Engagement in cloud-supported collaborative learning and student knowledge construction: a modeling study
International Journal of Educational Technology in Higher Education volume 17, Article number: 56 (2020)
Many universities, especially in low-income countries, have considered the potential of cloud-supported collaborative learning in planning and managing students’ learning experiences. This is because cloud tools can offer students the necessary skills for collaboration with one another and improving communication between all users. This study examined how cloud tools can help students engage in reflective thinking, knowledge sharing, cognitive engagement, and cognitive presence experiences. The impact of these experiences on students’ functional intellectual ability to construct knowledge was also examined. A quantitative questionnaire was used to collect data from 150 postgraduate students. A reflective–formative hierarchical model was developed to explain students' knowledge construction in the cloud environment. The findings revealed a positive influence of cognitive engagement, knowledge sharing, and reflective thinking on students’ knowledge construction. Outcomes from this study can help decision makers, researchers, and academicians to understand the potential of using cloud-supported collaborative tools in developing individuals’ knowledge construction.
Collaborative learning is a social interaction that involves “a community of learners and teachers, where members acquire and share an experience or knowledge” Zhu (2012, p. 128). Recent studies have indicated that cloud computing technologies may provide an effective collaborative learning community (Ali et al. 2018; Ghazal et al. 2018). These technologies can also provide interaction in learning by engaging learners with new ideas, accelerating knowledge sharing, enhancing students’ reflective thinking and analytical skills, and developing student capacity for “self-learning” (Aldoayan et al. 2019; Plisorn and Piriyasurawong 2019). Many universities have recently adopted cloud computing services such as Google Apps, Doc, and Space-Share in their educational system to stimulate sharing, reflection, communication, and to support collaboration between students (Al-Samarraie and Saeed 2018).
The use of cloud-supported collaborative learning tools in higher education can aid the teaching and learning processes (Martin et al. 2019). Meanwhile, cloud tools for collaborative learning may play a vital role in constructing a positive learning experience for students because its applications are more convenient for chatting, sending queries, recording audio meetings, processing documents, and delivering content (Zhaobin et al. 2013). It allows both instructors and students to construct and share knowledge and thus promote the latter’s advanced thinking (Zeng 2016). The current literature on the application of cloud computing tools in collaborative learning shows a need to assess how students’ engagement with these tools can stimulate students’ knowledge construction, especially in low-income countries (Ghazal et al. 2019; Kaba and Ramaiah 2020; Yang et al. 2019). In addition, previous studies on collaborative learning have rarely examined the relationship between student engagement in cloud-supported activities and the main antecedents of knowledge construction, which may contribute to both synchronous and asynchronous forms of communication (Muthanna and Sang 2018). According to Aldholay et al. (2018), understanding ways to support knowledge transfer among students and instructors outside the classroom is notably lacking. Such deficiency is commonly due to the absence of online participation. The majority of empirical studies (e.g., Ali et al. 2018; Baragash and Al-Samarraie 2018a, b) have indicated that the adoption of cloud computing tools for collaborative learning can be shaped with regard to the emerging participation of students. Consequently, the impact of cloud computing tools on the collaborative learning activity should be assessed based on the development in students’ knowledge and understanding in a specific situation (Xue et al. 2020).
This study constructed a new model to answer ‘How cloud-supported collaborative learning tools contribute to students’ knowledge construction in a university setting?’. Here, students’ engagement in cloud-supported collaborative learning activities has been investigated based on constructs of cognitive engagement, cognitive presence, knowledge sharing, and reflective thinking. We examined the association between these factors and students’ knowledge construction development. Participants were recruited from a public university in Yemen. Yemen is a developing country that faces a wide range of challenges in most areas of education. Therefore, the outcomes from this study can provide a better understanding for higher education institutions in Yemen and other low-income countries on how to integrate cloud-supported collaborative learning tools into the existing teaching practices. This study also offers some insights into the development of hybrid learning environments. It is also hoped that the proposed model can further improve individuals’ engagement in online discussion, which seeks to improve the quality of online learning programs through the development of knowledge construction process.
The proposed research model was formed based on the relationships between cognitive engagement, cognitive presence, knowledge sharing, reflective thinking, and knowledge construction. The following subsections explain the model's factors and the relations between them (see Fig. 1).
Cognitive engagement, as defined by Stoney and Oliver (1999), involves seeking, interpreting, analyzing, and summarizing information, and critiquing and reasoning through various opinions and arguments. According to Greene and Miller (1996), cognitive engagement can be further distinguished by the different behaviors that an individual experiences during a learning process. This delineation includes the continuum between deep and shallow engagement. Students who exhibit behaviors that allow them to master academic work are recognized as having deep cognitive engagement, whereas students who display behaviors such as rote memorization without developing mastery of the material are demonstrating shallow engagement. Zuh (2006) reported that cognitive engagement cannot be observed in an online learning environment, but it can be described as students’ active engagement in online discussion with each other. Markauskaite et al. (2006) used a sample of 226 students to investigate and compare students’ cognitive engagement with the personal and collaborative construction of knowledge in an online environment. The authors found that students were more engaged and likely to remember concepts better within an online collaborative learning environment than a traditional learning setting. Johnson and Delawsky (2013) examined the influence of project-based learning on chemistry students’ behavioral, cognitive, and emotional engagement. The authors found that collaboration time can significantly influence the cognitive engagement of students. They emphasized that the use of different teaching pedagogies to distinguish between the various types of engagement would encourage students to attain an in-depth understanding of the learning process. Chi (2009) argued that students demonstrate a high level of cognitive engagement with learning material activities that connect to their prior knowledge and experience from observing videos and information. In addition, students engaged in limited cognitive activity are likely to experience a limited knowledge construction experience. Chou and Chen (2008) employed a Web 2.0 (Wiki) tool to promote student online collaborative learning in a programming course. The authors recruited a total of 55 college students majoring in information technology and management. The results indicated that the technological tool motivated students to engage in collaborative learning and improved their knowledge about the course. Since effective learning (e.g., subject matter learning) requires deep cognitive engagement with the content; hence, cloud-supported collaborative learning may potentially assist in offering students a means of sharing and constructing their knowledge in a meaningful manner (Lee et al. 2020). Empirical evidence denoted a positive relationship between cognitive engagement and knowledge construction in an online setting. Therefore, this study proposes the following hypothesis:
H1 Students’ cognitive engagement in a computer-supported collaborative learning environment has a significant relationship with their knowledge construction.
Cognitive presence is a central dimension of an individual’s thinking, which describes the learning phases from the initial practical inquiry to the eventual knowledge construction and problem solving. It offers a vital element for explaining the critical thinking process and outcomes that are commonly used in higher education (Wilhelm-Chapin and Koszalka 2020). Based on previous studies (e.g., Cheung et al. 2020; Kilis and Yildirim 2019), cognitive presence is created in a community of inquiry, which partly depends on students’ communication (e.g., the use of asynchronous, text-based collaborative communication to facilitate deep and meaningful learning in higher education). Akyol and Garrison (2011) established a significant relationship between cognitive presence and knowledge construction when the students engaged in online discussion. Their study involved an online graduate course (16 students) on the topic of “Blended Learning.” The authors found that cognitive presence connotes the extent of a student’s ability to construct and approve meaning by constantly discussing solutions with others. In this context, Shea and Bidjerano (2009) involved a sample of 2159 university students participating in a multi-institutional fully online learning network. The authors identified higher levels of cognitive presence among students who establish connections with their classmates and thus promote effective collaborative knowledge construction. According to Gros and García-Peñalvo (2016), cognitive presence in an e-learning environment, provides a capacity to construct knowledge through learners’ communication with their peers. This can lead to the conclusion that the innovative use of cloud resources for asynchronous communication and collaboration may offer both students and lecturers a means of promoting knowledge construction and extending learning within a developed community of inquiry (Eteokleous and Ktoridou 2012). Furthermore, facilitating cognitive presence in the collaborative space can be through suggestions, discussions, information sharing, and synthesis and application of new ideas (le Roux and Nagel 2018). Based on these observations, this study posits the following hypothesis:
H2 Students’ cognitive presence in a computer-supported collaborative learning environment has a significant relationship with their knowledge construction.
Knowledge sharing plays an important role in facilitating and improving students’ understanding of basic concepts to construct knowledge (Lin et al. 2020; Wendell et al. 2019). According to Nandy (2015), students’ knowledge sharing could be improved by the “sense of self-worth,” “communication,” “attitude,” and “willingness to support”. Shin et al. (2018) investigated the effectiveness of visible-annotation types used in online discussions to enhance the accuracy of shared knowledge and the level of constructed knowledge in online environments. Using analysis of variance, Shin et al. (2018) reported a potential relationship between knowledge sharing and knowledge construction, suggesting that the accuracy of shared knowledge is an important factor for enhancing the level of knowledge construction in the online learning environment. Baraka (2012) examined the process of developing an e-learning environment that can support collaborative sharing and knowledge construction with greater participation of learners using “Share-Space.” They concluded that information sharing among learners is an essential element for constructing knowledge. The willingness to share knowledge depends on the availability of tools that support students to engage in such a process (Xue et al. 2020). The current study assumes that cloud-supported collaborative learning tools can facilitate collaborative knowledge sharing among team members by encouraging them to participate in collaborative activities through a “sense of self-worth” and “willingness to support knowledge” that support knowledge sharing behavior to promote knowledge construction. Hence, this study proposes the following hypothesis:
H3 Students’ knowledge sharing in a computer-supported collaborative learning environment has a significant relationship with their knowledge construction.
Numerous studies have pointed out that reflection plays an important role in the knowledge construction process (Renner et al. 2020; Tremblay et al. 2019). Reflective thinking refers to purposeful thought in which learners engage in active, persistent, and careful consideration of ideas for deeper understanding (Wilson and Murdoch 2013). Online tools are another means of promoting reflective thinking in collaborative learning activities. The review of the literature indicated that collaboration learning may contribute to increased reflective thinking (Brendel 2017), deep learning, and knowledge construction (Joshi and Chugh 2009). According to Xiao et al. (2008), promoting reflective thinking during collaborative learning activities can be achieved by supporting the active sharing of information among group members. Furthermore, a deeper understanding of reflective thinking processes would help students to progress in their collaborative tasks (Xiao and Carroll 2015). For instance, Khalid et al. (2015) conducted a study on 151 university students taking the Computer in Education course. The authors concluded that reflective thinking is not spontaneous but should be deliberately stimulated by a supported environment and group discussions, which may induce the construction of better knowledge and skills among students. The literature review on reflective thinking demonstrated that any changes to individual reflective thinking can be reasonably attributed to the learning environment. Thus, this study proposes the following hypothesis:
H4 Students’ reflective learning in a computer-supported collaborative learning environment has a significant relationship with their knowledge construction.
A quantitative research approach was used in this study to collect the data through an online survey questionnaire. A reflective–formative hierarchical model was developed for the knowledge construction construct in Partial Least Square-Structure Equation Modeling (PLS-SEM). The present study was approved by the first author’s institution. We also obtained informed consent from all respondents.
The preceding guidelines for using the G*Power software were used to determine the appropriate sample size as recommended by Faul et al. (2007). With an anticipated effect size of (d = 0.15, alpha = 0.05, and power = 0.95), the projected sample size required was 138. The sample size estimates were also informed by previous studies that adopted similar strategies. Yet, this study considered a bigger sample size of 180 students as it was believed that some students may refuse to accept or share responsibility for the group’s work, or may not participate for personal reasons. The sample consisted of postgraduate students of several disciplines at Aden University in Yemen. Students were recruited using a purposive sampling strategy. A 2-month online collaborative learning activity was given to all students.
A total of 180 students from different disciplines have participated in this study. The collaborative learning activity was managed and monitored by four instructors. The collaborative learning task was about performing advanced mathematical calculations using Google Drive Sheets. Six classes were formed, each class comprising 30–35 students. Students in these classes were divided into groups of 5–7 students. In each group, there was one group leader to lead the collaborative learning activity. Prior to the learning activity, the respondents received a brief demonstration of the purpose of the study via email. All students were given a 90-min demonstration on the first day of the cloud tool to explain the learning process. The cloud tool used in this study was Google Drive. Google Drive-Sheets is a free Google Cloud platform, which supports essential programming elements (formulas, functions, conditional formatting, and scripting) and allows for simultaneous collaboration and editing among online users. All the course materials (such as an e-book, spreadsheets syllabus, PDF files, and videos prototypes) were uploaded into a Google Drive’s shared folder (see Fig. 2). Students of the one group were encouraged to work with each other to make the necessary adjustments in the shared files, solve problems, comment, and discuss learning topics relevant to the course. These activities were believed to improve knowledge sharing and reflective thinking among students. The students were given 2 months to complete the learning activities that were managed by the four instructors. After completing the learning activity, “Revision history” data from Google Drive were used to track students’ participation in the collaborative learning activity. Then, students were asked to respond to an online questionnaire assessing the influence of the collaborative learning activity in Google Drive on their knowledge construction. Out of the 180 students, only 150 students were able to complete the collaborative learning activity and submit their responses.
We adapted items from previous studies that are related to the use of online tools and modify to be appropriate for our study’s setting. The questionnaire consisted of 69 items that were divided into two separate parts. The first part asked about the respondents’ demographic background (e.g., level of study, gender, age group, discipline, and Internet experience). The second part asked about the main variables:
Cognitive engagement is a second-order reflective construct, consisting of two first-order reflective constructs adapted from Greene and Miller (1996). Several published studies have validated this construct. This construct was measured using six items that assessed shallow and meaningful cognitive engagement.
Cognitive presence was assessed using 12 items adapted from the community of inquiry instrument by Garrison et al. (2001). These items examined aspects related to “triggering event,” “exploration,” “integration,” and “resolution or application”.
Knowledge sharing is a second-order reflective construct with two first-order reflective sub-constructs: “sense of self-worth” and “willingness to support.” A total of nine items were adapted from Nandy (2015) to measure this construct.
Reflective thinking was assessed using 16 items adapted from the reflective thinking questionnaire by Kember et al. (2000). The construct was measured using four first-order reflective sub-constructs named habitual action; understanding; reflection; and critical thinking.
Knowledge construction is a second-order formative construct that was measured with three first-order reflective constructs: “problem understanding,” “achieving perspective,” and “commitment.” A total of 20 items from Tillema and van der Westhuizen (2006) were used to measure this construct. The subconstruct of problem understanding consisted of seven items that evaluate the individual professional’s growth in understanding the topic and insights gained from the discussions. The subconstruct of achieving perspective consisted of seven items that evaluate the ideas expressed by others during team discussions. The subconstruct of commitment consisted of six items focusing on the active involvement of individuals during the discussion.
A five-point Likert scale was used to assess students’ perceptions of these constructs. To examine the validity of the questionnaire, two university professors with more than 30 years of experience were approached to validate the study instrument.
We used SEM to test the research hypotheses. The Smart PLS software was used for the overall data analysis and testing for the validity and reliability of the study instrument and the research model. Three steps were followed for analyzing the collected data. In the first step, we analyzed the reflective measurement model to indicate the internal consistency reliability, convergent validity, and discriminant validity (Hair et al.2011, 2014). In the second step, we analyzed the hierarchical model. In the third step, we applied four criteria for assessing the structural model to measure the hypothesized relationships between the constructs namely: coefficient of determination (R2), path coefficients, effect size (f2), and predictive relevance (Q2).
Results and discussion
Table 1 presents the frequencies and percentages of the demographic data. The respondents involved in this study were postgraduate students (89.3% Master and 10.7% Ph.D). A total of 80 students were male and 70 were female. The majority of students were over 30 years old (76%), 31 students were between 28 and 30 years, and only five students were between 25 and 27 years old. A total of 87 respondents had a high level of internet experience.
Measurement model evaluation and specification
Here, we used a regression analysis based on 150 cases (rows). The number of latent variables in the model was five. The number of indicators used in the model was 63 items. There were no items from reflective and formative latent variables removed. These items were sufficient for the analysis because they were used to measure the latent variables as expected. Factor loadings of 0.71 and above is considered excellent; 0.55 is considered good and very significant; 0.45 is considered fair 0.32, and below 0.32 is considered poor (Comrey and Lee 2013).
The measurement items and constructs were tested using reflective–reflective hierarchical component model (see Table 2). The first and second order constructs were considered as a reflective construct in this study. For example, the second-order construct of ‘cognitive engagement’ was manifested by two first-order constructs of cognitive engagement (shallow and meaningful) (see Additional file 1: Appendix S1, Fig. S1). Only knowledge construction was measured using reflective–formative hierarchical component model. The first and second order constructs were tested using reflective and formative model (see Additional file 1: Appendix S1, Fig. S2). However, removing any pair would violate the validity of the second-order construct. The reason for using the reflective–formative type of hierarchical component model for the dependent variable (knowledge construction) was because the relationship between problem understanding, achieving perspective, commitment, and knowledge construction was formative in nature, whereby each construct is measured by reflective indicators (Petter et al. 2007). This method of specifying the dependent constructs was also adopted by many previous studies (e.g., Hair et al. 2016; Petter et al. 2007; Ringle et al. 2012).
Convergent validity here was tested by extracting the factor loadings, cross-loadings, composite reliability (CR), and average variance extracted (AVE) of all the items on their respective constructs (see Table 3). It was illustrated that the validity of the measurement scale was convergent because of the high item loadings (i.e., greater than or equal to 0.5) on their associated latent variables. All values were found to be above the 0.5 thresholds, meaning that the measurement constructs produced adequate convergent validity. In addition, the internal reliability was also assessed using Cronbach's alpha and composite reliability, which should be greater than 0.7 for the reliability to be considered acceptable, 0.80 to be adequate and 0.90 to be excellent (Hair et al. 2014; Kock and Verville 2012). Table 3 shows the Cronbach’s alpha and the composite reliability results for the reflective latent variables. The composite reliability values were ranged between 0.77 and 0.93 which surpasses the prescribed estimation of 0.7. These coefficients are considered high, shown good support for the convergent validity.
Discriminant validity is the degree to which items differentiate among constructs or measures distinct concept by examining the correlation between the measures of potentially overlapping constructs (Fornell and Larcker 1981). In addition, the AVE of the latent variable should be higher than the squared correlations between the latent variable (Chin 1998, 2010; Fornell and Larcker 1981). Table 4 shows the latent variable correlation based on Fornell-Larcker criterion, where the values of the square root of AVE were higher than inter-correlation value between constructs.
In order to assess the collinearity value, the values of the variance inflation factors (VIFs) were obtained for all the latent variables and employed to measure discriminant validity. The collinearity values for all latent variables were lower than 3. This means that adequate VIFs were achieved for the reflective and formative latent variables, thus, sufficient discriminant validity.
Based on the results, it can be said that the measurement model was valid in terms of measuring the first order constructs through the measurement items. Then, we examined the hierarchical model in an attempt to determine the validity of measuring the second order constructs using the first order constructs. Figure 3 shows the research framework of the measurement model and the relationships between the latent variables.
Hierarchical model results
The second-order construct approach was used in this study, as recommended by Wetzels et al. (2009), for the development of the proposed model. First, two types of hierarchical latent variable model were reported in this study (e.g., reflective–reflective for independent variables and reflective–formative type for dependent variable). Second, we communicated aspects related to the approach used to estimate the hierarchical latent variable model (e.g., repeated indicator or two-stage approach for estimating knowledge construction). Figure 4 shows the second stage of the hierarchal model by reducing the reflective–formative constructs (problem understanding, achieving perspective, and commitment) into only one output (knowledge construction).
All path coefficients for the hierarchical model were significant (t-value > 2.56). It is worth mentioning that the values of R2 were very high, indicating that the variance of second order constructs can be fully explained by the first order constructs. This is because measurement items were repeated in the second order constructs (Wetzels et al. 2009). The values of Q2 were greater than zero, indicating that the first order constructs are robust to predict the second order constructs (see Table 5).
According to Garson (2016, p. 236), when applying the repeated indicator approach for reflective–reflective (input) models, the first order component (FOCs) was found to explain nearly all the variance in the second order component (SOC) (R2 approach 1.0). Therefore, a two-stage approach is recommended: (1) first the repeated indicator approach is used to get factor scores for the FOCs, then (2) the FOC factors scores are used as indicators for the SOC. In both stages, other latent variables should be included in the model as well (Hair et al. 2016, p. 236). Figure 5 shows the final simple model after carrying out a two-stage approach.
Structural model results
To assess the structured model, we used SMART PLS to generate five test fit indices; the path coefficient—hypothesis test, the coefficient of determination (R2), effect size (f2), predictive relevant (Q2), and goodness of fit (GOF). The result of the R2 coefficient of the endogenous latent variable or dependent latent variable (knowledge construction) was 0.598, reflecting a moderate effect. This means that the proportion of variation in the dependent variable (knowledge construction) can be moderately explained by all predictor variables (CE, CP, KS, and RT). The effect size (f2) indicates the relative effect of a particular exogenous (independent) latent variable on endogenous (dependent) latent variable(s) by means of a change in the R2. Table 6 provides a summary of the effect size results, indicating that reflective thinking has the largest effect on knowledge construction, while knowledge sharing and cognitive engagement had medium and small effect respectively, with no effect of cognitive presence on knowledge construction.
Then we examined the significance of the path coefficient-hypothesis. The analysis data in Table 7 showed that the relationship between cognitive engagement and knowledge construction seems to have the smallest impact, with significant level (β = 0.137, P < 0.05), and weak effect size (f2 = 0.042). The path coefficient from cognitive presence to knowledge construction was found to be non-significant (β = 0.011, P = 0.883), and had no effect on the knowledge construction (f2 = 0.000). This means that knowledge construction was not predicted by the cognitive presence. The results also showed that the path coefficient from knowledge sharing to knowledge construction was significant (β = 0.326, P < 0.001). This means that students’ knowledge construction can be moderately predicted by their knowledge sharing in the cloud-supported collaborative learning environment. The path coefficient from the students reflective thinking to the knowledge construction was significant (β = 0.481, P < 0.001), and also showed a large effect size (f2 = 0.350).
To find the predictive relevance of the structural model, we used the Stone-Geiser Q2 test of the independent latent variables (Roldán and Sánchez-Franco 2012). The result indicated that the Q-squared coefficients for the predictive relevance (validity) were associated with each latent variable in the model, through the dependent latent variables (Q2 = 0.543). Finally, we calculated the goodness of fit GoF using the following formula:
The outcome value of GoF was 0.593 for this study’s model. Thus, it can be concluded that the GoF model of this study was sufficient. Figure 6 shows the significant paths, quality of PLS-SEM results, and the relationships of cognitive engagement, cognitive presence, knowledge sharing and reflective thinking with knowledge construction.
This study found a significant relationship between students’ cognitive engagement and knowledge construction in the cloud space. This positive relationship might be due to the role of the environment in promoting students’ interaction and engagement in an active discussion, during which they constructed new understanding and knowledge. Our results likewise imply that a shallow cognitive engagement can be considered an extension of students’ learning in the cloud, which may potentially support the collaborative knowledge construction process. This finding supports previous studies such as that of Zuh (2006), indicating that stimulating individuals’ interaction and cognitive engagement during online-discussion activity is critical for constructing new understanding and knowledge. It also supplement the work of Mason (2011) denoting that cognitive engagement presented represents a conceived span of a crucible within which a rich mix of cognitive activities occurs and from which new knowledge emerges. This study assumes that using cloud-supported collaborated learning may allow students to become flexible in terms of communicating and learning about the topics taught anytime and anywhere to construct meaningful knowledge. This assumption similarly adds to the work of other scholars such as Shukor et al. (2014), which recommend that further research should comprehensively explore the influence of different variables on students’ strategies for constructing knowledge with regard to the cognitive engagement level.
The relationship between cognitive presence and knowledge construction, however, was not significant. This result is not in line with previous research such as the study of Kupczynski et al. (2010), which demonstrate the effectiveness of cognitive presence in allowing students to construct and confirm meaning through reflection and discourse. Öztürk (2015) posits that individuals’ level of experience in using the technology can potentially influence their level of cognitive presence. Building on this basis is the assumption that a high level of cognitive presence requires students to exercise critical thinking skills and entails communication with others based on engagement in collaborative learning activities. These conflicting findings may emanate from the students’ efforts in communicative processes (e.g., discussions, sending instant messages, and chats) and course tasks (e.g., quizzes, assignments, course projects) and activities that they are more likely to experience.
The results also showed a significant relationship between knowledge sharing and knowledge construction in the cloud space. This significant relationship was observed through frequent communication and the sharing of important information between students, which assisted to boosting their collaboration level and building strong ties with the learning community. The result extends the work of Stacey (2002), which found that students in the online collaborative environment can construct new knowledge by sharing and listening in a context that promotes effective interactions and collaborative practices. The results therefore suggest the students’ willingness to support and share knowledge with team members, which is reflected in the establishment of interpersonal and trusting relationships between group members, thereby spurring knowledge construction. This finding supports previous studies such as the work of Wang and Noe (2010) which measured the willingness to share knowledge and behaviors to understand individuals’ motivation to participate in online learning activities. Furthermore, it consistent with the finding of Dalkir and Beaulieu (2017), which denotes that one important type of knowledge sharing that occurs in a community involves the evolution of a best practice (an improved means of doing things) or lessons learned (learning from both successful and unsuccessful events).
In addition, a significant relationship between reflective thinking and students’ knowledge construction was also found. It is assumed that students’ engagement in the cloud-supported collaborative learning activity enabled learners to develop their higher-order thinking skills by prompting them to construct new knowledge, thus enabling individuals to test their constructed views on others and discuss their ideas through the available communication tools. The result of the present research also implied that students acquired both significant reflection benefits and valuable practical learning skills through online discussions. This result is consistent with the findings of Thaiposri and Wannapiroon (2015) that social networks and cloud computing offer various useful services available on the Internet, enhancing students’ critical thinking skills through teaching and learning activities by communicating and collaborating with one another. Furthermore, this result is in line with previous studies (e.g., Khalid et al. 2015; Murugaiah and Thang 2010) that thinking practices in the online learning environment would help students to foster the processes in collaborative learning activities and to become responsible for constructing their own knowledge. The results of this work imply that student reflection and critical reflection in the cloud environment encourage students to engage in reflective activities, thus contributing to the development of knowledge construction processes.
Implications of the study
This study demonstrated the potential of cloud tools in the formulation process of the collaborative activity itself. The most significant outcome was that the cloud environment is able to foster the development of students’ knowledge while learning how to collaborate online. With respect to instructional design, developing online courses with the aid of cloud computing technologies is essential for supporting collaborative knowledge construction in the cycle of deepening inquiry. Outcomes from this study may help in aligning different cloud adoption strategies to the practices of universities and learning institutions. In addition, this study offers a novel model that includes all the variables that are deemed necessary for promoting the knowledge construction process in online settings. The proposed model can help educational technologists to create a more sophisticated understanding of the relationship between the variables that influence the construction of collaborative knowledge in online, hybrid, and face-to-face settings. It is also possible that the use of online collaborative learning tools, such as Google Drive-Sheet, can promote the learning of advanced mathematical calculations for students enrolled in different university disciplines. For example, cloud-supported collaborative learning can be an important aspect of dialogic knowledge construction in that it allows students to interact with each other rather than with a computer. This can help students to engage in multiple dialogic communications that can be recorded and reflected upon later.
Limitations and future works
Despite the results, this study poses some limitations. First participants in this study included postgraduate students from only one university. Future studies could collect and examine data from different universities, by providing a large sample size. Additionally, this study could be replicated for undergraduate students or students in K-12 to determine whether the practices and activities in cloud-supported collaborative learning within higher education apply to the practices in K-12. The second limitation is related to students’ use of cloud-supported collaborative learning tools (e.g., Google Drive-Sheet) to learn advanced mathematical calculations, and therefore not generalizable to other subjects. Despite the existence of other cloud learning environments such as Microsoft Azure, Baihui, and Zoho, sheets course was only selected in this study without considering other Google Drive applications such as GD-Docs or GD-Slides. The final limitation is the use of linear statistical methods such as PLS-SEM. The relational transactions may be more complex in reality. For example, the relationship among variables and the outcomes may not be linear. Future research that adopts nonlinear statistical methods, experimental settings, or qualitative data to triangulate the findings is therefore recommended.
The findings of this study added to the current literature on technology utilization in higher education by demonstrating the forms of relationships exploring the key factors that influence knowledge construction. A comparative study using the same data instruments could be conducted to ascertain the similarities and differences between students’ knowledge construction in an identical environment (e.g., collaborative learning-based cloud computing). This study aimed to gain a deeper knowledge of the effects of cloud collaborative tools on student’s knowledge construction through active cognitive engagement, cognitive presence, knowledge sharing, and reflective thinking. In this regard, future research should consider additional success factors such as self-efficiency, which could also play a crucial role in improving knowledge construction and productivity. Finally, this work endeavored to address certain gaps and open some avenues to new areas in education, expecting to encourage further research on collaborative knowledge. The findings of this study alone would not do justice to this comprehensive research context. Future studies on the effects of different types of knowledge are thus recommended.
This study primarily aimed to provide an extensive vision of the implementation of cloud-supported collaborative tools to substantially improve students' collaboration by promoting the active discussion, sharing, and editing of learning resources to construct knowledge. The study addressed the factors contributing to students’ knowledge construction. The results indicated that students’ cognitive engagement, knowledge sharing, and reflective thinking in a cloud-supported collaborative learning environment had a significant relationship with knowledge construction. However, the findings also revealed the lack of any significant relationship between cognitive presence and knowledge construction in a cloud-collaborative environment. These outcomes may be related to the students’ efforts in communicative processes and course tasks. From a practical viewpoint, the findings provide an understanding of the cloud-supported collaborative tools in the higher education context toward developing students' knowledge construction; at the same time, the findings highlight the importance of cognitive engagement, cognitive presence, knowledge sharing, and reflective thinking relationships within knowledge construction.
Availability of data and materials
The datasets for this study will be shared upon request.
Akyol, Z., & Garrison, D. R. (2011). Understanding cognitive presence in an online and blended community of inquiry: Assessing outcomes and processes for deep approaches to learning. British Journal of Educational Technology, 42(2), 233–250.
Aldholay, A. H., Abdullah, Z., Ramayah, T., Isaac, O., & Mutahar, A. M. (2018). Online learning usage and performance among students within public universities in Yemen. International Journal of Services and Standards, 12(2), 163–179.
Aldoayan, M., Sahandi, R., John, D., & Cetinkaya, D. (2019). Collaborative cloud-based online courses: Issues and challenges. Paper presented at the Proceedings of the 2019 8th International Conference on Educational and Information Technology.
Ali, M. B., Wood-Harper, T., & Mohamad, M. (2018). Benefits and challenges of cloud computing adoption and usage in higher education: A systematic literature review. International Journal of Enterprise Information Systems (IJEIS), 14(4), 64–77.
Al-Samarraie, H., & Saeed, N. (2018). A systematic review of cloud computing tools for collaborative learning: Opportunities and challenges to the blended-learning environment. Computers & Education, 124(May), 77–91.
Baragash, R. S., & Al-Samarraie, H. (2018a). Blended learning: Investigating the influence of engagement in multiple learning delivery modes on students’ performance. Telematics and Informatics, 35(7), 2082–2098.
Baragash, R. S., & Al-Samarraie, H. (2018b). An empirical study of the impact of multiple modes of delivery on student learning in a blended course. The Reference Librarian, 59(3), 149–162.
Baraka, N. A. (2012). A web-based collaborative e-learning environment based on a model of social cognitive development theories. Islamic University of Gaza.
Brendel, N. (2017). Using weblogs to determine the levels of student reflection in global education. In C. Brooks, G. Butt, & M. Fargher (Eds.), The power of geographical thinking (pp. 119–135). Berlin: Springer.
Cheung, D.H.-C., Ng, A.K.-L., Kiang, K.-M., & Chan, H.H.-Y. (2020). Creating a community of inquiry in the science classroom: An effective pedagogy for teaching diverse students? Journal of Further and Higher Education, 44(1), 1–13.
Chi, M. T. (2009). Active-constructive-interactive: A conceptual framework for differentiating learning activities. Topics in Cognitive Science, 1(1), 73–105.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295–336.
Chin, W. W. (2010). How to write up and report PLS analyses. In Handbook of partial least squares, 655–690.
Chou, P.-N., & Chen, H.-H. (2008). Engagement in online collaborative learning: A case study using a web 20 tool. Journal of Online Learning and Teaching, 4(4), 574–582.
Comrey, A. L., & Lee, H. B. (2013). A first course in factor analysis. New York: Psychology Press.
Dalkir, K., & Beaulieu, M. (2017). Knowledge management in theory and practice. Cambridge: MIT Press.
Eteokleous, N., & Ktoridou, D. (2012). Community of inquiry developed through cloud computing for MIS courses. Paper presented at the Global Engineering Education Conference (EDUCON), 2012 IEEE.
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18, 382–388.
Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence, and computer conferencing in distance education. American Journal of Distance Education, 15(1), 7–23.
Garson, D. (2016). Partial least squares: Regression and structural equation models. North Carolina: Statistical Publishing Associates Publishing.
Ghazal, S., Al-Samarraie, H., & Aldowah, H. (2018). “I am still learning”: Modeling LMS critical success factors for promoting students’ experience and satisfaction in a blended learning environment. IEEE Access, 6, 77179–77201.
Ghazal, S., Al-Samarraie, H., & Wright, B. (2019). A conceptualization of factors affecting collaborative knowledge building in online environments. Online Information Review.
Greene, B. A., & Miller, R. B. (1996). Influences on achievement: Goals, perceived ability, and cognitive engagement. Contemporary Educational Psychology, 21(2), 181–192.
Gros, B., & García-Peñalvo, F. J. (2016). Future trends in the design strategies and technological affordances of e-learning. In Learning, design, and technology: An international compendium of theory, research, practice, and policy, 1–23.
Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks: Sage Publications.
Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks: Sage Publications.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152.
Johnson, C. S., & Delawsky, S. (2013). Project-based learning and student engagement. Academic Research International, 4(4), 560.
Joshi, M., & Chugh, R. (2009). New paradigms in the teaching and learning of accounting: Use of educational blogs for reflective thinking. International Journal of Education and Development using Information and Communication Technology, 5(3), C1.
Kaba, A., & Ramaiah, C. K. (2020). Predicting knowledge creation through the use of knowledge acquisition tools and reading knowledge sources. VINE Journal of Information and Knowledge Management Systems.
Kember, D., Leung, D. Y., Jones, A., Loke, A. Y., McKay, J., Sinclair, K., et al. (2000). Development of a questionnaire to measure the level of reflective thinking. Assessment & Evaluation in Higher Education, 25(4), 381–395.
Khalid, F., Ahmad, M., Karim, A. A., Daud, M. Y., & Din, R. (2015). Reflective thinking: An analysis of students’ reflections in their learning about computers in education. Creative Education, 6(20), 2160.
Kilis, S., & Yildirim, Z. (2019). Posting patterns of students’ social presence, cognitive presence, and teaching presence in online learning. Online Learning, 23(2), 179–195.
Kock, N., & Verville, J. (2012). Exploring free questionnaire data with anchor variables: An illustration based on a study of IT in healthcare. International Journal of Healthcare Information Systems and Informatics (IJHISI), 7(1), 46–63.
Kupczynski, L., Ice, P., Wiesenmayer, R., & McCluskey, F. (2010). Student perceptions of the relationship between indicators of teaching presence and success in online courses. Journal of Interactive Online Learning, 9(1).
le Roux, I., & Nagel, L. (2018). Seeking the best blend for deep learning in a flipped classroom–viewing student perceptions through the Community of Inquiry lens. International Journal of Educational Technology in Higher Education, 15, 1–28.
Lee, T., Pham, K., Crosby, A., & Peterson, J. F. (2020). Digital collaboration in design education: how online collaborative software changes the practices and places of learning. Pedagogy, Culture & Society, 1–15.
Lin, C.-Y., Huang, C.-K., & Ko, C.-J. (2020). The impact of perceived enjoyment on team effectiveness and individual learning in a blended learning business course: The mediating effect of knowledge sharing. Australasian Journal of Educational Technology, 36, 126–141.
Markauskaite, L., Sutherland, L., & Reimann, P. (2006). Interaction and cognitive engagement in the community of practice: The contradiction of terms? Paper presented at the Advanced Learning Technologies, 2006. Sixth International Conference on.
Martin, M. S., Hugues, R. E., & Puliatte, A. (2019). The use of cloud-computing to promote collaborative learning in higher education. In Preparing the Higher Education Space for Gen Z (pp. 32–45). IGI Global.
Mason, J. (2011). Cognitive engagement and questioning online. In Education in a technological world: communicating current and emerging research and technological efforts. Formatex, Badajoz, Spain.
Murugaiah, P., & Thang, S. M. (2010). Development of interactive and reflective learning among Malaysian online distant learners: An ESL instructor’s experience. The International Review of Research in Open and Distributed Learning, 11(3), 21–41.
Muthanna, A., & Sang, G. (2018). Conflict at higher education institutions: factors and solutions for Yemen. Compare: A Journal of Comparative and International Education, 48(2), 206–223.
Nandy, V. (2015). Impact of organizational context factors on individuals' self-reported knowledge sharing behaviors.
Öztürk, E. (2015). Facebook as a new community of inquiry environment: An investigation in terms of academic achievement and motivation. Journal of Baltic Science Education, 14(1), 20.
Petter, S., Straub, D., & Rai, A. (2007). Specifying formative constructs in information systems research. Mis Quarterly, 31, 623–656.
Plisorn, W., & Piriyasurawong, P. (2019). Model of intelligent cloud learning system to develop digital literacy and collaborative learning skills. Journal of King Mongkut’s University of Technology North Bangkok, 29(3), 518–526.
Renner, B., Wesiak, G., Pammer-Schindler, V., Prilla, M., Müller, L., Morosini, D., et al. (2020). Computer-supported reflective learning: How apps can foster reflection at work. Behaviour & Information Technology, 39(2), 167–187.
Ringle, C. M., Sarstedt, M., & Straub, D. (2012). A critical look at the use of PLS-SEM in MIS Quarterly.
Roldán, J. L., & Sánchez-Franco, M. J. (2012). Variance-based structural equation modeling: guidelines for using partial least squares. In Research methodologies, innovations and philosophies in software systems engineering and information systems, 193.
Shea, P., & Bidjerano, T. (2009). Community of inquiry as a theoretical framework to foster “epistemic engagement” and “cognitive presence” in online education. Computers & Education, 52(3), 543–553.
Shin, Y., Kim, D., & Jung, J. (2018). The effects of representation tool (Visible-annotation) types to support knowledge building in computer-supported collaborative learning. Journal of Educational Technology & Society, 21(2), 98–110.
Shukor, N. A., Tasir, Z., Van der Meijden, H., & Harun, J. (2014). Exploring students’ knowledge construction strategies in computer-supported collaborative learning discussions using sequential analysis. Educational Technology & Society, 17(4), 216–228.
Stacey, E. (2002). Learning links online: Establishing constructivist and collaborative learning environments. Paper presented at the Proceedings of the International Education & Technology Conference: Untangling the web-establishing learning links.
Stoney, S., & Oliver, R. (1999). Can higher order thinking and cognitive engagement be enhanced with multimedia. Interactive Multimedia Electronic Journal of Computer-Enhanced Learning, 1(2).
Thaiposri, P., & Wannapiroon, P. (2015). Enhancing students’ critical thinking skills through teaching and learning by inquiry-based learning activities using social network and cloud computing. Procedia-Social and Behavioral Sciences, 174, 2137–2144.
Tillema, H., & van der Westhuizen, G. J. (2006). Knowledge construction in collaborative enquiry among teachers 1. Teachers and Teaching: Theory and Practice, 12(1), 51–67.
Tremblay, M. L., Leppink, J., Leclerc, G., Rethans, J. J., & Dolmans, D. H. (2019). Simulation-based education for novices: Complex learning tasks promote reflective practice. Medical Education, 53(4), 380–389.
Wang, S., & Noe, R. A. (2010). Knowledge sharing: A review and directions for future research. Human Resource Management Review, 20(2), 115–131.
Wendell, K. B., Andrews, C. J., & Paugh, P. (2019). Supporting knowledge construction in elementary engineering design. Science Education, 103(4), 952–978.
Wetzels, M., Odekerken-Schröder, G., & Van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. Mis Quarterly, 33, 177–195.
Wilhelm-Chapin, M. K., & Koszalka, T. A. (2020). Graduate students’ use and perceived value of learning resources in learning the content in an online course. TechTrends, 64, 361–372.
Wilson, J., & Murdoch, K. (2013). Helping your pupils to think for themselves. Abingdon: Routledge.
Xiao, L., & Carroll, J. M. (2015). Shared practices in articulating and sharing rationale: An empirical study. International Journal of e-Collaboration (IJeC), 11(4), 11–39.
Xiao, L., Clark, S., Rosson, M. B., & Carroll, J. M. (2008). Promoting reflective thinking in collaborative learning activities. Paper presented at the 2008 Eighth IEEE International Conference on Advanced Learning Technologies.
Xue, L., Rienties, B., Van Petegem, W., & Van Wieringen, A. (2020). Learning relations of knowledge transfer (KT) and knowledge integration (KI) of doctoral students during online interdisciplinary training: an exploratory study. Higher Education Research & Development, 1–18.
Yang, Y., Hu, T., Ye, Y., Gao, W., & Zhang, C. (2019). A knowledge generation mechanism of machining process planning using cloud technology. Journal of Ambient Intelligence and Humanized Computing, 10(3), 1081–1092.
Zeng, Z. (2016). Design of a cloud services platform for a multimedia teaching environment. World Transactions on Engineering and Technology Education, 14(1), 173–178.
Zhaobin, L., Wenzhi, L., & Caidong, G. (2013). Cooperation sharing platform of network teaching resources based on cloud computing. Paper presented at the Computer Science & Education (ICCSE), 2013 8th International Conference on.
Zhu, C. (2012). Student satisfaction, performance, and knowledge construction in online collaborative learning. Journal of Educational Technology & Society, 15(1), 127–136.
Zuh, E. (2006). Interaction and cognitive engagement: An analysis of four asynchronous online discussions. Instructional Science, 34(6), 451–480.
The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for supporting this work through Research Group no. RG-1438-062.
Deanship of Scientific Research at King Saud University Research Group no. RG-1438-062.
The authors declare that they have no competing interests.
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An example of reflective–reflective hierarchical construct model of CE. Fig. S2. An example of reflective–formative hierarchical construct model of KC. Table S1. Questionnaire.
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Baanqud, N.S., Al-Samarraie, H., Alzahrani, A.I. et al. Engagement in cloud-supported collaborative learning and student knowledge construction: a modeling study. Int J Educ Technol High Educ 17, 56 (2020). https://doi.org/10.1186/s41239-020-00232-z