Validation of the Smart City as a Sustainable Development Knowledge Tool: The Challenge of Using Technologies in Education during COVID-19

sustainability Article Validation of the Smart City as a Sustainable Development Knowledge Tool: The Challenge of Using Technologies in Education during COVID-19 María del Carmen Olmos-Gómez 1,* , Mónica Luque-Suárez 2,*, Soraya Mohamed-Mohamed 1 and Jesús Manuel Cuevas-Rincón1 1Department of Research Methods and Diagnosis in Education, Faculty of Education and Sport Science, University of Granada, 52005 Melilla, Spain; [email protected] (S.M.-M.); [email protected] (J.M.C.-R.) 2Department of Sociology, Faculty of Education and Sport Science, University of Granada, 52005 Melilla, Spain *Correspondence: [email protected] (M.d.C.O.-G.); [email protected] (M.L.-S.) Received: 2 September 2020; Accepted: 5 October 2020; Published: 12 October 2020   Abstract: The objective of this research was to design and validate a questionnaire for teachers on the knowledge and use of smart city concepts and their usefulness in online learning during the COVID-19 pandemic based on a sustainable approach at different educational levels. It is important to analyze the use of online education based on the construction of intelligent learning environments, which favor the interaction between the environment and the student. With a quasi-experimental methodology, the psychometric properties of the questionnaire were analyzed through structural equation modeling (SEM), and the model was adjusted through a multivariate regression analysis to relate response patterns to a set of latent factors that cannot be directly observed, but exist in continuous dimensions of the people evaluated, and to create a valid and reliable instrument as a measurement tool using a sample of n=973 subjects. The sample distribution consisted of 22.36% primary school teachers, 59.01% high school teachers, and 18.56% university teachers. The results showed a high reliability and construct validity through two models, and adjustment of the original model produced better goodness-of-fit parameters. We conclude that the designed questionnaire is a useful and valid tool for understanding how teachers have approached online teaching during the pandemic and their knowledge and use of Smart City concepts. Keywords: Smart Cities; ICT; structural equation modeling (SEM); readjustment; e-learning 1. Introduction Cities are constantly changing, and if we take into account the pandemic caused by the SARS-CoV-2 virus (COVID-19) [ 1 – 3 ] that is spreading worldwide, the pace of change is even faster. As a result of this new reality, information and communication technologies (ICTs) are gaining momentum, and the concept of the Smart City is beginning to be heard more and more frequently [ 4 , 5 ]. This is the central focus of our research, which was carried out during a health crisis that has led the governments of almost all countries to order lockdowns of cities and schools [ 6 , 7 ] to prevent the spread of the infection, with significant implications for the world of education. Today’s society is very sensitive to environmental problems generated by humans [ 8 ]. According to previous studies [ 9 ], the population is not educated in current issues related to sustainable development. However, scientifically competent citizens and professionals are needed to address environmental threats to sustainability. With the aim of addressing this need, social networks are becoming an increasingly popular area of research, and recent studies and literature have shown that the media is Sustainability 2020,12, 8384; doi:10.3390/su12208384 www.mdpi.com/journal/sustainability Sustainability 2020,12, 8384 2 of 18 an effective tool to facilitate learning about sustainable development (SD) [ 10 – 17 ]. At first, the term Smart City focused on the relevance of ICTs for the achievement of a more competitive status and a more sustainable development [ 18 ]. When reviewing the literature on the concept of the Smart City, we encounter considerable controversy [ 19 – 24 ]. There are authors who associate this term with the cultural and professional growth of the members of a society who live in the same environment [ 25 ], while other authors associate it with the impact of ICTs and the expansion of their infrastructure [26]. The central definition used in our research, though, would be based on the consideration of Smart Cities in relation to ICTs and the opportunities that they provide for the benefit of citizens [ 27 ]. Therefore, the impact of the Smart City in education is important, since it saves costs in its management by using technological advances [28]. In essence, the idea of Smart Cities, according to the European Parliament [ 29 ], is based on the creation and connection of human capital, social capital, and infrastructure of information and communication technologies (ICTs) to promote a sustainable economic development and a better quality of life, understanding that Smart Cities comprise six areas: smart governance, smart people, smart living, smart mobility, smart economy, and smart environment. In Figure 1, we can observe how the information systems of Smart Cities will help improve infrastructures and develop a communication system that operates throughout such cities, providing an information and knowledge network in response to the services available. Thus, this convergence between ICTs and Smart Cities will, in turn, promote new ecosystems within the latter [30]. Figure 1. Developed from mapping of Smart Cities in the EU. In this research study, we focused on Smart Cities as platforms that allow maximization of the economy, environment, and well-being of citizens, and they also facilitate the transformation of the cities mentioned above towards a more sustainable behavior [ 31 , 32 ]. In order to do so, education authorities are responsible for developing online portals and web-based applications to deliver lectures or other teaching activities as a means of ensuring that education continues for the remainder of the academic year [ 33 ]. In order to achieve such a citizenry, it is necessary to enhance their education through the Sustainability 2020,12, 8384 3 of 18 use of alternative learning strategies, that is, by making use of technology such as ubiquitous learning, understood as “the possibility of accessing information anywhere or anytime” [ 34 ], cooperative learning [ 35 ], exploratory learning outside the classroom [ 36 ], and game-based learning [ 37 ]. Faced with this pandemic scenario, public administrations have to consider an evolution in city management models. For this, the use of ICTs is essential, and this translates into the Smart City concept, which, with its services, advances what is called the Internet of Things (IoT), that is, the Internet of the future [ 38 , 39 ]. Nowadays, with the growing popularity of multimedia and mobile devices, the IoT is becoming a reality, and mobile technology is beginning to enable new forms of learning environments that can be useful for students in different educational contexts, as well as for teachers [ 40 , 41 ]. Nowadays, the concept of Smart is used in educational research, giving rise to new concepts such as Smart Learning Environments, Smart Learning, Smart Education, Smart Classrooms, and Smart Universities [42]. This COVID-19 situation has marked a turning point in the way of facing learning as well as in the way of teaching. Therefore, health authorities should consider providing online psychoeducation [ 43 ], focusing on the level of know-how with regard to information and communication technologies (ICTs), attitudes, and planned future uses, as well as the actual ICTs used by teachers and teenagers in the knowledge society [44–46]. In addition, we must bear in mind that these virtual environments favor and promote social interaction, since they provide a collaborative learning framework between teachers and students [ 47 ]. Virtual classroom formats that can be used for comprehensive education include videos, forums, video calls, and photos. These technologies are characterized by the ability to save and broadcast a lesson in an electronic format [48]. It is precisely based on this approach that the use of mobile technologies plays a key role in teaching, although the vulnerability of these devices to privacy threats and data exploitation as a result of cyberattacks must be taken into account [49]. Therefore, as the educational potential of mobile social networks increases [ 50 ], their educational possibilities should be explored [51]. As a result of the COVID-19 pandemic, which is an unprecedented situation for all sectors of society and for education in particular (the subject addressed in this study), face-to-face classes have been urgently adapted to a remote format. In other words, what was first proposed as a replacement for teaching activities has led to non-face-to-face evaluation scenarios [ 52 , 53 ], which have encouraged a wider use of mobile technologies and the Internet. We are currently immersed in a knowledge society, characterized by information and communication technologies (ICTs) and globalization, which is based on knowledge rather than on the search for and collection of information [ 54 ]. In our society, technological advances are continuous and constant, which has created a “digital culture” [ 55 ]. Some teachers may have experienced apprehension when transitioning to online classes, but most of them have done so at a rapid pace and in a short period of time; in the long run, they all seem to be adjusting well, although the digital gap is more evident than ever [56]. Based on the considerations stated above, and as a novel contribution, this research aimed to: (1) Design and validate an instrument in order to analyze the impact of an online learning questionnaire, a Smart City tool, on teaching during a lockdown. This is important because no instrument currently exists to examine these parameters. A questionnaire was designed and validated in compliance with the established psychometric requisites for reliability. In order to confirm these characteristics, structural equation modeling (SEM) was used. This methodology enables latent analysis, which is consistent with the use of multivariate regression to relate patterns of answers to a group of factors that are not directly observed but exist in the people assessed [57]. (2) Obtain descriptive results for use of the Smart City tool of online learning in teaching during a lockdown. Sustainability 2020,12, 8384 4 of 18 2. Materials and Methods 2.1. Method Since the aim was to use a questionnaire to determine the acquired knowledge that exists with respect to ICT applications [ 58 ], how they are used by teachers, and the general use of links for accessing information on learning in a Smart City, this research used a quantitative methodology. The questionnaire was tested for its rigor, adaptability to context, validity, reliability, and its capacity to offer objective data with respect to the studied phenomenon. The two psychometric properties that should be demanded of any measurement tool are reliability and validity, which are both essential for making inferences from the relevant data and making decisions accordingly. A tool is considered trustworthy when various measurements of the same attribute or characteristic are internally coherent. The more stable the measurements of an attribute, the greater the reliability of the tool over repeated measurements. Mathematically, reliability is defined as the proportion of the true variance in the test results. This research used the questionnaire as a tool to collect data on opinions or information among teachers of education during COVID-19 on the use of the Smart City tool of an online learning questionnaire [ 58 – 60 ]. The aim was to establish theoretical processes, metrics, and validity to maximize the inferences that could be made from the questionnaire’s use. However, the scores of questionnaires, from a methodological and statistical point of view, may contain errors arising from multiple sources, which have to be minimized through various procedures [ 61 ]. To minimize such possible errors, a type of inferential analysis was performed that uses multivariate regression to relate response patterns to a set of latent factors that are not directly observed, but are detected in accordance with the substantive theory [ 57 ]; that is, an analysis based on the structural equation modeling (SEM) within a factor analysis was carried out. These analyses allowed us to test the constructed model to establish a hypothesis on the structure of the construct [ 62 ]. In addition, with the readjustment of the SEM model, some procedures and technical criteria were provided for the validation of model measurements [ 63 ] to establish that a conditional independence exists, understood as a set of mutually independent latent factors quantified by means of a dimensional structure based on substantive theory, which postulates the presence of a causal effect in the answers of the sample. The modeling of structural equations is a type of inferential analysis in which multivariate regression is used in order to relate response patterns to a set of latent factors that cannot be directly observed, but exist in continuous dimensions of the people evaluated. It also provides the most robust technical procedures and criteria for the validation of a questionnaire, specifying the latent features and the dimensions that represent them as variables of interest (factors) and relating the validated evidence with the dimensional structure of the instrument under evaluation through goodness-of-fit criteria and indices, which are complemented by a re-specification of the measurement model in order to improve the fit of the model by adding or eliminating relationships between the factors, all based on the analysis of the covariance structures of the observed variables and those reproduced by the model [63]. The first phase begins with the design of the questionnaire, and ends with the analysis of the information that it provides. In this analysis, the structure of the relationship between the latent variables represented by the dimensions of the instrument (knowledge of computer programs, use of ICTs, impact that ICTs have on the teaching process, learning style, the concept of smart cities, and knowledge and use of video and audio editing programs) and the responses to the items of the context questionnaire was established. In this model, the latent factors are the dimensions (factors) proposed in Figure 2, and the observed variables are the values obtained from the responses to the items of the questionnaire. Sustainability 2020,12, 8384 5 of 18 Figure 2. Schematic (path graph) of the Questionnaire on the Knowledge and Use of the Smart City and Its Usefulness in Online Learning (SCOLQ). Model 1 (M1). The second confirmatory phase involves analyzing the covariance structure in the database containing the 24 observed variables and trying to obtain evidence of validity to confirm that the measurement model coincides with the conceptual structure postulated in the dimensionality theory (factors were reduced from 6 to 5 to achieve a better goodness of fit) of the questionnaire when comparing two covariance structures. The first one is referred to as the covariance matrix of the observed variables (five factors). The second is known as the covariance matrix implied by the model (24 items). This distinction is observed in the graphical representation of the theoretical–conceptual structure of the instrument shown in Figure 2. We observed the weights of the coefficients and their relationship, finding out that when the latent variable increases, the observed variable increases too, according to the weight of the coefficient [ 63 ]. Finally, the re-specification of the model confirms the structure of four factors and 21 items as the final validation result, since the fit indices are more appropriate (Figure 3). According to Zhu and He [ 64 ], smart education, or intelligent education, is based on the construction of intelligent learning environments through the use of intelligent technology, making it possible to use intelligent pedagogies that offer individualized learning. This requires sharp thinking, considerable behavioral competence, and appropriate orientation; thus, the first dimension of our questionnaire was based on knowledge of computer programs and their use in the practice of teaching. All items linked to the knowledge and use of basic programs, blogs, etc. were integrated in this dimension. A smart learning environment favors the interaction between the environment and the student. Such learning cannot take place without the support of smart learning environments [ 65 ]. Middleton [ 66 ] pointed out that intelligent and personalized technologies encourage the participation of students in their own learning process, thus increasing autonomy in the aspects of connectedness and openness and resulting in more enriched personal environments. Sustainability 2020,12, 8384 6 of 18 Figure 3. Schematic (path graph) of the Questionnaire on the Knowledge and Use of the Smart City and Its Usefulness in Online Learning (SCOLQ). Model 2 (M2). In Liu, Hwang, and Wosinki’s [ 67 ] study on a smart learning environment, each student had a smart mobile device. This device allowed for annotations and comments, bookmarking, and access to multimedia links. This arrangement is called an e-textbook. In addition to offering an enrichment of learning, the device provided information on the evolution of student learning; the data were stored in the cloud, student performance was analyzed, and the results were presented through statistics. We also designed a third dimension that focused on the impact of ICTs on the teaching process. Intelligent learning requires the development an intelligent classroom that involves learning to encourage, collaborate, interact, and carry out self-directed learning through ICTs [ 68 ]. Today, the concept of “smart” is widely used in educational research, resulting in the creation of new concepts, such as the smart learning environment, smart learning, smart education, smart classroom, and smart university [ 69 ]. Its development is of vital importance in teaching. Thus, in the last section of the questionnaire, we developed items focused on the knowledge and use of Smart City tools as well as their usefulness in online learning. Although it is possible that the results obtained via a questionnaire include a certain margin of error for various reasons, this is reduced when the psychometric parameters are rigorously defined. This part of our research was based on a quantitative study using social/analytical/empirical research of a descriptive nature [70]. 2.2. Participants The sample participants were selected on a non-probabilistic basis. The study cohort was composed of teachers belonging to different educational cycles: primary, secondary, and university levels. The non-probabilistic sampling (convenience) data comprised a total of n=973 teacher participants. The sample distribution consisted of 22.36% primary school teachers, 59.01% high school teachers, and 18.56% university teachers. With respect to the religion variable, n=281 (28.87%) were Muslim, Sustainability 2020,12, 8384 7 of 18 n=509 (52.31%) were Catholic, n=12 (1.23%) were Jewish, n=169 (17.36%) were atheists, and n=2 (0.20%) were Hindu. The ages of participating teachers ranged between 28 and 63 years old (mean =48.5 years). A total of n=381 were male (39.15%) and n=592 were female (60.84%). 2.3. Instrument Each participant completed a modified version of the questionnaire on the smart city tool of online learning [ 60 ], limited and adapted to the border context of the city of Melilla and modified for the variable of religion. To select the initial items to be included in the questionnaire, a group of students (n=502) with largely similar characteristics to those of the sample respondents were asked to indicate all the items they considered to be best suited to the purposes of the research. After collecting this information, we proceeded to validate the items using an expert panel analysis via Delphi’s method [ 71 ]. Six experts assessed the instrument. They indicated when an item was unclear or poorly worded and made comments on its relevance, congruence, and adequacy. The experts who participated as judges in the content validation process were professors with PhDs in the areas of research, evolutionary psychology, and social sciences, which guaranteed their expertise in this process [ 71 , 72 ]. The final version of the instrument was developed in the exploratory phase. For this purpose, three rounds of content validity analysis were carried out among the experts. The percentage of agreement was 78% in the first round of validation, 81% in the second round, and 91% in the third round. The survey had two parts, with the first part devoted to sociodemographic information: age, gender, and religion. The second part included questions on the frequency of expressed opinions on the elements included in the questionnaire on a four-point Likert scale, where 1 =“none” and 4 =“a lot”. An exploratory factor analysis (EFA) was applied to test the validity of the construct. Factor analysis of the variables was carried out to ensure the validity and reliability of the tool. Validity was measured using the calculated Kaiser–Meyer–Olkin index (KMO), the value of which must be over 0.5. In this case, the KMO was 0.900, close to 1, indicating that it was appropriate to continue analyzing the validity and reliability of the questionnaire. The Bartlett sphericity test was used to ensure that the significance was adequate (p=0.000), allowing us to proceed to the factor analysis. A table of variance in a rotated component matrix provided the variance over all the variables studied. The results of the analysis highlighted five factors that explained the variance, with a total sum value of 68.051%. 2.4. Procedure Data Collection Procedure The questionnaire was administered over the months of March and April in 2020 and was approved by the directors of the participating schools, who collaborated in the research and indicated the best periods for data collection. The cooperation and social responsibility department of the University of Granada approved the questionnaire. Before completing the questionnaire, participants were provided with the following statement: “Before answering the following questionnaire, it is essential that you provide us with your informed, unequivocal, and expressed consent regarding the privacy and data protection regulations, in accordance with the provisions of Regulation (EU) 2016/679, of 27 April 2016(GDPR)”. Additionally, the ethical principles of the Declaration of Helsinki were followed at all times. The questionnaires were handed out to the students with a brief presentation and then read as a group to allow for any questions asking for clarification. Each questionnaire took approximately 25 min to fill out. Statistical analysis was carried out to establish construct validity and reliability of the questionnaire. We guaranteed that anonymity would be preserved throughout and that all information would be used for purely scientific research purposes. The information collection instrument was composed of 29 items grouped into five sections. The first section consisted of data identifying the participants (sex, age, and educational stage). For Sustainability 2020,12, 8384 8 of 18 the remaining four sections, a Likert scale was used. The scores were coded from 1 to 4 (1 =“none”; 2=“some” ; 3=“quite a bit” , and 4 =“a lot”). Four values were chosen to avoid intermediate values. 2.5. Data Analysis For the descriptive analysis and the exploratory factorial semi-confirmatory analysis of internal consistency, SPSS 24.0 (SPSS Statistics 24.0 Chicago, IL, USA, 2016) was used as the statistical program. For confirmatory factor analysis (CFA) of structural equation modeling (SEM), the statistical program LISREL v9.1 (Scientific Software International, Princeton, NJ, USA, 2010) was used. 3. Results The descriptive results of the response percentages and their standard deviations were obtained for the different questions. The standard deviations of the items were higher than 0.63, which reflected an appropriate discriminability. Likewise, the corrected item total correlation was positive for all items, which indicated a strong linear correlation, with values between 0.097 and 0.791 (Table 1). Table 1. Descriptive results of the Questionnaire on the Knowledge and Use of the Smart City and Its Usefulness in Online Learning (SCOLQ). Item Percentage (%) ¯ x 1 2 3 4 SD RI-t 1 3.1 9.9 36 50.9 3.34 0.784 0.654 2 1.9 11.2 32.9 54.04 3.39 0.759 0.504 3 3.1 8.7 29.2 59.0 3.44 0.781 0.590 4 9.3 39.8 26.1 24.8 2.66 0.954 0.575 5 1.9 9.3 28.0 60.9 3.47 0.743 0.566 6 73.9 19.9 5.0 1.2 1.33 0.631 0.228 7 5.6 8.1 28.0 58.4 3.39 0.860 0.648 8 3.1 11.2 32.3 53.4 3.36 0.802 0.550 9 5.6 18.3 27.3 48.4 3.18 0.930 0.692 10 22.4 29.8 25.5 22.4 2.47 1.07 0.575 11 5.0 13.7 23.0 58.4 3.34 0.896 0.577 12 17.4 23.0 34.2 25.5 2.67 1.040 0.654 13 16.8 17.4 34.8 31.1 2.80 1.059 0.611 14 5.0 13.0 31.7 50.3 3.27 0.873 0.643 15 11.8 21.1 31.7 35.4 2.90 1.017 0.665 16 6.2 11.8 30.4 51.6 3.27 0.901 0.791 17 7.5 16.1 40.4 36.0 3.04 0.906 0.705 18 17.4 26.1 35.4 21.1 2.60 1.007 0.562 19 5.6 19.9 41.6 32.9 3.01 0.869 0.653 20 7.5 19.3 37.3 36.0 3.01 0.925 0.617 21 47.2 25.5 18.6 8.7 1.88 0.999 0.369 22 13.0 19.9 45.3 21.7 2.75 0.940 0.733 23 11.2 15.5 41.6 31.7 2.93 0.959 0.696 24 8.7 10.7 30.4 50.3 3.22 0.955 0.672 25 54.0 24.2 16.8 5.0 1.72 0.915 0.097 26 16.1 32.9 36.0 14.9 2.49 0.936 0.384 27 14.3 32.3 32.9 20.5 2.59 0.970 0.525 28 11.2 25.5 47.8 15.5 2.67 0.870 0.593 29 45.3 29.8 16.1 8.7 1.88 0.977 0.366 Note: Percentage of the scale, mean, standard deviation, and corrected item total correlation. The scale values are 1=“none” , 2 =“some”, 3 =“quite a bit” and 4 =“a lot”; SD =standard deviation. RI-t =corrected item total correlation. With regard to the results obtained for the respondents in the sample for each of the items in the Questionnaire on the Knowledge and Use of the Smart City and Its Usefulness in Online Learning (SCOLQ), it was observed that teachers of education during the COVID-19 pandemic did not follow Sustainability 2020,12, 8384 9 of 18 a normal distribution (Table 2). This was indicated by the indices of asymmetry, kurtosis, and the Kolmogorov–Smirnov test. All the items expressed negative asymmetry, which implies that the professionals tended to select high values on the Likert scale, except for the following items: 4, 5, 6, 10, 21, 25, and 29. These items showed positive asymmetry, which means that the participants tended to choose low values on the scale. Table 2. Asymmetry, kurtosis, and Kolmogorov–Smirnov test results for the Questionnaire on the Knowledge and Use of the Smart City and Its Usefulness in Online Learning (SCOLQ). Items Asymmetry Kurtosis Z p 1−1.095 0.715 3.887 0.000 2−1.054 0.424 4.173 0.000 3−1.354 1.276 4.478 0.000 4 0.064 −1.276 3.140 0.000 5 1.317 1.097 4.664 0.000 6 1.998 3.861 5.600 0.000 7−1.389 1.177 4.369 0.000 8−1.113 0.569 4.078 0.000 9−0.806 −0.462 3.726 0.000 10 0.057 −1.243 2.460 0.000 11 −1.168 0.294 4.447 0.000 12 −0.263 −1.089 2.768 0.000 13 −0.455 −1.003 2.954 0.000 14 −1.020 0.200 3.813 0.000 15 −0.496 −0.903 2.699 0.000 16 −1.088 0.290 3.876 0.000 17 −0.710 −0.269 3.072 0.000 18 −0.173 −1.037 2.773 0.000 19 −0.556 −0.410 3.005 0.000 20 −0.613 −0.520 2.853 0.000 21 0.759 −0.646 3.614 0.000 22 −0.455 −0.615 3.457 0.000 23 −0.648 −0.471 3.283 0.000 24 −1.073 0.140 3.743 0.000 25 0.967 0.140 4.147 0.000 26 −0.037 −0.861 2.713 0.000 27 −0.068 −0.971 2.491 0.000 28 −0.356 −0.474 3.531 0.000 29 0.810 −0.448 3.427 0.000 Note: Z =Kolmogorov–Smirnov Z-statistic; p=bilateral significance test. Regarding the analysis of kurtosis, the indices are negative except for Items 1, 2, 3, 6, 7, 8, 11, 14, 16, 24, and 25. This showed that the scores are more grouped and located above the normal distribution curve. Exploratory analysis is a factorial analysis that measures construct validity within an instrument or scale using the principal components method with varimax rotation. Exploratory factor analysis was carried out using a Kaiser–Meyer–Olkin index calculation extraction method and the Bartlett sphericity test (KMO =0.900; 2 was 3045.648 for 406 degrees of freedom, significant at 0.000). The result of this initial analysis suggested that six values explained 68.051% of the total variance. The following table (Table 3) shows the results obtained in the component transformation matrix. 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