Integrating Sustainable Development Goals Through Problem Based Learning in Enhancing Motivation in Learning Mathematics in Ningbo,China

Abstract

This study aimed to design and develop the Sustainable Development Goals Problem-Based Learning (SDGPBL) Strategy to enhance students’ motivation in learning mathematics and then adopted a quasi-experimental mixed method to test the effectiveness of the strategy. Totally 68 senior one students (32 for experimental group and 36 for control group) from a high school in Ningbo participated in this experiment and seven students were selected to participate in the semi-structured interview. The ARCS motivation questionnaire surveys were conducted among the experimental and control group students before and after the intervention. Descriptive statistics of mean, percentage, standard deviation (SD), one-sample t-test, and independent sample t-test by SPSS Statistics 29 were utilized for quantitative data analysis. Thematic analysis method was used to analyze the transcript obtained from the interview session. The findings showed that the experimental group students’ motivation in learning mathematics has been improved after implementing the SDGPBL strategy and attention, relevance, confidence, and satisfaction were the successful dimensions of motivation in learning mathematics during this study, which indicated that the SDGPBL strategy has been successfully designed and developed. Majority of the students had positive comments about their learning experiences while using the SDGPBL strategy as some of the 21st century learning pillars and 10Cs of the students were developed, which showed that the learning strategy really support and improve their level of engagement in mathematics classroom.

Keywords

mathematics motivation problem-based learning education for sustainable development sustainable development goals ARCS

Introduction

Motivation is a key factor in influencing how well students perform in mathematics (Karakis et al., 2016). Many researchers have examined motivation as a mediating element and stressed its importance in affecting mathematical achievement (S.-Y. Chen & Lin, 2020; Ning, 2020; Tran & Nguyen, 2021; Wong & Wong, 2021). When learners are motivated, they tend to concentrate more, perform better in mathematical activities, and show persistence even when tackling complex problems (Arroyo et al., 2014). The application of information technology was often emphasized to enhance students’ motivation in learning mathematics (Bright et al., 2024). It is imperative to ensure and sustain students’ motivation toward learning mathematics (Schukajlow et al., 2017). Placing emphasis on getting students to use their mathematical skills in everyday situations and nurturing a genuine personal interest in the subject has also been highlighted (Fuqoha et al., 2018). Students who engaged in practical applications were more likely to recognize the relevance of mathematics to their lives and showed higher willingness to learn (Schoenherr, 2024). Based on the importance of motivation to mathematics achievement, strategies that can ensure and sustain students motivated should be the focus of studies. Therefore, this study focuses on improving students’ motivation in learning mathematics by integrating real life situations into mathematics classes.

China has regularly achieved outstanding results in the 2018 Program for International Student Assessment (PISA) which included 600,000 participants from four Chinese provinces (Beijing, Shanghai, Jiangsu, and Zhejiang). China ranked first across all three subjects among 79 participating countries and educational systems (V. Strauss, 2019). However, the study conducted by Gokhale (2019) showed that strong PISA results do not necessarily translate into higher motivation. At present, there is a noticeable decline in enthusiasm for mathematics in China, with many students intentionally avoiding the subject (Ng, 2018; Y. Zhang et al., 2023). R. Zhang et al. (2023) investigated the motivational levels of senior high school students using data collected from 623 Chinese learners over 2 years. Their results showed a blend of intrinsic and extrinsic motivation, though overall levels remained limited. The prevailing examination-focused education system could potentially lead to a decrease in students’ motivation for learning, which sheds light on why Chinese students, while showcasing adept numeracy skills, might not excel in addressing intricate open-ended problems (J. Cai et al., 2020). Under the influence of the belief that mathematics has no practical utility in real life, some students perceive mathematics as irrelevant to practical everyday situations. The emphasis of Chinese teachers on fostering student motivation is evident in their recognition of the practical applications of mathematics, with some believing that learners’ interest will grow once they understand its practical value (Wang, 2022).

The discourse and formulation of sustainable development revealed that education is essential for sustainability (Mckeown et al., 2002). Education is widely recognized as a cornerstone of sustainability, with the concept of Education for Sustainable Development (ESD) adopted internationally by the United Nations (UN). The purpose of ESD is to equip learners with the capacity to make well-informed choices and take responsible actions that uphold environmental integrity, economic stability, and social justice for both current and future generations, while honoring cultural diversity (UNESCO, 2017). The UN has set forth an extensive framework comprising 17 Sustainable Development Goals (SDGs) to address sustainability challenges, encompassing efforts such as combating climate change, eradicating poverty, promoting quality education, fostering sustainable cities, eliminating hunger, ensuring gender equality, providing clean air and water, advancing renewable energy, and encouraging responsible consumption and production (Semiz & Baykal, 2020). ESD emphasizes critical thinking and encourages student-centered classrooms, aligning with modern educational trends in the post-industrial era (Fekih Zguir et al., 2021). It emphasizes connecting lessons to the situations that happened in the students’ real life, encouraging their active participation and critical thinking. By implementing these approaches, students become more engaged with their surroundings and are motivated to actively contribute to finding solutions for contemporary challenges (Griswold, 2017).

Although integrating ESD into school subjects is challenging, it is also an opportunity to promote school subjects (Zowada et al., 2021). The relationships between ESD and a variety of research agendas in mathematics education have been established by the studies (Alsina, 2022). Embedding ESD into mathematics lessons can foster mathematically meaningful learning, enabling students to apply mathematical knowledge to address real-life issues (Coles, 2023; Hamilton & Pfaff, 2014). By incorporating sustainability, the aim is to highlight the practical relevance of mathematics for learners, while also nurturing values and positive attitudes toward the subject (Lafuente-Lechuga et al., 2020). Learning resources should align with the competencies promoted through ESD, and problem-solving tasks should be designed in accordance with the SDGs. This approach ensures that the provided mathematical material becomes more relevant and beneficial for students, simultaneously enhancing teachers’ skills and creativity in designing problems that reflect real-life scenarios (Widiati & Juandi, 2019). Integrated mathematics learning ESD is not only linked to mathematical knowledge but also applies mathematical knowledge to address real-life challenges (Martin et al., 2019). According to the situation mentioned above, this study aims:

To design and develop Sustainable Development Goals Problem-Based Learning (SDGPBL) strategy that will enhance students’ motivation in learning mathematics.

To test the effectiveness of the SDGPBL strategy in enhancing students’ motivation in learning mathematics.

To explore the process that students get motivated in learning mathematics and experiences during SDGPBL strategy.

Theoretical Background

ARCS Motivation Model

A widely recognized framework for studying motivation in education is the ARCS model, which stands for attention, relevance, confidence, and satisfaction (Li & Keller, 2018; Zabala-Vargas et al., 2022). The ARCS model developed by Keller (1984) is built on four elements: attention, relevance, confidence, and satisfaction (see Figure 1). The four components significantly influence students’ motivation during the process of learning mathematics (Khakpour et al., 2016). The expectancy-value theory is the theoretical foundation of ARCS model (Keller, 1987). In addition to focusing on instructional design, the ARCS model functions as a means of evaluating how teaching materials impact learner motivation and has been successfully utilized for more than 40 years (Dinçer, 2020).

Figure 1.

ARCS motivation model.

The aim of the attention component is to draw learners’ focus through a variety of instructional strategies and presentation formats (Izmirli & Izmirli, 2015). A practical way to keep students engaged in mathematics is to provide varied and creative approaches for presenting mathematical ideas, such as the use of computer software, multimedia resources, or group-based tasks, all of which help maintain students’ motivation (Novak, 2014). Teachers can enhance relevance by relating mathematics lessons to potential real-world applications in students’ futures (Izmirli & Izmirli, 2015).

Possible strategies include: (1) creating links between the mathematics curriculum and real-life situations, (2) meeting the educational needs of individual learners, (3) connecting current topics with future applications, (4) clearly stating learning objectives, (5) encouraging teamwork, and (6) giving learners personalized guidance (Milman & Wessmiller, 2016). Students’ confidence is tied to their ability to begin and successfully complete mathematical activities, which involves taking personal responsibility and developing self-efficacy (Hodges & Kim, 2013). Highlighting students’ skills and offering chances to apply them, as well as present their work, fosters pride in their achievements (Milman & Wessmiller, 2016). Satisfaction relates to how learners perceive the mathematical tasks they have accomplished.

Students often take pride in completing a mathematics task, and this pride naturally brings them a sense of personal satisfaction (Izmirli & Izmirli, 2015; Keller, 2009).

The ARCS model has been used in different ways to examine whether it improves learning motivation in recent years, and the results were proved to be encouraging (Chang & Chen, 2015). Many scholars have developed questionnaires based on the ARCS model to investigate students’ motivation in mathematics learning, and this approach has shown to be a reliable tool for evaluation (Bakar et al., 2010; Hsu, 2020; Wong & Wong, 2021). Therefore, this study chooses to use the ARCS-based questionnaire to investigate students’ motivation in learning mathematics.

Instructional-Learning Model Applying Problem-Based Learning Enabled by ICTs

Problem-based learning (PBL) approaches are designed to bridge the gap between theoretical concepts and their application in real-life contexts (Dreifuss-Serrano & Herrera, 2022). In the PBL setting, teachers guide students to tackle problems rooted in everyday situations, prompting them to analyze the scenario, identify the challenge, and work toward a solution (Amalia et al., 2017). While PBL enables students to connect real-life knowledge with required syllabus content, they may struggle to link it to prior knowledge (Botty et al., 2016). Research had consistently shown that PBL fosters mathematical problem-solving skills, mathematical reasoning, and communication skills more effectively than conventional teaching methods (Amalia et al., 2017; Napitupulu et al., 2016; Suparman et al., 2021). Motivation is another crucial factor influencing the success of PBL for that PBL positively impacts students’ motivation and collaborative learning (Hasrawati et al., 2020; Linda et al., 2018).

Nowadays, there exists a widespread consensus on the pervasive integration of information and communication technologies (ICTs) within the realm of education. Educational professionals and learners face a major challenge in narrowing the gap between the knowledge and skills offered by today’s educational systems and those needed to ensure individuals’ future well-being in an emerging knowledge-driven society (Makrakis & Kostoulas-Makrakis, 2017). The ESD was significantly impacted by the development in ICT (Al-Rahmi et al., 2020). Through the use of ICT-based infrastructure in technology-supported learning, it becomes possible to promote effective and sustainable educational practices via ICT tools and processes (Robinson et al., 2008). ICTs play an important role in people’s everyday lives and these tools are designed to facilitate the advancement of a sustainable future by addressing issues such as poverty reduction, enhancement of healthcare and education systems, and an overall enhancement in the quality of existence for a substantial number of individuals (Fernández-Portillo et al., 2019).

Makrakis and Kostoulas-Makrakis (2017) argued that the traditional 4Cs framework—communication, collaboration, creativity, and critical thinking—was insufficient and proposed an expanded 10Cs model. This broader model incorporates elements such as critical consciousness, connectivity, reflective thinking, shared responsibility, intercultural awareness, and knowledge construction, taking into account the sustainability crisis and the growing role of ICTs as tools for transformative learning. They stressed the need to progress from the 4Cs to the 10Cs in a time marked by rapid technological growth, the widespread use of ICTs, and the expansion of human knowledge, while tackling urgent sustainability challenges that pose risks to humanity’s future (Figure 2).

Figure 2.

ICTs as enabling tools for the 10Cs.

The sustainability crisis encompasses environmental, economic, social, and cultural challenges, as well as personal and moral dimensions stemming from human activities. Addressing these issues requires a shift in mindset that transforms how we exist in the world (learning to be), how we understand others by understanding ourselves (learning to live together), how we acquire knowledge (learning to know), and how we translate that knowledge into action (learning to do) (Makrakis & Kostoulas-Makrakis, 2017). In 2012, UNESCO introduced a fifth pillar of learning—“learning to give and share”—aimed at integrating volunteerism, social engagement, and education. Later, Makrakis and Kostoulas-Makrakis (2016) proposed a sixth pillar to further merge these elements with transformative learning. These six learning pillars are named 21st century learning pillars (shown as Figure 3). PBL is one of the educational and learning strategies that can greatly encourage such a change by stressing contemplation and action to provide problem solutions. Within this framework, PBL plays a vital role by encouraging student-centered approaches, fostering teamwork, promoting cooperative learning, and nurturing positive interdependence, proactive engagement, and personal responsibility, making it a valuable tool for advancing the 21st-century learning agenda (Gupta, 2022; Patange et al., 2019).

Figure 3.

21st century learning pillars.

The concept of 21st-century learning pillars combined with ICTs and the 10Cs framework has evolved into a more sustainable approach to education (Makrakis, 2017; Makrakis & Kostoulas-Makrakis, 2013). Drawing on these ideas, Makrakis and Kostoulas-Makrakis (2017) introduced a problem-based learning model. This model consists of several interconnected processes that explain PBL both as a teaching method and as a curriculum strategy. Each process incorporates a range of skills derived from the 10Cs (see Figure 4).

Figure 4.

The key skills integrated into the PBL processes.

Design and Development of Sustainable Development Goals Problem-Based Learning Strategy

The ARCS motivation model and the instructional-Learning Model Applying Problem-Based Learning Enabled by ICTs were applied to design the classroom instruction sustainable development goals problem-based learning (SDGPBL) strategy systematically in such a way that it will emphasis on “real-life” sustainability problems solving process, group collaboration and the integration of various skills drawn from the 10Cs, 21st learning pillars. This study applied ADDIE model to design and develop the classroom instruction for this study to enhance students’ motivation in learning mathematics. Content validation by experts was performed after designing and developing the strategy.

Analysis is the first stage of ADDIE model, which is the most crucial part and serves as the foundation for all other phases. In this study, the researcher explored the necessity of applying the SDGPBL approach in mathematics classrooms to boost high school students’ enthusiasm for learning mathematics. To assess the motivation levels of Ningbo high school students in mathematics and to inform the analysis phase of ADDIE, the first questionnaire survey was conducted. This highlights the importance of adopting a learning strategy that connects the SDGs’ real-world issues with problem-based learning, thereby encouraging students to see mathematics as a valuable tool for addressing practical challenges and increasing their motivation to study the subject. The ARCS model was applied to design the questionnaire as same as the questionnaire in pretest and posttest. A total of 384 high school students selected according to Morgan table from six high schools in Ningbo participated in this questionnaire survey. These 6 schools include 2 high-level high schools, 2 medium-level high schools, and 2 lower-level high schools. Table 1 presents the percentages and averages for each option within the four dimensions of the questionnaire, along with the overall motivation score. Results revealed that the mean overall motivation score was 2.80, below the benchmark of 3.0, with “confidence” scoring even lower at 2.63. Figure 5 showed that there have more negative respondents than positive respondents in each dimension and overall motivation. The results from the mean and percentage indicated that students’ motivation in learning mathematics was generally low.

Table 1.

Distribution of Percentage and Mean Scores on the Questionnaire Scale.

Scale	Percentage					Mean
Scale	Strongly disagree	Disagree	Neutral	Agree	Strongly agree	Mean
Attention	9.3	28.3	34.0	23.3	5.1	2.87
Relevance	10.0	28.1	35.2	22.6	4.1	2.83
Confidence	15.9	31.3	30.9	17.3	4.6	2.63
Satisfaction	11.7	23.0	39.0	21.2	5.1	2.85
Overall motivation	11.6	27.5	34.9	21.2	4.8	2.80

Figure 5.

Distribution of percentage on the questionnaire scale.

During the design phase, considering the methods available to students to collect data and the current critical situation with water resources and carbon emissions, and how relevant these two issues are to their daily lives. SDG 6 “Clean Water and Sanitation” and SDG 13 “Climate Action” were engaged in the mathematics classes. Students worked in groups to investigate water shortages which is related to SDG 6 and carbon emissions which is related to SDG 13 using the statistics knowledge learned in Chapter 9. Decisions were made on the topics, materials, and lessons that would be included in the program. Additionally, the objectives to be achieved after the program is implemented were determined, along with the strategies for accomplishing these objectives. A system plan and SDGs module were designed by the researcher to meet the learners’ requirements and provide guidance for the teacher. The plan included the identification of the topics to be addressed, time will be spent on each lesson, learning activities, lesson plan, tasks to be used and assessment instruments and media. The SDGs module presented the sustainable development goals, pillars of sustainability, 10Cs enabled by ICTs and 21st century learning pillars that to be developed in the SDGPBL strategy. The SDGs module provided to the teacher as guidance includes 4 contents: issues, lesson plan, SDGPBL strategy, and six parts of the activities. The researcher designed the SDGPBL based on the PBL Organizing framework as well as the ARCS motivation model that will have positive impact on learners’ motivation to learn mathematics.

During the development phase, the researcher collected all the information and the materials needed and then developed videos and materials, slides, group workshop, and group presentation assessment rubric. The main purpose of the videos and materials was to propose relevant questions based on them and attract students’ attention in class. These slides matched the teaching content of each lesson and served as an important reference for the teacher’s explanation at the part “linking claims to evidence.” Group workshop was an important reflection of group cooperation and group presentation assessment rubric provides a reliable standard for group presentation evaluations.

To ensure the implementation phase can be carried out successfully, the following three steps were taken into account: training the teacher, preparing the students, and creating the suitable learning environment. During the intervention, students work together as the center of the classroom to explore problems, collect and analyze data and propose solutions to problems following the steps of PBL enabled by ICTs and ARCS model. The teacher acted as facilitators in this process to explain knowledge to students, answer questions, check student activities and provide technical guidance. The whole experiment lasted for 7 weeks (Table 2).

Table 2.

Implementation Process of SDGPBL Strategy.

Week(s)/data	Lesson	Classroom	Lesson topics
1	—	—	ARCS motivation questionnaire (pretest)
2	1,2	Normal classroom	9.1.1 Simple Random Sampling 9.1.2 Stratified Random Sampling 9.1.3 Data Collection Methods
3	3,4	Normal classroom	9.2.1 Estimation of the Population Value Law 9.2.2 Estimation of Population Percentile
4	5,6	Normal classroom	9.2.3 Estimation of Population Central Tendency 9.2.4 Estimation of Population Dispersion
5	7,8	Computer classroom	Preparing for group presentation Making group presentation Assessing group presentation
6	—	—	ARCS motivation questionnaire (posttest)
7	—	—	Interview

In this study, evaluation happened at two levels, namely formative and summative. To gather data on each stage of the optimization process, formative evaluation is implemented. At the end of the program, a summative evaluation is carried out to thoroughly assess its influence on learning quality and students’ overall achievement (Widyastuti & Susiana, 2019). The formative evaluation was conducted throughout the entire process to assess the functionality of the SDGPBL strategy, and any feedback received was included into the development phase. developmental phase. The summative evaluation was conducted following the implementation of the final version of the instruction to evaluate the overall effectiveness of the SDGPBL strategy using the questionnaire survey and interview, which informed the researcher on the success of SDGPBL strategy in developing students’ motivation in learning mathematics.

Method

This research employed a quasi-experimental mixed-method design. To assess the effectiveness of the SDGPBL strategy, students’ motivation toward learning mathematics was measured both before and after the intervention. Motivation questionnaires were administered twice to both the experimental and control groups—once prior to the intervention and again afterward. The experimental group was taught using the SDGPBL method, while the control group continued with conventional instructional approaches. Within the experimental group, students were organized into eight small teams, each consisting of four members. The small group members worked together to use statistical knowledge and information technology software to explore and solve life problems related to SDGs. In the final stage, each group completed the group presentation through collaboration. The control group received instruction through a conventional teaching method, which primarily involved teacher-centered lectures and textbook-based exercises. The learning process focused on step-by-step procedural explanations, followed by individual practice on mathematical problems without the incorporation of technology or problem-based contexts.

Questionnaires based on Keller’s ARCS model were used to conduct the motivation surveys, and the results were analyzed to compare whether the students’ motivation was improved. Then seven students selected from the experimental group according to their posttest results were interviewed to get a closer understanding of the students’ motivation. Before conducting interview, the researchers adhered to ethical guidelines, which included outlining principles for conducting the study responsibly, obtaining informed consent from participants, and assigning identification codes to ensure confidentiality (Creswell et al., 2012; Nieuwenhuis, 2013). For data analysis, a deductive coding approach was applied to identify codes and themes within the interview transcripts by examining each student’s verbatim responses. The implementation process is shown as Figure 6.

Figure 6.

Implementation process.

Participants and the Selection Process

A total number of 68 senior one students (32 students in experimental group and 36 students in control group) from a high school in Ningbo participated in this study. Two classes are randomly selected as the samples of the study from the nine classes in the first grade of this high school using simple balloting method. In addition, the selected classes were randomly assigned to the experimental and control groups through a hat draw. From the experimental group, seven students were chosen to take part in the interview. The principle to select the students was based on their posttest result (Silverman, 2016; A. Strauss & Corbin, 2016), consisting of two students whose motivation was highest level, three students whose motivation was moderate level and two students whose motivation was lowest level. Before the experiment, the researchers informed the president and the parents of the participants about the details of the study and obtained their consent form. Participants also signed the consent form and a certificate of voluntary participation. To ensure that the students’ normal course progress at school was not affected, the researchers discussed the course progress with the teachers and the instructional design followed the normal progress.

Research Instrument

The same ARCS motivation questionnaire was the research instrument for both pretest and posttest. In the pretest, it was used to assess students’ prior experiences in learning mathematics, while the posttest measured their motivation after studying the topic “Statistics.” The experimental group was instructed using the SDGPBL approach, whereas the control group received traditional teaching. The questionnaire adopted a 5-point Likert scale (1 = “strongly disagree” to 5 = “strongly agree”), with each question offering five response options: Strongly Agree, Agree, Neutral, Disagree, and Strongly Disagree. To evaluate its reliability and validity, Cronbach’s Alpha (α) was calculated alongside expert review. An α value above .7 was considered evidence of strong reliability, and in this study, the coefficient reached .944. Table 3 presents the reliability for each scale. Two experts examined the questionnaire content, resulting in a revised version with 22 items that assessed attention, confidence, and satisfaction related to mathematics learning, as well as the perceived relevance of mathematics to students (6 items for “Attention,” 5 for “Relevance,” 5 for “Confidence,” and 6 for “Satisfaction”). For the semi-structured interviews, an interview protocol was used as the main instrument, enabling the researcher to confirm, verify, or challenge the impressions formed from other sources such as observations and documents.

Table 3.

Cronbach’s Reliability Analysis Test.

Scale name	Number of items	Cronbach’s α
Attention	6	.865
Relevance	5	.845
Confidence	6	.802
Satisfaction	5	.874
Total	22	.944

Data Collection and Data Analysis

Both quantitative and qualitative approaches were applied to gather the research data. For the pretest, questionnaires were used to assess students’ motivation to learn mathematics before the intervention. This pretest was carried out in both the experimental and control groups. The posttest was administered after the implementation of the SDGPBL strategy in the experimental group. Posttest data were collected using the same questionnaire to measure students’ motivation to learn mathematics in both groups. Qualitative data were obtained through a semi-structured interview, with each participant interviewed for 30 min. To analyze the survey results, motivation scores from the pretest and posttest were compared. Statistical analysis of the questionnaire data included descriptive measures (mean, percentage, and standard deviation), as well as one-sample t-tests and independent-sample t-tests, conducted using SPSS Statistics 29. The interview transcripts were examined through thematic analysis. The data obtained from the interview were descriptively coded at first and were further summarized by interpreting, interconnecting, and conceptualizing to find themes. Figure 7 as shown below is a flowchart illustrating the study process.

Figure 7.

Flowchart illustration of the study.

Results

ARCS Motivation Questionnaires

Table 4 shows the Shapiro–Wilk test outcomes used to check the normality of pretest and posttest results for both the experimental and control groups. The analysis revealed that the data were normally distributed, as the p-value exceeded .05. In addition, visual checks of histograms, Q-Q plots, and box plots confirmed that the scores from both the pretest and posttest were roughly normally distributed for the two groups.

Table 4.

Data Normality for the Pretest and Posttest Scores of Both Groups.

Test	Group	Kolmogorov-Smimova			Shapiro-Wilk
Test	Group	Statistic	df	Sig.	Statistic	df	Sig.
Pretest	Experimental	0.137	30	.161	0.963	30	.363
	Control	0.133	31	.173	0.967	31	.452
Posttest	Experimental	0.143	30	.119	0.935	30	.066
	Control	0.098	30	.200	0.980	30	.829

The pretest and posttest outcomes for the experimental group were analyzed using a one-sample t-test. Because some questionnaires were invalid, the pretest and posttest data could not be paired for certain students. Instead, the average score from the pretest was calculated and used as the benchmark for comparing the posttest mean scores through a one-sample t-test (Gleason et al., 2022; Huang et al., 2016). The results of this analysis are summarized in Table 5.

Table 5.

One-Sample t-test of Posttest Scores of the Experimental Group.

Group	N	M	SD	t	df	p	Mean diff.	Remark
Experimental group/posttest	30	4.08	0.54	8.526	29	<.001	0.85	Sig.

Note. Test value = 3.23.

The one-sample t-test results demonstrate that the posttest scores of the experimental group are noticeably higher than the pretest mean of 3.23. With a t-value of 8.562 and p-values less than .001, it can be concluded that the intervention has had a significant positive effect on the experimental group students’ motivation in learning mathematics. The mean difference of 0.85 provide further evidence of this improvement. To further study the differences of students’ motivation in learning mathematics between the pretest and posttest scores of the experimental group, One-sample t-test was used to analyze each dimension of ARCS motivation as shown in Table 6.

Table 6.

One-Sample t-test of Posttest Scores for Each Dimension of the Experimental Group.

Dimension	N	M	SD	Test value	t	df	p	Mean diff.	Remark
Attention	30	4.20	0.65	3.40	6.711	29	<.001	0.80	Sig.
Relevance	30	4.13	0.68	3.12	8.088	29	<.001	1.01	Sig.
Confidence	30	3.85	0.63	2.87	8.527	29	<.001	0.98	Sig.
Satisfaction	30	4.12	0.62	3.47	5.777	29	<.001	0.65	Sig.

All the p-values of four dimension were lower than .001. Results from the one-sample t-test for Attention, Relevance, Confidence, and Satisfaction revealed statistically significant increases in posttest mean scores compared to the pretest averages. The positive mean differences, along with confidence intervals that excluded zero, provide further evidence that the intervention had a clear and beneficial effect on students’ motivation to learn mathematics in each of these areas.

To determine whether a notable difference existed between the control group’s pretest and posttest mean scores, a one-sample t-test was conducted, as presented in Table 7. The pretest mean was 3.03, while the posttest mean was 3.17, resulting in a mean difference of 0.15. The standard deviations for the pretest and posttest were 0.72 and 0.55, respectively. For the control group’s posttest, the t-value was 1.482, and the p-value was .075, indicating that the change in scores from pretest to posttest was not statistically significant.

Table 7.

One-Sample t-test of Posttest Scores of the Control Group.

Group	N	M	SD	t	df	p	Mean diff.	Remark
Control group/posttest	30	3.18	0.55	1.482	29	.075	0.15	Not sig.

Note. Test value = 3.03.

To further study the differences of students’ motivation in learning mathematics between pretest and posttest scores of control group, one-samples t-test was used to analyze each dimension of ARCS as shown in Table 8. From Table 8, the results suggest that the control group did not experience significant changes in Attention and Confidence with t(29) = 0.204 and t(29) = 0.833 and p > .05. There was a significant improvement in Relevance with t(29) = 2.781 and p < .05, while Satisfaction showed a marginally significant decrease.

Table 8.

One-Sample t-test of Posttest Scores for Each Dimension of the Control Group.

Dimension	N	M	SD	Test value	t	df	p	Mean diff.	Remark
Attention	30	3.21	0.57	3.19	0.204	29	.420	0.02	Not sig.
Relevance	30	3.26	0.61	2.95	2.781	29	.005	0.31	Sig.
Confidence	30	2.81	0.83	2.68	0.833	29	.206	0.13	Not sig.
Satisfaction	30	3.45	0.37	3.58	−1.913	29	.033	−0.13	Sig.

Students’ performances were further examined through an independent sample t-test. The analysis compared the pretest and posttest scores of both the experimental and control groups to assess their initial equivalence and to evaluate the impact of the SDGPBL strategy. To determine whether any significant differences existed between the pretest scores of the two groups, an independent sample t-test was conducted, as shown in Table 9.

Table 9.

Independent-Sample t-test of the Pretest Scores.

Group	N	M	SD	t	df	p	Mean diff.	Remark
Experimental	30	3.23	0.76	1.050	59	.149	0.20	Not sig.
Control	31	3.03	0.72	1.050	59	.149	0.20	Not sig.

The test produced a value of 1.050 with a p-value of .149, which is above the .05 threshold. This means there was no meaningful difference in the pretest scores between the experimental and control groups. The mean gap was 0.20, and the 95% confidence interval [−0.18047, 0.57937] crossed zero, supporting the conclusion that the difference was not significant. To look further into variations in students’ motivation for learning mathematics, an independent-samples t-test was carried out for each ARCS dimension, as shown in Table 10.

Table 10.

Independent-Sample t-test of the Pretest Scores for Each Dimension.

Dimension	Group	N	M	SD	t	df	p	Mean diff.	Remark
Attention	Experimental	30	3.40	0.92	1.024	59	.155	0.21	Not sig.
Attention	Control	31	3.19	0.69	1.024	59	.155	0.21	Not sig.
Relevance	Experimental	30	3.12	0.85	0.742	59	.231	0.17	Not sig.
Relevance	Control	31	2.95	0.89	0.742	59	.231	0.17	Not sig.
Confidence	Experimental	30	2.87	0.90	0.826	59	.206	0.19	Not sig.
Confidence	Control	31	2.68	0.83	0.826	59	.206	0.19	Not sig.
Satisfaction	Experimental	30	3.47	0.65	−0.866	59	.195	−0.11	Not sig.
Satisfaction	Control	31	3.58	0.38	−0.866	59	.195	−0.11	Not sig.

For “Attention,” the t-value was 1.024 with a p-value of .155, higher than the .05 level, indicating no clear difference between the two groups. The mean gap stood at 0.21, and the 95% confidence range [−0.20246, 0.62679] included zero. In “Relevance,” the t-value reached 0.742 (p = .231), while “Confidence” recorded 0.826 (p = .206); neither result pointed to any meaningful variation. For “Satisfaction,” the t-value was −0.866 and the p-value .195, again suggesting no difference. All confidence intervals at the 95% level contained zero, confirming the lack of significant change. After the intervention, both groups took the same posttest to measure any effect of the treatment. Table 11 shows the posttest mean score comparisons.

Table 11.

Independent-Sample t-test of the Posttest Scores.

Group	N	M	SD	t	df	p	Mean diff.	Remark
Experimental	30	4.08	0.54	6.367	58	<.001	0.90	Sig.
Control	30	3.18	0.55	6.367	58	<.001	0.90	Sig.

The t-value is 6.367 with a p-value of <.001. Since the p-value is less than .05, it rejects the null hypothesis. This indicates that the scores of the experimental is greater than the control group in posttest significantly. The mean difference is 0.90, and the 95% confidence interval [0.61752, 1.18382] does not include zero, further confirming the significance of the difference. To further examine variations in students’ motivation to learn mathematics between the two groups after the posttest, an independent-samples t-test was conducted for each ARCS dimension, as presented in Table 12.

Table 12.

Independent-Sample t-test of the Pretest Scores for Each Dimension.

Dimension	Group	N	M	SD	t	df	p	Mean diff.	Remark
Attention	Experimental	30	4.20	0.65	6.262	58	<.001	0.99	Sig.
Attention	Control	30	3.21	0.57	6.262	58	<.001	0.99	Sig.
Relevance	Experimental	30	4.13	0.68	5.187	58	<.001	0.87	Sig.
Relevance	Control	30	3.26	0.61	5.187	58	<.001	0.87	Sig.
Confidence	Experimental	30	3.85	0.63	5.463	58	<.001	1.04	Sig.
Confidence	Control	30	2.81	0.83	5.463	58	<.001	1.04	Sig.
Satisfaction	Experimental	30	4.12	0.62	5.106	58	<.001	0.67	Sig.
Satisfaction	Control	30	3.45	0.37	5.106	58	<.001	0.67	Sig.

As shown in table 5.13, t(58) = 6.262 and p < .001 for “Attention”; t(58) = 5.187 and p < .001 for “Relevance”; t(58) = 5.463 and p < .001 for “Confidence”; t(58) = 5.106 and p < .001 for “Satisfaction.” It can be seen that there have statistically significant difference in scores between the experimental and control groups in posttest among all the dimensions through the results. The confidence interval does not include zero, confirming the significance of the difference. The experimental group achieved markedly superior scores compared to the control group across all dimensions in the posttest, suggesting that the intervention had a substantial positive effect.

Semi Structures Interview

The interview findings were organized into five overarching themes, which served as the framework for the interview protocol. One theme was aligned with the four dimensions of students’ motivation (Attention, Relevance, Confidence, and Satisfaction). The other two themes related to the influence of the 21st-century learning pillars, namely the 10Cs in the SDGPBL strategy, focusing on group collaboration and the teacher’s role, which reflected the learners’ perceptions of the new learning experience. In presenting the results, illustrative quotes from students were used to highlight each theme. Individual students were identified by assigning a letter that represented their motivation level, followed by a numerical code to specify the student. For example, “H2” referred to the second student categorized under high motivation.

ARCS Motivation

The results of this theme showed a positive outcome as it demonstrated all the four dimensions of the ARCS motivation. The demonstrated dimensions of motivation in learning mathematics complement the findings of the quantitative analysis (see Table 12) which shows a significant difference in the four dimensions of motivation in learning mathematics of attention, relevance, confidence and satisfaction. The students’ comments appeared to be positive and all of the students were able to respond to the questions asked by the researcher. From the comments of the seven students, it can be seen that the feedback on the “attention” dimension is the most. The SDGPBL strategy attracts students’ interest in various aspects and makes them pay more attention in the class.

H1: The videos played in class helped me focus more on the class.

M2: It’s more interesting than the previous class.

L2: It has many exploration activities and most of us are quite interested in this.

In addition, the relevance of mathematics to real life also makes them more interested in mathematics. The integration of sustainable issues into mathematics classes makes students more motivated to explore and solve problems.

H2: The teacher combines the real-life event with our lessons and introduces content other than mathematics knowledge, which makes me more interested.

M1: It is quite novel and is about sustainable development, so I am more interested in it than before.

L1: I think the previous classes were quite boring. Now I feel there is more time for discussion and it is closer to our lives, so I am quite interested… Because these issues are related to our lives, I am more focused in class.

21st Century Learning Pillars

The inquiry regarding students’ perspectives on SDG 6 and SDG 13 showed favorable results as it demonstrated five out of the six learning pillars. Students’ comments indicate that the integration of these two SDGs in mathematics classroom has made them respond to a sustainability. All the seven students learned about the issues related to these two goals and the challenges in sustainable development during the learning process of the SDGPBL strategy (Learning to know).

H2: The data collected in class reflects the issue of sustainable development more intuitively.

M3: During the exploration process, I can feel from the data that the water shortage and carbon emissions problems are indeed serious.

L1: After learning about water shortage issue in class, I feel that water waste is serious in life … Through our group’s preparation, I found that many behaviors in life can produce excessive carbon emissions and affect the climate.

Through this intervention, these seven students gained ideas about how to make their own lives sustainable (Learning to be) and change their previous behaviors and the events in the society that are not in line with the values of sustainability (Learning to transform oneself and society). The results of this analysis show that the application of the SDGPBL strategy in mathematics learning has a positive impact on sustainable development.

H1: Water shortage and carbon emissions are closely related to our lives, and we can improve these two problems in our own life (Learning to be) … I hope everyone can do their part for sustainable development. Personally, I will try my best to contribute to it, such as save water (Learning to transform oneself and society).

L2: I am the editor of our school magazine. I hope to have the opportunity to write some articles related to sustainability to let more people understand sustainability (Learning to be, Learning to transform oneself and society).

10Cs

Eight aspects of 10Cs are reflected in students’ experience in using knowledge of the topic “Statistics” to solve SDGs-related problems. In this process, students first realized that it’s our co-responsibility to achieve sustainability and then developed critical consciousness and critical thinking about SDGs-related issues. They collaborated and communicated with group members to solve the problems step by step. Besides, students also had critical reflections on these sustainability issues from their daily lives. This indicates that learning process has taken place (Buckler, 2016) as there is a change in the students’ behaviors. Although not achieving all 10Cs under the SDGPBL strategy, the students gained a lot in this process.

H1: Everyone can play their part in sustainable development (co-responsibility).

To solve the water and carbon emissions issues raised in class, we discussed and collaborated in group to come up with solutions (collaboration, communication).

L1: It’s indeed that my actions have impacts on sustainable development (critical consciousness).

M3: Based on the results of the data analysis, I think we can appropriately shorten our shower time and save water (Critical thinking and problem solving).

Group Collaboration

Six out of seven students agreed that the group collaboration have positive impact on them. They commented positively on the group work aspect of the SDGPBL strategy. They think the process of group collaboration is interesting, active, necessary, and practical (Barkley et al., 2014; Berlanga et al., 2024; Zubiri-Esnaola et al., 2020). The students pointed that they could concentrate more when completing group tasks, which helps them master mathematics knowledge better. The most profound feeling these students have been the sense of accomplishment they get after completing the group work (Onyekwere & Azubuike, 2023; Tiantong & Teemuangsai, 2013).

H2: It is a great sense of accomplishment when group members work together to solve problems (Have a sense of accomplishment).

M3: I think group presentation is helpful for us to learn mathematics knowledge. It can help us review the mathematics knowledge (Helpful to acquire knowledge).

L1: Working in group has increased my interest in exploring problems in depth (interesting).

Only one student had negative comments on group collaboration. He thought that group work took up too much class time and thus he could not learn as much mathematics knowledge as possible in the limited class time. At the same time, the uneven contribution of group members to group work also gave him a bad impression of group collaboration. However, the student still found the group work process interesting.

M2: I think I contribute more to the group work, and I think it’s really unfair that other group members don’t contribute as much as I do.

I think that sometimes the explore activities in class last too long, taking up about 20 min. In this case, in fact, not much knowledge is learned in class, and I don’t support group collaboration that much. But I still think it is interesting.

Teacher’s Role

All students agreed that the teacher’s role have changed in the new learning experience and they found that the teacher was no longer the leader in the classroom. From the codes “The teacher is more like a guide,”“The teacher is like an organizer,” it can be seen that the mathematics classroom under the SDGPBL strategy is no longer teacher centered. The code “Give students more time to explore on their own” can reflect that the students take a greater role in the learning process now and the teacher play a role in assisting the students. This indicates that learning process has taken place (Buckler, 2016) as there is a change in the teacher’ role.

H1: The teacher walked around the classroom to see if our group work was going well and help us solve any problems (providing help and answering questions when we need it).

M2: The teacher provides us with more opportunities to show ourselves … we are given time to explore the activities by ourselves (Give students more time to explore on their own).

L3: He guided us more on how to take the next step rather than told us directly what to do (The teacher is more like a guide).

Discussion

Design and Development of Sustainable Development Goals Problem-Based Learning Strategy

The SDGPBL strategy provided the students with lots of ideas and opportunities to have some new experiences to learn mathematics in a broader context leading to motivating students to learn the content. The mathematics content taught using the SDGPBL strategy has the potential to be comprehended, internalized, and retained more effectively than content taught using conventional methods, where students are passive learners because the activities in the SDGPBL strategy are designed to enhance students’ motivation in learning mathematics through integrating real-life problems related to SDGs into mathematics classroom. According to Hammond (2000), smaller collaborative groups make it simpler to motivate students who are not actively participating. This leads to a greater sense of presence and involvement, as well as enhanced individual contributions. Preparing students for group projects, organizing groups, assigning group-work tasks, and guiding student involvement via teachers’ discourse with small groups and with the class help the teacher to promote good group dialogue (Fereday & Muir-Cochrane, 2006). This study indicated that the group collaboration component in SDGPBL strategy exerted a beneficial influence on students’ motivation to learn mathematics in the classroom. The role of the teacher has also changed due to the setting of the group collaboration part, and the teacher became the facilitator of the group collaboration.

Nevertheless, the SDGPBL strategy is helpful to enhances students’ learning in the mathematics classroom, as demonstrated by the study’s findings. The designed learning instruction proves to be effective in improving students’ performance and motivation in mathematics. A notable limitation of this study lies in the restricted scope of instructional content, as only the topic “Statistics” was integrated with the SDGPBL strategy in the instructional design. “Statistics” was selected due to its strong alignment with PBL principles, especially its focus on data interpretation and real-world problem-solving. In contrast, other mathematical domains like algebra, geometry, or calculus involve different conceptual structures and problem-solving demands. These differences may offer new opportunities for adapting and refining the SDGPBL framework.

The Effectiveness of the SDGPBL Strategy in Enhancing Students’ Motivation in Learning Mathematics

Results of students’ motivation were substantiated by posttest result of both experimental and control groups. The result of the independent sample t-test from Table 11 showed a statistically significant mean difference, implying that the student that used the SDGPBL strategy were more motivated after undergoing the intervention than the students taught using conventional method. This was supported by the Cohen’s d value for the test as it appeared to have an effect size that is large in the difference of mean (Cohen et al., 2002). The findings conform to the finding that connecting lessons to the situations that happened in the students’ life, encouraging their active participation and critical thinking, students become more engaged with their surroundings and are motivated to actively contribute to finding solutions for contemporary challenges (Griswold, 2017). The comparison of posttest mean scores for each dimension of students’ motivation in mathematics was carried out using an independent sample t-test. As shown in Table 12, all four dimensions “attention, relevance, confidence, and satisfaction” showed significant differences between the two groups. The findings reveal that students in the experimental group scored notably higher in these areas compared to those in the control group. This suggests that the SDGPBL approach played a meaningful role in enhancing motivation toward mathematics learning across all measured aspects (see Table 12). The Cohen’s d values showed a substantial effect size in the mean differences (Cohen et al., 2002). These findings further support the role of the SDGPBL approach in improving learners’ motivation in mathematics.

According to the ARCS framework, increasing student motivation involves four key elements: (1) drawing and keeping learners’ attention; (2) making clear why the material is worth learning; (3) helping students believe they can succeed through their own effort; and (4) encouraging feelings of achievement and satisfaction (Keller, 1987). It can be concluded that using the SDGPBL strategy for students to learn mathematics succeed improving their performances as well as their motivation in learning mathematics. This result is similar to the findings of Y. Chen (2019), Gu and Lee (2019), Hsu (2020) and Wong and Wong (2021) who found out that the students’ motivation in learning mathematics can be enhance through the inventions which catching students’ attention, connecting real life with mathematics, inspiring students’ confidence, and satisfaction. The findings of this study indicate that introducing SDGS-related problems in the mathematics classroom through videos and materials can capture students’ attention in class (attention); Students were aware of the relevance of mathematics to real life using the knowledge of statistics to analyze SDGS-related problems (relevance); Students gained confidence in working in small groups to explore and solve problems and complete the group presentation (confidence); Students had great sense of accomplishment after solving problems through exploring by themselves (satisfaction). These are all attested and demonstrated by the student’s comments during the conduct of interview when they were asked about the learning process in the SDGPBL strategy intervention.

This study was conducted in a single medium-level high school, which means that the sample primarily represented students with moderate academic performance in mathematics. Under these conditions, the SDGPBL strategy was found to effectively enhance students’ learning motivation, as reflected in the four ARCS dimensions: Attention, Relevance, Confidence, and Satisfaction. However, the potential impact of the SDGPBL strategy on students from higher-level and lower-level schools remains an open question. If experimental conditions had permitted, a stratified sampling approach involving students from high-, medium-, and low-level high schools to form both experimental and control groups would have been preferable. Such a design would enable direct comparison of the SDGPBL strategy’s effects across different academic levels, providing deeper insights into how variations in prior knowledge, learning habits, and motivation influence its effectiveness.

The Process that Students Get Motivated in Learning Mathematics and Experiences During SDGPBL Strategy

The verbatim quotes from the students’ interview provided additional confirmation of the findings from the quantitative study as the students achieved all the four dimensions of motivation in learning mathematics which shows the same result as in Table 12 of a significant differences between the experimental and control groups posttest mean scores of all the four dimensions. It revealed that learners were able to develop all the four dimensions of motivation in learning mathematics after integrating the SDGPBL strategy into their learning process. The most frequent identified code is “attention” which appeared in 21 different places based on their responses. It is in line with the studies of Hao and Lee (2021), Ma and Lee (2021) and Ithnin et al. (2023) who pointed that attention was consistently the most significant factor influencing students’ motivation. The second most commonly occurring code in the students’ direct quotes was “relevance,” appearing in fourteen instances. This indicates that students could relate mathematics to real-life situations. Moreover, according to the students verbatim quotes, “confidence” was the third most frequently identified code, appearing in 12 different places. “Satisfaction” was the fourth most frequent code, coded in nine different places. This indicates both are important, though less common than other codes. Confidence-building strategies significantly improved students’ willingness to participate in Zhu and Huang (2023) and satisfaction strategies, such as recognizing achievements and providing rewarding experiences, are essential for keeping students motivated over time (Goksu & Islam Bolat, 2021). Therefore, both quantitative and qualitative outcomes show a significant difference in the motivation in learning mathematics processes of attention, relevance, confidence, and satisfaction.

Majority of the students’ comments about their learning experiences while using the SDGPBL strategy were positive as they were improved by six learning pillars, 10Cs, group collaboration and the teacher’s assistance during the learning experience. Except for “learning to give/share,” a total of five out of six learning pillars have been achievement according to the codes obtained by the seven students’ responses. “Learning to know” and “Learning to be” both are the most frequent identified code achieved which appeared in eleven different places based on the students’ responses. This result is consistent with the improvement of students’ motivation in learning mathematics in this study, for that “Learning to know” encourages curiosity, analytical skills, and the ability to learn independently (Lawale & Adams, 2010) and “Learning to be” is crucial for personal accomplishment and the development of a sense of belonging and purpose (Delors, 1996). The code “Learning to transform oneself and society” appeared eight times and all the seven students mentioned about it. This indicates that in this study, not only the students’ motivation in learning mathematics has been improved, but also shifted their unsustainable values, behaviors, and actions, working together to transform themselves and contribute to building a more sustainable society.

The students’ response to their experience in using knowledge of the topic “statistics” to solve the SDGs-related problems was coded as 10Cs. Out of the 10Cs, students achieved eight in total, with “Collaboration” and “Communication” emerging as the most frequently identified codes, each appearing seven times in the students’ responses. Through problem-based learning, students are encouraged to cooperate in small groups to address real-world issues (Alexander et al., 2024; Trullàs et al., 2022). In this study, the students in the experimental group were divided into groups of four and a total of eight groups for learning and exploring. Therefore, students should have more collaboration and communication in the small groups. In addition, the students developed critical consciousness and critical thinking in the process and then had critical reflection. PBL encourages students to critically reflect on their knowledge and its application to issues related to the SDGs, enhancing their critical consciousness and competence in addressing sustainability issues (Grindsted & Nielsen, 2022). The issues of water shortage and carbon emissions introduced in the classroom in this study are very relevant to the lives of these students, and it is easier to stimulate these critical abilities of students.

Learning in small groups was a new experience for these students in learning mathematics and the teacher’s role has changed along with the change of learning strategy. Most of the students’ comments about the group collaboration while using the SDGPBL strategy were positive as they think group collaboration is “active, interesting, necessary, practical, novel, efficient, helpful to acquire knowledge.” They commented that they better focused and had a sense of accomplishment when completing tasks in group, which consistent with the study findings of Osman et al. (2011) and R. Zhang et al. (2023). Besides, there were some negative comments such as “I think it’s unfair that some group members don’t contribute much,”“I think that too much time is spent on group activities and not enough knowledge is learned.” But the student still found the group work process interesting. Students perceived the teacher as no longer the leader of the classroom but an organizer or guide, and the teacher assisted the students while giving them more time to explore and solve the problems on their own. These support the idea that in PBL settings, teachers assist students in exploring and solving problems independently, reinforcing the shift from traditional leadership to a more supportive role (Keiler, 2018). However, it is worth noting that one of the interviewees mentioned that there was an uneven contribution of group members in the group collaboration process, which affected his attitude to the explore activities. Gross et al. (2025) demonstrated that allowing individuals to choose interactions can minimize uneven contributions and foster a more effective cooperation mechanism, transcending limitations imposed by strict group boundaries. This study’s design could better facilitate interactions among group members during group formation, thereby addressing the issue of unequal participation.

Conclusion

The SDGPBL strategy was designed systematically in such a way that it gave more emphasis on the relevance with SDGs issues, group collaboration and student-centered. It includes a systematic process by which lesson materials are designed, developed, and implemented (Forest, 2014) as well as provide series of guidelines that describe the way it would be carried (Forest, 2014; Türel & Demirli, 2010). The quantitative analysis results shows that the students’ motivation in learning mathematics has improved significantly after the use of the SDGPBL strategy instruction leading to a conclusion that the SDGPBL strategy has been successfully designed and developed. It also showed that all the four dimensions of ARCS motivation where statistically significant leading to a conclusion that: attention, relevance, confidence, and satisfaction were successful during this study. This method was successful in enhancing students’ motivation in learning mathematics.

Seven students took part in interviews aimed at understanding their experiences and what motivated them to learn mathematics while being taught through the SDGPBL strategy instruction. The interview was transcribed and analyzed using thematic analysis. Students’ comments on their learning processes in SDGPBL strategy show that they have demonstrated attention, relevance, confidence, and satisfaction. Majority of the students had positive comments about their learning experiences while using the SDGPBL strategy as some of the six learning pillars and 10Cs of the students were developed, which shows that the learning strategy really support and improve their level of engagement in mathematics classroom. Besides, students’ comments about group collaboration and the teacher’s role in the class were mostly positive. It is therefore concluded that students have enhanced their motivation and developed their learning pillars and 10Cs as their responses complement the findings in the quantitative data (and they also have positive experiences while using the SDGPBL strategy.

It is necessary to mention some limitations even the SDGPBL strategy was proved to be effective. It took a long time to train the teacher and students to use technology software in this study. It is recommended that subsequent researchers incorporate technology software that teachers and students were already proficient in into their instructional design. This will save teachers’ training time and ensure the application of information technology in the study. The topic “Statistics” was selected to be combined with SDG 6 and SDG 13 for instruction design. The limited topics limits the choice of sustainable development goals. More SDGs are expected to be integrated into mathematics classes in conjunction with the SDGPBL strategy.

Footnotes

Acknowledgements

My deep appreciation goes to my parents for the vital role they played in raising me up and their tireless supports morally and financially. My gratitude also goes to Prof. Zhang Fengjuan and Prof. Zaleha Ismail for their help and their contribution to this article.

ORCID iDs

He Mengyao

Zhang Fengjuan

He Xueting

Ethical Considerations

In order not to affect the participants’ normal mathematics course learning progress in school, the researchers had meetings with the mathematics teachers to determine the class topics and content before the experiment. The instruction was designed based on the school’s course progress. Besides, due to the limited class time in school, the researcher and the mathematics teacher discussed and decided to let students complete some group cooperation tasks which would take a long time on the weekend. Many studies have shown that motivation to learn mathematics has positive impact on students’ mathematics achievements. In China, high school learning is very important for students and mathematics is essential as a major learning subject in high school. Students’ achievements will determine their future university choices. Therefore, this study has a potential positive impact on students’ mathematics achievement and their future university choices while improving their motivation to mathematics. This study involved high school participants. Before conducting the study, the participants, president and parents were informed of the contents of the study in detail and invited to fill out a consent form. Only those who signed to participate, students’ data were included in the study.

Consent to Participate

Informed consent was obtained from participants.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

Alexander

White

Varol

Appel

Lieneck

(2024). Team- and problem-based learning in health services: A systematic literature review of recent initiatives in the United States. Education Sciences, 14(5), 515. https://doi.org/10.3390/educsci14050515

Al-Rahmi

W. M.

Alzahrani

A. I.

Yahaya

Alalwan

Kamin

Y. B.

(2020). Digital communication: Information and communication technology (ICT) usage for education sustainability. Sustainability (Switzerland), 12(12), 5052. https://doi.org/10.3390/su12125052

Alsina

. (2022). On integrating mathematics education and sustainability in teacher training: Why, to what end and how? In Ortega-Sánchez

(Ed.), Controversial issues and social problems for an integrated disciplinary teaching. Integrated science (Vol. 8., pp. 9–21). Springer. https://doi.org/10.1007/978-3-031-08697-7_2

Amalia

Surya

Syahputra

Surya

(2017). The effectiveness of using Problem Based Learning (PBL) in mathematics problem solving ability for junior high school students. International Journal of Advance Research and Innovative Ideas in Education, 3(2), 3402-3406. https://www.researchgate.net/publication/318982082

Arroyo

Woolf

B. P.

Burelson

Muldner

Rai

Tai

(2014). A multimedia adaptive tutoring system for mathematics that addresses cognition, metacognition and affect. International Journal of Artificial Intelligence in Education, 24(4), 387–426. https://doi.org/10.1007/s40593-014-0023-y

Bakar

K. A.

Ayub

A. F. M.

Luan

W. S.

Tarmizi

R. A.

(2010). Exploring secondary school students’ motivation using technologies in teaching and learning mathematics. Procedia - Social and Behavioral Sciences, 2(2), 4650–4654. https://doi.org/10.1016/j.sbspro.2010.03.744

Barkley

E. F.

Major

C. H.

Cross

K. P.

(2014). Collaborative learning techniques: A handbook for college faculty. John Wiley & Sons, Eds.

Botty

H. M. R. H.

Shahrill

Jaidin

J. H.

H. C.

Chong

M. S. F.

(2016). The implementation of problem-based learning (PBL) in a year 9 mathematics classroom: A study in Brunei Darussalam. International Research in Education, 4(2), 34–47.

Bright

Welcome

N. B.

Arthur

Y. D.

(2024). The effect of using technology in teaching and learning mathematics on student’s mathematics performance: The mediation effect of students’ mathematics interest. Journal of Mathematics and Science Teacher, 4(2), em059. https://doi.org/10.29333/mathsciteacher/14309

10.

Buckler

(2016). Achieving continuous improvement and innovation through learning models. In The Learning Organization, 23, 3214–3223.

11.

Cai

Chen

Zhang

Song

(2020). Exploring the impact of a problem-posing workshop on elementary school mathematics teachers’ conceptions on problem posing and lesson design. International Journal of Educational Research, 102, Article 101404. https://doi.org/10.1016/J.IJER.2019.02.004

12.

Chang

N.-C.

Chen

H.-H.

(2015). A motivational analysis of the ARCS model for information literacy courses in a blended learning environment. Libri, 65(2), 129–142. https://doi.org/10.1515/libri-2015-0010

13.

Chen

S.-Y.

Lin

S.-W.

(2020). A cross-cultural study of mathematical achievement: From the perspectives of one’s motivation and problem-solving style. International Journal of Science and Mathematics Education, 18(6), 1149–1167. https://doi.org/10.1007/s10763-019-10011-6

14.

Chen

(2019). Effect of mobile augmented reality on learning performance, motivation, and math anxiety in a math course. Journal of Educational Computing Research, 57(7), 1695–1722. https://doi.org/10.1177/0735633119854036

15.

Cohen

Manion

Morrison

(2002). Research methods in education. Routledge. https://doi.org/10.4324/9780203224342

16.

Coles

(2023). Towards a socio-ecological practice of mathematics teacher education. Avances de Investigación En Educación Matemática, 23, 19–35. https://doi.org/10.35763/aiem23.5489

17.

Creswell

Klassen

A. C.

Plano Clark

V. L.

Smith

K. C.

Meissner

H. I.

(2012). Best practices in mixed methods for quality of life research. Quality of Life Research, 21(3), 377–380. https://doi.org/10.1007/s11136-012-0122-x

18.

Delors

(1996). Learning: The treasure within. UNESCO.

19.

DİNÇER

(2020). The effects of materials based on ARCS Model on motivation: A meta-analysis. İlköğretim Online, 19, 1016–1042. https://doi.org/10.17051/ilkonline.2020.695847

20.

Dreifuss-Serrano

Herrera

P. C.

(2022, December 2–4). SDGs for the assessment of voluntourism learning experiences [Conference session]. 2022 IEEE International Humanitarian Technology Conference, IHTC 2022, Ottawa, ON, Canada (pp. 27–31). https://doi.org/10.1109/IHTC56573.2022.9998409

21.

Fekih Zguir

Dubis

Koç

(2021). Embedding Education for Sustainable Development (ESD) and SDGs values in curriculum: A comparative review on Qatar, Singapore and New Zealand. Journal of Cleaner Production, 319, Article 128534. https://doi.org/10.1016/j.jclepro.2021.128534

22.

Fereday

Muir-Cochrane

(2006). Demonstrating Rigor using thematic analysis: A hybrid approach of inductive and deductive coding and theme development. International Journal of Qualitative Methods, 5(1), 80–92. https://doi.org/10.1177/160940690600500107

23.

Fernández-Portillo

Almodóvar-González

Coca-Pérez

J. L.

Jiménez-Naranjo

H. V.

(2019). Is sustainable economic development possible thanks to the deployment of ICT? Sustainability (Switzerland), 11(22), 6307. https://doi.org/10.3390/su11226307

24.

Forest

(2014). University teachers and instruction. In Forest

(Ed.), University teaching: International perspectives (Vol. 35). Routledge.

25.

Fuqoha

A. A. N.

Budiyono

Indriati

(2018). Motivation in mathematics learning. Pancaran Pendidikan, 7(1), Article 151. https://doi.org/10.25037/pancaran.v7i1.151

26.

Gleason

A. M.

Melhem

Dimitri

Al Hmaimat

(2022). Nursing students’ anxiety about distance education during COVID19: A quasi-experimental non-equivalent group pre/post-survey. International Journal of Nursing and Health Care Research, 6(12), 1368. https://doi.org/10.29011/2688-9501.101368

27.

Gokhale

S. S.

(2019, March 16). Do PISA scores relate to happiness? [Conference session]. 2019 IEEE Integrated STEM Education Conference (ISEC), Princeton, United States (pp. 111–116). https://doi.org/10.1109/ISECon.2019.8882045

28.

Goksu

Islam Bolat

(2021). Does the ARCS motivational model affect students’ achievement and motivation? A meta-analysis. Review of Education, 9(1), 27–52. https://doi.org/10.1002/rev3.3231

29.

Grindsted

T. S.

Nielsen

T. T.

(2022). Spaces of learning – practising the SDGs through geographical fieldwork methods in a nature park. International Journal of Sustainability in Higher Education, 23(8), 105–119. https://doi.org/10.1108/IJSHE-04-2021-0132

30.

Griswold

(2017). Creating sustainable societies: Developing emerging professionals through transforming current mindsets. Studies in Continuing Education, 39(3), 286–302. https://doi.org/10.1080/0158037X.2017.1284054

31.

Gross

Götz

Reher

Toscano

(2025). Free mobility across group boundaries promotes intergroup cooperation. Communications Psychology, 3(1), 10. https://doi.org/10.1038/s44271-025-00192-y

32.

Lee

(2019). Promoting students’ motivation and use of SRL strategies in the web-based mathematics learning environment. Journal of Educational Technology Systems, 47(3), 391–410. https://doi.org/10.1177/0047239518808522

33.

Gupta

(2022). The impact and measurement of today’s learning technologies in teaching software engineering course using design-based learning and project-based learning. IEEE Transactions on Education, 65(4), 703–712. https://doi.org/10.1109/TE.2022.3169532

34.

Hamilton

Pfaff

T. J.

(2014). Sustainability education: The what and how for mathematics. PRIMUS, 24(1), 61–80. https://doi.org/10.1080/10511970.2013.834526

35.

Hammond

(2000). Communication within on-line forums: The opportunities, the constraints and the value of a communicative approach. Computers & Education, 35(4), 251–262. https://doi.org/10.1016/S0360-1315(00)00037-3

36.

Hao

K.-C.

Lee

L.-C.

(2021). The development and evaluation of an educational game integrating augmented reality, ARCS model, and types of games for English experiment learning: An analysis of learning. Interactive Learning Environments, 29(7), 1101–1114. https://doi.org/10.1080/10494820.2019.1619590

37.

Hasrawati, Ikhsan

Hajidin (2020). Improving students’ problem-solving ability and learning motivation through problem based learning model in senior high school. Journal of Physics: Conference Series, 1460(1), Article 012027. https://doi.org/10.1088/1742-6596/1460/1/012027

38.

Hodges

C. B.

Kim

(2013). Improving college students’ attitudes toward mathematics. TechTrends, 57(4), 59–66. https://doi.org/10.1007/s11528-013-0679-4

39.

Hsu

Y.-C.

(2020). Exploring the learning motivation and effectiveness of applying virtual reality to high school mathematics. Universal Journal of Educational Research, 8(2), 438–444. https://doi.org/10.13189/ujer.2020.080214

40.

Huang

J. T.

Reynolds

S. D.

DiGiovanni Evans

Zimmermann

Joyce

C. J.

Katz

J. T.

Buzney

(2016). Fine arts curriculum improves observational skills of dermatology trainees: A pilot study. British Journal of Dermatology, 175(4), 815–817. https://doi.org/10.1111/bjd.14616

41.

Ithnin

H. S.

Mohd Nasir

Ramli

(2023). Engaging tertiary students: A comparative study of online learning tools with arcs motivation model integration. Information Management and Business Review, 15(4(I)), 41–48. https://doi.org/10.22610/imbr.v15i4(I).3651

42.

Izmirli

O. S.

(2015). Factors motivating preservice teachers for online learning within the context of ARCS motivation model. Turkish Online Journal of Distance Education, 16(2), Article 5. https://doi.org/10.17718/tojde.26620

43.

Karakis

Karamete

Okcu

(2016). The effects of a computer-assisted teaching material, designed according to the ASSURE instructional design and the ARCS model of motivation, on students’ achievement levels in a mathematics lesson and their resulting attitudes. European Journal of Contemporary Education, 15(1), 105–113. https://doi.org/10.13187/ejced.2016.15.105

44.

Keiler

L. S.

(2018). Teachers’ roles and identities in student-centered classrooms. International Journal of STEM Education, 5(1), 34. https://doi.org/10.1186/s40594-018-0131-6

45.

Keller

J. M.

(1984). The use of the ARCS model of motivation in teacher training. In Shaw

Trott

A. J.

(Eds.), Aspects of educational technology (Vol. 17, pp. 140–145). Kogan.

46.

Keller

J. M.

(1987). Development and use of the ARCS model of instructional design. Journal of Instructional Development, 10(3), 2–10. https://doi.org/10.1007/BF02905780

47.

Keller

J. M.

(2009). Motivational design for learning and performance: The ARCS model approach. Springer Science & Business Media.

48.

Khakpour

Franke

Gortschakow

Uhrlandt

Methling

Weltmann

K.-D.

(2016). An improved arc model based on the arc diameter. IEEE Transactions on Power Delivery, 31(3), 1335–1341. https://doi.org/10.1109/TPWRD.2015.2473677

49.

Lafuente-Lechuga

Cifuentes-Faura

Faura-Martínez

. (2020). Mathematics applied to the economy and sustainable development goals: A necessary relationship of dependence. Education Sciences, 10(11), 1–12. https://doi.org/10.3390/educsci10110339

50.

Lawale

Bory-Adams

(2010). The decade of education for sustainable development: Towards four pillars of learning. Development, 53(4), 547–550. https://doi.org/10.1057/dev.2010.76

51.

Keller

J. M.

(2018). Use of the ARCS model in education: A literature review. Computers & Education, 122, 54–62. https://doi.org/10.1016/j.compedu.2018.03.019

52.

Linda

Effendi

Z. M.

Idris

(2018, July 7). The influence of problem-based learning and motivation on Students' learning outcomes [Conference session]. First Padang International Conference on Economics Education, Economics, Business and Management, Accounting and Entrepreneurship (PICEEBA 2018), Padang, Indonesia. https://doi.org/10.2991/piceeba-18.2018.8

53.

Lee

C. S.

(2021). Evaluating the effectiveness of blended learning using the ARCS model. Journal of Computer Assisted Learning, 37(5), 1397–1408. https://doi.org/10.1111/jcal.12579

54.

Makrakis

(2017). Unlocking the potentiality and actuality of ICTs in developing sustainability-justice curricula and society. Knowledge Cultures, 5(2), 103–122. https://doi.org/10.22381/KC5220177

55.

Makrakis

Kostoulas-Makrakis

(2013). A methodology for reorienting university curricula to address sustainability: The RUCAS-tempus project initiative. In Caeiro

Filho

Jabbour

Azeiteiro

(Eds.), Sustainability assessment tools in higher education institutions (pp. 323–344). Springer International Publishing. https://doi.org/10.1007/978-3-319-02375-5_18

56.

Makrakis

Kostoulas-Makrakis

(2016). Bridging the qualitative–quantitative divide: Experiences from conducting a mixed methods evaluation in the RUCAS programme. Evaluation and Program Planning, 54, 144–151. https://doi.org/10.1016/j.evalprogplan.2015.07.008

57.

Makrakis

Kostoulas-Makrakis

(2017). An instructional-learning model applying problem-based learning enabled by ICTs. In Anastasiades

Zaranis

(Eds.), Research on E-learning and ICT in education (pp. 3–16). Springer. https://doi.org/10.1007/978-3-319-34127-9_1

58.

Martin

S. N.

Suryadi

Juandi

(2019). Students’ difficulties in solving the mathematics word problems with the context of Education for Sustainable Development (ESD). Journal of Physics: Conference Series, 1157(4), 042051. https://doi.org/10.1088/1742-6596/1157/4/042051

59.

Mckeown

Hopkins

C. A.

Rizzi

Chrystalbridge

(2002). With assistance from Education for Sustainable Development Toolkit Economy Environment. http://www.esdtoolkit.org

60.

Milman

N. B.

Wessmiller

(2016). Motivating the online learner using Keller’s ARCS Model. Distance Learning, 13, 67–71.

61.

Napitupulu

E. E.

Suryadi

Kusumah

Y. S.

(2016). Cultivating upper secondary students’ mathematical reasoning-ability and attitude towards mathematics through problem-based learning. Journal on Mathematics Education, 7(2), 117–128.

62.

C. C.

(2018). High school students’ motivation to learn mathematics: The role of multiple goals. International Journal of Science and Mathematics Education, 16(2), 357–375. https://doi.org/10.1007/s10763-016-9780-4

63.

Nieuwenhuis

(2013). Qualitative research design and data gathering technique. In Maree

(Ed.), First steps in research (pp. 70–98). Van Schaik.

64.

Ning

(2020). Discipline, motivation, and achievement in mathematics learning: An exploration in Shanghai. School Psychology International, 41(6), 595–611. https://doi.org/10.1177/0143034320961465

65.

Novak

(2014). Toward a mathematical model of motivation, volition, and performance. Computers & Education, 74, 73–80. https://doi.org/10.1016/j.compedu.2014.01.009

66.

Onyekwere

L. A.

Azubuike

E. S.

(2023). Institutional collaborative culture and task accomplishment in universities. Asian Journal of Arts, Humanities and Social Studies, 6(2), 105–120.

67.

Osman

Duffy

T. M.

Chang

Lee

(2011). Learning through collaboration: Student perspectives. Asia Pacific Education Review, 12(4), 547–558. https://doi.org/10.1007/s12564-011-9156-y

68.

Patange

A. D.

Bewoor

A. K.

Deshmukh

S. P.

Mulik

S. S.

Pardeshi

S. S.

Jegadeeshwaran

(2019). Improving program outcome attainments using project-based learning approach for: UG coursemechatronics. Journal of Engineering Education Transformation, 33, 1–8.

69.

Robinson

Michael

Landra

(2008). Facilitating learning. In Routledge (Ed.), Educational technology: A definition with commentary (pp. 15–48). Routledge.

70.

Schoenherr

(2024). Personalizing real-world problems: Posing own problems increases self-efficacy expectations, intrinsic value, attainment value, and utility value. British Journal of Educational Psychology, 94(2), 407–424. https://doi.org/10.1111/bjep.12653

71.

Schukajlow

Rakoczy

Pekrun

(2017). Emotions and motivation in mathematics education: Theoretical considerations and empirical contributions. ZDM, 49(3), 307–322. https://doi.org/10.1007/s11858-017-0864-6

72.

Semiz

G. K.

Baykal

I. I.

(2020). Middle school pre-service mathematics teachers’ opinions related to mathematics education for sustainability. Eurasian Journal of Educational Research, 20(89), 1–26. https://doi.org/10.14689/ejer.2020.89.6

73.

Silverman

(2016). Interpreting qualitative data (5th ed.). SAGE Publishing Inc.

74.

Strauss

Corbin

(2016). Basics of qualitative research (5th ed.). Sage Publications, Inc.

75.

Strauss

(2019). China is No. 1 on PISA — But here’s why its test scores are hard to believe. The Washington Post. Retrieved November 29, 2022, from https://www.Washingtonpost.Com/Education/2019/12/04/China-Is-No-PisaHeres-Why-Its-Test-Scores-Are-Hard-Believe/

76.

Suparman

Juandi

Tamur

(2021). Review of problem-based learning trends in 2010-2020: A meta-analysis study of the effect of problem-based learning in enhancing mathematical problem-solving skills of Indonesian students. Journal of Physics: Conference Series, 1722(1), Article 012103. https://doi.org/10.1088/1742-6596/1722/1/012103

77.

Tiantong

Teemuangsai

(2013). Student Team Achievement Divisions (STAD) Technique through the Moodle to Enhance Learning Achievement. International Education Studies, 6(4), 87–99. https://doi.org/10.5539/ies.v6n4p85

78.

Tran

L. T.

Nguyen

T. S. S.

(2021). Motivation and mathematics achievement: A Vietnamese case study. Journal on Mathematics Education, 12(3), 449–468. https://doi.org/10.22342/jme.12.3.14274.449-468

79.

Trullàs

J. C.

Blay

Sarri

Pujol

(2022). Effectiveness of problem-based learning methodology in undergraduate medical education: A scoping review. BMC Medical Education, 22(1), 104. https://doi.org/10.1186/s12909-022-03154-8

80.

Türel

Y. K.

Demirli

(2010). Instructional interactive whiteboard materials: Designers’ perspectives. Procedia - Social and Behavioral Sciences, 9, 1437–1442. https://doi.org/10.1016/j.sbspro.2010.12.346

81.

UNESCO. (n.d.). Education for sustainable development. https://En.Unesco.Org/Themes/Education-Sustainable-Development-Esd.

82.

Wang

(2022). Analysis and countermeasures of common problems in mathematics learning for senior high school students. Mathematics Learning and Research, 17, 143–145.

83.

Widiati

Juandi

(2019). Philosophy of mathematics education for sustainable development. Journal of Physics: Conference Series, 1157(2), Article 022128. https://doi.org/10.1088/1742-6596/1157/2/022128

84.

Widyastuti

Susiana (2019). Using the ADDIE model to develop learning material for actuarial mathematics. Journal of Physics: Conference Series, 1188, 012052. https://doi.org/10.1088/1742-6596/1188/1/012052

85.

Wong

S. L.

Wong

S. L.

(2021). Effects of motivational adaptive instruction on student motivation towards mathematics in a technology-enhanced learning classroom. Contemporary Educational Technology, 13(4), ep326. https://doi.org/10.30935/cedtech/11199

86.

Zabala-Vargas

S. A.

García-Mora

Arciniegas-Hernández

Reina-Medrano

de Benito-Crosetti

Darder-Mésquida

(2022). Didactic strategy mediated by games in the teaching of mathematics in first-year engineering students. Eurasia Journal of Mathematics, Science and Technology Education, 18(2), em2082. https://doi.org/10.29333/ejmste/11707

87.

Zhang

Shi

Zhang

(2023). Research on the quality of collaboration in project-based learning based on group awareness. Sustainability, 15(15), 11901. https://doi.org/10.3390/su151511901

88.

Zhang

Yang

Sun

Kaiser

(2023). The reciprocal relationship among Chinese senior secondary students’ intrinsic and extrinsic motivation and cognitive engagement in learning mathematics: A three-wave longitudinal study. ZDM – Mathematics Education, 55(2), 399–412. https://doi.org/10.1007/s11858-022-01465-0

89.

Zhu

Huang

(2023). The application of ARCS motivation model in college oral English teaching. Applied Mathematics and Nonlinear Sciences, 8(2), 1549–1558. https://doi.org/10.2478/amns.2023.1.00086

90.

Zowada

Belova

Eilks

(2021). Enhancing education for sustainable development through geographical perspectives in chemistry teaching. International Journal of Science and Mathematics Education, 19(1), 87–109. https://doi.org/10.1007/s10763-019-10043-y

91.

Zubiri-Esnaola

Vidu

Rios-Gonzalez

Morla-Folch

(2020). Inclusivity, participation and collaboration: Learning in interactive groups. Educational Research, 62(2), 162–180. https://doi.org/10.1080/00131881.2020.1755605