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Article

Examining a Primary Education Approach Using Digital Storytelling: Chinese Industrial Heritage as a Vehicle to Support Learning

by
Xin Bian
1,2,*,
Andre Brown
2 and
Bruno Marques
2
1
School of Art and Design, Shandong Women’s University, Ji’nan 250300, China
2
Wellington School of Architecture, Victoria University of Wellington, Wellington 6011, New Zealand
*
Author to whom correspondence should be addressed.
Heritage 2025, 8(11), 477; https://doi.org/10.3390/heritage8110477
Submission received: 31 August 2025 / Revised: 27 October 2025 / Accepted: 10 November 2025 / Published: 14 November 2025
(This article belongs to the Special Issue Progress in Heritage Education: Evolving Techniques and Methods)
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Versions Notes

Abstract

Digital storytelling has emerged as an innovative approach that integrates technology with education, demonstrating growing research and practical value in cultural heritage preservation. This study focuses on China’s industrial heritage and conducts empirical research with primary school students (Years 1–6) to examine how digital storytelling enhances engagement in industrial heritage education in particular, but also how industrial heritage reflects and links to wider cultural and historical issues. The research analyzes six key educational dimensions: learning interest, functional preferences, content comprehension, supervisory expectations, creative expression, and willingness to participate. Hypothesis testing revealed significant positive correlations among these dimensions (p < 0.05), and the overall regression model explained 51% of the variance in students’ willingness to participate (R2 = 0.51). Grade-level analysis further demonstrated distinct developmental patterns: younger students preferred gamified interactions with parental supervision, middle-grade students gradually shifted toward personalized learning approaches, and senior students focused more on value-driven and inquiry-based content. A temporary decline in interest and willingness around Year 5 highlighted a key transitional period requiring targeted scaffolding for abstract and creative learning tasks. Based on these insights, the study innovatively proposes a “Sapling Growth” educational framework that systematically combines digital storytelling technology with children’s cognitive development patterns. This progressive three-stage instructional design achieves dynamic alignment between teaching content and students’ cognitive abilities. The framework integrates cultural depth with interactive features, establishing a theoretical pathway to enhance learning processes, strengthen cultural identity, and promote sustainable industrial heritage preservation, while providing a foundation for interdisciplinary integration across educational technology, cultural heritage conservation, and child development fields.

1. Introduction

Cultural heritage plays a fundamental role in shaping collective identity and transmitting knowledge across generations. UNESCO’s Hangzhou Declaration [1] emphasizes that culture and education are mutually reinforcing pillars of sustainable development. Heritage is not limited to the conservation of monuments but is a dynamic process of meaning-making and transmission [2,3]. However, rapid urbanization and economic transformation have reduced children’s opportunities to engage with local cultural heritage through direct, lived experiences [2,3]. As screen-based entertainment and standardized curricula replace informal cultural learning, younger learners encounter fewer pathways to explore community histories and shared memories [4,5].
The transformation of China, from the 1950s, into a leading industrial nation is fundamentally important in relation to understanding the China of today. The industrial changes and artefacts are closely linked to parallel cultural and social changes. However, many early industrial sites have also been repurposed or demolished due to redevelopment, accelerating material loss and weakening the potential for intergenerational transmission of meanings, values and history [2,3]. The absence of such transmission makes it harder for children to grasp the historical context and cultural foundations of the associated social development. Learning about industrial heritage helps them understand the material and technological bases of modern life and fosters cultural identity through appreciation of labor, innovation, and community memory.
Industrial heritage occupies a distinctive position within the broader cultural heritage framework, balancing conservation and transformation. It refers to the material and intangible remains of industrialization, including factories, machinery, workers’ housing, transport systems, and the skills and memories associated with industrial production [1]. In China, this heritage embodies the nation’s twentieth-century transition from agrarian to industrial society and represents collective achievements of technological progress, social reform, and national modernization [2]. These sites hold deep educational value because they connect scientific principles with human stories of effort, resilience, and creativity [6]. However, despite this significance, industrial heritage remains underrepresented in public education and is often perceived as remote from children’s daily lives [7].
Industrial heritage occupies a distinctive position within the broader cultural heritage framework, balancing conservation and transformation. As Lei et al. [6] note, industrial heritage management involves continuous negotiation between safeguarding authenticity and fostering adaptive reuse. Tensions often arise when heritage sites are leveraged for economic or aesthetic renewal under culture-led development policies, which risk reducing heritage to a branding tool rather than a medium for education and memory [2]. Recognizing and managing industrial heritage therefore require a multidimensional and interdisciplinary approach that integrates conservation, planning, education, and public participation. International practices have demonstrated the educational potential of industrial heritage reuse. The Ironbridge Gorge in the United Kingdom integrates industrial heritage interpretation with school-based learning programmes and museum–school partnerships, while the Cité du Cinéma in Saint-Denis, France, has transformed a former Alstom power plant into a centre for film production and higher-level training in media and the arts [6]. In contrast, China’s flagship redevelopments, such as the Shougang Industrial Park in Beijing, the Anshan Steelworks in Liaoning, and the Jiangnan Shipyard in Shanghai, have achieved wide public recognition, yet their integration as educational platforms remains limited [3]. Success in advertising and cultural branding has not translated into classroom presence or sustained meaning-making for children.
Given this context, digital storytelling provides a potential means of revitalizing educational activities through heritage learning and cultural transmission. By combining a narrative structure with digital technology, there is the potential to transform abstract or complex historical content into accessible and emotionally resonant [8,9]. In the field of Human–Computer Interaction (HCI), researchers have explored immersive visualization systems that reconstruct historical narratives and participatory online platforms for community storytelling [10,11]. For children, digital storytelling inspired by traditional cultural forms such as Chinese shadow puppetry has been shown to enhance understanding, engagement, and cultural identity [5,12,13,14]. As a continuation of oral tradition, such an approach preserves intangible aspects of heritage, including voices, emotions, and practices that conventional documentation cannot fully capture [15,16].
Globally, children are spending increasing amounts of leisure time on digital entertainment, reducing opportunities for cultural exploration and creative inquiry [17,18]. In China, this broader trend intersects with specific challenges in the transformation of major industrial sites. Projects have raised the public profile of industrial heritage through urban renewal and cultural tourism, yet their role in basic education remains marginal. At the primary level, industrial heritage content is essentially absent from textbooks; where students are aware of it, such awareness typically stems from informal exposure such as self-selected online videos or family-led site visits rather than systematic classroom instruction [7]. Digital storytelling offers a timely and contextually grounded response by aligning narrative-based, interactive learning with children’s media habits and developmental characteristics [19,20,21,22].
Responding to this gap, the present study investigates how digital storytelling can enhance primary school students’ engagement with industrial heritage learning. The research focuses on Years 1 to 6, and analyses five learner-centred dimensions: learning interest, functional preference, content comprehension, supervisory expectations, and creative presentation. It explores how these factors shape willingness to engage across developmental stages. Drawing on Piaget’s theory of cognitive development [23,24,25], the study further examines developmental differences in engagement patterns and proposes a “Sapling Growth” educational framework that systematically aligns digital storytelling design with children’s evolving cognitive abilities. This approach aims to integrate cultural depth with technological interactivity, balancing engagement and authenticity to strengthen comprehension, sustain interest, and cultivate cultural identity in industrial heritage education [26,27,28].

2. Theoretical Framework

To more effectively enhance the willingness of Chinese primary school students in Years 1 to 6 (ages 6–12) to engage in digital industrial heritage learning, this study proposes five key dimensions based on Piaget’s theory of cognitive development: content comprehension, learning interest, supervisory expectations, creative presentation, and functional preference [25,29]. The core idea behind these dimensions is that children at different stages of cognitive development demonstrate notable differences in thinking styles, sources of interest, behavioral norms, and operational abilities. Therefore, digital learning content and formats must align with their cognitive characteristics to effectively stimulate their motivation to learn.
Accordingly, many early childhood and primary education programs have incorporated Piaget’s theory, which forms part of the foundation for constructivist learning [25]. Discovery learning and supporting the child’s developing interests are central instructional strategies within these approaches and are closely aligned with the dimensions proposed in this study [30]. By recognizing children’s cognitive developmental stages, digital learning experiences can better support their engagement with and understanding of industrial heritage, thereby enhancing overall learning outcomes.

2.1. Content Comprehension

According to Piaget’s theory of cognitive development, children in the concrete operational stage (approximately ages 7 to 11) begin to develop logical thinking abilities, enabling them to understand concepts such as cause and effect, sequencing, and classification [25]. Therefore, learning content must be aligned with their cognitive capacity to be effectively understood and absorbed. Research by Anderson et al. [26] found that children aged 7–12 are more likely to engage in exploratory behavior when they possess prior knowledge of the subject. Van Schijndel et al. [27] further demonstrated that interactive, hands-on experiences significantly enhance children’s knowledge retention, which in turn promotes continued interest in learning. Similarly, Heath et al. [31] showed that deeper knowledge engagement leads to more active learning behaviors. Based on these findings, the following hypothesis is proposed:
Hypothesis 1. 
Children’s comprehension of content is positively associated with their willingness to engage.

2.2. Learning Interest Dimension

Learning interest plays a crucial role in motivating student engagement, and this can be explained through Piaget’s concept of assimilation and accommodation [25]. When the complexity of a learning task matches a child’s existing cognitive structures, it creates cognitive disequilibrium—a state that stimulates curiosity and drives the learner to adapt, thus generating interest and enhancing intrinsic motivation to learn. Empirical research supports this theoretical perspective. Research suggests that interest plays a critical role in enhancing student engagement, particularly in conceptual learning domains. Hidi (2000) and Renninger (1992) assert that sustained interest in a subject can lead to increased knowledge retention and persistence in learning [28,32]. Hidi [33] further supports this, showing that interest is positively correlated with deep-level indicators of understanding and achievement, with interest explaining about 10% of the variance in learning outcomes [34,35]. In the context of educational tools, it has been shown that when students are interested in a topic, they are more likely to engage in the learning process, ask questions, and apply deeper cognitive strategies [36]. This leads to the following hypothesis:
Hypothesis 2. 
Children’s learning interest is positively associated with their willingness to engage.

2.3. Supervisory Expectations Dimension

Supervision expectations reflect the importance of external guidance in children’s learning processes. According to Piaget’s theory of moral and cognitive development, children’s behavior regulation gradually evolves from heteronomous morality—where rules are imposed by authority figures—to autonomous morality, in which they internalize rules and self-regulate [37]. In the early developmental stages, external supervision plays an important role in shaping children’s behavior, as they have not yet fully internalized rules or understood the consequences of their actions. In this process, parents and teachers still have a significant impact on the formation of learning habits.
In the context of integrating technology into children’s education, Crouse et al. [38] emphasize that digital tools must meet the expectations of parents and educators regarding screen time. Antfolk et al. [35] and various health organizations advocate limiting children’s screen exposure to prevent potential long-term effects. Empirical studies also show a link between extended screen time and attention problems in adolescents [39]. Similarly, Cristia and Seidl [17] note that government agencies, such as the German Federal Ministry of Health, recommend screen time restrictions to protect children’s wellbeing. Barr [40] stresses the importance of managing screen time in education, as excessive use can reduce learning interest and affect children’s mental development. To address these concerns, Kotiash et al. [41] recommend that educational content be short, interactive, and dynamic, a strategy that not only captures attention but also supports effective learning [38]. Therefore, balancing educational goals with health considerations requires designing content that aligns with parental expectations while maintaining engagement. This leads to the following hypothesis:
Hypothesis 3. 
Meeting the supervisory expectations of parents and teachers is positively associated with children’s willingness to engage.

2.4. Creative Presentation Dimension

The concept of creative presentation is rooted in Piaget’s description of symbolic thinking in children during the preoperational stage [25]. At this stage, students are highly sensitive to vivid, concrete, and imaginative visual and situational elements. Creative designs can enhance emotional engagement and cognitive participation, fostering deeper involvement in the learning process. Creativity is the ability to generate novel and task-appropriate ideas or products [42]. It depends on both domain-specific skills [43] and general abilities [44], involving both divergent and convergent thinking [42]. Mobile applications not only support broad learning but also offer creative experiences [45]. As children engage with various apps daily, understanding how creative content motivates learning is crucial [46]. Many children’s apps integrate creativity and play to support exploratory learning in areas like visual arts, storytelling, and programming [45,47]. These apps boost engagement through interactivity and multimodal expression [48]. More importantly, creative content can ignite interest in unfamiliar subjects, expanding knowledge and deepening learning [49]. While many creativity-focused apps center on visual arts, they help develop skills like fine motor control and colour recognition [50]. Role-playing apps allow children to learn through simulation [42,43]. Research shows that integrating creativity with learning content improves children’s learning outcomes [45,51,52]. Hybrid designs increase engagement and foster active thinking [53]. These interactive, playful apps spark curiosity, encouraging children to explore unfamiliar knowledge [49]. This leads to the following hypothesis:
Hypothesis 4. 
Children’s creative presentation is positively associated with their willingness to engage.

2.5. Function Preference Dimension

Functional preference reflects the differences in operational abilities among students of different ages. Younger students tend to prefer simple and intuitive interaction methods, while older students, having reached more advanced developmental stages, can engage with a more complex and structured learning system [54]. As digital learning becomes more common, apps are no longer just knowledge carriers but also guides that help primary school children explore unfamiliar subjects [19]. Since many children lack background knowledge and self-directed learning strategies [55], apps must provide clear guidance, intuitive interfaces, and engaging support [42]. However, many existing tools fall short in addressing these needs [12,14]. Perceived ease of use, which refers to how effortless the tool is to operate, is a key factor [56]. Apps that are easy to navigate, with logical flows and meaningful feedback, reduce cognitive load and support sustained engagement [21]. Interactivity also plays a vital role, especially for young learners in hands-on subjects like science, where real-time feedback and simulations help bridge gaps in understanding [48]. Given that primary students rely on concrete thinking [57], design features like visual guidance, task breakdown, and contextual examples are essential for learning abstract or technical content such as science, programming, or music [58,59]. Rather than relying solely on app ratings or downloads, it is important to evaluate whether the app effectively supports learning in new domains [17]. Future research should investigate how functional designs can enhance outcomes and adapt to diverse educational contexts [21,27,45]. This leads to the following hypothesis:
Hypothesis 5. 
Children’s function preference is positively associated with their willingness to engage.
This study investigates whether digital storytelling applications can effectively promote student engagement with industrial heritage by examining five key dimensions: learning interest, content presentation, functional preference, supervisory expectations, and creative presentation. These dimensions are considered as factors that may influence students’ willingness to engage with the learning content. The conceptual framework illustrating the relationships between these dimensions and students’ willingness is presented in Figure 1.

3. Materials and Methods

3.1. Research Design

This study used a structured questionnaire. Instrument development followed established scale-development procedures [60,61]: (1) a literature review identified constructs and items; (2) an expert review with primary-level teachers and a heritage interpreter assessed content relevance and readability, leading to minor wording refinements; and (3) a small pilot with non-participating classes checked validity and comprehensibility. After ethics approval, the main survey was administered in classrooms during break periods by classroom teachers. Researchers did not administer or observe questionnaire completion. Afterward, teachers briefly communicated students’ general comments to the researchers; these comments were not treated as qualitative interview data and were used only as contextual reference for interpreting the survey results. The final instrument was then used in a large-scale survey to analyze behavioral characteristics and developmental trends across grade levels.

3.2. Participants

After ethics approval, we recruited students from a public primary school in Qingdao, China, using classroom-based cluster invitations rather than individual screening. All students in Years 1–6 were invited through their homeroom teachers, and participation was entirely voluntary. Participation was voluntary with parental consent and student assent. To avoid disrupting instruction, all paper-based questionnaires were completed during break time and were supervised by classroom teachers. After data-quality screening (completeness and response-consistency checks), a total of 177 valid responses from Year 1 to Year 6 were retained for analysis. The grade distribution of the analytic sample was as follows: Year 1 = 33 (18.64%), Year 2 = 27 (15.25%), Year 3 = 30 (16.95%), Year 4 = 27 (15.25%), Year 5 = 33 (18.64%), Year 6 = 27 (15.25%).
The sample-size rationale is based on five predictors, including Green [62], which recommends N ≥ 50 + 8 m = 90 for overall model tests, and N ≥ 104 + m = 109 for ensuring stable estimation of individual regression coefficients at the model level. Our total N = 177 exceeds both thresholds, supporting adequate power and reliability parameter estimation.

3.3. Measures

This study employed a questionnaire consisting of 26 items in total, including 2 demographic questions (grade level and gender), and 24 items measuring six key dimensions. These dimensions included learning interest (4 items), content comprehension (4 items), functional preference (4 items), supervisory expectations (4 items), creative presentation (4 items), and final willingness (4 items). In the analysis, the first five dimensions were treated as independent variables, while the final willingness dimension to engage with digital storytelling tools for industrial heritage learning was treated as the dependent variable.
To ensure appropriateness for students in Grades 1 to 6, the items were adapted using simplified language and visual symbols (Figure 2). A 5-point Likert scale with emoji anchors (1 = “strongly disagree” to 5 = “strongly agree”) was used for scoring [63]. Dimension scores were calculated by averaging item responses, with higher scores indicating a stronger willingness to engage. The relative importance of each dimension was determined through standardized regression coefficients derived from subsequent analyses. Additionally, the model was calibrated for different grade levels based on Piaget’s theory [28] of cognitive developmental stages.

3.4. Reliability and Validity of the Sample

Following recommended procedures [61], we conducted data-quality checks: questionnaires were screened for completeness and consistency; responses with substantial missingness or patterned answering were excluded; and score distributions were reviewed for skewness/kurtosis. Internal consistency of the attitude scale was excellent (Cronbach’s α = 0.938) [62], and alpha-if-item-deleted diagnostics showed no meaningful improvement, indicating that all items contributed positively.
For construct validity, we assessed factorability using the Kaiser–Meyer–Olkin (KMO) index and Bartlett’s test of sphericity. The KMO was 0.913 (exceeding the 0.80 benchmark), and Bartlett’s test was significant (χ2 = 4964.456, p < 0.001), supporting the suitability of the data for factor-analytic modeling.

4. Hypothesis Results

To test the hypotheses H1–H5 proposed previously, this study conducted regression analyses using SPSS 25.0 software. Analyses were performed for five dimensions—learning interest, content presentation, function preference, supervision, and creative presentation—to examine their respective impacts on the dimension of students’ willingness to learn about industrial heritage. The results of these regression analyses are presented in Table 1.
According to the above Table 1, the dimensions of learning interest, function preference, content comprehension, supervisory expectations, learning preferences, and creative presentation are all significantly positively correlated with the final willingness dimension (r > 0, p < 0.01).
Based on the values in Table 2, where F = 35.629 and p = 0.000 < 0.05, the model is successfully established. The R2 value of 0.510 indicates that 51% of the variance in the dependent variable is explained by the independent variables, demonstrating a strong explanatory power. With VIF values below 10 and tolerance greater than 0.1, there is no multicollinearity among the variables.
Each dimension—learning interest, function preference, content comprehension, supervisory expectations, and creative presentations showed a significant positive effect on final willingness, with B-values of 0.140, 0.262, 0.106, 0.147, and 0.147, respectively, all passing the significance test at the 5% level. This indicates that higher scores in these dimensions lead to higher final willingness.
The hypothesis validation analysis confirms that all five dimensions (learning interest, function preference, content comprehension, supervisory expectations, and creative presentation) positively correlate with students’ willingness to engage with industrial heritage content. These findings suggest that mobile digital storytelling applications show promise for facilitating industrial heritage education, particularly when incorporating developmentally appropriate design features. Notably, the significant results for supervisory expectations highlight the importance of parental oversight mechanisms in application design, especially regarding usage time management features. This consideration will be further elaborated in the discussion of grade-level differences. Additionally, the integration of key factors such as learning interest, content comprehension, creative presentation, and function preference should be carefully balanced to optimize learning outcomes.

Grade-Level Differences from a Cognitive Development Perspective

This study examined the acceptance of digital storytelling tools for industrial heritage learning among primary school students (Years 1–6), revealing significant age-related differences across multiple dimensions—learning interest, function preference, content comprehension, supervisory expectations, and creative presentation, and overall willingness to engage (Table 3). The results supported the research hypotheses (H1–H5), underscoring the importance of tailoring digital storytelling tools to children’s stage-specific cognitive development.
As reflected in the data (Table 3 and Figure 3), each year level demonstrated distinct patterns of engagement and preference, reinforcing the need for a comprehensive, age-responsive design strategy that enables personalized learning experiences. These findings resonate with established developmental theories, such as Piaget’s stages of cognitive development, and highlight practical challenges unique to industrial heritage education, particularly in presenting abstract or historically distant content in a way that is meaningful and engaging to young learners.
Figure 3 is a visual summary of Table 3, illustrating the performance levels of primary school students from Year 1 to Year 6 across six dimensions: learning interest, function preference, content comprehension, supervision expectations, creative presentation, and final willingness. The number of dots represents the strength of each dimension, while colors indicate relative levels of three dots (Yellow) for high, two dots (Green) for medium, and one dot (Red) for low. This figure clearly reflects the developmental trends in students’ preferences and needs when using digital learning tools.
Year 1: Early Exploration Driven by Interest and Shaped by Strong Supervision
Year 1 students’ cognitive development aligns with Piaget’s preoperational stage [18], engaging with learning tools through vivid sensory experiences. As shown in Table 3 and visualized in Figure 3, their responses can be categorized into three levels: high-level engagement (learning interest, supervisory expectations, final willingness, function preference), mid-level engagement (creative presentation), and low-level engagement (content comprehension). These patterns highlight the strengths of young learners in curiosity and visual interaction, while also reflecting their limitations in processing abstract content. Survey data indicates high scores in the dimensions of learning interest (M = 3.78), supervisory expectations (M = 3.71), final willingness (M = 3.95), and function preference (M = 3.92). Most students showed strong enthusiasm for learning, but also clearly stated that their parents limit the time they can spend using tablets or phones. Additionally, students generally prefer interactive and game-like design features. The Creative Presentation dimension scored (M = 3.21), indicating that students responded positively to creative storytelling tasks. However, the content comprehension dimension (M = 2.41) scored notably low, highlighting a cognitive gap: most students were unfamiliar with the term “industrial heritage,” had difficulty understanding its meaning, and had never been exposed to it before. This suggests that while students are eager to engage in novel and interesting learning experiences, they still face significant challenges in understanding and retaining educational content related to industrial heritage.
These findings affirm Piaget’s theoretical insights [64]: children in this stage benefit from colorful, interactive designs and adult support. To bridge the gap between interest and understanding, learning tools should offer simple structures, vivid feedback, and scaffolded knowledge, gradually shifting the focus of engagement from form to meaning. This developmental sensitivity makes Year 1 a key entry point for cultivating awareness of industrial heritage.
Year 2: The Beginning of Developmental Transition and Structured Learning
An interesting finding is that Year 2 students demonstrate high levels across all dimensions. The survey shows that students exhibit a strong interest in learning (M = 3.83), with many clearly expressing a keen enthusiasm for industrial heritage. In the content comprehension dimension (M = 3.04), many students reported prior exposure to industrial heritage through site visits, media, or daily observations. Although their understanding may still be imprecise, these prior experiences lay the foundation for future, more systematic and in-depth learning. Despite potential bias, these experiences can be effectively activated and expanded. The results in the function preference dimension (M = 3.59) indicate that Year 2 students adapt well to task-based learning, suggesting that teaching should follow a progressively structured approach. Some students mentioned that they particularly enjoy activities like “spot the difference,” “puzzle-solving tasks,” and “completing levels with animated characters.” These gamified designs not only align with their cognitive development but also foster learning interest in a relaxed, enjoyable atmosphere. In the creative presentation dimension (M = 3.54), students show a preference for presenting content through storytelling, beginning to link these stories to key knowledge points. This supports previous findings that students prefer “illustrated explanations,” highlighting the importance of strong visual support. The supervisory expectations dimension reveals a decrease compared to Year 1, with the score remaining relatively high (M = 3.54). Some students mentioned that their parents now hope they can learn new knowledge through electronic devices, shifting parental focus from “time management” to “content guidance.” Finally, in the final willingness dimension (M = 3.66), feedback indicates that many students exhibit a very high willingness to learn about industrial heritage, suggesting that high learning interest effectively promotes their engagement with the subject.
This overall trend reflects Year 2 students’ readiness to transition from intuitive cognition to more structured learning, where proper guidance and strong visual support can foster deeper understanding and engagement.
Year 3: Awakening of Autonomy and Relaxation of Supervision
Year 3 students demonstrate a clear increase in autonomy when learning about industrial heritage. Survey data reveals a noticeable decline in the learning interest dimension (M = 3.57), with a score of only two points. While some students remain highly interested in industrial heritage, others express little to no interest. This shift reflects a transformation in their cognitive development: they are beginning to develop judgment and selection skills, actively choosing content based on personal preferences and understanding, rather than showing uniform interest in all new topics. The function preference dimension scored relatively high (M = 3.65), indicating a strong interest in personalized learning pathways. For example, some students mentioned they prefer a learning model where they can “choose their own character and path”. This autonomy in choice enhances their sense of control, aligning with their growing independence and decision-making abilities. In terms of content comprehension, students showed a stable cognitive level (M = 3.00). Several students shared that they had seen “old factories” or “steam trains” during museum visits or while watching documentaries. While their understanding remains basic, they have formed initial impressions, which lay the foundation for deeper learning in the future. Creative presentation scored 3.05, with most students expressing a preference for creative story content. For instance, some students mentioned they “enjoy games where they can decide how the story unfolds”. This branching narrative style increases their sense of involvement and exploration, helping them understand story development and cause-and-effect relationships. Regarding their willingness to use the tools, students showed a strong inclination to continue learning about industrial heritage (M = 3.64). Many students said, “As long as the content is interesting, we are eager to keep using these tools for learning.” This underscores the importance of engaging content in maintaining their learning motivation. Parental supervision scores showed a slight decrease (M = 3.40), reflecting a change in parental attitudes from strict time control to a focus on content quality. One student mentioned, “My mum says it’s okay to use it longer as long as it’s for learning.” This indicates that families are increasingly supportive of autonomous learning, fostering a more relaxed and encouraging environment.
Overall, Year 3 students are transitioning from reliance on adult guidance to independent exploration, showing a preference for personalized, choice-driven learning. However, moderate adult support remains crucial in sustaining their motivation to learn.
Year 4: Developmental Transition and the Cultivation of Higher-Order Thinking
Year 4 students show clear signs of cognitive transformation in their learning of industrial heritage. Among all dimensions, the highest score is in creative expression (M = 3.76), indicating that students expect the content to be creative and engaging. For example, they prefer tools that allow them to participate in story development and creation. In simulations of industrial heritage exploration, students can role-play and direct the storyline, which greatly enhances their interest and depth of learning. The functionality preference dimension (M = 3.30) shows that students favor interactive, content-rich tools and reject overly simple interfaces, aligning with Piaget’s developmental stage theor [24]. In the content comprehension dimension (M = 3.09), students begin to demonstrate early systematic thinking, making simple conceptual connections such as between “old factories” and “steam trains.” Although their understanding remains basic, museum visits and documentaries provide initial impressions that lay the groundwork for future learning. The parental supervision dimension (M = 3.39) indicates a shift from direct control to resource-based support, as parents increasingly emphasize learning quality rather than screen-time restrictions. Learning interest (M = 3.52) remains relatively high, while willingness to use learning tools (M = 3.63) shows strong motivation. Some students commented, “If the content is both interesting and challenging, I’d be more willing to keep using it.”
Overall, Year 4 students are transitioning from teacher- and parent-directed learning to actively constructing their own knowledge. Educational tools should support this shift by combining deeper content with creative expression opportunities, thereby enhancing cognitive engagement and fostering creative development.
Year 5: Developmental Decline and the Challenge of Tool Adaptation
As highlighted in the regression analysis, Year 5 students showed the lowest scores in final willingness, suggesting a temporary decline in motivation and engagement compared with both lower and upper grades. The multidimensional analysis further clarifies this finding. Learning interest (M = 3.08) and final willingness (M = 3.17) are the lowest among all grades, and supervisory expectations are also low (M = 2.52). This pattern marks a key transitional phase in cognitive development. In content comprehension (M = 3.03), students show basic understanding but have not yet integrated more abstract concepts, shifting from concrete operational toward formal operational thinking [24]. Although they begin to grasp abstract ideas such as steelmaking, full synthesis remains difficult, and function preference (M = 3.11) indicates only moderate receptivity to complex interactive tasks.
This pattern aligns with the mid-primary shift from “learning to read” to “reading to learn,” when discourse becomes more expository and academic vocabulary demands increase, reducing perceived competence and motivation if supports are not adjusted [65,66]. Classic synthesis work also shows that around Grades 4 to 5, early differences can widen as task complexity increases, a mechanism often described as the Matthew effect [67]. At the same time, creative presentation (M = 2.97) is the lowest across all grades, suggesting less engagement with open-ended, narrative-based tasks. This may reflect a short-term dip in self-confidence or rising academic pressure, with students focusing on task completion rather than creative expression. Motivational studies explain this concurrent decline in interest and willingness: as task demands rise, students reassess expectations and values, lowering engagement unless learning provides scaffolded knowledge, explicit vocabulary previews, and stepwise creation support [68,69,70]. Empirical research further shows that simplifying abstract content and coupling narrative with immediate feedback can help maintain attention, comprehension, and motivation in upper-primary learners [71,72].
Overall, the Year 5 downturn in willingness is not an isolated fluctuation but part of a broader developmental and motivational transition. Year 5 appears to be a sensitive stage when learning motivation and creative participation tend to decline. Educational tools for this age group should provide staged guidance for abstract thinking, structured support, and opportunities for creative, open-ended participation. With clear frameworks and timely feedback, students can express what they have learned in meaningful ways and move through this stage more effectively. Such support directly addresses the upper-primary threshold in text complexity and vocabulary load and may mitigate the developmental slump typical of the Grade 4 to 5 transition [65,66].
Year 6: Independent Learners and the Demand for Specialist Tools
Year 6 students exhibit the typical characteristics of rational and purposeful learners in their study of industrial heritage. Survey data shows a high score in the learning interest dimension (M = 3.74), indicating that students remain highly engaged and motivated, particularly when the content aligns with their growing intellectual curiosity and developing sense of values. Their understanding of the content is also strong, with the content comprehension dimension scoring the highest across all year groups (M = 3.38). This suggests that their cognitive development has entered the formal operational stage. The function preference dimension (M = 3.51) supports this, showing a clear inclination towards more specialized, task-oriented educational tools, in contrast to earlier preferences for simpler interfaces. Rather than favouring surface-level activities, students now appreciate in-depth, narrative-led formats. This is reflected in their performance in the creative presentation dimension (M = 3.39), with some expressing a desire to draw on real-life experiences to articulate their understanding of industrial heritage. The supervisory expectations dimension remains relatively low (M = 3.04), suggesting that parental oversight is gradually being replaced by independent learning habits. The final willingness dimension score (M = 3.47) further indicates a strong level of interest and expectation towards using tools for learning about industrial heritage. While not the highest score recorded, it still reflects students’ growing focus on the practical and functional value of educational tools, rather than on purely entertaining features.
Overall, these patterns suggest that Year 6 students are entering a mature stage of learning: they seek out meaningful and structured content, value authenticity and depth, and display increasing autonomy and intrinsic motivation. Educational design at this level should therefore prioritize inquiry-driven learning experiences and support learners in constructing knowledge independently and purposefully.

5. Discussion

5.1. Grade-Level Developmental Patterns

The previous systematic analysis of different grade levels of students in terms of content comprehension, learning interest, supervisory expectations, creative presentation, and functional preference is clearly summarized in Figure 4.
The chart shows that the development of students in grades 1–6 exhibits distinct stages: in the lower grades (grades 1–2), students are primarily driven by learning interest, preferring gamified interactions and simple content, while relying on parental supervision; in the middle grades (grades 3–4), students enter a transitional phase, beginning to seek personalized learning paths and independent exploration; in the upper grades (grades 5–6), students shift to value-driven motivations, focusing on professional tools and real-world relevance, while demonstrating stronger critical thinking and self-management skills. Overall, students’ content comprehension, learning interest, and autonomy exhibit a progressive development from dependency to independence, from entertainment-oriented to functional, reflecting the dynamic evolution of learning behaviors as students age.

5.2. Resonance and Dialogue with Existing Research

Overall, the grade-differentiated patterns observed in this study align with [25] description of stage-based cognitive development in primary years and correspond to the learning preference shifts reported in digital heritage education, where younger students favour visual and narrative content while older learners engage more with inquiry- and creation-based tasks [12]. When learning is organized through narrative with immediate interactive feedback, attention, comprehension, and learning motivation rise together, converging with findings on digital storytelling in primary settings [71,72]. The observed Year 5 dip in interest and willingness corresponds to the motivational adjustment commonly reported during the upper-primary transition, reflecting pre-adolescent reappraisals of effort, competence, and task value [33]. In addition, the positive association of supervisory expectations with learning willingness is consistent with evidence that guided parental mediation, combining light supervision with content guidance, supports self-regulation and learning quality in middle childhood [69,70]. Unlike most studies situated in museums or field trips, the present study focuses on classroom- and home-mediated industrial-heritage learning, extending previous findings to a less examined context. Collectively, these correspondences anchor our findings in established developmental and motivational frameworks while extending them to the underexplored context of industrial-heritage learning, highlighting how grade-specific design strategies can better align with children’s cognitive and motivational growth.

5.3. Operationalizing the “Sapling Growth” Framework

This research constructs an industrial heritage education APP development model for primary school students, using the “Sapling Growth” premise as its core metaphorical system, as shown in Figure 5. The metaphor of ‘Sapling Growth’ symbolizes children’s progressive learning trajectory, in which knowledge, interest, and self-regulation develop like the gradual growth of a sapling into a mature tree. Each educational stage (emergent, developing, and proficient) represents a distinct phase of cognitive and motivational maturation, with learning support and supervision gradually shifting from external assistance to internal autonomy. This staged increase in narrative complexity and learner control is consistent with recommended scaffolding patterns in digital-heritage design for primary learners [12].
The system developed is divided into two main functional modules: student devices and parent devices, building a closed-loop educational support system around learning tasks, incentive mechanisms and parental supervision. The student devices mainly consist of three sections—development stage, core functions and incentive mechanisms—covering the complete process from content design to evaluation feedback. The parent devices focus on three functional aspects: stage filtering, key metrics and warning system, achieving precise monitoring and positive reinforcement of students’ learning behaviors.
The development stage is divided into emergent stage (Year 1–2), developing stage (Year 3–4) and proficient stage (Year 5–6) based on students’ grade levels and cognitive abilities, showing high alignment with the student development dimensions summarised in Figure 3. The emergent stage meets younger students’ preference for gamified interactive content through industrial cartoon series and puzzles, while the parent devices provide real-time activity monitoring and attention distraction alert, corresponding to this stage’s characteristic of requiring full parental supervision. The developing stage features interactive mini-games like industrial process sorting, addressing middle-grade students’ emerging demand for personalised learning paths. The parent devices’ achievement milestone notification and collaborative task analysis functions reflect the transitional feature of supervision gradually shifting to resource support. The proficient stage supports older students’ value-driven participation motivation through documentary series case designs and creative design projects, while the self-management functions of sunlight value and unlocked heritage items count precisely match this stage’s developmental trend of mature self-regulation skills.
Each development stage incorporates distinct learning activity types, including content delivery formats (such as industrial cartoon series, science animation series and documentary series), interactive learning activities (including puzzles, matching games, industrial process sorting and creative design projects), and evaluation methods (like image-based multiple choice, step-sequencing tasks, and student presentations with Q&A discussions), with corresponding 5-point, 10-point or 15-point sunlight point evaluation levels. The sunlight points system runs through the entire learning process, enabling stage progression through the tree growth model (nutrients → growth → unlock new stages), map unlocking through the exploration map model (energy → unlock map nodes), and skill enhancement through the role-playing levels model (experience points → promotion → skill enhancement), which greatly stimulates students’ exploratory motivation and participation. The game mechanics in student devices (from drag-and-drop operations to creative design) directly reflect the linear deepening process of content comprehension, while the indicator tracking in parent devices (from daily average usage duration monitoring to achievement milestone notification) dynamically adjusts support strategies to correspond with the U-shaped development trajectory of learning interest.
The adult interface can automatically filter and match stage-appropriate content according to grade level, and visually display students’ sunlight points growth curve, unlocked heritage items count and daily average usage duration, helping parents comprehensively understand learning status. Simultaneously, the attention distraction alert and achievement milestone notification mechanisms prompt parents to intervene timely or provide positive feedback at key junctures, further strengthening the educational function of the family support system. In creative expression, the system design also demonstrates a progressive trend from simple storytelling to real-case-based creation: the emergent stage emphasizes concise and interesting story expression, the developing stage gradually guides students to develop branching narrative abilities, and the proficient stage encourages creative project design based on real-world scenarios.
Overall, the design of this model reflects three key innovations: first, it strictly follows the cognitive development patterns of children to systematically assign learning tasks; second, it deeply integrates gamified motivational mechanisms with educational goals to enhance both learning interest and effectiveness; and third, it transforms industrial heritage education into a visualised and measurable growth experience, supported by intelligent feedback on the adult interface, ultimately enabling students to understand industrial heritage through engagement and to develop final willingness through exploration.

6. Limitations and Future Research

This study has several limitations. First, although the sample size was good, all participants were drawn from a single public primary school within one region rather than multiple schools across that or other regions. This limits between-school variation and therefore constrains the generalizability of the findings. Each grade’s sample size was also relatively small, so the results should be regarded as indicative rather than conclusive.
Second, the analysis focused mainly on short-term engagement outcomes, without examining long-term knowledge retention or the processes through which cultural awareness develops. Balancing historical accuracy with creative storytelling also remains a continuing challenge in digital heritage education.
Finally, the proposed “Sapling Growth” framework is still at the conceptual stage and has not yet been validated through large-scale classroom practice. Future research should include participants from multiple schools and regions, adopt longitudinal designs to trace sustainable learning outcomes, and test the practical effectiveness of the framework in authentic educational settings. It would also be valuable to test the framework in international contexts to examine its cross-cultural adaptability.

7. Conclusions

This study examined whether mobile digital storytelling can support primary students’ learning about industrial heritage and how design features should align with children’s cognitive development across Years 1 to 6. Using a grade-spanning survey and validated scales, we assessed five predictors—learning interest, function preference, content comprehension, supervisory expectations, and creative presentation—in relation to students’ willingness to use digital storytelling for industrial heritage learning. The analyses showed that all five predictors were positively associated with willingness and that their importance varied by grade in ways consistent with developmental transitions. Younger students benefited from concrete, game-like formats and close adult guidance, whereas older students responded more to inquiry-based learning, authentic tasks, and opportunities for independent creation. A decline in interest and willingness around Year 5 suggests a transitional period in which scaffolding for abstract concepts and structured creative support becomes particularly important.
The study offers three main contributions. First, it introduces a developmental perspective into industrial heritage education, an area that has been largely overlooked at the primary level. Second, it translates empirical findings into a staged design framework, the “Sapling Growth” model, which specifies age-appropriate content forms, interaction modes, parental guidance features, and assessment approaches that can be applied in classroom and app design. Third, it proposes a practical measurement method for linking design factors to students’ willingness to engage, using reliable and interpretable scales that can inform both teaching and design practice.
Methodologically, this work integrates theory-informed construct development, grade-level calibration, and regression modeling to evaluate the relative influence of design factors. The measurement instrument demonstrated high internal consistency and solid structural validity, supporting its applicability in educational design and evaluation. Unlike previous studies that treat digital storytelling as a single intervention, this study identifies which design components are most effective at specific ages, providing concrete guidance for educators and developers.
Several limitations should be acknowledged. The sample was drawn from one public primary school, and the number of participants per grade was limited, so the results should be viewed as indicative rather than conclusive. The analysis focused on short-term engagement and self-reported willingness rather than long-term knowledge retention or transfer. Moreover, the “Sapling Growth” framework remains conceptual and has yet to be tested in large-scale classroom applications.
Future studies should expand sampling to multiple schools and regions, conduct longitudinal research to trace learning retention and cultural awareness development, and implement controlled classroom trials comparing the staged design with conventional instruction. Design experiments should pay particular attention to the Year 5 transition by incorporating guided concept introductions, step-by-step creative support, and time-management tools for families. Finally, mixed-method approaches combining observation and interviews could further illuminate how narrative structure, feedback timing, and parental participation interact to influence student engagement and learning outcomes in real educational contexts.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/heritage8110477/s1.

Author Contributions

Conceptualization, X.B.; methodology, X.B.; software, X.B.; validation, X.B.; formal analysis, X.B.; investigation, X.B.; resources, X.B.; data curation, X.B.; writing—original draft preparation, X.B.; writing—review and editing, A.B. and B.M.; visualization, X.B.; supervision, A.B. and B.M.; project administration, A.B. and B.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by China Scholarship Council, grant number 202108250018.

Data Availability Statement

The data presented in this study are available within the article and its Supplementary Materials. Due to ethical considerations involving research with minors, the raw survey data are not publicly available. Anonymized data may be obtained from the corresponding author upon reasonable request.

Acknowledgments

I would like to extend my deepest gratitude to my supervisor for their invaluable guidance, unwavering support, and insightful recommendations throughout this research. Their expertise has been pivotal to the successful completion of this study. I am also grateful to the teachers and students who participated in this research, whose engagement and feedback provided critical data and perspectives that significantly enriched the findings. Finally, I wish to thank the editors and reviewers of this journal for their thorough evaluation and constructive feedback, which have contributed greatly to the refinement of this paper.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
KMOKaiser–Meyer–Olkin
HCIHuman-Computer Interaction

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Heritage 08 00477 g001
Figure 1. Conceptual framework.
Figure 1. Conceptual framework.
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Figure 2. Emoji likert scale with sample items (Note: 1 = “strongly disagree” to 5 = “strongly agree”).
Figure 2. Emoji likert scale with sample items (Note: 1 = “strongly disagree” to 5 = “strongly agree”).
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Figure 3. Year-level differences in educational engagement across multiple dimensions.
Figure 3. Year-level differences in educational engagement across multiple dimensions.
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Figure 4. Developmental trajectories in industrial heritage learning.
Figure 4. Developmental trajectories in industrial heritage learning.
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Figure 5. Dynamic development framework for digital industrial heritage education APP.
Figure 5. Dynamic development framework for digital industrial heritage education APP.
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Table 1. Correlation analysis.
Table 1. Correlation analysis.
Learning InterestFunction PreferenceContent ComprehensionSupervisionCreative PresentationFinal Willingness
Learning Interest1
Function Preference0.402 **1
Content Comprehension0.373 **0.261 **1
Supervisory Expectations0.517 **0.400 **0.351 **1
Creative Presentation0.337 **0.324 **0.177 *0.179 *1
Final Willingness0.522 **0.579 **0.383 **0.506 **0.407 **1
Note. Entries are unstandardized regression coefficients unless otherwise specified. Final willingness = willingness to use digital storytelling for industrial-heritage learning. SD = standard deviation; VIF = variance inflation factor; F = F-statistic. Two-tailed tests: * p < 0.05; ** p < 0.01.
Table 2. Test of hypothesis.
Table 2. Test of hypothesis.
Unstandardized CoefficientsStandardized CoefficientstSignificance95.0% Confidence IntervalCollinearity Statistics
BStd. ErrorBetaLower BoundUpper BoundToleranceVIF
Constant0.9180.210 4.3680.0000.5031.334
Learning Interest0.1400.0550.1732.5540.0120.0320.2480.6241.604
Function Preference0.2620.0490.3355.4040.0000.1670.3580.7441.344
Content Comprehension0.1060.0500.1262.1370.0340.0080.2050.8191.221
supervisory Expectations0.1470.0470.2063.1420.0020.0550.2390.6661.502
Creative Presentation0.1360.0440.1803.0870.0020.0490.2230.8401.190
R20.510
Adjusted R20.496
FF = 35.629, p = 0.000
Note. Entries are Pearson’s r. Final willingness = willingness to use digital storytelling for industrial-heritage learning. SD = standard deviation. Two-tailed tests: p < 0.05; p < 0.01. Variables: learning interest, function preference, content comprehension, supervisory expectations, creative presentation, and final willingness.
Table 3. The analysis of differences in variables across years.
Table 3. The analysis of differences in variables across years.
VariableGradeNMSDFp ValueLSD
Learning InterestYear1333.781.112.4630.0355 < 1, 2, 6;
Year2273.830.78
Year3303.571.12
Year4273.521.13
Year5333.080.79
Year6273.740.98
Function PreferenceYear1333.920.922.3960.0391 > 4, 5;
3 > 5;
Year2273.591.01
Year3303.650.83
Year4273.301.15
Year5333.111.11
Year6273.511.14
Content ComprehensionYear1332.410.893.5020.0051 < 2, 3, 4, 5, 6;
Year2273.040.92
Year3303.000.83
Year4273.091.10
Year5333.030.83
Year6273.381.09
Supervisory ExpectationsYear1333.711.214.9330.0005 < 1, 2, 3, 4;
6 < 1;
Year2273.540.75
Year3303.401.02
Year4273.391.06
Year5332.521.07
Year6273.041.35
Creative PresentationYear1333.211.292.2740.0492 > 5;
4 > 3, 5;
Year2273.541.11
Year3303.051.21
Year4273.760.80
Year5332.970.95
Year6273.390.95
Final WillingnessYear1333.950.913.3870.0065 < 1, 2, 3, 4;
6 < 1;
Year2273.660.67
Year3303.640.54
Year4273.630.82
Year5333.170.81
Year6273.470.95
Note. N = group size; M = mean; SD = standard deviation; F = one-way ANOVA; p = two-tailed p-value; LSD = Fisher’s Least Significant Difference post hoc. Codes: 1 = Y1, 2 = Y2, 3 = Y3, 4 = Y4, 5 = Y5, 6 = Y6 (e.g., 5 < 1,2,6 means Y5 < Y1,Y2,Y6, α = 0.05). Report p < 0.001 instead of 0.000. Note. N = group size; M = mean; SD = standard deviation; F = one-way ANOVA; p = two-tailed p-value; LSD = Fisher’s Least Significant Difference post hoc. Codes: 1 = Y1, 2 = Y2, 3 = Y3, 4 = Y4, 5 = Y5, 6 = Y6 (e.g., 5 < 1,2,6 means Y5 < Y1,Y2,Y6, α = 0.05). Report p < 0.001 instead of 0.000.
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Bian, X.; Brown, A.; Marques, B. Examining a Primary Education Approach Using Digital Storytelling: Chinese Industrial Heritage as a Vehicle to Support Learning. Heritage 2025, 8, 477. https://doi.org/10.3390/heritage8110477

AMA Style

Bian X, Brown A, Marques B. Examining a Primary Education Approach Using Digital Storytelling: Chinese Industrial Heritage as a Vehicle to Support Learning. Heritage. 2025; 8(11):477. https://doi.org/10.3390/heritage8110477

Chicago/Turabian Style

Bian, Xin, Andre Brown, and Bruno Marques. 2025. "Examining a Primary Education Approach Using Digital Storytelling: Chinese Industrial Heritage as a Vehicle to Support Learning" Heritage 8, no. 11: 477. https://doi.org/10.3390/heritage8110477

APA Style

Bian, X., Brown, A., & Marques, B. (2025). Examining a Primary Education Approach Using Digital Storytelling: Chinese Industrial Heritage as a Vehicle to Support Learning. Heritage, 8(11), 477. https://doi.org/10.3390/heritage8110477

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