Next Article in Journal
Short-Term Effects of Eco-Friendly Fertilizers on a Soil Bacterial Community in the Topsoil and Rhizosphere of an Irrigated Agroecosystem
Next Article in Special Issue
Education and Diet in the Scientific Literature: A Study of the Productive, Structural, and Dynamic Development in Web of Science
Previous Article in Journal
Towards Financial Sustainability of the Hospital Sector in Poland—A Post Hoc Evaluation of Policy Approaches
Previous Article in Special Issue
Development of ICT-Based Didactic Interventions for Learning Sustainability Content: Cognitive and Affective Analysis
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 12
Issue 12
10.3390/su12124802
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Sustainable Technology Integration in Underserved Area Schools: The Impact of Perceived Student Change on Teacher Continuance Intention

by
Hye Jeong Kim
1 and
Hwan Young Jang
2,*
1
Graduate School of Education, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea
2
Department of Education, Dongguk University, Pildong-ro 1 gil 30 Jung-gu, Seoul 04620, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(12), 4802; https://doi.org/10.3390/su12124802
Submission received: 22 May 2020 / Revised: 6 June 2020 / Accepted: 9 June 2020 / Published: 12 June 2020
(This article belongs to the Special Issue ICTs in Sustainable Education Environments)
Browse Figures
Versions Notes

Abstract

:
This study aims to examine the determining factors of teachers’ continuance intention to integrate technology in a smart classroom of schools in underserved areas. Smart classrooms provide a supportive learning environment for students by equipping them with advanced multi-functional and mobile technologies. A smart classroom can provide opportunities for teaching and learning by facilitating curriculum implementation and encouraging student success. The sustainable integration of technology in a smart classroom depends on the teacher’s ability to effectively utilize digital technology in the classroom. We assessed teachers’ perceptions of their technology integration by building a research model for sustainable technology integration in an underserved area in South Korea. For this, we included four aspects of teachers’ perceptions: the frequency of technology integration, the effort toward instructional practices, student change, and continuance intention. Data were gathered via a self-administered online survey with a sample of teachers who are participating in a smart school program and were analyzed using partial least squares structural equation modeling. The findings of the study show that teachers in smart classrooms are motivated to continue technology integration when they experience positive changes among students after employing smart classroom technologies. The research findings can contribute to the efforts of educators, scholars, and policy-makers to pursue sustainable development in underserved area schools.

1. Introduction

With the increased availability of smart technologies and high-speed Internet, educational informatization has become an important aspect of enriching students’ experiences as well as improving educational administration and management [1]. Emerging technologies are becoming an increasingly vital part of education, both in terms of providing instructional tools and in shaping curricula. Most school curricula have adopted information and communication technologies as an integral part of teaching and learning. The availability of digital technology has been perceived as a social equalizer and a means of sustainable development in education [2]. Yet, rural areas with students from low-income families, who have low motivation and lower academic performance, are still at a digital disadvantage thanks to various constraints such as the availability of financial and human resources and the limits of geographical isolation [3,4]. Digital technologies offer opportunities for excluded learners and learners at risk of exclusion by providing resources for teachers and students in underserved area schools [5]. However, with unequal access to high-quality educational resources, the gap between urban and rural education remains [2].
In harnessing digital technologies for sustainable development in underserved area schools, the key to successful implementation is teachers who have the pedagogical capability to integrate technology into existing curricula [6,7,8]. The role of the teacher in a technology-integrated classroom is to effectively utilize digital technology to promote student engagement and reach target learning outcomes [9]. However, rural schools face ongoing practical disadvantages. For example, teachers are often responsible for multiple subjects and grades in a single classroom while also holding additional responsibilities such as leading extracurricular activities and providing support programs and infrastructure for students’ success [10]. The challenges teachers face can hinder the innovation that might bridge the gap in student performance caused by the geographical location of the school and/or the social background of the students [11]. Major factors for the sustainable integration of technology in the classroom include professional development in technology integration; the accessibility of technology equipment; the development of technology-integrated curricula; school climate and culture; and teacher motivation, which can be influenced by self-efficacy, internal and external values, and the intention to use [6,12].

2. Background and Hypotheses

The traditional teacher-centered approach to learning can be amended by digital technology, which provides the tools to transform classrooms into student-centered environments and enhance the engagement of both teachers and students [13,14,15]. Smart classrooms integrate advanced technologies to increase teachers’ instructional capabilities and engage students in their learning experience [13,15,16,17,18]. The multi-functionalities and connectivity of advanced technologies as well as pedagogical capabilities in smart classrooms enable a more student-centered environment by facilitating a 1:1 setting. Smart classrooms provide an alternative solution to educational opportunities not typically available in underserved area schools. The adoption of advanced technologies in smart classrooms requires teachers to integrate technology into their pedagogical activities and to continue doing so in the long term [12].

2.1. Technology Integration in Smart Classrooms

Smart classrooms can broaden teachers’ pedagogical imaginations and students’ active engagement without alienating them during instruction. Smart classrooms are equipped with modern technologies such as tablets, interactive whiteboards, high-speed networks, learning management systems, applications, and other educational resources to support the teacher and students [19,20,21]. The emergence of mobile handheld devices in the classroom can foster a more interactive environment that encourages students to actively engage in the learning process and contribute to their learning experience [14,22]. Interactive and cooperative digital learning technologies enable engagement, such as cloud-based learning management systems, touch-screen electronic whiteboards with shared classroom materials, authoring tools for digital textbooks, and learning resources for mobile handheld devices [23]. When smart classrooms were conceptualized [24,25], they were focused on the use of technology in traditional teaching contexts. Then, evolving mobile technologies facilitated hyper-personalized learning [19], more advanced interactivity between teacher and students and among students, and access to a wide variety of instructional content and applications.
One of the goals of smart classrooms is to address student disengagement from learning. The classroom ratio of one teacher to many students has been the norm for a long time. Student disengagement in public schools has been a serious detriment to their academic achievement and satisfaction [26], and can often be attributed to factors such as a high student-to-teacher ratio, limited classroom space, socioeconomic status, and other socio-cultural factors [27,28,29]. Students in public schools in underserved areas are more likely to be disengaged in the classroom due to the lack of available resources, geographical isolation, and limited support [26]. Smart classroom technology can resolve disengagement when the goals of technology use and teachers’ preparedness are aligned, and when students demonstrate positive changes in the new, technology-driven environment [30,31]. The primary reason students do not succeed in school is lack of appropriate methodology and materials to motivate student interest, thus indicating the need for technology [31]. Technology can assist teachers in capturing learners’ attention in a variety of ways that traditional methods cannot [31]. Due to the availability of many types of technology, learners with different needs and learning styles can be engaged and motivated toward learning [30,31]. For example, instructional presentation has been improved through the use of interactive whiteboards, PowerPoint, and VoiceThread, which support visual and auditory learners [30,31]. The use of tablets can further improve learning based on students’ interest; several educational apps are available and students can choose what appeals to them [32].

2.2. Teachers’ Technology Integration and Perceived Student Change

When teachers implement new instructional practices, a negative response from students can discourage teachers from continuing to use the new method or explore other practices [33]. According to Howley, Wood, and Hough [6], teachers in rural schools are influenced by students’ attitudes, preparedness for using technology, and the availability of technology, rather than the school’s location or students’ socioeconomic status. Technology can play a significant role in students’ lives and education, and teachers have a profound influence on the way technology is integrated into lessons. The integration of technology in smart classrooms is possible only when teachers take the time to become familiar with the technology and acquire the equipment needed to support learning. Beyond this, teachers must find ways to incorporate the new technology into their teaching. This must be done in such a way that the teaching provides complex cognitive engagement that, in turn, invites students to become invested in the learning process [34]. Teachers’ appropriate integration of technology in smart classrooms may have a positive influence on student change. We therefore hypothesize the following relationship:
Hypothesis 1 (H1).
The frequency of technology-integrated instruction in smart classrooms has a positive influence on teacher-perceived student change.

2.3. Teachers’ Technology Integration and Effort toward Instructional Practices

The effectiveness of technology integration in schools is dependent on teachers’ actual practice, the ability to integrate technology, their training in technology, the availability of educational software, and the degree to which school infrastructure supports integration [35,36]. In smart classrooms, teachers need to implement new technologies based on a student-centered approach to teaching that fosters technology-appropriate skills, knowledge, and attitudes. The technologies in smart classrooms support student-centered learning in more constructivist ways [15]. Recently, the shift from teacher-centered to student-centered learning in the educational paradigm has altered teachers’ beliefs about successful instruction. It is important to be consistent in the use of technology even if teachers apply it in simple ways [37]. Teachers’ effort toward instructional practices, including professional development and the application of innovative instructional methods, can manifest in several ways at the personal, school, district, and national levels. These efforts can impact instructional effectiveness, knowledge, skills, and student growth as perceived by teachers. However, teachers’ ability to practice in innovative environments such as smart classrooms has been hampered by the absence of professional development and related equipment. Thus, teachers in favor of technology integration should ensure the localization of their professional experiences to increase the effectiveness of instructional practices in the classroom [38,39]. Teachers’ efforts to adopt technology-based instructional practices into the classroom can catalyze technology-integrated classrooms, whereas teachers who make little effort have little influence on classroom changes. Thus, we hypothesize the following relationships:
Hypothesis 2 (H2).
The frequency of technology-integrated instruction in smart classrooms has a positive influence on teachers’ effort toward instructional practices.
Hypothesis 3 (H3).
Teachers’ effort toward instructional practices in smart classrooms has a positive influence on teacher-perceived student change.

2.4. Perceived Student Change and Teacher Continuance Intention

According to technology acceptance models regarding technology integration [40,41,42], teachers’ continuance intention toward technology use is connected to their actual use of technology in classrooms [9]. Thus, teachers’ continuance intention toward technology integration in smart classrooms is an important predictor of long-term practice. However, teachers in rural schools have difficulties sustaining technology integration due to insufficient time, technical limitations, and inadequate support [43]. These difficulties may partly be the result of a passive approach toward technology integration―an unsupportive environment can provoke negative attitudes toward the technology-integrated environment. Teachers’ perception of student characteristics and behaviors can also influence teachers’ implementation and continuance of new and innovative practices [33]. To examine the relationship between student change and teacher continuance intention toward technology integration in smart classrooms, we postulate the following:
Hypothesis 4 (H4).
The frequency of technology-integrated instruction in smart classrooms has a positive influence on teachers’ continuance intention toward technology integration in the classroom.
Hypothesis 5 (H5).
Teachers’ efforts toward instructional practices in smart classrooms have a positive influence on teachers’ continuance intention toward technology integration in the classroom.
Hypothesis 6 (H6).
Teacher-perceived student change has a positive influence on teachers’ continuance intention toward technology integration in the classroom.
The research model for this study is shown in Figure 1.

3. Materials and Methods

3.1. Sample and Data Collection

A total of 54 respondents from 21 elementary and middle schools participated in this study, including teachers who work in smart classrooms donated by a community relations program for underserved rural schools and education institutions (e.g., hospital schools and community youth centers). The classrooms have been provided with high-tech learning environments and mentoring groups since 2012. The program donates to around ten educational institutions each year. Teachers who were working in such classrooms at the time of this study were invited to participate in a survey to assess their experience in the smart classrooms. Therefore, the number of possible respondents was limited. Basic background information regarding the participants is shown in Table 1.
An online survey was conducted via SurveyMonkey, an online survey tool, to collect data regarding teachers’ experiences operating in smart classrooms.

3.2. Measures

All variables were measured according to criteria developed by the researchers. The background information for the teachers included gender, the school level they were teaching, age, years of teaching experience, and their technology proficiency level. Additionally, the frequency of technology integration was measured by three items using a 7-point scale (Cronbach’s alpha = 0.77); for example, “How many times have you used the smart classroom environment for your instruction in the last month?” Teacher-perceived student change in smart classrooms was measured by four items [44,45] using a 5-point Likert scale ranging from (1) strongly disagree to (5) strongly agree (Cronbach’s alpha = 0.92); for example, “During my instruction in the smart classroom, students were actively engaged.” Continuance intention was measured by two items [42,46,47] using a 5-point Likert scale ranging from (1) strongly disagree to (5) strongly agree (Cronbach’s alpha = 0.79); for example, “I have continuously used the smart classroom environment because of students’ satisfaction with it in previous classes.” Teachers’ effort toward instructional practices (EE) was measured by two items [15,48,49] using a 5-point Likert scale ranging from (1) strongly disagree to (5) strongly agree (Cronbach’s alpha = 0.91); for example, “I made an effort to improve my instructional skills for the smart classroom.”

3.3. Data Analysis

Data were analyzed using partial least squares structural equation modeling (PLS-SEM) to evaluate the quality of the model and test the hypotheses. We used PLS-SEM because it can handle non-normal data, as well as smaller sample sizes [50]―our sample size of 54 teachers was relatively small. To validate the model, we used convergent validity and discriminant validity [50]. To examine loadings, weights, average variance extracted (AVE), composite reliabilities (CR), and t-values, we performed the bootstrapping process with 5000 resamples. Fornell and Larcker propose a cutoff value of 0.70 or greater as being acceptable for CR [51]. Both validities can offer proof of the goodness of fit of the model regarding the collected data [40]. The statistical analysis in this study was conducted using SPSS 26.0 for descriptive statistics of the data and using SmartPLS [52]. This study followed the guidelines of PLS-SEM provided by Hair and colleagues [50,53].

4. Results

4.1. Evaluation of the Measurement Model

The properties of the measurement model were examined using convergent validity and discriminant validity. Convergent validity can explain the indicators’ variance, which is the shared variance between each item and its associated construct. The standardized loadings for each factor model should exceed 0.70 [50]. As shown in Table 2, the standardized loadings of all items exceeded this threshold. Additionally, the AVE, which measures the variance captured by the indicators relative to the variance caused by the measurement errors of the indicators, should exceed 0.50 [51]. The acceptance level of AVE was 0.5 or higher, which means that 50% or more of the indicators’ variance is accounted for by the construct. Table 2 shows that all constructs met this threshold.
To confirm the construct reliability, the values of Cronbach’s alpha and composite reliability should exceed the threshold of 0.70 [54]. As shown in Table 2, the alpha from each response showed a range of 0.766 or higher, yielding an internal consistency that exceeded the necessary threshold. The CR values were in the range of 0.866 to 0.957, which exceeded the threshold of 0.80, showing that the reliability of respondent scores was adequate with relatively little errors [54]. Thus, the results of the standardized loadings, Cronbach’s alpha, CR, and AVE support the convergent validity and reliability of the measurement model.
The discriminant validity was assessed using the Fornell–Larcker scale and cross-loadings, as shown in Table 3. To confirm discriminant validity, the square root of AVE should be higher than all correlations between constructs [55], which are given by the bold diagonal elements in Table 3. The off-diagonal values represent the correlations between the constructs. As can be seen in Table 3, the square root of each construct’s AVE was higher than the correlations between the constructs. The square root of the AVE for a construct should be significantly higher than the variance between that construct and other constructs within the model [50]. The square root of the AVE should also be greater than 0.5 [55]. Table 4 shows the analysis of the correlations among the measurement items to confirm that they do not cross-load on other factors. All items loaded higher than their own construct and had a value higher than 0.5. Thus, all values were higher than the requisite thresholds and the discriminant validity in the measurement model was confirmed.

4.2. Structural Model Evaluation and Hypothesis Testing

After evaluating the measurement model, we examined the structural model proposing that there are causal relationships between constructs. The bootstrapping method was run using 5000 samples, as well as bias-corrected and accelerated (BCa) confidence intervals with a two-tailed test at a 0.05 level. The structural model was assessed according to the significance of the path coefficients, the level of predictive power of the model using R2 value and Q2 of the endogenous variables, and the f2 effect size.
All path coefficients of the model were statistically significant. As shown in Table 5 and Figure 2, the results support all four hypothesized paths. The relationship between IF and EE was accepted, with an effect size that proved to be small (β = 0.330, p = 0.017). The relationship between EE and PS was accepted, also with a small effect size (β = 0.319, p = 0.009). The relationship between IF and PS was accepted, with a medium effect size (β = 0.410, p = 0.000). The relationship between PS and CI was accepted, with an effect size that proved to be large (β = 0.685, p = 0.000).
The coefficient of determination of the R2 value was determined by the predictive accuracy of the model. The R2 value can be confirmed at 0.67 or higher, with values ranging between 0.33 and 0.67 being considered moderate, values in the range of 0.19 to 0.33 as weak, and values lower than 0.19 as unacceptable. Hair et al. [50] suggested that a value of 0.25 indicates a weaker predictive relationship while a value of 0.50 indicates a medium predictive relationship. The R2 for EE was 0.131, meaning that 13.1% of the variance in EE can be explained by this model. The R2 for PS was 0.365, meaning that 36.5% of the variance in PS can be explained by this model. The R2 for CI was 0.510, meaning that 51.0% of the variance in CI can be explained by this model. Additionally, the predictive relevance of the model was assessed by calculating Stone–Geisser’s Q2 values [56,57]. The Q2 values were 0.07 for EE, 0.334 for CI, and 0.258 for PS. All values of Q2 reached acceptable levels of predictive relevance [58].
Further, we examined the effect size for the antecedents of endogenous variables using the value of Cohen’s f2 [59] shown in Table 5. According to Cohen’s guidelines, f2 is a useful measure of effect size, where f2 ≥ 0.02, f2 ≥ 0.15, and f2 ≥ 0.35 represent small, moderate, and large effects of the exogenous latent variable, respectively [59,60].
As shown in Table 6, to assess the effects of specific paths in the model, we examined the mediation effect of student change (PS), teacher effort toward instructional practices (EE), and integration frequency (IF) on continuance intention (CI) using specific indirect effects after testing the bootstrapping routine with the 5000 samples, and no sign changes for determining the 95% and BCa confidence intervals in PLS-SEM [61,62,63]. The results of the mediation analysis showed that teacher-perceived student change is the only significant mediator between integration frequency (IF) and continuance intention (CI).

5. Discussion

The goal of this study was to examine the role of teacher-perceived student change and teacher continuance intention in integrating technology in smart classrooms in underserved area schools. We developed six hypotheses to examine the role of key variables on teachers’ continuance intention toward technology-integrated instruction in smart classrooms in rural schools.
Overall, except for hypotheses H4 and H5, our hypotheses were mostly supported by the data collected in the study. Also, we found that teacher-perceived change is the only significant mediator between the frequency of technology integration in smart classrooms and continuance intention. Hence, if those teachers who are integrating technology in underserved area schools experience positive changes in student engagement, they will be motivated to sustain technology integration in smart classrooms.
To sustain technology integration in the classroom, teachers need to be both passionate and professional [64]. In underserved schools with limited environments and educational resources, teachers have an even greater role in bringing about positive changes in the classroom. Smart classrooms as an innovative learning environment in underserved area schools should bolster teachers’ continuous effort to help students actively engage in the classroom through professional development and instructional practices.
Importantly, the direct effect of frequent technology integration in smart classrooms on continuance intention is mediated by teacher-perceived student change in such classrooms, which may work as a motivational factor for teachers to sustain instruction long term. The functionalities and pedagogical possibilities of smart classrooms can enhance students’ experiences in learning. Providing active and interactive technologies in a smart classroom may augment rural students’ learning experiences and motivation [65]. Further, people’s behavioral intention toward technology can strongly predict their actual use of technology. Smart classrooms can provide a richer learning environment for students and teachers in rural areas if teachers can consistently and effectively integrate technology into their classroom teaching. In this study, we expected to validate a research model to explain teachers’ experiences in smart classrooms in order to promote more successful integration of technology in underserved area schools. The characteristics of underserved area schools were taken into consideration to develop a plan for initiating positive change among students via technology integration.
Technology integration tends to confront several challenges, including the lack of training, nonexistent equipment, and insufficient time in the curriculum to incorporate technology, among others [6], often because of the challenges of adapting an appropriate educational program to accommodate instruction goals or strategies [66]. However, recent classroom technologies with greater mobility, interconnectivity, and multi-functionality can help to overcome the limits of previous classroom technology integration using primarily personal computers. Technology integration is an authentic instructional strategy to enhance students’ ability to connect what they learn to their everyday lives [67]. Through technology, teachers can not only improve their teaching efficiency, but extend and transform learning [68].
Contrary to our expectations, we found that frequent integration itself does not directly predict teachers’ continuance intention toward integration in smart classrooms. The relationship can be explained by teacher motivation. Teachers’ ultimate role is to support student growth through curriculum and pedagogical expertise within enriched educational environments, including resources, policies, and budget. However, in the limited environment of underserved areas, an innovative learning environment cannot be sustained by teachers alone without additional effort or motivation, such as positive changes observed in students.
This implies that technology integration in smart classrooms should be designed to ensure the enrichment of student learning, rather than rote adoption without a plan. Teachers need to experience students’ positive responses, such as strong engagement in learning activities for their level of performance, gaining knowledge in subjects, or enjoying the activities in a way that reinforces their motivation to learn. When teachers perceive positive changes in students, they tend to sustain their efforts and try new methods going forward. Positive change is a strong motivator for teachers in smart classrooms.
This study has several limitations that should be considered. First, the findings may not generalize to a more representative sample because the survey data came from underserved rural areas in South Korea, among teachers who are engaged in a specific smart school program. Second, all of the measurements of the study are limited to the teachers’ self-reported perceptions. Third, the sample size is relatively small, which might limit the power of the study. Thus, increasing the sample size to provide stronger evidence for the research model will be an important undertaking for future studies. It may also prove beneficial to expand the research model to include additional predictors of teacher’s technology integration in rural areas. Fourth, it is necessary to examine qualitative responses from the teachers to expand the research model as well as generalize it in different contexts.

6. Conclusions and Future Research

In conclusion, our results show that teacher perceived student change affects their continuance intention as it relates to technology in smart classrooms. The results indicate that teachers’ continuous integration of technology in smart classrooms might be encouraged through motivation and additional training on using technology in their lessons. Hence, we recommend that school leaders and teacher educators support the continuous integration of technology by helping teachers use a more student-centered approach that builds toward student satisfaction and achievement. The study results will help teacher educators to plan training for in-service teachers in underserved area schools. In future studies, the research model’s predictive ability and explanatory powers should be further validated regarding their usefulness in urban contexts. While our data are from underserved area schoolteachers in South Korea, additional studies may be able to generalize the findings to other areas and countries. Furthermore, comparative studies across countries could be conducted to identify the cross-country invariant variables that influence teachers’ continuous intention to integrate technology in underserved area schools.

Author Contributions

H.J.K. conceived the research idea and designed the research framework; H.J.K. analyzed the data; and H.Y.J. and H.J.K. wrote the draft. All authors have read and agreed to the published version of the manuscript.

Funding

The authors acknowledge support from the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (project NRF-2017S1A5A8021541). The views expressed in this article are those of the authors and do not necessarily reflect those of the grant agencies.

Acknowledgments

The authors want to thank their anonymous reviewers.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Tosun, N.; Bariş, M.F. Using information and communication technologies in school improvement. Turkish Online J. Educ. Technol. 2011, 10, 223–231. [Google Scholar]
  2. Wang, J.; Tigelaar, D.E.H.; Admiraal, W. Connecting rural schools to quality education: Rural teachers’ use of digital educational resources. Comput. Hum. Behav. 2019, 101, 68–76. [Google Scholar] [CrossRef]
  3. Salas-Pilco, S.Z.; Law, N.W.Y. ICT curriculum planning and development: Policy and implementation lessons from small developing states. In ICT-Supported Innovations in Small Countries and Developing Regions; Springer: Cham, Switzerland, 2018; pp. 77–98. [Google Scholar]
  4. Warschauer, M.; Knobel, M.; Stone, L.A. Technology and equity in schooling: Deconstructing the digital divide. Educ. Policy 2004, 18, 562–588. [Google Scholar] [CrossRef] [Green Version]
  5. Tyler, T.R. Is the internet changing social life? it seems the more things change, the more they stay the same. J. Soc. Issues 2002, 58, 195–205. [Google Scholar] [CrossRef]
  6. Howley, A.; Wood, L.; Hough, B. Rural elementary school teachers’ technology integration. J. Res. Rural Educ. 2011, 26, 1–13. [Google Scholar]
  7. Miranda, H.; Russell, M. Predictors of teacher-directed student use of technology in elementary classrooms: A multilevel SEM approach using data from the USEIT study. J. Res. Technol. Educ. 2011, 43, 301–323. [Google Scholar] [CrossRef]
  8. Tyler-Wood, T.L.; Cockerham, D.; Johnson, K.R. Implementing new technologies in a middle school curriculum: A rural perspective. Smart Learn. Environ. 2018, 5, 22. [Google Scholar] [CrossRef]
  9. Kim, H.J.; Jang, H.Y. Factors influencing students’ beliefs about the future in the context of tablet-based interactive classrooms. Comput. Educ. 2015, 89, 1–15. [Google Scholar] [CrossRef]
  10. Willis, J.C.; Templeton, N.R. Investigating the establishment and sustainability of professional learning communities in rural east Texas: The principals’ perspectives. Rural Educ. 2018, 38, 30–37. [Google Scholar] [CrossRef] [Green Version]
  11. The Organisation for Economic Co-operation and Development (OECD). Creating Effective Teaching and Learning Environments: First Results from TALIS; The Organisation for Economic Co-operation and Development (OECD): Paris, France, 2009. [Google Scholar]
  12. Lin, J.M.C.; Wang, P.Y.; Lin, I.C. Pedagogy * technology: A two-dimensional model for teachers’ ICT integration. Br. J. Educ. Technol. 2012, 43, 97–108. [Google Scholar] [CrossRef]
  13. Macleod, J.; Hao, H.; Zhu, S.; Li, Y. Understanding students’ preferences toward the smart classroom learning environment: Development and validation of an instrument. Comput. Educ. 2018, 122, 80–91. [Google Scholar] [CrossRef]
  14. Sessoms, D. Interactive instruction: Creating interactive learning environments through tomorrow’s teachers. Int. J. Technol. Teach. Learn. 2008, 4, 86–96. [Google Scholar]
  15. Matzen, N.J.; Edmunds, J.A. Technology as a catalyst for change: The role of professional development. J. Res. Technol. Educ. 2007, 39, 417–430. [Google Scholar] [CrossRef]
  16. Niemeyer, D. Hard Faction on Smart Classroom Design: Ideas, Guidelines, and Layouts; The Scarecrow Press, Inc.: Lanham, MD, USA, 2003. [Google Scholar]
  17. Saini, M.K.; Goel, N. How smart are smart classrooms? A review of smart classroom technologies. ACM Comput. Surv. 2019, 52, 1–28. [Google Scholar] [CrossRef] [Green Version]
  18. Lee, J.; Lee, H.; Park, Y. The smart classroom: Combining smart technologies with advanced pedagogies. Educ. Technol. 2013, 53, 3–12. [Google Scholar]
  19. Alelaiwi, A.; Alghamdi, A.; Shorfuzzaman, M.; Rawashdeh, M.; Hossain, M.S.; Muhammad, G. Enhanced engineering education using smart class environment. Comput. Hum. Behav. 2015, 51, 852–856. [Google Scholar] [CrossRef]
  20. Chan, T.-W.; Roschelle, J.; Hsi, S.; Kinshuk; Sharples, M.; Brown, T.; Patton, C.; Chernivasky, J.; Pea, R.; Norris, C.; et al. One-to-one technology-enhanced learning: An opportunity for global research collaboration. Res. Pract. Technol. Enhanc. Learn. 2006, 1, 3–29. [Google Scholar] [CrossRef] [Green Version]
  21. Al-Qirim, N. Determinants of interactive white board success in teaching in higher education institutions. Comput. Educ. 2011, 56, 827–838. [Google Scholar] [CrossRef]
  22. Terras, M.M.; Ramsay, J. The five central psychological challenges facing effective mobile learning. Br. J. Educ. Technol. 2012, 43, 820–832. [Google Scholar] [CrossRef] [Green Version]
  23. Kim, H.J.; Kim, H. Approches multidirectionnelles pour une école numérique en Corée du Sud. Rev. Int. d’éducation Sèvres 2014, 67, 129–136. [Google Scholar] [CrossRef] [Green Version]
  24. Shi, Y.; Xie, W.; Xu, G.; Shi, R.; Chen, E.; Mao, Y.; Liu, F. The smart classroom: Merging technologies for seamless tele-education. IEEE Pervasive Comput. 2003, 2, 47–55. [Google Scholar]
  25. Yau, S.S.; Gupta, S.K.S.; Karim, F.; Ahamed, S.I.; Wang, Y.; Wang, B. Smart classroom: Enhancing collaborative learning using pervasive computing technology. In Proceedings of the American Society for Engineering Education Annual Conference Proceedings, Nashville, TN, USA, 22–25 June 2003. [Google Scholar]
  26. Mann, S.J. Alternative perspectives on the student experience: Alienation and engagement. Stud. High. Educ. 2001, 26, 7–19. [Google Scholar] [CrossRef]
  27. Finn, J.D.; Cox, D. Participation and withdrawal among fourth-grade pupils. Am. Educ. Res. J. 1992, 29, 141–162. [Google Scholar] [CrossRef]
  28. Brown, M.R.; Higgins, K.; Paulsen, K. Adolescent alienation: What is it and what can educators do about it? Interv. Sch. Clin. 2003, 39, 3–9. [Google Scholar] [CrossRef]
  29. Rokach, A.; Bauer, N.; Orzeck, T. The experience of loneliness of Canadian and Czech youth. J. Adolesc. 2003, 26, 267–282. [Google Scholar] [CrossRef]
  30. Liang, T.; Huang, Y.; Tsai, C. An investigation of teaching and learning interaction factors for the use of the interactive whiteboard technology. Educ. Technol. Soc. 2012, 15, 356–367. [Google Scholar]
  31. Lee, L. A learning journey for all: American elementary teachers’ use of classroom wikis. J. Interact. Online Learn. 2012, 11, 90–102. [Google Scholar]
  32. Aronin, S.; Floyd, K.K. Using an iPad in inclusive preschool classrooms to introduce STEM concepts. Teach. Except. Child. 2013, 45, 34–39. [Google Scholar] [CrossRef]
  33. Könings, K.D.; Brand-Gruwel, S.; van Merriënboer, J.J.G. Teachers’ perspectives on innovations: Implications for educational design. Teach. Teach. Educ. 2007, 23, 985–997. [Google Scholar] [CrossRef]
  34. Warschauer, M. The paradoxical future of digital learning. Learn. Inq. 2007, 1, 41–49. [Google Scholar] [CrossRef]
  35. Comi, S.L.; Argentin, G.; Gui, M.; Origo, F.; Pagani, L. Is it the way they use it? Teachers, ICT and student achievement. Econ. Educ. Rev. 2017, 56, 24–39. [Google Scholar] [CrossRef] [Green Version]
  36. Gil-Flores, J.; Rodríguez-Santero, J.; Torres-Gordillo, J.J. Factors that explain the use of ICT in secondary-education classrooms: The role of teacher characteristics and school infrastructure. Comput. Hum. Behav. 2017, 68, 441–449. [Google Scholar] [CrossRef]
  37. Ertmer, P.A. Teacher pedagogical beliefs: The final frontier in our quest for technology integration? Educ. Technol. Res. Dev. 2005, 53, 25–39. [Google Scholar] [CrossRef]
  38. Longhurst, M.L.; Jones, S.H.; Campbell, T. Factors influencing teacher appropriation of professional learning focused on the use of technology in science classrooms. Teach. Dev. 2017, 21, 365–387. [Google Scholar] [CrossRef]
  39. Clarke, G.; Zagarell, J. Teachers and technology: A technological divide. Child. Educ. 2012, 88, 136–139. [Google Scholar] [CrossRef]
  40. Joo, Y.J.; Lee, H.W.; Ham, Y. Integrating user interface and personal innovativeness into the TAM for mobile learning in Cyber University. J. Comput. High. Educ. 2014, 26, 143–158. [Google Scholar] [CrossRef]
  41. Sánchez-Prieto, J.C.; Olmos-Migueláñez, S.; García-Peñalvo, F.J. Informal tools in formal contexts: Development of a model to assess the acceptance of mobile technologies among teachers. Comput. Hum. Behav. 2016, 55, 519–528. [Google Scholar] [CrossRef]
  42. Teo, T.; Lee, C.B.; Chai, C.S.; Wong, S.L. Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia: A multigroup invariance analysis of the Technology Acceptance Model (TAM). Comput. Educ. 2009, 53, 1000–1009. [Google Scholar] [CrossRef]
  43. Howley, A.; Howley, C. Planning for technology integration: Is the it agenda overrated or underappreciated? Educ. Plan. 2008, 17, 1–17. [Google Scholar]
  44. Mucherah, W.M. The influence of technology on the classroom climate of social studies classrooms: A multidimensional approach. Learn. Environ. Res. 2003, 6, 37–57. [Google Scholar] [CrossRef]
  45. Christophel, D.M. The relationships among teacher immediacy behaviors, student motivation, and learning. Commun. Educ. 1990, 39, 323–340. [Google Scholar] [CrossRef]
  46. Bhattacherjee, A. Understanding information systems continuance: An expectation-confirmation model. MIS Q. Manag. Inf. Syst. 2001, 25, 351–370. [Google Scholar] [CrossRef]
  47. Venkatesh, V.; Davis, F.D. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Manag. Sci. 2000, 46, 186–204. [Google Scholar] [CrossRef] [Green Version]
  48. An, Y.J.; Reigeluth, C. Creating technology-enhanced, learner-centered classrooms: K–12 teachers’ beliefs, perceptions, barriers, and support needs. J. Digit. Learn. Teach. Educ. 2011, 28, 54–62. [Google Scholar] [CrossRef]
  49. Palak, D.; Walls, R.T. Teachers’ beliefs and technology practices: A mixed-methods approach. J. Res. Technol. Educ. 2009, 41, 417–441. [Google Scholar] [CrossRef]
  50. Hair, J.F.J.; Hult, G.T.M.; Ringle, C.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd ed.; SAGE Publication: Thousand Oaks, CA, USA, 2016. [Google Scholar]
  51. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  52. Ringle, C.M.; Wende, S.; Becker, J.-M. SmartPLS 3 2015. Available online: http://smartpls.com (accessed on 22 May 2020).
  53. Hair, J.; Hollingsworth, C.L.; Randolph, A.B.; Chong, A.Y.L. An updated and expanded assessment of PLS-SEM in information systems research. Ind. Manag. Data Syst. 2017, 117, 442–458. [Google Scholar] [CrossRef]
  54. Nunnally, J.; Bernstein, I. Psychometric Theory, 3rd ed.; McGraw-Hill: New York, NY, USA, 1994. [Google Scholar]
  55. Fornell, C.; Bookstein, F.L. Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. J. Mark. Res. 1982, 19, 440–452. [Google Scholar] [CrossRef] [Green Version]
  56. Geisser, S. A predictive approach to the random effect model. Biometrika 1974, 61, 101–107. [Google Scholar] [CrossRef]
  57. Stone, M. Cross-validatory choice and assessment of statistical predictions. J. R. Stat. Soc. Ser. B 1974, 36, 111–147. [Google Scholar] [CrossRef]
  58. Chin, W.W.; Newsted, P.R. Structural equation modeling analysis with small samples using partial least square. Stat. Strateg. Small Sample Res. 1999, 1, 307–341. [Google Scholar]
  59. Cohen, J. Statistical Power Analysis for the Behavioral Sciences, 2nd ed.; Lawrence Earlbaum Associates: Hillsdale, NJ, USA, 1988. [Google Scholar]
  60. Selya, A.S.; Rose, J.S.; Dierker, L.C.; Hedeker, D.; Mermelstein, R.J. A practical guide to calculating Cohen’s f 2, a measure of local effect size, from PROC MIXED. Front. Psychol. 2012, 3, 111. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Nitzl, C.; Roldan, J.L.; Cepeda, G. Mediation analysis in partial least squares path modelling: Helping researchers discuss more sophisticated models. Ind. Manag. Data Syst. 2016, 116, 1849–1864. [Google Scholar] [CrossRef]
  62. Streukens, S.; Leroi-Werelds, S. Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results. Eur. Manag. J. 2016, 34, 618–632. [Google Scholar] [CrossRef]
  63. Hayes, A. Introduction to Mediation, Moderation, and Conditional Process Analysis; Guilford Publication: New York, NY, USA, 2013. [Google Scholar]
  64. Wizel, M. Preparing educational hackers. In Contemporary Pedagogies in Teacher Education and Development; Weinberger, Y., Libman, Z., Eds.; IntechOpen: London, UK, 2018; pp. 101–118. [Google Scholar]
  65. Lee, C.C.; Hao, Y.; Lee, K.S.; Sim, S.C.; Huang, C.C. Investigation of the effects of an online instant response system on students in a middle school of a rural area. Comput. Hum. Behav. 2019, 95, 217–223. [Google Scholar] [CrossRef]
  66. Drent, M.; Meelissen, M. Which factors obstruct or stimulate teacher educators to use ICT innovatively? Comput. Educ. 2008, 51, 187–199. [Google Scholar] [CrossRef]
  67. Wallace, J.; Rennie, L.; Malone, J.; Venville, G. What we know and what we need to know about curriculum integration in science, mathematics and technology. Curric. Perspect. 2001, 21, 9–16. [Google Scholar]
  68. McCormick, R.; Scrimshaw, P. Information and Communications Technology, Knowledge and Pedagogy. Educ. Commun. Inf. 2001, 1, 37–57. [Google Scholar] [CrossRef]
Sustainability 12 04802 g001 550
Figure 1. The research model.
Figure 1. The research model.
Sustainability 12 04802 g001
Sustainability 12 04802 g002 550
Figure 2. Results of the research model. * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 2. Results of the research model. * p < 0.05, ** p < 0.01, *** p < 0.001.
Sustainability 12 04802 g002
Table
Table 1. Descriptive statistics.
Table 1. Descriptive statistics.
Background InformationFrequency%
GenderFemale2442.6
Male3057.4
School LevelElementary school3768.5
Middle school1731.5
Age20–2535.5
26–301120.4
31–351527.8
36–401222.2
41–45713.0
46 (or older)611.1
Teaching Experience5 years or less23 42.6
6–10 years10 18.5
11–15 years11 20.3
16–20 years4 7.4
21–25 years3 5.6
26 years or more3 5.6
Table
Table 2. Item reliability and convergent validity analysis.
Table 2. Item reliability and convergent validity analysis.
Latent VariableIndicatorLoadingAlphaCRAVE
Continuance Intention
(CI)		CI10.9350.7910.9040.825
CI20.881
Perceived
Student Change
(PS)		PS10.9130.9160.9410.799
PS20.933
PS30.874
PS40.854
Integration Frequency
(IF)		IF10.7320.7660.8660.685
IF20.831
IF30.910
Effort toward Instructional Practices (EE)EE10.9510.9110.9570.918
EE20.964
Note: CR = Composite Reliability; AVE = Average Variance Extracted; Alpha = Cronbach’s Alpha.
Table
Table 3. AVE and the correlations of all constructs for discriminant validity.
Table 3. AVE and the correlations of all constructs for discriminant validity.
Latent DimensionsCIPSIFEE
Continuance Intention (CI)0.908
Perceived Student Change (PS)0.6850.894
Integration Frequency (IF)0.4460.5150.828
Effort toward Instructional Practice (EE)0.3810.4540.3300.958
Note: The bold diagonal elements represent the square root of AVE; the off-diagonal elements represent correlations between the constructs.
Table
Table 4. Cross-loading of discriminant validity analysis.
Table 4. Cross-loading of discriminant validity analysis.
Latent VariableIndicatorCIPSIFEE
Continuance Intention
(CI)		CI10.9350.6990.4690.390
CI20.8810.5240.3240.291
Perceived
Student Change
(PS)		PS10.5930.9130.5270.437
PS20.6140.9330.4910.341
PS30.6520.8740.4540.424
PS40.5880.8540.3610.422
Integration Frequency
(IF)		IF10.3020.3940.7320.237
IF20.3440.3120.8310.346
IF30.4460.5440.9100.249
Effort toward Instructional Practice (EE)EE10.3470.4130.2720.951
EE20.3810.4540.3540.964
Note: The bold diagonal elements represent the square root of AVE; the off-diagonal elements represent correlations between the constructs.
Table
Table 5. Hypotheses, path coefficients, and results.
Table 5. Hypotheses, path coefficients, and results.
HypothesisPathPath CoefficientT-Statisticsp-Valuef2Conclusion
H1IF → PS0.4103.6290.0000.232Supported
H2IF → EE0.3302.4000.0170.122Supported
H3EE → PS0.3192.6130.0090.141Supported
H4IF → CI0.1190.1200.3220.232Not supported
H5EE → CI0.0740.5240.6000.008Not supported
H6PS → CI0.6857.0600.0000.437Supported
Note: IF = Integration frequency; EE = Effort toward instructional practice; PS = Perceived student change; CI = Continuance intention.
Table
Table 6. A summary of mediation effects.
Table 6. A summary of mediation effects.
Mediation PathEffectT-Statisticsp-Value95% Bias-Corrected Confidence IntervalMediation Effect
IF → EE → CI0.0240.4470.655LLCI: −0.003, ULCI: 0.175No
IF → PS → CI0.2422.5920.010LLCI: 0.091, ULCI: 0.451Yes
EE → PS → CI0.1891.7250.085LLCI: 0.026, ULCI: 0.435No
IF → EE → PS → CI0.0621.1600.246LLCI: 0.003, ULCI: 0.212No
IF → EE → PS0.1051.4560.145LLCI: 0.008, ULCI: 0.288No
Note: IF = Integration frequency; EE = Effort toward instructional practices; PS = Perceived student change; CI = Continuance intention; LLCI = Lower-level confidence interval; ULCI = Upper-level confidence interval.

Share and Cite

MDPI and ACS Style

Kim, H.J.; Jang, H.Y. Sustainable Technology Integration in Underserved Area Schools: The Impact of Perceived Student Change on Teacher Continuance Intention. Sustainability 2020, 12, 4802. https://doi.org/10.3390/su12124802

AMA Style

Kim HJ, Jang HY. Sustainable Technology Integration in Underserved Area Schools: The Impact of Perceived Student Change on Teacher Continuance Intention. Sustainability. 2020; 12(12):4802. https://doi.org/10.3390/su12124802

Chicago/Turabian Style

Kim, Hye Jeong, and Hwan Young Jang. 2020. "Sustainable Technology Integration in Underserved Area Schools: The Impact of Perceived Student Change on Teacher Continuance Intention" Sustainability 12, no. 12: 4802. https://doi.org/10.3390/su12124802

APA Style

Kim, H. J., & Jang, H. Y. (2020). Sustainable Technology Integration in Underserved Area Schools: The Impact of Perceived Student Change on Teacher Continuance Intention. Sustainability, 12(12), 4802. https://doi.org/10.3390/su12124802

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop