazar (2021) found the effect of active learning on attitude variables was moderate and considered important. The success of online collaborative learning implementation depends on the students’ positive attitude (Kasiyah et al., 2017). Individual attitudes influenced the level of actions, efforts, and commitments (Glasman & Albarracín, 2006). These results support the view that a properly applied student-centered teaching and learning approach will lead to a more positive attitude, particularly towards economics learning.
5. Recommendations and Implications
The researchers suggested some further research be implemented due to the limitations in this quasi-experimental study. The sample is limited to three Form Six schools in the state of Melaka. It is therefore recommended that future research be extended to study locations in other states as well as to obtain comparative studies by involving economics students at the matriculation level. This is because economics students in matriculation and Malaysian Higher School Certificate have almost similar characteristics. Research involving multiple institutions can strengthen the findings for generalizing research results. The findings help the teaching staff to plan student attitude development programs in their respective institutions. This study uses a quasi-experimental design, thus the results obtained are limited to the effects of the intervention using a questionnaire instrument only. Therefore, the researcher suggested a
survey to identify the relationship or influence of the variables. In addition, to obtain more information and do in-depth exploration, a combination of qualitative approaches involving interview and observation methods is also recommended. Thus, more comprehensive findings and more accurate pictures can be obtained.
The findings have implications for teachers to plan effective learning approaches to enhance students' positive attitudes. Teachers are responsible to produce excellent students not only from the cognitive aspect but also from personality. Furthermore, this learning approach is in line with generation Z’s lifestyle which is synonymous with the use of devices, technologies, and the internet (Brodsky et al., 2021). In addition, the administrators should pay attention to the pedagogical needs and competencies of teachers to integrate technology in teaching and learning. This indirectly increases the readiness of teachers to face any possibility in the future.
Sudden changes were found to be stressful for teachers and students (Dvořáková et al., 2021;
Murdhiono et al., 2021; Rao & Rao, 2021). Among the challenges that are often discussed are readiness, internet, devices, and learning satisfaction (Adedoyin & Soykan, 2020; Danchikov et al., 2021). Therefore, policymakers need to pay attention to the need for expert support, infrastructure, and adequate financial assistance so that the positive attitude of educators towards new learning approaches is formed. The formation of a positive attitude encourages individuals to apply the new learning approach to face a more challenging learning environment (Ssemugenyi & Seje, 2021).
6. Conclusion
In conclusion, this study proves that there are significant differences in students' attitudes based on the learning approaches tested. Students' attitudes play an important role in the success of collaborative activities, which leads to collaborative learning. Therefore, teachers need to ensure that students receive exposure to collaborative learning, accessibility, and usability as well as group awareness of the implementation of teaching and learning activities.
Collaborative skills are one of the aspects emphasized in 21st-century learning (Sahin, 2009) and can be developed through electronic communication tools (Khalil & Enber, 2017).
Therefore, the learning approach tested proves that active learning can impart a positive attitude compared to the conventional approach. This learning approach should be considered as one of the effective economics learning approaches implemented in the post-Covid-19 phase.
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