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Volume 18
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Article

Sustaining Learning Practices: Exploring the Roles of External Engagement for Engineering Graduates

by
Pornthipa Ongkunaruk
1,
Panuwat Rodchom
1,
Bordin Rassameethes
2 and
Kongkiti Phusavat
1,*
1
Department of Industrial Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10903, Thailand
2
Department of Technology and Operations Management, Faculty of Business Administration, Kasetsart University, Bangkok 10903, Thailand
*
Author to whom correspondence should be addressed.
Sustainability 2026, 18(7), 3218; https://doi.org/10.3390/su18073218
Submission received: 20 January 2026 / Revised: 20 March 2026 / Accepted: 23 March 2026 / Published: 25 March 2026
(This article belongs to the Special Issue Sustainable Higher Education: Innovative Teaching and Learning, and Leadership for Creating Impacts on Local Society and Globally)
Versions Notes

Abstract

This exploratory study addresses the shift in employment preferences among recent engineering graduates toward small and medium enterprises (SMEs) and startups, highlighting the importance of learning. Learning, instead of conventional training, has been pivotal for the sustainability of small businesses. The study evaluated industrial engineering students’ perception of learning following a 2023 change, which replaced traditional exams with professional presentations and business reports based on enterprise visits. Using a mixed-methods approach with 218 third-year students, the findings demonstrate that external engagement relates positively to the perception of learning. Given the rising interest among new engineering graduates in SMEs and startups, these findings offer useful background for preparing workplaces to support and sustain business operations.

1. Introduction

Surveys regarding higher education often focus on teaching outcomes by involving instructors, managers, and executives from large enterprises [1,2,3]. While this practice benefited corporate partners, they often lowered students’ voices, including behavior, attitude, perception, and actual experiences, to a secondary position [4,5,6]. A gradual shift has emerged which shows more efforts towards engaging directly with students about their workplace expectations and learning experiences, particularly for students in their final years of study [4,7,8,9,10]. The shift is driven by a more student-centered approach towards learning with more emphasis on industrial partnership and real-world settings.
The findings from annual surveys are often applied to initiate improvements in curriculum and pedagogy, directly enhancing graduate employability [9,11]. Past studies indicate this data helps bridge the gap between employer expectations and actual skill achievement. These surveys are critical for strengthening university partnerships with the private sector for senior and collaborative projects and internships, which support the broader aim of long-term sustainable workforce development [12,13].
Furthermore, the findings from a traditional university survey offer useful background for small and medium enterprises (SMEs) and startups, which must continuously adapt to students’ expectations and experiences to remain competitive in the labor market [14,15]. Both SMEs and startups are faced with limited budgets which affect the ability to provide formal training to all. Despite these limitations, they remain vital drivers of socio-economic development, particularly in Southeast and East Asia [16,17]. For instance, in Thailand, SMEs and startups account for nearly 40% of GDP and provide significant employment opportunities across various educational levels [15,18]. Nevertheless, capacity building remains a primary challenge to their survival.
This challenge is further highlighted by the fact that most engineering curriculums and preparation are traditionally designed to meet the specialized technical demands of large enterprises, leaving a gap regarding general skills such as learning and communication required by the SMEs and startups [19,20]. On the other hand, a significant shift is observed as recent engineering graduates increasingly express their preference to seek employment in SMEs and startups, due to personal needs and a dynamic labor market [15,20]. These smaller businesses offer flat organizational structures and flexible arrangements which are favorable to work-life balance. In these environments, newly graduated engineers can foster broad skill acquisition and immediately apply classroom knowledge [12,13]. Conversely, entry-level roles in large corporations have become intensely competitive as Artificial Intelligence systematizes many technical tasks, prompting graduates to seek alternative career paths in SMEs and startups [15,20].
To further demonstrate the gap in skill requirements, Thailand’s industrialization efforts since the 1980s have profoundly shaped the demands for engineering education [13,21]. Multinational corporations in the automotive and electronics sectors demand specialists with in-depth knowledge in process optimization. On the other hand, SMEs and startups often require employees to be versatile, handling frequent job rotations, broad responsibilities, and rapid changes in scope. As a result, tasks within SMEs and startups are characterized as on-the-job or learning while working, and emphasize flexibility, autonomy, and adaptability [14,20,22,23]. Furthermore, these smaller businesses expect employees to act as role models to others, manage fast-paced competition, and demonstrate a willingness to work beyond defined job descriptions.
Learning includes cognitive, emotional, and practical abilities for the effective acquisition and application of knowledge [24,25,26]. In the context of SMEs and startups, which often lack the financial capital for standardized training, engineering graduates need to have positive learning perception to survive rapidly changing business environments and to ensure operational sustainability [18,24,27,28].
Consequently, Work-integrated Learning (WIL) has been introduced into engineering curriculums to enhance hands-on experience through more active industrial engagements such as site visits, collaborative projects, guest lectures, and internships [1,5,21,27]. WIL is expected to improve students’ learning interests and recognition of the importance of learning [1,13,21]. In other words, young engineers need to become more proactive in learning to cope with the continuous changes in business environments [5,11,29]. Consequently, gaining more insights into student perceptions of learning is vital for sustaining business operations in SMEs and startups [11,22,24,26].
Thus, the question for this exploratory study is as follows. How do active industry– academic partnerships shape engineering students’ perception of learning? The question is important because it addresses the need to align engineering education with the capacity building of SMEs and startups due to the shift in the employment of young engineering graduates. Simply put, the focus is on exploring the relation between external engagement and students’ perception of learning.

2. Literature Review

During the past decade, learning has become an integral component of work in SMEs and startups due to challenges in providing formal training [16,23,25]. Formal training often requires taking employees away from work simultaneously, which disrupts deadlines, commitment, and operational continuity. As a result, SMEs and startups are increasingly relying on learning for capacity building. For enterprises operating in Industry 4.0 or 5.0, learning reflects a broader definition of performance which goes beyond task completion or merely executing predefined tasks [27]. Work performance consists of task, contextual, and adaptive components [30,31]. Task performance focuses on the technical work and responsibilities embedded in the job description.
On the contrary, contextual performance deals with non-compulsory activities that are not part of the required tasks but support business operations, such as sharing ideas and mistakes and providing feedback to peers. Adaptive performance highlights the ability to amend or adjust activities to meet emerging and challenging conditions at work, such as the use of remote work for more operational efficiency and the use of digital technology to minimize cost. Learning helps achieve contextual and adaptive performance through social interactions, open communication, conversations, and feedback to keep pace with ongoing and emerging changes in technology, customers, and the business environment [30,31]. In the contemporary business environment, employability for engineering graduates has shifted towards learning due to its relationship with contextual and adaptive performance [7,13,20,29,32]. This shift underlines the importance of problem-solving, communication, collaboration, and ethical responsibility in today’s competitive and volatile environments [14,17,22,26,33].
Regular surveys of engineering students’ perceptions of learning provide critical background for preparing SMEs and startups to integrate new talent by preparing their workplace to accommodate learning as well as upskilling and reskilling efforts [3,4,34]. Unlike large and international firms that can attract new graduates through brand prestige, SMEs and startups must proactively adapt to their evolving expectations. For SMEs and startups, while formal education often addresses technical challenges, continuous learning facilitates the accumulation of knowledge needed for long-term sustainability of organizational competitiveness [23,27].
Therefore, continuous understanding of how university graduates perceive learning is crucial [27,35]. To ensure that learning remains relevant, active engagement with practitioners has been encouraged in higher education [25,33,34]. Finally, the shift in employment preferences among recent engineering graduates, who increasingly value SMEs and startups as crucial learning spaces, has emerged [36,37]. From their perspective, these spaces offer hands-on experiences that bridge theory with practicality. Despite a lack of structured career paths in SMEs and startups, engineering graduates prefer flexibility and autonomy, which are essential for modern work-life balance and employability.

3. Problem Background

The Industrial Study course serves as a cornerstone of WIL for third-year industrial engineering students at Kasetsart University. Traditionally, the pedagogical approach relied primarily on-site visits to various enterprises to be assessed by descriptive reporting on company’s background. However, this model faced criticism for failing to foster deep understanding of complex industrial operations and real-world problems. The disruption caused by the COVID-19 pandemic during 2020–2022 resulted in a suspension of these visits. Upon resumption in 2023, the landscape had changed, as large enterprises, which went through their post-pandemic restructuring stage, became less accessible. As a result, the Industrial Study course turned toward SMEs and startups for practical knowledge and understanding.
To revitalize this course in a more meaningful way, the Department introduced two transformative changes: (1) replacing traditional examinations with professional presentations, and (2) replacing passive company descriptions with analytical business reports focusing on industry trends and operational processes. Moreover, enterprise representatives and owners are invited to provide direct feedback which bridges the gap between theory and practicality. These changes help students gain more understanding of the nature of work and business environments in SMEs and startups.
An exploratory study on engineering students’ perceptions of learning is essential to align classroom environments with real-world settings which is critical for improving employability [1,13,16]. This study helps examine possible relations between the two significant changes in WIL and students’ learning. Note that there are other Industrial Engineering courses which require strong collaboration with business operators such as Quality Management and Production Planning.

4. Objective

This exploratory study evaluates industrial engineering students’ perception to learning because of a revised Industrial Study course. This evaluation also includes the connection between learning and industry–academic partnership or external engagement.

5. Methodology

This study employed a mixed-methods approach combining descriptive quantitative survey analysis and qualitative thematic analysis of open-ended responses. A survey, designed to assess students’ perceptions of learning after completing the Industrial Study course over two consecutive academic years, was developed. The survey evaluated students’ learning and their overall course experience. All closed-ended items were measured using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Open-ended questions were included to obtain additional qualitative feedback. Then, a pilot test was conducted with 30 students to evaluate clarity and internal consistency reliability. Reliability was assessed using Cronbach’s alpha coefficient. The results indicated acceptable internal consistency (α ≥ 0.70), confirming the instrument’s suitability. Afterwards, the questionnaire was administered via Google Forms to students who had completed the Industrial Study course during the two academic years.
Next, to describe students’ responses, the mean, minimum, maximum, and standard deviation for the survey items were calculated. Furthermore, skewness and kurtosis statistics were examined to evaluate the normality of the data distribution. Furthermore, responses to open-ended questions were analyzed using thematic analysis to identify recurring patterns and themes. All statistical analyses were conducted using SPSS Statistics 30.0 software. The normality of the data distribution was assessed using skewness and kurtosis statistics. Skewness values between −0.5 and 0.5 were considered approximately symmetric, values between −1 and −0.5 or between 0.5 and 1 indicated moderate skewness, and values less than −1 or greater than 1 indicated high skewness. Kurtosis values close to zero suggested a normal distribution, negative values indicated light tails (platykurtic), and positive values indicated heavy tails (leptokurtic) [38,39]. Data were considered approximately normally distributed when skewness and kurtosis values fell within acceptable ranges, supporting the use of parametric statistical interpretations.
The survey focuses on two learning areas for third-year engineering students, consistent with the course outline which emphasizes learning process and opportunity to learn [12,13,22,23]. The learning process consists of the acquisition, application, and sharing of knowledge. Opportunity to learn addresses learning interest, including relatedness and relevance. The survey adopts five core competencies, namely systems thinking, decision-making, logical thinking, communication/collaboration, and attitude. These competencies are crucial for contextual and adaptive performance required by SMEs and startups [21,30,40,41]. Modifications were made by linking logical thinking with application, system thinking with sharing, communication and collaboration with communication, decision making with relatedness, attitude with relevance. Note that, acquisition was linked with desirable behavior expected from students at the beginning of and throughout the course.
In this study, acquisition and application of knowledge was operationalized through behaviors related to logical reasoning and self-paced learning [29,30,31]. Assessing willingness to share knowledge is completed by evaluating students’ capacity to explain complex concepts to peers in a simplified manner. Then, students need to be able to interact with peers and colleagues through social skills [9,27]. Relatedness is defined as the students’ capacity to integrate acquired knowledge within the broader scope of industrial engineering and effectively demonstrate these connections to others [5,21]. Relevance refers to the professional mindset and motivation toward the engineering profession, representing a role model for others to learn and follow [20,32].
To complement the quantitative data, the survey incorporated open-ended questions. These items were designed to draw personal insights into reflections, specifically regarding their interactions with instructors, peers, and industry practitioners during the site visits. The questionnaire is administered by using Google Forms, and its internal consistency reliability was assessed prior to full deployment. All closed-ended items are measured using a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). See Table 1 for the survey items.

6. Results

A total of 218 students who have completed the Industrial Study course over the past two years, academic years 2023–2024 and 2024–2025, volunteered to participate in the survey. Students provided informed consent before participation. They could choose to withdraw from the survey without any negative consequence on their academic performance. The duration for completing the survey was two weeks. The number of participants in the survey represented about 85% of the combined two groups of third-year engineering students.
Prior to presenting the findings, the reliability of the survey instrument was evaluated using Cronbach’s alpha to assess the internal consistency of the constructs. Overall, the learning-related categories demonstrated acceptable reliability for further analysis. The initial four-item Acquisition (AC) scale (AC1–AC4) yielded an alpha coefficient of 0.683, slightly below the commonly accepted threshold of 0.70. Item–total statistics indicated that removing AC3 would increase the alpha to 0.721. Although this adjustment marginally improved reliability, the original coefficient exceeded the minimum acceptable threshold of 0.60. Furthermore, AC3 exhibited acceptable distributional properties and contributed conceptually to the construct. Therefore, all four items were retained to preserve content validity. The reliability results are presented in Table 2.
The next step examines the normality of each survey item through skewness (asymmetry of data distribution—assessment of students’ responses cluster more on one side of the mean) and kurtosis (peakedness and extremity of tail values—assessing the level of concentrated responses from students around the mean and the likelihood of extreme values or outlier expressed by students) [42,43]. Normality tests serve two primary purposes [43,44,45]. The first one helps evaluate the quality and symmetry of the student responses for simple and straightforward insights while the second one plans to use these insights for potential future parametric analysis. The parametric tests are not utilized in this specific phase due to the current scope focusing solely on students. Note that the next phase intends to include SMEs and startups for gap analysis.
Most items follow a normal distribution, apart from C4, RV1, RV2, and RV3. These four items exhibit negative skewness, reflecting a high concentration of strong agreement among students regarding team communication, professional ethics, sense of responsibility, and punctuality. The resulting left-skewed distribution indicates that while most students perceive their performance positively in these areas, a small minority expressed strong disagreement. See Table 3.
Qualitative data was collected from students through open-ended reflections upon course completion. These responses were analyzed using thematic analysis, based on Braun and Clarke’s framework [46]. This process involved data familiarization, initial coding, and theme development, review, and refinement. To ensure trustworthiness, coding decisions were revisited after a two-week interval to ensure consistency. Then, codes were synthesized into broader themes aligned with the course’s learning objectives, with the coding structure further refined to ensure conceptual clarity and consistency.
The analysis followed an inductive coding process, in which similar ideas were grouped and refined into higher-level themes. Overall, fourteen key themes emerged from the open-ended reflections, illustrating students’ perspectives on their learning after course completion. Based on the findings, the two most frequent themes were the application of industrial engineering tools or Theme 1 and the acquisition of knowledge through enterprise visits or Theme 3. These were followed by the ability to adapt to a working environment or Theme 12 and communication skills at work or Theme 6. Teamwork and Collaboration Skills or Theme 5 was identified as the fifth most frequent theme. See Table 4.
The short description of each theme is as follows.
Theme 1: Learning New Applications for Industrial Engineering Tools—Students consistently indicated that the course significantly enhanced their proficiency in applying core industrial engineering methodologies, including IDEF0 process modeling, root cause analysis, and systematic problem decomposition. These competencies were identified as highly relevant not only for senior projects and internships but also for operational analysis and future professional roles. Applicability to SMEs and startups, essential for operational sustainability, is also mentioned.
Example of a response from this theme:
“I gained a deeper understanding of production processes and learned that even within the same industry, manufacturing processes can differ significantly. I developed a better understanding of supply chains, learned how to read IDEF diagrams, analyze root causes of problems, and became more interested in studying supply chain and logistics in greater depth.”
Theme 2: Understanding of Industrial Systems and Supply Chain Processes—Students have gained a holistic understanding of industrial operations, encompassing work processes, logistics flows, and supply chain integration. A significant number of students reported an enhanced ability to visualize end-to-end processes, spanning from upstream to downstream activities.
Example of response from this theme:
“I can apply what I learned by planning tasks more effectively and preparing better for other courses. I can use logical thinking and systematic problem-solving approaches in future work, considering operations from upstream to downstream.”
Theme 3: Experiential Learning through Enterprise Visits—Enterprise visits served as a critical learning component, effectively bridging the gap between theory and practice. Students reported that observing actual operational environments allowed them to operationalize classroom theories and gain a deeper understanding of practical constraints often omitted during lectures. Furthermore, the out-of-class setting significantly enhanced student motivation and fostered collaborative interactions among group members.
Example of response from this theme:
“I learned how different factories operate and what their production processes look like. This helped me identify which type of factory and field I am most interested in working in.”
Theme 4: Development of Systematic and Analytical Thinking for Learning Skills- The coursework significantly boosted confidence in applying structured problem-solving techniques, including root cause identification and bottleneck analysis to operational sustainability. Students also reported improved proficiency in applying engineering logic to identify inefficiencies in industrial processes, a key competence for developing sustainable solutions.
Example of response from this theme:
“I learned to read documents carefully and thoroughly and to seek additional knowledge independently.”
Theme 5: Teamwork and Collaboration Skills—Students developed critical teamwork competencies, including task delegation, coordination, communication, and conflict management, deemed to be essential for sustainable practices. Working with unfamiliar peers mirrored real-world environments, significantly enhancing collaborative, social, and intercultural skills.
Example of response from this theme:
“I can apply teamwork, presentation, and communication skills in my future career.”
Theme 6: Communication and Presentation Skills—Students reported significant improvements in their professional competencies. Specifically, they highlighted enhanced skills in oral presentations, report writing, slide design, and English-language communication. These skills are viewed as essential for learning and contribute to both academic success and future employability.
Example of response from this theme:
“I developed skills in report writing, classroom presentations, slide preparation, and creating engaging bullet points to make presentations more effective.”
Theme 7: Time Management and Work Planning—The coursework strengthened students’ abilities to manage time, prioritize tasks, and meet deadlines through various projects and group assignments. Time management was viewed as critical when students were faced with multiple deadlines across different courses.
Example of response from this theme:
“I learned how to plan work as a team, divide responsibilities, and manage time efficiently. These skills can be applied in real workplaces with larger teams. I also gained exposure to various products and saw how knowledge from different courses is applied in actual production processes.”
Theme 8: Integration of Knowledge across Courses—Students recognized the value of the ability to integrate knowledge from multiple industrial engineering subjects, such as production planning, logistics, work study, quality management, industrial management, and data analysis, into a cohesive problem-solving framework.
Example of response from this theme:
“I can apply skills in information searching and summarizing data to other tasks. Presentation skills can also be adapted for future professional work.”
Theme 9: Career Awareness and Professional Identity—Exposure to diverse business environments allowed students to reflect on personal career interests, strengths, and preferences. Some students reported that they had gained better clarity on what their long-term goals could potential be and perspective of future business operations in the country. Appreciation of SMEs and startups for a future career was mentioned.
Example of response from this theme:
“I learned planning, teamwork, and systematic problem-solving skills, as well as how to apply technical knowledge in real situations, such as process design, data analysis, and quality control. This enhanced my engineering thinking skills and professional communication abilities, which can be applied to future employment, job applications, or even personal business development.”
Theme 10: Preparation for Internships, Capstone Projects, and Future Employment—Exposure to diverse business environments enabled students to critically reflect on personal career interests and strengths. Students gained greater clarity regarding their long-term professional goals, with a specific focus on opportunities within SMEs and startups that prioritized sustainability.
Example of response from this theme:
“The knowledge gained can be applied to work and research activities.”
Theme 11: Self-Regulated and Lifelong Learning Skills—The course encouraged independent learning behaviors, such as information searching, critical questioning, asking questions, sharing ideas and problems, providing feedback, and continuous development beyond formal instruction.
Example of response from this theme:
“The experience helped me decide which factory processes interest me most and reflect on which part of the production process I would like to work in. It also encouraged me to explore knowledge in multiple areas, not only in my preferred field.”
Theme 12: Professional Readiness and Workplace Mindset for SMEs and Startups—Students reported an increase in the level of awareness of professional and workplace behaviors, including responsibility, calmness, adaptability, respect, punctuality, care and giving, and effective communication which were critical for business continuity and success of SMEs and startups.
Example of response from this theme:
“I developed better future planning skills, including career analysis, career selection, self-development, and life planning, especially as a fourth-year student who must think more seriously about the future.”
Theme 13: Learning on Academic Planning and Decision-Making—The insights gained from the course support students’ decisions regarding selection of elective courses, senior project topics, and academic pathways in their final year.
Example of response from this theme:
“Observing different factories with varied production processes provided foundational knowledge that can support future career decisions and help determine which specialized courses to select in the final year.”
Theme 14: Critical Reflection on Learning Experience—Reflective critiques on workload balance, team dynamics, and course structure were described. This description indicated higher- order thinking which was needed for future learning and development.
Example of response from this theme:
“The course provided guidance for choosing a future career path and helped me better understand my interests and strengths.”

7. Discussion

The findings indicate a positive perception of learning among the 218 third-year industrial engineering students. With all items scoring above the 3.5 threshold, a value recognized as favorable in Likert-scale interpretation, it indicates an encouraging sign for learning from more engagement with practitioners [44,47]. The findings highlight two essential issues. First, students’ perceptions reveal a high level of confidence in communication skills and organizational strategy and method identification. Second, these results can help SMEs and startups refine their workplace strategies to accommodate engineering graduates.
The highest mean values, from 3.77 to 4.05, indicate that most students agree with the statements C3, C2, AC1, AP1, and RL3. These findings suggest that positive course experiences relate to communication skills when faced with complex challenges [44,47]. Furthermore, students expressed confidence in their ability to independently steer actions toward goals when provided with adequate guidance [48,49]. Students recognize the importance of environmental factors influencing business strategy and performance. In addition, the high frequency of themes T1, T3, T12, T6, and T5 suggests that learning for engineering students is connected to technical knowledge, interpersonal and soft skills, and professional and practice experiences. These findings highlight the need to link between the classroom environment and business practices and settings [50,51].
Furthermore, it is crucial to analyze survey items exhibiting a left-skewed distribution, specifically RV3 (punctuality), RV2 (reflection), RV1 (professional ethics), and C4 (team communication). While most students responded positively, a small but significant minority expressed strong disagreement, which resulted in their scores being disproportionately below the average level. For RV, which reflects the relevance of the engineering profession, there is a lack of clarity regarding the expectation of an engineer [9,21]. To narrow this perception, increased instructor engagement and industrial expectations from business owners are needed. Regarding team communication, the observed outliers may be attributed to individual personality traits, such as extreme introversion, which can influence this perception.
Given the necessity of integrating learning within the workplace, these findings offer useful background for SMEs and startups to better accommodate incoming engineering graduates. Specifically, fostering an environment that encourages the practice of soft skills more openly such as sharing ideas or articulating problems is important. This environment also serves as a foundation to improve engagement with these graduates [51,52]. Despite their essential role in SMEs and startups, a deficiency in these soft skills remains prevalent among university graduates [28,53]. Consequently, it is imperative for SMEs and startups to remain familiar with the perceived proficiency of soft skills among engineering students [13,53].
Since engineering students perceive learning as an integral part of their study, a safe learning environment appears to be an important consideration for SMEs and startups. Such an environment can be characterized by peer-to-peer learning, constructive feedback from business owners, and the psychological safety to learn from mistakes, which enhance confidence, social learning, and coaching in the workplace [6,17,24,54].

8. Implications

The findings indicate a strong consensus among third-year industrial engineering students regarding their learning behaviors when emphasizing external participation. These findings indicate that two specific interventions, despite the lack of historical comparative data, foster positive perceptions of learning among third-year engineering students. Therefore, increased engagement with business executives and owners, especially in WIL like Industrial Study, is encouraged. With this perception, learning can be more effectively embedded among engineering graduates, which possibly leads to positive professional behaviors and attitude.
Potentially, for engineering education, embedding real-world environments represents one of many opportunities that help graduates improve their perception of learning. This perception is necessary when they need to integrate into the dynamic ecosystems of SMEs and startups [14,55]. Because SMEs and startups often lack formal training infrastructures, they rely on learning, which is regarded as part of work, to sustain their capacity building and business operations [10,22,26].
In alignment with double-loop learning theory, external interaction is vital for engineering education [56,57]. Beyond merely correcting mistakes or errors as part of single-loop learning, students need to engage with business operators to challenge their underlying assumptions such as importance of soft skills in the workplace. This engagement is part of double-loop learning, which helps students realize why they need to constantly learn. Constructive feedback from business executives during presentations fosters critical thinking, planning, and communication skills, ensuring that learning is not only reflective but also can sustain self-development.
The study shows that shifting to increased interactions with business owners and entrepreneurs enhances students’ perception of learning, evidenced by the high average score after the normality test. For third-year industrial engineering students, this pedagogical transition results in improved knowledge application of theories, as confirmed by thematic analysis. These findings indicate that higher education’s learning environments should mirror professional ecosystems. This strategy can enhance the business sustainability of SMEs and startups, which serve as the economic backbone in emerging economies like Thailand [15,18].

9. Limitations and Future Research

While this study offers promising results on student perspectives of learning after the redesign of the Industrial Study course, this positive viewpoint is not without limitations. A primary limitation is the absence of comparative data from previous students who experienced the same course before 2023. However, a longitudinal or direct comparative analysis of student viewpoints before and after the 2023 adjustment was not feasible, limiting the ability to statistically quantify the specific effects of these two pedagogical practices over time.
Additional limitations should also be pointed out as follows. This study employed a cross-sectional descriptive design; therefore, causal inferences regarding the effectiveness of the course redesign cannot be made. Participation was voluntary, which may introduce the bias for self-selection. Additionally, demographic information was not collected to preserve anonymity and encourage candid responses. Therefore, subgroup analyses and non-response bias assessment were not possible. In addition, other issues need to be recognized when exploring further into how external feedback could be better utilized to enhance learning skills of engineering students. These issues include self-report bias, social desirability bias, common-method bias, single-institution context, and the lack of objective performance measures after learning.
Given the lack of comparative data, future research should explore the underlying reasons for the positive consensus among third-year industrial engineering students, specifically using open-ended survey to capture their narrative depth such as personal feeling and experiences. Factors driving learning interests, such as constructive feedback from outside, should also be examined, including the identification of prevailing assumptions among engineering students about learning. These assumptions highlight the second-loop learning. More understanding of students’ insights can help other courses to explore the possibility of improving how they learn and to determine what learning environment in classroom should be.
Future research should incorporate demographic items and comparative or longitudinal designs to strengthen validity and analytical depth to overcome the bias-related issues. Given that digital competence is crucial for operational sustainability of SMEs and startups, further study is needed to explore if increased interaction with business executives relates to higher learning interests in Artificial Intelligence [58]. Understanding this relation is vital for long-term viability of these enterprises. Finally, future research should explore active partnerships with SMEs and startups to monitor the effects of revised pedagogical practices on students’ actual contextual and adaptive performance.

10. Conclusions

This exploratory study demonstrates that increasing the involvement of business professionals in the Industrial Study course relates significantly to students’ learning, enhancing the adaptability that is crucial for SMEs and startups. Learning while working is essential for the workforce development of SMEs and startups, which is often overlooked for sustainability. While the lack of a formal before-and-after comparison is a limitation, this study highlights critical pedagogical shifts for future research to measure impact on contextual and adaptive performance of engineering graduates.

Author Contributions

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

Funding

This project is funded by National Research Council of Thailand (NRCT) through Kasetsart University (N42A660996).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Kasetsart University Research Ethics Committee (KUREC-SSR68/148 on 17 September 2025).

Informed Consent Statement

Informed consent was provided and/or explained to all participants involved in the survey study.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

WILWork-integrated Learning
SMEsSmall and Medium Enterprises

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Table 1. Illustration of Survey Items.
Table 1. Illustration of Survey Items.
LearningCodeDetails
AcquisitionAC1I recognize the importance of the course syllabus and have read it with a great deal of understanding on activities for the course fulfillment.
AC2I have watched the introductory video clips recommended by the course syllabus.
AC3I have downloaded the referenced e-book recommended by the course syllabus.
AC4I have watched all relevant video clips which correspond to operations and work processes of the enterprises to be visited.
ApplicationAP1I can identify and compare the advantages and disadvantages of different strategies and management methods currently practiced in industries.
AP2I can assess, analyze and write a business model at the enterprise level, including key supply chains by using IDEF0 (Integration Definition for Function Modeling).
AP3I can analyze problems, identify their root causes, and propose solutions to an enterprise by using DMAIC (Define, Measure, Analyze, Improve, Control) on one page.
SharingS1I am willing to share and explain critical activities and work processes in the enterprise’s operations to others.
S2I am willing to share and explain problems that may arise in the enterprise and its industry to others.
S3I am willing to share and explain the methods relating to planning, managing, and controlling in the enterprise to others.
CommunicationC1I can provide formal or informal presentations about my work effectively in English or Thai.
C2I can create and develop presentation materials that can address essential issues and questions, based on enterprise visits.
C3I can write reports, short notes, summary, and emails clearly, based on enterprise visits.
C4I can manage communication within my team well.
RelatednessRL1As an industrial engineer, I should demonstrate appropriate methods and approaches to measure the enterprise’s operational performance.
RL2As an industrial engineer, I should demonstrate appropriate strategies and approaches to solve critical problems faced by the enterprise.
RL3As an industrial engineer, I should demonstrate whether a current performance management system remains effective for the enterprise.
RelevanceRV1It is important that, as an engineer, I need to uphold professional ethics and maintain integrity all the time with the need to refrain from any actions for personal gain.
RV2I believe an engineer should be responsible for completing the assigned tasks while continuing to share, learn, and reflect on mistakes or errors made in the team.
RV3As an engineer, I believe in the importance of being punctual on a consistent basis, which is a good example for others in a team.
Table 2. Reliability Test for Survey Items.
Table 2. Reliability Test for Survey Items.
CategoryItemsCronbach’s AlphaInterpretation
Acquisition (AC)AC1–AC40.683Acceptable
Application (AP)AP1–AP30.762Acceptable
Sharing (S)S1–S30.912Excellence
Communication (C)C1–C40.796Acceptable
Relatedness (RL)RL1–RL30.831Good
Relevance (RV)RV1–RV30.929Excellence
Table 3. Descriptive Statistics of items.
Table 3. Descriptive Statistics of items.
ItemNMinMaxMeanS.D.SkewnessKurtosisInterpretation
AC1218153.960.884−0.5740.026Normality
AC2218153.590.952−0.33−0.122Normality
AC3218153.681.163−0.556−0.467Normality
AC4218153.561.015−0.323−0.17Normality
AP1218153.880.788−0.4680.279Normality
AP2218153.650.864−0.159−0.405Normality
AP3218153.550.916−0.187−0.285Normality
S1218153.750.856−0.334−0.03Normality
S2218153.750.856−0.334−0.241Normality
S3218153.720.826−0.38−0.038Normality
C1218153.70.935−0.362−0.397Normality
C22181540.812−0.461−0.052Normality
C3218154.050.852−0.584−0.109Normality
C4218154.370.812−1.4582.719Left−skewed
RL1218153.650.842−0.097−0.355Normality
RL2218153.740.864−0.245−0.376Normality
RL3218153.770.886−0.3740.028Normality
RV1218154.50.757−1.7663.818Left−skewed
RV2218154.50.787−1.7883.483Left−skewed
RV3218154.470.781−1.9154.795Left−skewed
Table 4. Illustration of the Results from the Thematic Analysis.
Table 4. Illustration of the Results from the Thematic Analysis.
CodeThemeFrequency%
T1Learning New Applications for Industrial Engineering Tools7534.40
T2Understanding of Industrial Systems and Supply Chain Processes2411.01
T3Experiential Learning through Enterprise Visits7132.57
T4Development of Systematic and Analytical Thinking for Learning Skills3817.43
T5Teamwork and Collaboration Skills5525.23
T6Communication and Presentation Skills6127.98
T7Time Management and Work Planning3817.43
T8Integration of Knowledge across Courses52.29
T9Career Awareness and Professional Identity 4319.72
T10Preparation for Internships, Capstone Projects, and Future Employment4520.64
T11Self-Regulated and Lifelong Learning Skills73.21
T12Professional Readiness and Workplace Mindset for SMEs and Startups6630.28
T13Learning on Academic Planning and Decision-Making5022.94
T14Critical Reflection on Learning Experience188.26
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Ongkunaruk, P.; Rodchom, P.; Rassameethes, B.; Phusavat, K. Sustaining Learning Practices: Exploring the Roles of External Engagement for Engineering Graduates. Sustainability 2026, 18, 3218. https://doi.org/10.3390/su18073218

AMA Style

Ongkunaruk P, Rodchom P, Rassameethes B, Phusavat K. Sustaining Learning Practices: Exploring the Roles of External Engagement for Engineering Graduates. Sustainability. 2026; 18(7):3218. https://doi.org/10.3390/su18073218

Chicago/Turabian Style

Ongkunaruk, Pornthipa, Panuwat Rodchom, Bordin Rassameethes, and Kongkiti Phusavat. 2026. "Sustaining Learning Practices: Exploring the Roles of External Engagement for Engineering Graduates" Sustainability 18, no. 7: 3218. https://doi.org/10.3390/su18073218

APA Style

Ongkunaruk, P., Rodchom, P., Rassameethes, B., & Phusavat, K. (2026). Sustaining Learning Practices: Exploring the Roles of External Engagement for Engineering Graduates. Sustainability, 18(7), 3218. https://doi.org/10.3390/su18073218

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