Title: Exploring socio-technological attributes For Smart classroom innovation
Chapter One: Introduction
This Chapter establishes the foundation for this research by outlining the background, motivation and problem statement of the research. In addition, research aim, objectives, questions, context, concepts, significance and contribution. Finally, the chapter concludes with research approach and thesis structure.
1.1. Research Background
Everyday life remains progressively digitized and interconnected in terms of information and technology use, triggering an evolution in learning behavior. Noticeably, digital natives increasingly turn towards digital devices to enhance their learning (Manichander, 2009), as older forms of information conveyance become more marginalized in favor of the virtually instant access offered by smart technology. While online and offline environments have for the most part evolved in leaps and bounds, the classroom is still a relatively traditionally configured space that is limited in reflecting the external evolution of society and culture. Hence, the smart classroom is becoming a fundamental part of learning in the 21st century (Niemeyer, 2003), and the concept represents a socio-technological solution (Uskov et al., 2015) to addressing the evolving needs of learners and teachers.
Technological and social aspects have been central to the development of smart classrooms. Advances in the development of integrated intelligent systems (Zheng, Yang, Feng, Fu, & Shi, 2019), system models and ontologies (Huang et al., 2019), learning environments (Cheong & Koh, 2018; Benakli, Kostadinov, Satyanarayana, & Singh, 2016), analytic tools and analysis (Wang, Li, & Li, 2019; Di, Danxia, & Chun, 2019), and mobile and social media applications (Dos Santos, 2019) have been the foundation upon which smart classrooms are based. At the same time, social aspects feature heavily as the development of smart classrooms focuses on personalizing the learning environment and teaching methods for effective learning and teaching (Munawar, Toor, Aslam, & Hamid, 2018; Tlili et al., 2019). Furthermore, smart classrooms are designed with a motive of enhancing engagement between students, teachers, and the learning content (Gupta, Ashwin, & Guddeti, 2019; Pingxiao, 2017; Tissenbaum & Slotta, 2019). The effectiveness of smart classrooms is likely to be assessed based on their adoption (Benakli et al. 2016; Cheong & Koh, 2018).
The interconnection is particularly notable in smart classrooms entailing physical spaces interwoven with sensors, actuators, computational elements, and displays to form a seamless continuous network to facilitate student-centred learning (Jun & Hong, 2014; Kwet & Prinsloo, 2020; Shahkarami, Amani, Yazdani, & Jahandideh, 2015). Smart technologies can transform teaching and learning (Song, Zhong, Li, & Du, 2014; Li-Shing, Jui-Yuan, & Tsang-Long, 2019; Cebrián, Palau, & Mogas, 2020; Nishantha, Pishva, & Hayashida, 2009). The desire for improved interactions, real-time feedback, data collection, and rich data fuels the emergence of smart classrooms (Wang, Li, & Li, 2019). However, this development does not fully reflect in classrooms as most retain traditional configurations. Instructors complain that technology-use is time-consuming as they spend more time ensuring the technology runs smoothly (Miraoui, 2018; Li & Li, 2020). The slow adoption is because the user experience was likely not a priority as the focus was mainly on developing content (Piamsa-nga & Poovarawan, 2020). Nevertheless, the World Bank (2020) shows that the Coronavirus Disease 2019 (COVID-19) pandemic highlights the essence of adopting the technology. There is a pressing need to re-imagine education, exploit technology advancements, and innovate accordingly (Yeh & Walter, 2016; World Bank, 2019; Johnes, Portela, & Thanassoulis, 2017).
Amidst the potential of technological development (Cloete, 2017), the smart classroom concept is projected to improve learning quality, but its adoption has been slow because of resource constraints and teachers’ unpreparedness (Xie, 2018; Alenazy, 2017). Nevertheless, incorporating technology promises to enhance learners’ higher-order thinking, and appropriate measures should therefore be in place to ensure quality delivery (Di, Danxia, & Chun, 2019; Dong et al., 2019). Quality of learning environments is a priority for the safety and comfort of learning environments and to develop appropriate teaching and learning skills (Songkram, 2017; Abdellatif, 2019). The desire for quality explains the focus on smart classroom configuration and design (Dong et al., 2019; Wu, 2016). While Bdiwi, de Runz, Faiz, and Cherif (2019) based their assessment of quality on collaborative learning, Van De Bogart and Wichadee (2016) focused on users’ attitudes, and Kilburn, Kilburn, and Davis (2016) emphasized students’ learning experiences.
The solution to limited adoption of smart classrooms lies in disruptive information system (IS) innovation possible by setting new purposes of smart technologies in teaching and learning (Flavin, 2012; Christensen, McDonald, Altman, & Palmer, 2018; Kumaraswamy, Garud, & Ansari, 2018). As smart classrooms exhibit the potential of being a fundamental part of learning in the 21st century (Niemeyer, 2003), Uskov et al. (2015) recommend a socio-technological solution for the technology to meet learners’ and teachers’ needs. Technological and social dimensions applicable must be addressed to realize disruptive IS in smart classrooms (Li-Shing et al., 2019; Cebrián et al., 2020). Advances in multiple technology dimensions (Zheng, Yang, Feng, Fu, & Shi, 2019; Huang et al., 2019; Cheong & Koh, 2018; Benakli, Kostadinov, Satyanarayana, & Singh, 2016; Wang, Li, & Li, 2019; Di, Danxia, & Chun, 2019; Dos Santos, 2019) serve as the foundation upon which smart classrooms are based. At the same time, social aspects feature heavily as the development of smart classrooms focuses on interactivity, engagement, and personalization (Li-Shing et al., 2019; Cebrián et al., 2020).
Some teachers resist smart classrooms as novelty generates divergent expectations (Holzmann, Schwarz, & Audretsch, 2020). Resistance to smart classrooms results from computer anxiety, which emerges as a system independent construct that influences the perceived use of the system (Venkatesh, 2000). In contrast, preferences for connectedness promote task-technology fit, which then influence consequent perceived intention for continued smart classroom innovation (Rovai, 2002). In enhancing the task-technology fitness, technical support should be considered for its mediating influence (Igbaria, Zinatelli, Cragg, & Cavaye, 1997; Lin & Wang, 2011). The pursuit for smart classrooms to facilitate student-centred learning (Shahkarami et al., 2015) is assessed based on task-technology fit embedded in technology, human actors, and institutional attributes (Zigurs & Buckland, 1998).
Technological and social factors need to be addressed to promote perceived intention for continued smart classroom innovation. From a technological perspective, the task-technology fit theory helps predict technology adoption as perceived fit influences in intention to use (Lin & Wang, 2011; Bere, 2018). Computer anxiety and readiness for connectivity are antecedent variables to task-technology fit and technical support mediates its impact on perceived intention (Cheng-Min & Yu, 2019; Lin & Wang, 2011). In relation to social factors, the attitude advanced by perceived systems and service quality (Garrison, 2009). Kilburn et al. (2016) and Tsai, Cheng-Min, Hong-Mau, and Bor-Wen (2018) also add that high-quality smart classrooms positively influence individuals’ attitudes that promote the intentions. As Muhaimin, Mukminin, Pratama, and Asrial (2019) recommended, it would be crucial to consider the moderating role of training teachers in influencing the intentions. The training mediates attitudes regarding smart classrooms and affects perceived intention for continued smart classroom innovation (Kim & Jang, 2020). Evidently, teachers are bound to assume a central role when seeking to address these technological and social dimensions of smart classrooms (Tanase &Velica, 2015). Appropriate measures on these dimensions to ensure teachers deliver in smart classroom environments should be a priority.
1.2. Research Motivation and Problem Statement
Education is the cornerstone of a thriving economy and society. Every economy depends on a variety of resources in order to function properly and to grow. One of the most important of these resources is the human capital or the workforce. Investing in the education, training, and preparation of the workforce is vital for the sustainable development of any economy. Heavy investments in education have been made by governments in the Middle East and North Africa (MENA) region. The education accelerated growth in the primary, secondary, and tertiary education levels. Despite this, the results were disappointing. Graduating students lack the skills and abilities required in the employment markets. The employment markets are stagnating due to mismanagement. The results are high numbers of educated people coupled with mass unemployment (Walkins, 2011). Although many advanced technological educational tools are used in the educational system in GCC, their potential is still far from being utilized fully. Utilizing technology in education, in an efficient manner, and as a strategic tool, would enhance the quality of the GCC educational system. Many methods and tools could be implemented in order to achieve that. Such as mobile learning, smart classrooms, virtual reality, augmented reality, and many others. Combining technological and traditional educational tools to the classroom creates an engaging and unique learning experience for the students and enhances their digital skills.
This study explores the potential of smart classroom that integrate a new ICT tools to form a new smart learning environment. Although recent reviews of smart learning environments address problems such as technology evaluation (Lai and Bower, 2019), student engagement (Schindler et al., 2017), technology-supported peer assessment (Fu et al., 2019), and the use of smart boards (Mun and Abdullah, 2016), literature is limited in capturing and presenting the range of underpinning socio-technological challenges of smart classrooms. Considering these challenges, this study focus on filling gaps in knowledge on socio-technological factors (Cebrián et al., 2020; Takawale & Kulkarni, 2016) that determine learning paths (Yang et al., 2018). These considerations relate to the assumptions that technology use creates opportunities to support individual and collaborative learning. This study contributes to filling gaps on instructional requirements of smart learning environments. Smart learning environments add value to education by enabling the creation of personalized, intelligent as well as adaptive learning setting. Furthermore, these learning environments aid in reducing the cognitive load of students and enables ontological construction and sense making among learners (Al-Qirim, 2011). Concentrating on emerging gaps in existing literature serves as the basis for insights on how learners and educators promptly make better decisions and learn in flexible and technology-supported interactive environments (Mohamed & Lamia, 2018; Zhu, 2020). However, literature lacks a review that systematically analyzes social and technological challenges of smart classrooms. This is the motivation for this study to review the emphasis on their methodologies, theories, and domains of smart classroom research, and the social and technological challenges that serve as the focus for studies on about smart classrooms. Furtherly, limited coverage on research dedicated to the quality attributes of smart classrooms – as opposed to smart classrooms being attributes for the quality of education. The insights are likely to uncover new areas that future research should examine.
The aim of this research is to explore the smart classroom attributes. The next section outlines the aims, objectives and research questions of the study.
1.3. Research aim, objectives and questions
This section illustrates the research aim that explores the attributes that influence smart classrooms from the perspective of higher educational teachers from GCC universities. Derived from systematic literature review, interviews, and questionnaire that focus on social and technological dimensions of learning in smart classrooms followed by research objectives and questions.
Research aim
This study aims at exploring the socio-technological attributes that influences the perceived intention for smart classrooms innovation for higher educational teachers.
Research objectives
1. Review literature on smart classroom attributes.
2. Capture higher education teachers’ perspectives on socio-technological attributes that influences the smart classroom innovation.
3. Identify challenges influencing smart classrooms innovation to learning in a GCC context.
4. Explore the attribute of smart classroom from the perspective of GCC higher education teachers.
5. Propose smart classroom framework/guidelines to elevate GCC higher education.
Research questions
Thus, the research questions guiding this study are:
RQ1. What are the smart classrooms attributes in the literature?
RQ2. What are the attribute themes influences the smart classroom from the perspective of GCC higher education teachers?
RQ3. What are the main attributes of smart classrooms influences the perceived intention for continued smart classroom innovation from the perspective of GCC higher education teachers?
RQ4. How can higher education teachers overcome the challenges of smart classrooms?
1.4. Research context and concepts
Research context
In the context of current research addressing evaluations and requirements for smart classrooms (MacLeod et al., 2018; Cebrián et al., 2020; Mohamed & Lamia, 2018; Zhu, 2020; Lai and Bower, 2019), this study seeks to inform analytical frameworks for evaluating the use of technology, preferences toward the smart classroom learning environment, determining the challenges, sustainable, resource-efficient, personalized and adaptive learning environments, and the potential of smart learning environments. The notion that identifying socio-technological challenges facing smart classrooms is vital to realizing the potential of smart classrooms serves as the premise for this study. Accordingly, the focus of this present work sheds lights on potential future trends that facilitate deeper insights into multi-disciplinary and multi-faceted development of smart classrooms.
This research explores the concept of smart classrooms by using different methods in sequent, systematic literature review, interviews, and questionnaire with high education academicians. The originality of the study is its coverage of this vital but often overlooked concept, which is likely to be vital in the ultimate utilization of any technological development in the education sector. The need for this study results from the perceived lag in the adoption of new technologies in the sector, which is likely because of weak integration. In which, the disruptive IS innovation is central to the shift in intention of acceptability and adoption of a technology in a sector. This study advances the existing literature through systematic literature review then conducted interviews and questionnaire to illustrate this impact with the rationale being that the same can apply to promote widespread adoption and intention to use the smart classrooms.
This study begins by reviewing the literature that leads to identify the challenges regarding smart learning systems and smart classrooms. The study uses data obtained from higher education educators from GCC countries. The region has increased attention on its knowledge economy as part of the ongoing impetus on economic diversification to extricate the region out of the plummeting oil-prices crisis (Saxena & Al-Tamimi, 2018). Even though Gupta and Jain (2017) advance that old teaching and learning models fail to meet the challenge of rapid quality and quantity deficit in education, this study seeks to demonstrate whether the new ones based on smart learning service systems and e-learning applications meet these pressing demands. The data for this study was collected from administering questionnaire to higher education academics in GCC countries. As Madrid, Ahmed, and Kumar (2019) observe, higher education industry in GCC countries is investing heavily to attract and retain students. The respondents are likely to offer valuable insights on the subject.
Furtherly, in implementing change management, the success in improving social and technological attributes will depend on leadership’s ability to shift from adopting the smart classroom technology to adapting it to local conditions, consequently enhancing it and meeting local needs. This study shows that emphasis should be on adapting smart classrooms to the unique contexts of the GCC countries because there are complex, unique dynamics of the region’s pedagogical aspects and education needs.
Research concepts
This section defines the key concepts of this study while the terms and concepts that are discussed in other chapters of the thesis are presented in the Glossary of this thesis (page)
Smart classroom:
Quality:
Quality in education:
Innovation:
Innovation in education:
Technology Task Fit:
1.5. Research Significance and contribution
Research significance
The significant of this study is that educational projects focusing on smart education have been adopted globally in recent years as governments aim to reform their educational systems and improve educational infrastructures (Zhu, Ming-Hua, & Riezebos, 2016; Yang et al., 2018). Ha and Soo-Young (2019) note that governments and IT companies are actively pursuing and implementing smart learning projects to prepare teachers for the future of education because of the prevailing paradigm that smart classrooms can offer what traditional classrooms could hardly afford. Thus, the development of smart technology has been accompanied by the introduction of the latest technological equipment, such as smart boards in educational environments to make the learning process more effective (Bıçak, 2019). It becomes essential to assess the value of such investment; considering past concerns that investing heavily in technology does not necessarily translate to improved students’ performance (OECD, 2016; Yang et al., 2018). This study sheds light on the experience that academics have with smart learning service/management system and e-learning applications.
Research noting that students are not gaining the skills needed for the 21st professional careers (Trucano, 2017). Policymakers argue that innovations aimed at reimagining the role of education could drive substantial welfare gains (U.S. Department of Education, 2017). Significantly, practitioners note concerns over efficiency and productivity of educational expenditures and urge that technology-based solutions to improving and re-imagining education lie in technological innovations (OECD, 2016; Flavin & Quintero, 2018) such as smart classrooms that integrate smart devices and technologies (e.g. sensors and microcontrollers) to improve the learning process (Chamba-Eras, Aguilar, Guamán, & Valdiviezo-Diaz, 2018). The insights on technology and interaction dimensions in smart classrooms can assist in better understanding changes necessary to enhance quality of smart learning. As well, the study would be relevant for initiatives targeting to promote the adoption of smart learning systems. Reflecting on the findings can assist in identifying the changes necessary to promote the adoption of smart learning among teachers.
Research contribution
To address the contribution of this study the limitations were extracted. The analysis of articles stated art of literature on smart classrooms defined the social and technological challenges of smart classroom research using a systematic approach, and analyze the research efforts aimed at confronting these challenges to set a research agenda for future studies.
Theoretically, this review contributes to and advances knowledge regarding managing smart classroom systems in two ways. First, the technological imperatives within the review shed light on some of the intricacies associated with virtual and distance-learning (Shi et al., 2010). The desire to be able to communicate remotely is driving the push to advance scientific studies designs for virtual platforms that support digital collaboration. Such cases highlight the value of having organized, centralized applications for shared learning. Smart classrooms promise integration with such platforms that serve to not only to increase the accessibility of educational materials and benefits to all stakeholders, but also to guarantee uninterrupted delivery and quality of educational content (Aguilar et al., 2015). Smart classrooms, based on well-researched principles, could facilitate easy transitions to such virtual and distance-based domains (Munawar et al., 2018). While virtual and distance-learning interests continue to grow, this review of smart classrooms retains its importance due to the fast-paced nature of developments in virtual technologies, which continues to demonstrate the immense potential for growth into the future (Lee et al., 2013). Second, the social dimensions of the study add to the discourse on inclusive and supportive learning, which seeks to incorporate a comprehensive diversity of learners with exceptional educational needs (Chaudhary et al., 2014; Kim et al., 2018). In this case, the application of smart classroom technology and its integration with digital learning platforms promises a high degree of adaptation and improvements in the educational experience for those with exceptional educational needs (Bakken et al., 2016; Uskov et al., 2015). The suggestion is that insights on the complexities and complexes associated with the technical principles and social interaction (and behavior) within smart classrooms could be harnessed for enhanced management of educational computer systems that boosts inclusivity in education.
The need for this study results from the perceived lag in the adoption of new technologies in the sector, which is likely because of weak integration. Disruptive IS innovation is central to the shift in acceptability and adoption of a technology in a sector. This study uses interviews to illustrate this impact with the rationale being that the same can apply to promote widespread adoption of smart classrooms.
The study specifically seeks to enlighten educators and policymakers on the technological and social dimensions of quality applicable in deploying smart classrooms that meet high quality levels and standards. This has the potential for a multiplier effect throughout the education sector with implications for national and regional competitiveness. This study aligns with arguments proposing that the cornerstone of any effective 21st century learning framework demands transformative strategies that integrate the smart classrooms concept (Al-Hunaiyyan, Al-Sharhan, & Alhajri, 2017).
Focusing on attitudes and perceived intentions and the role that training practices have in mediating their relationship. The study are likely to have a considerable impact on policy frameworks selected to promote the adoption of smart classrooms. The mediating role of training for smart classrooms is an area that has received limited attention in prior studies.
Further, the attention on technology task fit attribute for smart classroom innovation and the impact on perceived intention for continued smart classroom. The study is keen to ensure policymakers in GCC countries gain insights into the impact that techno-anxiety may be the hindrance to adopting smart classroom by comparing with adoption by teachers who exhibit strong preference for connectedness that significantly influence the approach that policymakers would consider in promoting smart classrooms.
1.6. Research approach/ Scope
Methodology
The mixed methods design with qualitative and qualitative approaches used in this research study in order to achieve the research study objectives. In addition to explores the attributes that influence smart classrooms from the perspective of higher educational teachers from GCC and measuring the intention use for continued smart classroom innovation. First, the systematic approach, and analyze the research efforts aimed at confronting these challenges to set a research agenda for future studies. Second, the semi-structured interview was conducted to explore attributes that influence the smart classrooms attributes from the perspective of GCC higher education teachers. Third, questionnaire was applied to explore smart classrooms attributes that increase the perceived intention for smart classrooms innovation.
Figure xx. Research Design
Participants
All participants were from GCC higher education teachers from public, private, and semi-government universities, colleges, and institutes. The data collected were divided into two parts. First, academic staff interviewed. Second, higher education teachers from all GCC educational organizations.
Limitations
This research has several limitations that result from flaws in data collection, focus on the GCC region, and flawed qualitative and quantitative methodology approaches.
For both interview and questionnaire, data collection was through virtual interviews as part of the “social distancing” measures meant to curb the spread of COVID-19. Consistent with Krouwel et al. (2019), the viewing perspective in virtual interviews limits access to body language. Reliance on camera implies that both the interviewer and interviewees contend with peculiar eye contacts. Moreover, virtual interviewing increased the likelihood of social interruptions because the interviewer and interviewee are in separate locations. Apart from virtual interviews, multiple aspects need to be considered in implementing smart classrooms but this study ignores them. Other equally vital factors have not been covered. Examples of these factors include failure due to inadequate staff training, lack of resources, and burnout due to heavy workloads. In addition, the research only focuses on GCC countries. The regional context implies that the study findings may not be reliable elsewhere. Furthermore, the non-random sample used does not reflect general education stakeholders. Failure to randomize the sample introduces the risk of bias. Thus, the study cannot be generalized to a broader population because the non-random sampling introduces biases and potentially skews the findings. There is a possibility that the researcher unconsciously sampled specific individuals based on certain factors. Another critical limitation of the study is its adoption of a qualitative methodology. The main concern in using qualitative methodology relates to the validity of generalizing results to the larger population. Even though random sampling is not a guarantee for perfect generalizability, the failure to use it is a notable concern. The researcher loses a broader perspective of the subject by focusing on detailed information. The need to do a survey remains so quantitative approach applied. Despite these, the exploratory nature of this research potential to provide advances knowledge and richness of the data due to improve the quality of smart classrooms.
1.7. Thesis Structure
The thesis is structured as follows.
Chapter 1
Chapter 2 carries out an in-depth review of the literature in order to identify the research gap and critically evaluates the theoretical base of the arguments on smart classroom.
Chapter 3 introduces the different methodological aspects of this research study. In addition to research design, data collection, samples, data collections, key variables and their measurement, and the survey questionnaire.
Chapter 4 presents the data synthesis of the systematic literature review, analysis of the interview as a qualitative method by using ATLAS ti and quantitative data from questionnaire by using SPSS.
Chapters 5 discussed the results of the data analysis and explain results.
Chapter 6 outlines the conclusion based on the data analysis and the possible ideas for future research implications on smart classrooms, Follows by the appendices relates to this study.