Sustainable Development Goals from Theory to Practice Using Spatial Data Infrastructure: A Case Study of UAEU Undergraduate Students

Abstract

The importance of Education for Sustainable Development (ESD) in influencing students’ understanding of and behavior toward sustainability cannot be overstated. However, prior studies have shown that students generally lack comprehension of how to apply geography instruction in relation to territories and their contribution to the Sustainable Development Goals (SDGs). Although the relationship between the SDGs and academic institutions has been the subject of numerous research, it is still unknown how much knowledge students have on the subject. Therefore, this research aims to raise awareness about SDGs and evaluate the knowledge of undergraduate geography students regarding the SDGs. Additionally, it investigates the impact of Spatial Data Infrastructure (SDI) and quality education as pedagogical tools on students’ sustainability consciousness. A questionnaire was designed, validated, and administered to students at the United Arab Emirates University from both geography and non-geography cohorts to assess their knowledge. Statistical analysis indicated high reliability of the constructs. The results revealed significant insights through descriptive, ANOVA, and multiple comparisons analysis with the Tukey HSD test. Specifically, geography students who participated in an SDG Awareness and Knowledge Program within the Technological Pedagogical Content Knowledge (TPACK) model demonstrated statistically significant differences in various aspects of SDG awareness and knowledge, SDG awareness using SDI skills, and the importance of quality education and the integration of SDG knowledge in pedagogy, as compared to other geography and non-geography students who did not receive the SDG awareness program. This research is expected to provide valuable knowledge about SDGs through effective pedagogical skills, benefiting both student and educator community.

1. Introduction

In order to improve the standard of living for future generations, sustainable development—the UN’s overarching paradigm—seeks to develop environmental, social, and economic goals in a balanced manner [1]. To maintain the well-being of both humanity and the planet, the United Nations General Assembly’s 70th session outlined 17 Sustainable Development Goals (SDGs) [2], a framework for future global development from 2015 to 2030 after the Millennium Development Goals (MDGs) expired [3]. The fourth goal, quality education, emphasizes the significance of education for the advancement of global sustainability [4]. The objective, which aims to “ensure inclusive and equitable quality education and promote lifelong learning opportunities for all”, is essential to achieving all other objectives [5]. From this angle, Goal 4 implies that by the year 2030, everyone in the world will be able to access high-quality education and lifelong active learning [6]. It also aims to enhance knowledge, skills, and competencies [7]. Students should be able to consider alternative global views from both domestic and international perspectives considering the age of internationalization [8]. Education institutions, schools, teachers, and instructors have a responsibility and duty to help students develop into global citizens and contribute to sustainable global development [9].

The majority of research on SDG awareness has focused on adults or college students, while little open research has examined how university students, particularly UAE Students, understand and are aware of the SDGs [10,11]. As a result, educational institutions and teachers must help students keep track of global issues and develop a sustainability consciousness [12].

If education or intervention are lacking, senior high school students’ awareness and knowledge of the SDGs will be constrained [13]. Even though Education for Sustainable Development (ESD) is widely accepted and used throughout the world, there is little research on its outcomes and efficiency [14]. The current knowledge level and cognition should be examined to further develop educational theory and behavior in order to foster the sustainability competencies of university students and to measure the efficacy of ESD.

Education undeniably plays a vital role in the attainment of the SDGs [15]. The integration of the SDGs into education at various levels contributed significantly to their achievement [16]. Moreover, different educational stages and training programs can be implemented to enhance awareness and foster the advancement of the SDGs. Higher education institutions play a pivotal role in the attainment of the SDGs [17]. Numerous authors have emphasized the significance of these institutions in relation to the SDGs [18]. Consequently, university activities can be directed towards social transformation and the pursuit of the SDGs. Examples of such transformative universities include initiatives like transdisciplinary research addressing societal needs, knowledge dissemination, and the implementation of teaching methodologies like service learning [19].

Within the realm of teaching, higher education institutions play a crucial role in fostering the development of competencies among students. The term “competence” refers to the integration of knowledge, skills, and attitudes that can be applied within a specific context [20]. University education can be geared towards equipping students with professional skills (specific competences) or can adopt an integrated approach by nurturing both specific and cross-cutting competences [21].

The United Arab Emirates (UAE) understands the importance of educational institutions in promoting sustainable development and increasing student awareness in order to successfully integrate the SDGs into society. Education is crucial in influencing people’s attitudes, actions, and behaviors toward sustainability. The UAE recognizes the significance of increasing university students’ awareness and knowledge of the SDGs. In order to engage and empower university students in the pursuit of the SDGs, initiatives are being taken because it is widely acknowledged that youth are the driving force behind sustainable development. These programs are designed to give students the chance to learn about the various SDGs, comprehend how they are interconnected, and consider ways to help them achieve them. The UAE wants to foster a generation that is actively involved in creating a sustainable future by educating university students about the SDGs.

This research stands out for its innovative approach in several dimensions. First, it addresses a gap in existing literature by focusing on undergraduate geography students’ understanding of the Sustainable Development Goals (SDGs). While previous studies have explored SDG awareness among adults and college students, there is limited open research on how university students, particularly in the UAE, comprehend and are aware of the SDGs. This research contributes valuable insights in this specific context. Second, the research integrates Spatial Data Infrastructure (SDI) and quality education as pedagogical tools to enhance students’ sustainability consciousness. By combining SDI with advanced technologies like ArcGIS Online, the study promotes territorial knowledge and fosters students’ competencies within a technological environment. This innovative combination of pedagogical and technological elements enhances the effectiveness of the SDG Awareness and Knowledge Program. Additionally, the research adopts the Technological Pedagogical Content Knowledge (TPACK) model to design and implement the SDG awareness program. This integration of TPACK with SDI skills provides a cutting-edge approach in the field of education, fostering innovation in disciplinary perspectives and pedagogical methodologies.

Overall, the study evaluates the impact of the SDG awareness program on geography students compared to non-geography students through statistical analysis. This comparative analysis adds to the uniqueness of the research, providing valuable insights into the effectiveness of the program in enhancing SDG awareness among students from different academic backgrounds. This research’s novel aspects also lie in its focus on the integration of SDI and Quality Education as pedagogical tools, the utilization of the TPACK model, and the comparative analysis of the program’s impact on different student groups. These innovative dimensions contribute significantly to the field of Education for Sustainable Development and offer valuable insights for future actions in promoting SDGs within higher education institutions.

This article’s remainder is structured as follows: Section 2 defines the research objective and hypothesis, Section 3 describes the methodology, and Section 4 presents the results and analysis. Section 5 discusses the principal conclusions and discussions in conclusion.

2. Theoretical Background

2.1. The Role of Education in Attaining SDGs

When referring to “competence” in the context of higher education, it is the integration of information, abilities, and attitudes that may be used in a particular situation [22]. This is consistent with the definition offered by [23], who defined competence as the fusion of real-world experience and academic understanding that enables an individual to complete a given task successfully. There is substantial literature showing how university education can foster specific and cross-cutting competences. Ref. [24] argued that universities could adopt a comprehensive approach to education by equipping students with both specific professional skills and broader, transferable competences. In [25], a model called Higher Education for Sustainability Competencies (HESC) has been developed to evaluate the theoretical and practical knowledge achieved by students through their university educational path and environment. The authors investigated the efforts made by lecturers with a non-education background in a large Indonesian university of education to develop their pedagogical competence, and made recommendations based on the results of the research [26].

2.2. Importance of Higher Education Institutions for SDGs

Several authors have emphasized the significance of higher education institutions in relation to the SDGs. Ref. [27] contended that universities, through their teaching, research, and service functions, could play a transformative role in achieving the SDGs. In addition, Ref. [28] highlighted that the integration of SDGs into university curricula worldwide significantly contributes to their achievement. In [29], the authors proposed the adoption of dialogical and developmental approaches in a single action case, the SDGs Seminars at the Universidad Politecnica de Madrid, to diagnose organizational and individual readiness for change considering cognitive, affective, and behavioral components, and identify consequences in organizational structures and culture. Additionally, in [30], the authors developed an assessment framework for educational institutions to evaluate the contribution of their educational programs to sustainability by reviewing the alignment of their intended learning outcomes to the enabling conditions for a vision of sustainability based on the Sustainable Development Goals (SDGs).

2.3. University Education and Competence Development

When referring to “competence” in the context of higher education, it is the integration of information, abilities, and attitudes that may be used in a particular situation [22]. This is consistent with the definition offered by [23], who defined competence as the fusion of real-world experience and academic understanding that enables an individual to complete a given task successfully.

There is substantial literature showing how university education can foster specific and cross-cutting competences. Ref. [31] argued that universities could adopt a comprehensive approach to education by equipping students with both specific professional skills and broader, transferable competences. In [32], a model called Higher Education for Sustainability Competencies (HESC) has been developed to evaluate the theoretical and practical knowledge achieved by students through their university educational path and environment. The authors investigated the efforts made by lecturers with a non-education background in a large Indonesian university of education to develop their pedagogical competence and made recommendations based on the results of the research [33].

2.4. UAE’s Initiative in Promoting SDGs

The UAE government has recognized the importance of promoting sustainable development and has implemented several initiatives to increase awareness and knowledge of the SDGs among university students [34]. This assertion is consistent with reference [22] findings, which highlight the UAE government’s role in integrating the SDGs into higher education curricula. In [23], the authors presented an analysis of the sustainable development goals in the Arab region with particular emphasis on the example of the UAE, and analyzed projects and their implementation, including Vision 2021, the Green Economy initiative, National Innovation Strategy, the Energy 2050 Strategy, initiatives for tolerance and world peace, humanitarian aid, or activities related to the fight against climate change. The United Arab Emirates (UAE) stands out as one of the top donors globally based on its contributions compared to its Gross National Income (GNI) as mentioned in this paper, and the current status of UAE’s cooperation to International Development and the country’s Official Development Assistance (ODA) to the Sustainable Development Goals (SDGs) will be determinant in order to understand the relations with the Caribbean region and the prospects for future collaboration [24].

2.5. The Impact of SDG Awareness Program

Several studies have demonstrated the effectiveness of SDG awareness programs or frameworks [25,26]. For instance, Refs. [3,11,35] found that such a program significantly improved students’ understanding and awareness of the SDGs. In [10], the authors assess the level of awareness and knowledge on SDGs among university students in Yogyakarta, Indonesia and find that 89.5% of students are aware and 62.5% of students have high knowledge about SDGs. A study aimed at assessing the level of understanding on sustainable development goals (SDGs) among university students at scientific colleges in Yarmouk University, Jordan found that scientific students understand the scope of SDGs and they can contribute to support, encourage, and achieve the development of these goals [36].

2.6. Innovative Approaches to Teaching SDGs

This study’s innovative approach is supported by previous studies that have used similar methods. For example, Refs. [37,38,39,40] demonstrated that the use of Spatial Data Infrastructure (SDI) significantly enhanced students’ sustainability consciousness. Additionally, in [39], the integration of geography and GIS through teaching experimentation, as applied to a real case study in the Apulia region (Monti Dauni Area) under the national strategy for inner areas, can be observed to understand how to use GIS as an active tool in education for sustainability as well as the awareness of the value of local resources and active citizenship. Moreover, Refs. [41,42,43] supported our assertion that integrating the TPACK model into the teaching process can foster innovation in disciplinary perspectives and pedagogical methodologies.

2.7. Quality Education and the Integration of SDG Knowledge in Pedagogy

The significance of quality education (SDG 4) has been widely recognized in academic literature. The United Nations itself notes the role of quality education as a “foundation to improving people’s lives and sustainable development” [44]. Further, in their extensive research, Hanushek and Woessmann in [45,46,47] demonstrated that the quality of education, rather than mere access to education, is more significantly associated with individual earnings and countries’ economic growth.

Incorporating SDGs into pedagogy is not merely an additive measure but rather an essential framework to prepare students for the multifaceted challenges of the 21st century. According to UNESCO (2017), Education for Sustainable Development (ESD) is crucial for equipping learners with the knowledge, skills, values, and attitudes needed to contribute to a more inclusive, just, peaceful, and sustainable world. ESD, in essence, integrates all SDGs into learning [45,46].

This literature review provides substantial evidence to support our arguments and research proposals. It highlights the critical role of education in attaining SDGs, the significance of higher education institutions in promoting SDGs, and the potential impact of innovative teaching methodologies on enhancing SDG awareness among university students.

3. Research Objectives and Hypothesis

This research is being conducted at United Arab Emirates University (UAEU), located in Al Ain, Abu Dhabi, UAE. The aim of this study is to understand the knowledge and awareness of the SDGs among UAEU students, as well as their prior knowledge and information sources on the subject. Investigations are also carried out to ascertain the effect of spatial data infrastructure as a pedagogical tool on students’ sustainability consciousness, the significance of quality education, and the incorporation of SDG knowledge in pedagogy. All these objectives are fulfilled under the paradigm of quality education with a focus on providing a comprehensive and well-rounded learning experience along with the necessary knowledge and awareness regarding SDG and Spatial Data Infrastructure (SDI).

• To assess the level of knowledge and awareness of the Sustainable Development Goals (SDGs) among undergraduate students at United Arab Emirates University (UAEU).
• To explore the sources of information and prior knowledge that UAEU students have about the SDGs.
• To examine the impact of using Spatial Data Infrastructure (SDI) as a pedagogical tool on students’ sustainability consciousness.
• To compare the differences between student groups participating in an SDG Awareness and Knowledge Program and those who do not.

Based on these objectives the development of the Hypotheses of this research are as follows:

Drawing from prior research on education and sustainable development, it is hypothesized that there exists a positive association between undergraduate students’ knowledge of the Sustainable Development Goals (SDGs) and their exposure to SDG-related content across different academic groups. Literature suggests that immersive exposure to SDGs fosters greater understanding and engagement among students [3,48,49,50,51]. Additionally, students’ knowledge and information sources related to the SDGs are limited [3], highlighting the need for comprehensive formal and non-formal education to promote learning about the SDGs. Based on the reviewed literature, the subsequent hypothesis for the study is proposed:

Hypothesis 1 (H₁). 

Undergraduate students’ knowledge of the SDGs is positively associated with their exposure to SDG related content across academic groups.

The utilization of Spatial Data Infrastructure (SDI) as an instructional tool significantly enhances students’ awareness of sustainability matters within diverse academic groups. Existing studies highlight the potential of innovative pedagogical methods, such as incorporating geospatial information, to elevate students’ understanding of environmental and social issues [37,38,52,53,54]. By incorporating SDIs and geospatial technology, students can explore spatial patterns, conduct geographic analysis, and gain a deeper understanding of sustainability issues [55]. Therefore, the above literature postulates the hypothesis for the following study:

Hypothesis 2 (H₂). 

The utilization of Spatial Data Infrastructure (SDI) as a pedagogical tool significantly influences students’ sustainability consciousness across academic groups.

The effective integration of SDGs knowledge within pedagogical practices can significantly elevate the quality of education that aligns with the Sustainable Development Goals across a wide range of academic groups. Previous research underscores the pivotal role of education systems in nurturing a holistic understanding of sustainability [26,28,56]. An interdisciplinary pedagogical approach that embeds sustainability can stimulate students’ problem-solving competencies for sustainability-related issues and develop their strategic competencies, including systems thinking and anticipatory competencies [57]. Embedding sustainability in university curricula requires an interdisciplinary approach that analyzes the interconnection between the SDGs, subject learning points, and relevant aspects of sustainability [58]. Teacher education institutions can integrate SDGs in their curriculum by training students in the integration of SDGs in educational projects, such as Educational Robotics, and reinforcing their Teacher Digital Competence [59]. Therefore, the above literature postulates the following hypothesis for the study:

Hypothesis 3 (H₃). 

The Quality Education and the Integration of SDG Knowledge in pedagogy hold significant importance across academic groups.

By aligning the hypotheses with existing literature, this study aims to contribute to a comprehensive understanding of the relationship between knowledge of SDGs, pedagogical tools, and sustainability consciousness among undergraduate students.

4. Methodology

4.1. SDG Awareness and Knowledge Program

To provide SDG awareness and assess student’s knowledge related to it. Students from the geography department are given awareness and knowledge regarding the SDGs by incorporating the Technological Pedagogical Content Knowledge (TPACK) [60] model. The workflow of the SDG Awareness and Knowledge Program model based on the TPACK model applied in this research study is shown in Figure 1.

Figure 1 shows seven different modules based on the TPACK model to enhance awareness and knowledge among geography students during a class. The modules are as follows: online search for published articles; gamification; program research project; videos, SDG website, and PowerPoint lectures; students conference participation; public events; geospatial labs and statistical analysis labs. The description of each module is mentioned in Figure 1.

To investigate the role of Spatial Data Infrastructure as a pedagogical tool in impacting student outcomes in terms of their sustainability consciousness. A module is created for the students on Spatial Data Infrastructure based on ArcGIS to facilitate their learnings on SDGs. The objectives of this module are as follows:

• Understand and explain concepts related to SDIs and SDGs [61] including geodata, different types of poverty, and types of migrations.
• Utilize the ArcGIS Online tool within an ArcGIS framework to effectively integrate geodata obtained from SDI services [62].
• Prepare comprehensive reports that analyze the situation represented on the map and formulate relevant questions pertaining to geography and scientific aspects related to the territory.
• Communicate proposed solutions to global and local issues, taking action at the community level.
• Collaborate effectively within a team, with a focus on assigning and fulfilling specific roles during decision-making processes.
• Engage in respectful debates and ensure equitable speaking time for the spokesperson of each group.

The following pedagogical strategies will be used to introduce and raise awareness of the SDGs:

• Implement the flipped classroom technique, allowing for the introduction of open-ended, true–false, and multiple-choice questions to engage students actively in the learning process.
• Assign individual reading tasks to students, focusing on the SDGs to be studied. Encourage students to reflect on the readings to develop a deeper understanding of the goals and their implications.
• Facilitate cooperative learning in groups of four students within the Geoinformatics classroom. Guide students in creating a map using the ArcGIS Online tool [63], utilizing the SDI services available through web resources [64] or reliable virtual data repositories. This hands-on activity provided practical experience in visualizing geospatial data related to the chosen SDGs.

Detailed samples of Student’s Assignments, Labs, Projects, GIS Applications, Brainstorming, SDG Impact Assessment Tool, the SDG Mapper, and other SDG material related to the SDG Awareness and Knowledge Program are available in Supplementary Files.

4.2. Research Flow

After giving awareness to a group of students from the geography department, a survey questionnaire is designed according to the hypothesis, and then groups are conducted from whom survey analysis is conducted. The subsequent section describes the variables for the research according to each hypothesis, groups formation, and data collection.

The research flow is outlined in Figure 2, which also includes information about the tools used, the groups involved, the SDG Awareness and Knowledge Program, the regression variables, and the kind of statistical analysis used. It provides a clear understanding of the relationships between these elements by acting as a visual representation of the overall research structure.

With both descriptive and inferential implications, the current study used an observational research design. Its main goal is to collect empirical data on how well-informed both geography and non-geography students are about the SDGs and the function of SDI in terms of sustainability consciousness. It was a multicenter research project because three different sample groups were created to achieve this.

4.3. Data Collection

A comprehensive questionnaire consists of three sections, as given in

Table A1. Complete list of Questionnaires.

Sr.#	Items	Type
1	College
	SDGAKN
2	Source of Awareness of SDG	MSMCQ
3	Importance of Knowledge about SDG	MSMCQ
4	Alignment of SDG with a Project or Assignment	MSMCQ
5	Student’s Familiarity with Number of SDGs	MSMCQ
6	Organization Responsible for SDGs	SSMCQ
7	The target year for achieving the SDGs	SSMCQ
8	Number of SDGs	SSMCQ
9	Picture depicting Clean Water and Sanitation (SDG 6)	SSMCQ
10	Picture depicting No Poverty (SDG 1)	SSMCQ
11	Picture depicting No Poverty (SDG 1)	SSMCQ
12	Picture depicting Zero Hunger (SDG 2)	SSMCQ
13	Picture depicting Good Health and Well-Being (SDG 3)	SSMCQ
14	Picture depicting Quality Education (SDG 4)	SSMCQ
15	Picture depicting Affordable and Clean Energy (SDG 7)	SSMCQ
16	Picture depicting Sustainable Cities and Communities (SDG 11)	SSMCQ
17	Picture depicting Climate Action (SDG 13)	SSMCQ
18	Frequency of usage of the SDG Mapper Tool	LTQ
19	The SDG Mapper Tool ease	LTQ
20	Usefulness of SDG mapper tool in identifying the relevant SDG	LTQ
21	Quality of the data visualizations on the SDG Mapper Tool?	LTQ
22	Usage the SDG Mapper Tool to Identify areas where more work is needed to achieve the SDGs	LTQ
23	Usage of the SDG Mapper Tool to prioritize actions that would have the greatest positive impact on the SDGs	LTQ
24	Helping in achieving organization’s sustainability goals through SDG Mapper Tool	LTQ
25	Recommendation of SDG Mapper Tool to others.	LTQ
26	Frequency of usage of the SDG Impact Assessment Tool	LTQ
27	Usefulness of SDG Impact Assessment Tool	LTQ
28	Usefulness of SDG Impact Assessment Tool in assessing the impact of my research project on the SDG.	LTQ
29	Sharing insights from the SDG Impact Assessment Tool with others in organization or classmates.	LTQ
30	Usefulness of SDG Impact Assessment Tool in identifying the positive and negative impacts of my research project to the Sustainable Development Goals (SDGs).	LTQ
31	Usefulness of SDG Impact Assessment Tool in suggesting actions that would strengthen the positive impact of my research project on certain SDG goals and eliminating the negative impact.	LTQ
32	Quality of the data visualizations on the SDG Impact Assessment Tool	LTQ
33	Familiarity with the SDGs	LTQ
34	Awareness with the underlying philosophy of the SDGs	LTQ
35	Importance of having knowledge of the SDGs	LTQ
36	Ability to bring about positive change in the world with SDGs?	LTQ
37	Economic development is necessary for sustainable development	LTQ
38	Improving people’s health and opportunities for a good life contribute to sustainable development	LTQ
39	Reducing water consumption is necessary for sustainable development	LTQ
40	Preserving nature is not necessary for sustainable development.	LTQ
41	A culture where conflicts are resolved peacefully through discussion is necessary for sustainable development	LTQ
42	Sustainable development demands that we humans reduce all sorts of waste.	LTQ
43	People who exercise their democratic rights are necessary for sustainable development (for example, they vote in elections, involve themselves in social issues, express their opinions)	LTQ
44	Reinforcing girls’ and women’s rights around the world is necessary for sustainable development	LTQ
45	Respecting human rights is necessary for sustainable development.	LTQ
46	To achieve sustainable development, all the people in the world must have access to good education.	LTQ
47	To achieve sustainable development, companies must treat their employees, customers and suppliers in a fair way.	LTQ
48	Preserving many different natural species is necessary for sustainable development.	LTQ
49	Having respect for other cultures is necessary for sustainable development.	LTQ
50	Sustainable development demands a fair distribution of, for example, food and medical care among people in the world.	LTQ
51	Wiping out poverty in the world is necessary for sustainable development.	LTQ
52	Sustainable development demands that we switch to renewable resources (renewable resources include, for example, wind power, solar panels, ethanol, cardboard	LTQ
53	Sustainable development demands that people understand how the economy functions.	LTQ
54	For sustainable development, big infectious diseases such as HIV/AIDS and malaria must be stopped.	LTQ
55	For sustainable development, people need to be educated in how to protect themselves against natural disasters.	LTQ
SDGAWS
56	Number of SDGs most effectively supported by Spatial Data Infrastructure (SDI)	MSMCQ
57	Primary purpose of GIS technology	SSMCQ
58	Applications of GIS and remote sensing in the field of geography	SSMCQ
59	SDI related to No Poverty (SDG 1)	SSMCQ
60	SDI related to Quality Education (SDG 4)	SSMCQ
61	SDI related to Quality Education (SDG 4)	SSMCQ
62	SDI related to Sustainable Cities and Communities (SDG 11)	SSMCQ
63	SDI related to Good Health and Well-Being (SDG 3)	SSMCQ
64	SDI related to Clean water and Sanitation (SDG 6)	SSMCQ
65	SDI related to Clean water and Sanitation (SDG 6)	SSMCQ
66	SDI related to Affordable and Clean Energy (SDG 7)	SSMCQ
67	SDI related to Affordable and Clean Energy (SDG 7)	SSMCQ
68	SDI related to Affordable and Clean Energy (SDG 7)	SSMCQ
69	SDI related to Sustainable Cities and Communities (SDG 11)	SSMCQ
70	SDI related to Sustainable Cities and Communities (SDG 11)	SSMCQ
71	SDI related to Responsible Consumption and Production (SDG 12)	SSMCQ
72	SDI related to Responsible Consumption and Production (SDG 12)	SSMCQ
73	SDI related to Responsible Consumption and Production (SDG 12)	SSMCQ
74	SDI related to Responsible Consumption and Production (SDG 12)	SSMCQ
75	Importance of geography students to learn about the SDGs?	LTQ
76	Familiarity with Spatial Data Infrastructure (GIS and Remote Sensing)?	LTQ
77	Utilization of GIS and remote sensing is vital in addressing SDG	LTQ
78	Level of confidence in using GIS and remote sensing technology to support decision-making and problem-solving in SDG geography-related fields?	LTQ
79	Level of interest to pursue further studies or research in the area of GIS and remote sensing that linked with SDG?	LTQ
80	Believing that Spatial Data Infrastructure (GIS and Remote Sensing) can support the implementation or exploring the SDGs?	LTQ
81	Believing that GIS and remote sensing technologies can be used to monitor progress towards the SDGs?	LTQ
82	Importance for geography students to learn about the link between the SDGs and Spatial Data Infrastructure (GIS and Remote Sensing)?	LTQ
83	Level of agreement Geography students should be involved in projects that use GIS and remote sensing technologies to support the SDGs?	LTQ
84	UAE government is making an effort to achieve the SDGs through Spatial Data Infrastructure (GIS and Remote Sensing)?	LTQ
85	Taken courses or received training in the use of GIS or remote sensing technology.	LTQ
86	Participated in a project or assignment that involved using GIS and remote sensing technologies to support the implementation of the SDGs?	LTQ
IQESDP
87	Level of agreement with the statement: “It is important for all students, regardless of their field of study, to have education and understanding about the Sustainable Development Goals (SDGs)”?	LTQ
88	Should a mandatory course specifically on SDGs be incorporated into the undergraduate curriculum for all fields of study? (OS)	LTQ
80	Incorporating a proper understanding of the SDGs into the curriculum is important for the development of the country, as the youth are considered the backbone of the country?	LTQ
90	SDGs can be better achieved through interdisciplinary cooperation and collaboration?	LTQ
91	Current education system provides enough information about the SDGs and their importance?	LTQ
92	Quality and inclusive education is a priority in achieving SDG 4?	LTQ
93	Familiarity with these organizations DEK, CAA and WASC (OS)	LTQ

of the Appendix A. The first section focuses on gathering sociological data about the students. The second section aims to assess the students’ awareness and knowledge of SDGs under the variable SDGAKN. The third section aims to assess the students’ SDG awareness using SDI skills under the variable SDGAWS, and lastly, the fourth section examines the importance of quality education and the integration of SDG knowledge in pedagogy among students under the variable IQESDFP.

The second and third sections are composed of three types of questions: multiple-select multiple-choice questions (MSMCQ), single-select multiple-choice questions (SSMCQs), and Likert-type questions (LTQ), whereas section four is composed of only Likert-type questions. The detailed source of data for each variable is given in

Table 1. Source of Data against each Hypothesis and Variables.

Sr. #.	Hypothesis	Dependent Variables	Constructs
1	H1	SDGAKN	SDG Awareness and Knowledge
2	H2	SDGAWS	SDG Awareness using SDI
3	H3	IQESDP	Importance of quality education and integration of SDG knowledge in pedagogy

.

To ensure the questionnaire’s quality, a comprehensive literature review was conducted and adapted from [35,62]. Subsequently, the initial draft of the questionnaire underwent scrutiny by independent experts [65]. Two experts in Social Science Education, who possess over a decade of teaching and research experience, reviewed the questionnaire and made minor modifications to the wording of the questions. To collect data, students completed the questionnaire online while maintaining anonymity [39]. The Google Form^® tool (Google LLC, Mountain View, CA, USA) [14] was employed for this purpose. The response rate reached 72%, which is consistent with similar studies found in the existing literature [66]. Prior to participation, students were provided with information about the research’s objectives, and their informed consent was obtained.

4.4. Sample Description

To fulfill the research objectives, the sample for this study comprised of three distinct groups of students from the geography department and various departments of the UAEU. The details of these three groups, including their respective sample sizes, are provided in

Table 2. Descriptions along with Sample Size.

Groups	Description	Sample Size
Group 1	Students from the geography department who have SDI skills and received the SDG Awareness and Knowledge program	32
Group 2	Students from the geography department who have SDI skills but have not received the SDG Awareness and Knowledge program	48
Group 3	Students from a non-geography department who do not have SDI skills and have not received the SDG Awareness and Knowledge program	32

.

4.5. Data Process

The Microsoft Office Excel 2021 and Statistical Package for the Social Sciences (SPSS) software version 23 for Windows, created by IBM in Chicago, IL, USA, was used to analyze the obtained data. The Cronbach’s alpha test is used to evaluate the questionnaire’s reliability. When an alpha coefficient greater than 0.8 is attained, previous studies in literature suggest that a group of items can be regarded as a component of the same construct [42]. The results are then subjected to descriptive analysis to provide a more thorough understanding. Levene’s test is used to determine whether the data were normal before moving on to parametric inferential analysis. One-way ANOVA and other parametric tests were used to identify significant differences between the groups. Specific significant differences were further identified using post hoc tests like Dunnett T3 and Tukey’s test. All tests had a significant level of 0.05, which corresponds to a 95% confidence level.

5. Analysis and Results

5.1. Questionnaire Validation

Before beginning the analysis, several tests were carried out to determine the validity of the LTQ data that had been gathered. These tests included Cronbach’s alpha (CA) and factor analysis (FA), which computed composite reliability (CR). The internal consistency of the data was assessed using CA, and a value greater than 0.7 is typically regarded as satisfactory. The computed CA values for the questions in this study were found to be significantly higher than 0.82, indicating excellent internal consistency of the collected data. Additionally, FA was performed to further validate the internal consistency by calculating the CR for all the questions.

The results demonstrated that the CR was greater than 0.882 for all questions, surpassing the threshold of 0.7, thus affirming the findings derived from the FA test.

Table 3. Reliability Test Results for Each Construct.

Variables	Involved Questions	Mean	Std. Deviation	Cronbach’s Alpha (CA)	Composite Reliability (CR)
SDGAKN	Q2–Q55	2.45	0.54	0.93	0.90
SDGAWS	Q6–Q86	1.87	0.49	0.88	0.82
IQESDP	Q87–Q93	2.26	0.77	0.86	0.90

provides a summary of the test results.

The CR is calculated using the Equation (1)

$$CR=\frac{{\left(\sum _{i=1}^n{\lambda }_i^2\right)}^2}{{\left(\sum _{i=1}^n{\lambda }_i^2\right)}^2+\sum _{i=1}^n{\delta }_i}$$

(1)

where the value of λ represents the standardized factor loading, which comes from the rotated component matrix after performing Factor Analysis of the items in SPSS, i is the item number, δ represents error variance, and N represents the total number of items.

5.2. Hypothesis 1. Undergraduate Students’ Knowledge of the SDGs Is Positively Associated with Their Exposure to SDG Related Content across Academic Groups

5.2.1. Descriptive Analysis of MSMCQ

• Source of Awareness of the SDGs

The descriptive analysis results provide key statistics on the respondents’ awareness of the SDGs based on different points of awareness. The highest frequency and percentage of awareness were observed for the option “During my undergraduate program courses at the university”, with 66% of Group 1, 46% of Group 2, and 28% of Group 3 selecting this option. University research activities also showed significant awareness, with 56% of Group 1, 63% of Group 2, and 50% of Group 3 indicating it as their point of awareness. The statistics of other responses are given in

Table 4. Source of Awareness of SDG.

At What Point Did You First Become Aware of the Sustainable Development Goals	Groups
	Group 1		Group 2		Group 3
	Count %		Count %		Count %
As part of my high school curriculum and activities	2	6%	14	29%	3	9%
University students club	6	19%	16	33%	5	16%
During my undergraduate program courses at university	21	66%	22	46%	9	28%
University research activities	18	56%	30	63%	16	50%
Through family discussions or awareness	1	3%	7	15%	4	13%
Through media sources (such as television, newspapers, websites, or social media)	3	9%	3	6%	5	16%
Online learning platforms (Khan Academy, Coursera, Udemy, etc.)	2	6%	1	2%	1	3%
Other	0	0%	4	8%	4	13%

:

The strong presence of Group 1 in the response related to “During my undergraduate program courses at university” can be attributed to their participation in the SDG Awareness and Knowledge program. Conversely, Group 2 demonstrates dominance in the area of university research activities. Notably, Group 3 also exhibits favorable responses in this category, indicating that the UAEU has undertaken significant initiatives concerning SDGs. Moreover, the university has introduced a dedicated general education course on sustainability for all students across various disciplines, leading to a noticeable escalation in research endeavors across multiple subjects at the university.

• Importance of Knowledge about SDGs

The descriptive analysis provides significant data on the respondents’ perceptions of the value of being aware of the SDGs, including key statistics. Respondents highlighted that being aware of the SDGs allows them to connect their learning with global challenges and solutions, which was considered the most crucial reason for valuing SDG knowledge. Among the groups, 47% of Group 1, 31% of Group 2, and 41% of Group 3 selected this response. A considerable percentage of respondents also highlighted the significance of SDGs in enhancing general knowledge. These findings highlight the diverse benefits associated with SDG knowledge, emphasizing its role in fostering global awareness, aiding in career choices, and promoting active citizenship. Additional responses are presented in

Table 5. Importance of Knowledge about SDGs.

In Your Opinion, Why Is It Important to Have Knowledge about the Sustainable Development Goals (SDGs)?	Groups
	Group 1		Group 2		Group 3
	Count	%	Count	%	Count	%
As a citizen, it aligns with the goals of my country	5	16%	9	19%	5	16%
It helps in general knowledge	7	22%	14	29%	11	34%
If we know about it, we can plan our career path accordingly	5	16%	9	19%	3	9%
It helps us to relate our learning with the global problems and solutions	15	47%	15	31%	13	41%

, providing further statistical insights.

Most of the students in Group 1 strongly associate the importance of SDG knowledge with their understanding of global issues and potential solutions. This correlation can be attributed to their participation in the SDG Awareness and Knowledge Program, where they were exposed to the identification of global problems alongside their possible solutions. It is worth noting that other groups also demonstrate positive responses to this question, which can be attributed to the university’s provision of an SDG course that is accessible to students across all disciplines.

• Alignment of SDG with a Project or Assignment

The descriptive analysis reveals the key statistics of the alignment between past projects or assignments and specific SDGs. For each SDG, the distribution of the three groups (Group 1, Group 2, and Group 3) is shown in Figure 3. It provides insights into the distribution of projects or alignments across various SDGs.

The highest number of counts in SDGs is in SDG 3 (Good Health and Well-Being), which has 18 (56%) in Group 1, 18 (38%) in Group 2, and 7 (22%) in Group 3. On the other hand, SDG 10 (Reduced Inequalities) has the lowest count across all three groups. This descriptive analysis allows us to identify the relative frequency of project or assignments with different SDGs.

By visualizing Figure 3, it is possible to determine the significant group by examining the highest count for each SDG. Based on the counts in the aggregated column, Group 1 is the most significant group among the three groups for most SDGs because of the SDG Awareness and Knowledge Program. Group 1 has the highest count in SDGs 3, 9, 11, 13, 15, 16, and 17. This suggests that individuals in Group 1, who believe their projects or assignments align with the goals of their country, have a higher frequency of alignment with multiple SDGs compared to the other two groups. Therefore, Group 1 can be considered the most significant group in terms of project alignment with the SDGs.

• Student’s Familiarity with the Number of SDGs

The descriptive analysis presents key statistics on the familiarity of three groups (Group 1, Group 2, and Group 3) with various SDGs. The results reveal variations in the level of familiarity with different SDGs among the groups. The highest level of familiarity with SDGs like No Poverty (SDG 1), Good Health and Well-Being (SDG 3), Quality Education (SDG4), Industry, Innovation and Infrastructure (SDG 9), Reduced Inequalities (SDG 10), Responsible Consumption and Production (SDG 12), Climate Action (SDG 13), Life Below Water (SDG 14), Life on Land (SDG 15), Peace, Justice, and Strong Institutions (SDG 16), and Partnerships for the Goals (SDG 17) were found in Group 1, among notable findings. The SDG awareness program likely provided these students with focused information and knowledge related to these SDG goals, resulting in their heightened familiarity. Group 2 demonstrates greater familiarity with SDGs like Zero Hunger (SDG 2), Good Health and Well-Being (SDG 3), Quality Education (SDG4), Gender Equality (SDG 5), Clean Water and Sanitation (SDG 6), Affordable and Clean Energy (SDG 7), Decent Work and Economic Growth (SDG 8), and Responsible Consumption and Production (SDG 12). This can be attributed to the broader exposure and general knowledge about these goals that students in Group 2 may have gained through their regular coursework and other educational experiences. Group 3 generally demonstrates the lowest familiarity with most SDGs, as shown in Figure 4.

As non-geography students, their academic curriculum and educational experiences might not have focused extensively on sustainability and global development issues, including the SDGs. Consequently, they may have had limited opportunities to gain knowledge and familiarity with the various SDGs.

To determine the significant group among the three, the percentages and aggregated counts can be examined. In this case, Group 1 appears to be the most significant, as it consistently has the highest familiarity percentages and counts across multiple SDGs. Group 2 also demonstrates a notable level of familiarity, particularly with Zero Hunger (SDG 2). Group 3 consistently exhibits the lowest familiarity across the board. These results indicate that Group 1 has a more thorough understanding of the SDGs than the other two groups, perhaps as a result of more exposure to, familiarity with, or involvement with the subjects covered by the SDG Awareness and Knowledge Program.

5.2.2. Descriptive Analysis on SSMCQs

The rigorous analysis was conducted to evaluate students’ knowledge and awareness regarding the SDGs using the SSMCQs. The SSMCQs were thoughtfully designed to cover various dimensions of the SDGs, including their objectives, targets, and thematic areas. These questions were administered to all three groups of students, and their responses were meticulously recorded and analyzed. The analysis involved examining the percentage of correct answers for each question, enabling an assessment of the overall level of knowledge and awareness among the students regarding the SDGs.

Table 6. Students’ scores on SDG awareness and knowledge.

Sr. #	SDGAKN—Items	Groups						Dominance
		Group 1		Group 2		Group 3
		Count	%	Count	%	Count	%
1	Organization Responsible for SDGs	20	63%	20	42%	11	34%	Group 1
2	The target year for achieving the SDGs	25	78%	33	69%	16	50%	Group 1
3	Number of SDGs	25	78%	26	54%	14	44%	Group 1
8	Picture depicting Clean Water and Sanitation (SDG 6)	31	97%	44	92%	23	72%	Group 1
9	Picture depicting No Poverty (SDG 1)	21	66%	26	54%	10	31%	Group 1
10	Picture depicting No Poverty (SDG 1) a	28	88%	36	75%	23	72%	Group 1
11	Picture depicting Zero Hunger (SDG 2)	30	94%	41	85%	24	75%	Group 1
12	Picture depicting Good Health and Well-Being (SDG 3)	31	97%	44	92%	27	84%	Group 1
13	Picture depicting Quality Education (SDG 4)	30	94%	45	94%	26	81%	Group 1 and Group 2
14	Picture depicting Affordable and Clean Energy (SDG 7)	32	100%	42	88%	26	81%	Group 1
15	Picture depicting Sustainable Cities and Communities (SDG 11)	32	100%	41	85%	27	84%	Group 1
16	Picture depicting Climate Action (SDG 13)	30	94%	22	46%	12	38%	Group 1

^a A different picture illustrating SDG 1.

presents the results of a descriptive analysis conducted on the construct of SDG Awareness and Knowledge among Students (SDGAKN). It displays the scores and percentages of students within each group. Each row corresponds to a specific question posed to the groups, while the scores indicate the number of students who provided correct answers. The final column displays the dominant group that achieved the highest scores compared to the other groups.

Table 6 illustrates the highest scores attained for each question, while the last column indicates the dominance of each group for each question. Notably, Group 1 consistently achieved the highest score across all questions, underscoring their outstanding performance. It clearly demonstrates the significant improvement in students’ outcomes regarding SDG awareness and knowledge resulting from the SDG Awareness and Knowledge Program. Additionally, students in Group 2 and Group 3 also demonstrate commendable performance in these questions, likely attributed to their familiarity with SDGs through the University’s SDG course.

5.2.3. Inferential Analysis on SSMCQs

To gather additional evidence for the hypothesis, an inferential analysis was undertaken, focusing on assessing the impact of different student groups on the SDG Awareness and Knowledge Program. The groups into which the students were placed are referred to as the independent variables in this study. These groups are Group 1, Group 2, and Group 3. Meanwhile, the dependent variable is determined by the scores obtained by the students, as indicated by the descriptive analysis of the SSMCQs as presented in Table 6.

To ascertain whether there is a significant difference in the mean scores among the groups, a one-way ANOVA test was conducted. Prior to conducting the ANOVA test, it is necessary to perform the Homogeneity of Variance test and collect Descriptive Statistics in

Table 7. Descriptive Statistics of all variables.

Sub Tables	Groups	Count	Mean	St. D.	95% Confidence Interval for Mean		Minimum	Maximum
					Lower Bound	Upper Bound
(A) SDGAKN on SSMCQs	Group 1	12	87.42	13.014	79.15	95.69	63	100
	Group 2	12	73.00	19.325	60.72	85.28	42	94
	Group 3	12	62.17	20.993	48.83	75.50	31	84
	Total	36	74.19	20.473	67.27	81.12	31	100
(B) SDGAKN on LTQ	Group 1	32	2.2549	0.54461	2.0586	2.4513	1.32	3.34
	Group 2	48	2.4263	0.64027	2.2404	2.6122	1.26	3.71
	Group 3	32	2.7281	0.70304	2.4747	2.9816	1.26	4.39
	Total	112	2.4636	0.65405	2.3411	2.5860	1.26	4.39
(C) SDGAWS on SSMCQ	Group 1	18	80.50	9.960	75.55	85.45	53	94
	Group 2	18	79.94	7.870	76.03	83.86	63	90
	Group 3	17	66.76	15.258	58.92	74.61	19	88
	Total	53	75.91	12.829	72.37	79.44	19	94
(D) SDGAWS on LTQ	Group 1	32	1.8828	0.63283	1.6547	2.1110	1.08	3.00
	Group 2	48	2.0642	0.78818	1.8354	2.2931	1.00	4.25
	Group 3	32	2.6875	0.80517	2.3972	2.9778	1.00	5.00
	Total	112	2.1905	0.81323	2.0382	2.3427	1.00	5.00
(E) IQESDP on LTQ	Group 1	32	2.2305	0.55821	2.0292	2.4317	1.13	3.13
	Group 2	48	2.2604	0.70797	2.0548	2.4660	1.13	4.00
	Group 3	32	2.6563	0.81814	2.3613	2.9512	1.00	5.00
	Total	112	2.3650	0.72152	2.2299	2.5001	1.00	5.00

. For example, the descriptive statistics for the SDGAKN construct are outlined in Table 7A, which was generated using SPSS.

The sample data reveal a significant difference in the mean scores of the three levels of the group variable. Students in Group 1 score higher than the other two groups. The key question is whether this difference reaches significance.

In order to assess the equality of variances across groups, Levene statistics are employed to test the variances of each comparison group. The results reveal a significant p-value of 0.028, indicating that the assumption of homogeneous variances has not been satisfied. Consequently, instead of using ANOVA, this study employs the Robust Welch test. The significant value obtained from the Welch test is 0.006, providing evidence of a significant difference among the means of the groups. To identify specific pairs of means with significant differences, a Dunnett T3 post hoc test is conducted.

A statistically significant difference between the groups was discovered by the one-way ANOVA (F(2,33) = 5.873, p = 0.007). This finding indicates a statistically significant difference among the means of the grouping variable’s various levels. To identify specific pairs of means with significant differences, a post hoc Dunnett T3 test is conducted.

Upon examination of the multiple comparisons given in

Table 8. Multiple Comparisons for SDGAKN on SSMCQ.

(I) Groups	(J) Groups	Mean Difference (I − J)	Sig.	95% Confidence Interval
				Lower Bound	Upper Bound
Group 1	Group 2	14.417	0.125	−3.09	31.92
	Group 3	25.250 *	0.007	6.61	43.89
Group 2	Group 1	−14.417	0.125	−31.92	3.09
	Group 3	10.833	0.481	−10.38	32.05
Group 3	Group 1	−25.250 *	0.007	−43.89	−6.61
	Group 2	−10.833	0.481	−32.05	10.38

* The mean difference is significant at the 0.05 level.

, significant values are observed for mean differences among pairs of different levels of the grouping variable (Group 1, Group 2, and Group 3). Group 1 scored considerably better than Group 3 according to a Dunnett T3 post hoc test (p = 0.007). Group 1 and Group 2 (p = 0.125) and Group 2 and Group 3 (p = 0.099) did not differ significantly from one another.

5.2.4. Inferential Analysis on LTQ

The examination was carried out to assess the extent of agreement regarding SDG Awareness and Knowledge among Students (SDGAKN). The survey consisted of thoughtfully constructed questions on a Likert scale, encircling different aspects of the SDGs, such as objectives, targets, and thematic areas. The survey was then distributed to a group of students, and their responses were diligently collected and subjected to thorough analysis.

To determine if there is a significant difference in the level of agreement among different groups, the one-way ANOVA test was employed. Before conducting the ANOVA test, the Homogeneity of Variance test was performed, along with descriptive statistics. Descriptive statistics generated by SPSS for the construct SDGAKN are presented in Table 7B. The sample data demonstrate a difference in the mean scores among the three levels of group variable, with Group 1 having consistently lower means compared to the other groups. The key question is whether this difference in mean scores is statistically significant. The Levene statistic was used to determine whether the variances in each comparison group were comparable in order to satisfy the ANOVA test’s requirements. A significant value larger than 0.05, indicating no discernible difference across variances, was the desired outcome. The Levene statistic’s obtained significance value is 0.350, which is not significant and indicates that the homogeneity of variance assumption has been met. To guarantee the accuracy of the analysis, a Tukey HSD post hoc test was run in addition to the ANOVA.

The one-way ANOVA analysis produced a statistically significant result with (F(2, 109) = 4.605, p = 0.012), indicating significant variation in the means of the grouping variable. Post hoc analysis using the Tukey HSD test further revealed specific pairings of means with significant differences. Notably, the variable SDGAKN showed a statistically significant mean difference between Group 1 and Group 3, as evidenced by the obtained p-value of 0.010, which falls below the predetermined alpha level of 0.05 as shown in

Table 9. Multiple Comparisons for SDGAKN on LTQ.

(I) Groups	(J) Groups	Mean Difference (I − J)	Sig.	95% Confidence Interval
				Lower Bound	Upper Bound
Group 1	Group 2	−0.17138	0.465	−0.5151	0.1723
	Group 3	−0.47319 *	0.010	−0.8497	−0.0967
Group 2	Group 1	0.17138	0.465	−0.1723	0.5151
	Group 3	−0.30181	0.097	−0.6455	0.0419
Group 3	Group 1	0.47319 *	0.010	0.0967	0.8497
	Group 2	0.30181	0.097	−0.0419	0.6455

* The mean difference is significant at the 0.05 level.

. These statistical findings validate our research hypothesis 1

5.3. Hypothesis 2. The Utilization of Spatial Data Infrastructure (SDI) as a Pedagogical Tool Significantly Influences Students’ Sustainability Consciousness across Academic Groups

5.3.1. Descriptive Analysis on MSMCQ

• Number of SDGs most effectively supported by SDI

The descriptive analysis of the question reveals the responses of three distinct groups regarding the SDGs that they believe can be best supported by Spatial Data Infrastructure (GIS and Remote Sensing). Figure 5 provides insights into the distribution of percentages across various SDGs for each group. Group 1 (N = 32) reveals the highest percentages in SDG 4 (50%), SDG 6 (56%), SDG 7 (56%), SDG 9 (59%), SDG 11 (56%), SDG 12 (25%), SDG 13 (17%), SDG 14 (22%), SDG 15 (47%), SDG 16 (19%), and SDG 17 (17%). Following this, Group 2 (N = 48) exhibits the second-highest percentages, with coverage in SDG 2 (31%), SDG 3 (27%), SDG 5 (33%), SDG 8 (33%), and SDG 10 (19%). Group 3 (N = 32), on the other hand, shows a prevalence only in SDG 1 (41%). Considering these findings, it can be concluded that Group 1 significantly prioritizes the SDGs that can be effectively supported by Spatial Data Infrastructure. Furthermore, it is worth noting that Group 1 demonstrates the highest frequency across five SDGs, highlighting their inherent background in SDI within the field of geography. Additionally, some of the frequencies observed in Group 3 provide evidence of the University’s SDG-supportive course, which is accessible to students across different departments.

5.3.2. Descriptive Analysis on SSMCQs

Furthermore, the SSMCQs were specifically designed to assess students’ knowledge of the SDGs within the context of SDI. The utilization of SDI, a technological framework that enables the collection, management, analysis, and dissemination of geospatial data, played a crucial role in evaluating students’ understanding of the SDGs. By incorporating SDI into the assessment process, the integration of spatial information allowed for a more comprehensive evaluation of students’ comprehension of the SDGs. This approach provided an opportunity to explore how students applied their knowledge of the SDGs within a spatial context, taking advantage of the capabilities provided by SDI.

Table 10. Students score of Awareness of SDG using SDI.

Sr. #	SDGAWS—Items	Groups						Dominance
		Group 1		Group 2		Group 3
		Score	%	Score	%	Score	%
1	Primary purpose of GIS technology	28	88%	34	71%	22	69%	Group 1
2	Applications of GIS and remote sensing in the field of geography	25	78%	33	69%	23	72%	Group 1
3	SDI related to No Poverty (SDG 1)	25	78%	37	77%	21	66%	Group 1
8	SDI related to Quality Education (SDG 4)	26	81%	42	88%	20	63%	Group 2
9	SDI related to Quality Education (SDG 4) a	25	78%	40	83%	23	72%	Group 2
10	SDI related to Sustainable Cities and Communities (SDG 11)	24	75%	38	79%	20	63%	Group 2
11	SDI related to Good Health and Well-Being (SDG 3)	24	75%	37	77%	23	72%	Group 2
12	SDI related to Clean Water and Sanitation (SDG 6)	17	53%	30	63%	6	19%	Group 2
13	SDI related to Clean Water and Sanitation (SDG 6) b	23	72%	38	79%	22	69%	Group 2
14	SDI related to Affordable and Clean Energy (SDG 7)	22	69%	33	69%	14	44%	Groups 1 and 2
15	SDI related to Affordable and Clean Energy (SDG 7) c	28	88%	39	81%	22	69%	Group 1
16	SDI related to Affordable and Clean Energy (SDG 7) d	27	84%	40	83%	23	72%	Group 1
17	SDI related to Sustainable Cities and Communities (SDG 11)	29	91%	40	83%	23	72%	Group 1
18	SDI related to Sustainable Cities and Communities (SDG 11) e	30	94%	42	88%	28	88%	Group 1

^a A different SDI illustrating SDG 4,^b A different SDI illustrating SDG 6, ^c A different SDI illustrating SDG 7, ^d A different SDI illustrating SDG 7, ^e A different SDI illustrating SDG 1.

presents the results of a descriptive analysis conducted on variable SDGAWS. It displays the scores and percentages of students within each group. Each row corresponds to a specific question posed to the groups, while the scores indicate the number of students who provided correct answers. The final column displays the dominant group that achieved the highest scores compared to the other groups. Remarkably, Group 1 and Group 2 exhibit nearly equivalent performance in this construct. This similarity can be attributed to the fact that both groups belong to the same geography department. In contrast, Group 3 demonstrates a comparatively lower performance as they originate from different departments.

However, it is worth noting that some students from Group 3 answered correctly, likely influenced by their participation in the University’s SDG-supportive course, which is available to students across various departments.

5.3.3. Inferential Analysis on SSMCQs

To find out if there is a significant difference in the mean scores between the groups, a one-way ANOVA test was run. A homogeneity of variance test and descriptive statistics were performed before the ANOVA test. Descriptive statistics for the SDGAWS construct are presented in Table 7C.

Descriptive statistics generated by SPSS reveal differences in mean scores among the three levels of group variable, with Group 1 scoring higher than the other two groups. To meet the requirement for the ANOVA Analysis, the Levene statistic was used to assess the equality of variances across groups. The non-significant result (0.508) from Levene statistics suggests that the assumption of homogeneity of variance is satisfied. The ANOVA analysis yielded a statistically significant difference between the groups, as shown by (F(2, 50) = 8.098, p = 0.001). Specific group differences were investigated using post hoc Tukey HSD testing. According to the results, Group 1 outperformed Group 3 substantially (p = 0.002), and Group 2 outperformed Group 3 significantly (p = 0.003). However, there was no discernible distinction between Group 1 and Group 2 (p = 0.988). It is important to note that Groups 1 and 2’s resemblance can be due to their shared departmental association with geography and their SDI abilities as shown in

Table 11. Multiple Comparisons for SDGAWS on SSMCQ.

(I) Groups	(J) Groups	Mean Difference (I − J)	Sig.	95% Confidence Interval
				Lower Bound	Upper Bound
Group 1	Group 2	0.556	0.988	−8.60	9.71
	Group 3	13.735 *	0.002	4.45	23.02
Group 2	Group 1	−0.556	0.988	−9.71	8.60
	Group 3	13.180 *	0.003	3.89	22.47
Group 3	Group 1	−13.735 *	0.002	−23.02	−4.45
	Group 2	−13.180 *	0.003	−22.47	−3.89

* The mean difference is significant at the 0.05 level.

.

5.3.4. Inferential Analysis on LTQ

A comprehensive investigation was conducted to assess the level of student concurrence with the SDGs through the utilization of the SDI (SDGAWS). Thoughtfully designed survey questions were employed on a Likert scale to encompass the objectives, targets, thematic areas, and other relevant aspects of the SDGs. A representative sample of students responded to these questions, and their answers were meticulously recorded and analyzed.

The one-way ANOVA test enables us to ascertain whether there is a significant variation in the degree of agreement between the groups. Prior to the ANOVA test, descriptive statistics and the homogeneity of variance test are necessary. The Table 7D shows the descriptive statistics of the variable SDGAWS.

The data analysis of the sample indicates a difference in the mean scores among the three levels of the group variable, implying that students in different groups have varying means. The lower mean values observed in Group 1 suggest the dominance of this group over the others. The main question is whether this difference in means is statistically significant. To fulfill the requirement of the ANOVA test, the equality of variances among the comparison groups was tested using the Levene statistic. The obtained significance value for the Levene statistic is 0.502, indicating no significant difference in variances. This confirms that the assumption of homogeneity of variance is met, allowing the analysis to proceed with the Tukey HSD post hoc test in conjunction with the ANOVA test.

A significant outcome of the ANOVA analysis was obtained, with (F(2,109) = 10.332, p = 0.001). This shows that the means of the various levels of the grouping variable differ statistically significantly. A post hoc Tukey HSD test was used to identify the precise pairs of means that show significant differences.

The mean differences between Groups 1 and 3, as well as between Groups 2 and 3, were found to be statistically significant for the SDGAWS variable after applying the Tukey HSD test. According to

Table 12. Multiple Comparisons for SDGAWS on LTQ.

(I) Groups	(J) Groups	Mean Difference (I − J)	Sig.	95% Confidence Interval
				Lower Bound	Upper Bound
Group 1	Group 2	−0.18142	0.543	−0.5894	0.2266
	Group 3	−0.80469 *	<0.001	−1.2517	−0.3577
Group 2	Group 1	0.18142	0.543	−0.2266	0.5894
	Group 3	−0.62326 *	0.001	−1.0313	−0.2152
Group 3	Group 1	0.80469 *	<0.001	0.3577	1.2517
	Group 2	0.62326 *	0.001	0.2152	1.0313

* The mean difference is significant at the 0.05 level.

, the calculated p-values were <0.001 and 0.001, respectively. These statistical findings validate our research Hypothesis 2.

5.4. Hypothesis 3. The Quality Education and the Integration of SDG Knowledge in Pedagogy Hold Significant Importance across Academic Groups

Inferential Analysis on LTQ

An in-depth analysis was carried out to evaluate the extent of consensus on the significance of Quality Education and Integration of SDG Knowledge in Pedagogy (IQESDP). The survey consisted of meticulously designed Likert scale questions covering diverse dimensions of the SDGs, such as objectives, targets, and thematic areas. These survey questions were presented to a group of students, and their answers were carefully recorded and subjected to thorough examination.

The one-way ANOVA test was employed to examine whether there existed a significant difference in the level of agreement across different groups. Prior to conducting the ANOVA test, a Homogeneity of Variance test and descriptive statistics were performed. The descriptive statistics of the IQESDP variable are presented in Table 7E, providing an overview of the data distribution and central tendencies. The descriptive statistics obtained from the SPSS analysis indicate a difference in the mean scores among the three levels of group variable. Specifically, the data analysis reveals varying means for students in different groups. The lower mean values suggest a higher dominance of the respective group, considering the scale where 1 represents “Strongly Agree” and 5 represents “Strongly Disagree”. Notably, Group 1 consistently exhibits lower mean scores across all variables. Hence, the main question is whether the difference in mean scores is statistically significant. The Levene statistic was used to determine the homogeneity of variances prior to running the ANOVA test. The Levene statistic’s derived p-value is 0.350, which is not statistically significant. As a result, the homogeneity of variance is satisfied, and to make sure the analysis is reliable, a post hoc Tukey HSD test was carried out along with the ANOVA test.

A significant result from the ANOVA analysis was obtained, with (F(2,109) = 3.856, p = 0.024). This shows that the means of the various levels of the grouping variable differ statistically significantly. A post hoc Tukey HSD test was used to find specific pairings of means that had different values that are statistically significant. The post hoc Tukey HSD test indicated that the mean difference between Group 1 and Group 3, as well as between Group 2 and Group 3, were statistically significant for the IQESDP variable. The respective p-values were 0.045 and 0.040, as presented in

Table 13. Multiple Comparisons for IQESDP on LTQ.

(I) Groups	(J) Groups	Mean Difference (I − J)	Sig.	95% Confidence Interval
				Lower Bound	Upper Bound
Group 1	Group 2	−0.02995	0.981	−0.4115	0.3516
	Group 3	−0.42578 *	0.045	−0.8438	−0.0078
Group 2	Group 1	0.02995	0.981	−0.3516	0.4115
	Group 3	−0.39583 *	0.040	−0.7774	−0.0143
Group 3	Group 1	0.42578 *	0.045	0.0078	0.8438
	Group 2	0.39583 *	0.040	0.0143	0.7774

* The mean difference is significant at the 0.05 level.

.

The findings suggest that quality education and the integration of SDG knowledge in pedagogy hold considerable importance across academic groups. Group 1, comprising students from the geography department who participated in an SDG Awareness and Knowledge Program, demonstrated a heightened understanding of these concepts. Similarly, both Group 1 and Group 2, who had inherent exposure to the SDG-supportive course at the university, recognized the significance of integrating SDG knowledge into pedagogy. These statistical findings validate the research Hypothesis 3.

6. Discussions and Conclusions

This paper represents a cutting-edge approach in multiple dimensions. First, it brings forth innovation in its disciplinary perspective by introducing a fresh outlook on content and reflections related to the SDGs. Second, it showcases pedagogical innovation by requiring the adoption of novel methodologies, including collaborative approaches. Furthermore, it embraces technological advancements by integrating Spatial Data Infrastructures and ArcGIS Online into its implementation.

The synergistic combination of TPACK and Spatial Data Infrastructures aims to promote territorial knowledge and enhance students’ competencies within a technological environment, ultimately fostering a deeper understanding of the SDGs and empowering students to initiate actions that contribute to their attainment. It is worth noting that the utilization of a cartographic language in the ArcGIS platform has been validated, considering the specific needs and motivations of the students. These competencies can be integrated into the curriculum of all subjects and incorporated into instructors’ pedagogy.

Moreover, university education plays a vital role in increasing students’ awareness of sustainable development through various approaches:

• By fostering college students’ self-perception of SDGs and emphasizing the significance of sustainability.
• By cultivating a positive attitude among college students towards the SDGs through engaging teaching methods such as project-based learning, case studies, and collaborative learning.
• By stimulating college students’ interest and motivation towards the SDGs through interactive activities like games and group learning, taking into account their specific needs and preferences when designing projects.
• By enhancing college students’ self-efficacy related to the SDGs, thereby encouraging environmentally sustainable behavior and pro-environmental actions.

Through these means, university education can effectively raise students’ awareness of sustainable development and empower them to actively contribute to the goals and principles of sustainability.

This paper initiated an SDG awareness and knowledge campaign targeted at geography students at the United Arab Emirates University. The study further conducted a rigorous analysis to evaluate the score and level of agreement on constructs SDG Awareness and Knowledge (SDGAKN), SDG Awareness using SDI (SDGAWS), and the importance of quality education and integration of SDG knowledge in pedagogy (IQESDP) among students using an online questionnaire. The participants were divided into three distinct groups: Students from Geography Department who possess SDI skills and undergone the SDG Awareness and Knowledge program, students from Geography Department who possess SDI skills but have not received the SDG Awareness and Knowledge program, and students from Non-Geography Department who do not possess SDI skills and have not received the SDG Awareness Knowledge program. By comparing the mean scores and levels of agreement among the groups, the results from the descriptive and inferential analysis demonstrated that Group 1 exhibited the highest level of agreement and mean scores in SDG Awareness and Knowledge among Students, SDG Awareness using SDI among Students, and importance of quality education and integration of SDG knowledge in pedagogy (IQESDP) compared to Group 2 and Group 3. However, Group 2 also showed notable results due to a partial awareness of SDGs and the presence of some mapping skills inherited from the geography department and the University’s SDG course designed for all the undergraduate students.

The findings and discussions from this study yield significant recommendations for future actions in the following ways: In order to enhance students’ awareness regarding the Sustainable Development Goals (SDGs), several future recommendations emerge from this study. First, implementing specific training programs targeted at addressing the lack of SDG awareness among students is imperative. This training should be thoughtfully contextualized within the framework of Education for Sustainable Human Development. Moreover, promoting and teaching the SDGs necessitates the development of specific and cross-cutting competencies, which should be seamlessly integrated throughout the entire curriculum. The survey used in this study holds significant value as a tool for assessing the development of these competencies and guiding the teaching and learning process. Additionally, incorporating WebGIS or QGIS, free open-access software, as part of pedagogy, can be instrumental in successfully implementing the SDG Awareness Program and further fostering students’ engagement with sustainable development principles.

The findings of this study highlight the imperative for collaborative efforts at the university level to foster the promotion of the SDGs. Universities hold a pivotal position in advancing the SDGs, and their contribution is instrumental in propelling sustainable development initiatives.

7. Limitations of the Research

This research has identified specific limitations that may warrant attention in future investigations. These limitations are outlined as follows:

Sample Size: The sample size of the study might be limited, as it focused solely on undergraduate students from a specific university. This may affect the generalizability of the findings to a broader student population. Other universities can be considered in the UAE or other countries as well.

Short-Term Assessment: The research primarily focused on short-term effects of the SDG awareness program, and the study might not capture the long-term impact of such initiatives on students’ behavior and contributions to sustainability.

Lack of Comparison with Non-Geography Students: While the study compared geography students with and without the SDG awareness program, it did not include a comparison with non-geography students who received similar interventions. This could have provided additional insights into the effectiveness of the program across different disciplines.

Limitation of Questionnaire Design: While the questionnaire was validated and considered reliable, it might not have fully captured all aspects of students’ understanding and perceptions of all seventeen SDGs.

In order to better understand the connection between teaching for sustainable development and students’ knowledge of and activities toward the Sustainable Development Goals, future study could overcome these constraints. When interpreting the study’s conclusions, these limitations should be considered.