Developing the Methodological Competence of Future IT Engineers in the Republic of Kazakhstan Through Problem-Oriented Learning

Abstract

Kazakhstan’s transition toward Industry 4.0 and 5.0 increases demand for IT professionals capable of solving complex engineering problems. This study theoretically substantiated and empirically evaluated a didactic model for developing future IT engineers’ methodological competence through systemic problem-oriented learning. A quasi-experimental study involved 150 undergraduates from four Kazakhstani universities. In an integrated Smart Campus environment, students addressed authentic, industry-informed problems through an approach combining Challenge-Based Learning with the Four-Component Instructional Design model. Tasks included designing critical information infrastructure with provision for post-quantum cryptography and developing an intelligent energy-management system using fuzzy logic and the NSGA-III and MOEA/D algorithms. Performance on two engineering cases was independently evaluated using a 0–100 analytical rubric; scores were examined using the Shapiro–Wilk test, independent-samples Student’s t-tests, and Cohen’s d. The experimental group outperformed the control group in Case 1 (79.36 vs. 70.33; p = 0.003; d = 0.50) and Case 2 (80.61 vs. 71.12; p = 0.002; d = 0.53). Analysis of 45 reflective essays and 12 interviews indicated changes in methodological decision making, attitudes toward uncertainty, and perceived professional relevance. The findings support integrating authentic challenges with structured complex-learning design, while the Quadruple Helix provides an institutional framework connecting universities, industry, government, and society.

1. Introduction

The evolution of Industry 4.0 and the transition to Industry 5.0 have profoundly transformed the professional requirements for information technology (IT) specialists. The contemporary engineering practice is distinguished by a substantial increase in the complexity of computing systems and the necessity to integrate interdisciplinary approaches. In such circumstances, the conventional model of IT education, centered on the transmission of advanced skills (hard skills) and the study of particular programming languages or frameworks, reveals its inadequacies. The necessity for the establishment of pre-adaptive competence becomes paramount. This entails the capacity of a specialist to proactively address non-standard situations by independently devising novel algorithmic solutions. The foundation of such pre-adaptiveness is methodological competence, understood as an integrative quality of an engineer’s personality, allowing him to consciously choose, adapt, and combine mathematical apparatus, optimization methods, and systems design approaches to solve unstructured problems.

Despite the acknowledged importance of methodological competence, a gap persists in the higher education system of the Republic of Kazakhstan (RK) between theory-oriented academic training and the actual demands of the high-tech corporate sector. Graduates frequently exhibit a robust comprehension of fundamental algorithms; nevertheless, numerous individuals encounter substantial cognitive obstacles when confronted with authentic, open-ended problems that are characterized by incomplete initial conditions and conflicting optimality criteria.

According to recent World Bank reports on the development of the digital economy in Central Asia (World Bank, 2024), the IT sector in the Republic of Kazakhstan faces an acute shortage of professionals with systems engineering skills who can work under conditions of high technological uncertainty. The industry needs specialists who can not only apply standard frameworks but also conceptualize architectural solutions. This need is further evidenced by the growing demand for engineers capable of applying evolutionary optimization algorithms to resource allocation in a well-founded manner and using fuzzy logic to process noisy data generated by complex systems.

Consequently, a need arose to transform didactic approaches—specifically, to shift from the reproductive transmission of knowledge to the practice-oriented construction of experience in close collaboration with the IT industry.

One response to this challenge has been the adoption of problem-oriented learning approaches, particularly Problem-Based Learning (PBL), which emphasizes knowledge acquisition through the analysis and resolution of professionally relevant problems (Barrows, 1986; Hmelo-Silver, 2004; Savery, 2006). In complex IT engineering contexts, this approach has been further developed through Challenge-Based Learning (CBL), in which the learning process is organized around open-ended, authentic challenges that require students to develop solutions and engage with external stakeholders (Nichols et al., 2016; Johnson et al., 2015; Malmqvist et al., 2015). The Four-Component Instructional Design (4C/ID) model complements this challenge-oriented approach by structuring complex learning through sequences of authentic learning tasks, the provision of supportive and procedural information, and part-task practice (van Merriënboer, 1997; van Merriënboer et al., 2002, 2024).

The CBL strategy involves the immersion of students in addressing global, interdisciplinary “challenges,” thereby fostering the development of systems thinking and social responsibility. The 4C/ID model, in turn, employs a structured approach to facilitate the mastery of complex cognitive skills through the resolution of authentic, multifaceted “learning tasks,” accompanied by a progressive reduction in instructional support. The integration of these approaches fosters the development of an educational environment where methodological competence is cultivated not in isolation, but as a means for addressing specific engineering problems.

The present study proposes a model for developing methodological competence, which has been implemented and evaluated across multiple universities in Kazakhstan. Kazakhstan currently boasts a rapidly growing IT outsourcing and product development sector, making the alignment of higher education with corporate market needs a priority. A distinguishing aspect of this study is the implementation of the “Smart Campus” concept as a cohesive, end-to-end authentic environment—or “sandbox”—for the integration of CBL and 4C/ID methodologies. Through the integration of anonymized research data and real-world case studies provided by partner technology companies in Kazakhstan, the researchers successfully developed valid educational scenarios. Rather than working with abstract examples, students were tasked with ensuring the fault tolerance of critical IT infrastructure. To this end, they employed complex mathematical and algorithmic tools to strike a balance between network security and efficiency.

The aim of the study is theoretical substantiation and empirical validation—using a mixed-methods approach—of the effectiveness of employing CBL and 4C/ID technologies to develop the methodological competence of future IT specialists within the framework of corporate-university partnerships.

To achieve the stated goal, the article formulates the following research questions:

How will the integration of CBL and 4C/ID approaches, implemented within the framework of the “Smart Campus” concept, affect quantitative indicators of student academic success in the fields of systems engineering and algorithmic optimization?

How does working on authentic tasks initiated by IT companies in Kazakhstan transform the qualitative indicators of students’ reflective readiness to update the methodological foundations of design under conditions of uncertainty? What role does inter-institutional collaboration between universities and corporate universities play in creating educational conditions that foster the development of pre-adaptive competence in future engineers? The subsequent sections of the article present a detailed literature review, a description of the mixed-methods research design, and the results of a pedagogical experiment illustrating a qualitative shift in the cognitive patterns of future specialists.

2. Literature Review

2.1. Problem-Based Learning as the Basis for Competence-Oriented Engineering Training

For several decades, PBL has remained one of the most extensively researched and widely applied methods in the training of engineering, medical, and technical professionals. This approach, initially developed within the context of medical education, has gradually evolved into a universal educational methodology. The objective of this pedagogical approach is to cultivate students’ capacity for autonomous knowledge acquisition, the analysis of intricate professional scenarios, and the formulation of sound judgments (Barrows, 1986, 1996; Albanese & Mitchell, 1993; Dochy et al., 2003).

The traditional educational model, which relies primarily on the transmission of established knowledge from instructor to student, contrasts with the PBL approach. PBL organizes the learning process around solving problems that are either real-world or closely simulate professional practice. In this approach, the problem itself serves as the starting point for learning activities. These activities necessitate information gathering, the analysis of alternative solutions, and the collaborative discussion of findings. This pedagogical approach is designed to facilitate the integration of theoretical knowledge and practical experience, while concurrently cultivating a research-oriented mindset and developing independent learning skills (Barrows, 1986, 1996).

The conceptual foundations of PBL were formulated by H. S. Barrows, who viewed the problem situation not as a means of testing knowledge, but as the primary mechanism for its acquisition. Barrows (1986) demonstrated that learning based on solving professionally relevant tasks fosters the development of clinical reasoning, argumentation skills, and the ability to apply knowledge in novel situations. These notions were subsequently elaborated upon by Barrows (1996), who argued for a transition from a teacher-centered instructional paradigm to one that emphasizes students’ independent cognitive engagement (Albanese & Mitchell, 1993; Dochy et al., 2003).

Research by Hmelo-Silver (2004) and Savery (2006) contributed to the development of PBL theory by examining the approach through the lens of constructivist learning theory. According to their research, the problem-solving process fosters the active construction of knowledge, the development of metacognitive mechanisms, and the ability to self-organize educational activities. The efficacy of PBL is not solely determined by the utilization of practical problems; rather, it is contingent upon the caliber of pedagogical support, the arrangement of student collaboration, and the framework for reflecting on learning outcomes (Albanese & Mitchell, 1993; Dochy et al., 2003; Hmelo-Silver, 2004; Savery, 2006).

The efficacy of PBL has been validated by numerous systematic reviews and meta-analyses. For instance, following a comprehensive analysis of numerous studies, Strobel and van Barneveld (2009) concluded that traditional teaching methods can yield superior results when assessing the short-term retention of factual material. However, PBL has been shown to significantly outperform traditional methods in terms of long-term knowledge retention, the ability to apply knowledge in new contexts, and the professional success of graduates. A meta-analysis by Dochy et al. (2003) reached similar conclusions, demonstrating a statistically significant impact of PBL on the development of professional competencies, communication skills, and the capacity for independent learning.

It is noteworthy that researchers are currently engaged in active exploration of the application of PBL in engineering education. The nature of engineering work is characterized by the high complexity of the tasks involved, the need to integrate knowledge from diverse fields, and the constant need to adapt to rapidly changing technological conditions. Consequently, conventional pedagogical approaches—which prioritize the transmission of knowledge—have been inadequate in cultivating the professional readiness of prospective engineers, a problem that persists in the Republic of Kazakhstan. The adoption of PBL has been demonstrated to facilitate a more congruent alignment between the educational process and the demands of practical engineering. In such practices, there are no pre-existing, ready-made solution algorithms. Furthermore, decision-making requires a comprehensive analysis of technical, organizational, and economic factors (Strobel & van Barneveld, 2009; Walker & Leary, 2009).

Recent studies by Loyens et al. (2015) and Yew and Goh (2016) also demonstrate that the effectiveness of PBL increases significantly when integrated with digital educational technologies, collaborative project tools, and virtual laboratories. The utilization of digital platforms facilitates the highly effective organization of collaborative engineering problem-solving, ensures continuous feedback among participants in the educational process, and allows for the simulation of professional scenarios of varying complexity (Loyens et al., 2015). Moreover, advancements in artificial intelligence and intelligent tutoring systems have engendered new opportunities for personalizing problem-based learning, adapting task complexity, and providing automated support for students’ decision-making processes (Yew & Goh, 2016).

However, despite the substantial body of existing research, most current PBL models focus primarily on developing professional knowledge and practical problem-solving skills. Significantly less attention has been paid to fostering methodological competence—understood as the ability to independently select research methods, design strategies for solving novel problems, evaluate the limitations of applied approaches, and adapt to rapidly changing technological conditions. This aspect is particularly relevant in the context of the digital transformation of engineering practice, where professionals must not only apply existing knowledge but also continuously master new technologies and professional methods. It is precisely this under-researched aspect that serves as a basis for the further development of modern competency-based educational models.

The fundamental distinction between PBL and CBL lies not merely in their use of authentic problems, but in the structure and scope of the learning task. PBL typically begins with a clearly defined professional problem around which the processes of inquiry and solution development are organized. CBL, by contrast, places students within a broader context in which the challenge itself must be further defined, the interests of multiple stakeholders must be considered, and practically feasible solutions must be developed iteratively. This distinction is fundamental to the present study because the target competence extends beyond solving a predefined engineering problem to include the selection and adaptation of methodological tools under conditions of uncertainty. Thus, CBL ensures the authenticity and stakeholder orientation of the learning environment, while the 4C/ID model provides the instructional architecture required to scaffold increasingly complex activities as students progress through the learning process.

2.2. Challenge-Based Learning as an Evolution of Problem-Based Learning

Despite the proven effectiveness of PBL, the continued evolution of engineering education—including in the Republic of Kazakhstan—has revealed certain limitations of this approach. Classic PBL models focus primarily on finding a solution to a pre-formulated problem. However, modern professional engineering practice requires not only solving existing problems but also independently identifying new challenges, considering the interests of various stakeholders, analyzing the social and economic consequences of decisions, and developing new products. This context has led to the emergence of the CBL concept. CBL is viewed as an evolution of PBL principles (Nichols et al., 2016; Johnson et al., 2015; Malmqvist et al., 2015; Piironen et al., 2009; Membrillo-Hernández et al., 2019; Gallagher & Savage, 2023).

Unlike PBL, where the learning process begins with a specific professional problem, CBL is structured around a complex challenge. Such challenges do not have a single correct solution; they typically reflect real-world tasks encountered in the professional practice of IT engineers. Addressing them requires integrating technical knowledge, research methods, project work, teamwork, and communication with external stakeholders. According to Nichols et al. (2016), the educational process is organized as a sequence of interconnected stages: defining a “Big Idea,” formulating an essential question, setting the challenge, conducting research, developing a solution, implementing it, and reflecting on the results. This structure facilitates a shift from the mere reproduction of knowledge to its active application in settings that closely mirror actual professional practice.

The increasing interest in CBL is also driven by the evolving requirements for university graduates in the Republic of Kazakhstan within the context of the digital economy. According to Johnson et al. (2015), CBL is regarded as one of the most promising approaches for cultivating 21st-century competencies. The authors of the study noted that contemporary employers prioritize graduates’ ability to work in interdisciplinary teams, independently seek information, make autonomous decisions, and rapidly adapt to changing technological requirements over the mere volume of knowledge acquired (Johnson et al., 2015). Consequently, CBL has emerged not only as a novel educational technology but also as a tool for developing the flexible professional competencies in demand within modern engineering practice.

It is noteworthy that CBL has gained traction in engineering education, a field characterized by open-ended and multi-criteria professional tasks. Research conducted under the CDIO initiative has demonstrated that CBL facilitates a more integrated approach to fundamental training, engineering design, and practical activity. For instance, Malmqvist et al. (2015) compared various models of CBL implementation in engineering programs and concluded that this approach ensures higher levels of student engagement, fosters systems thinking, and improves the ability to apply knowledge in novel professional situations. Concurrent findings were reported by Piironen et al. (2009) and their colleagues, who demonstrated the efficacy of CBL in training engineers working on real-world technical projects in collaboration with industry partners (Piironen et al., 2009).

A distinguishing characteristic of contemporary CBL models is the active involvement of industry representatives in the educational process. In contrast to traditional PBL, in which instructors primarily formulate problems, in concurrent engineering-based learning (CBL), real-world industrial challenges serve as the direct source of learning content. Research by Membrillo-Hernández et al. (2019) demonstrated that collaboration between universities and leading industrial companies enhances the practical relevance of educational projects. This initiative enables students to access contemporary production technologies and cultivates competencies that align directly with employer expectations. The authors emphasized that engagement with industry partners influences not only the quality of professional training but also the development of an entrepreneurial mindset, communication skills, and the ability to make engineering decisions under resource constraints.

It is noteworthy that several authors perceive CBL not as a standalone alternative to PBL, but rather as its logical progression. For instance, Gallagher and Savage (2023) demonstrated that the majority of contemporary CBL models maintain the fundamental tenets of problem-based learning while augmenting the educational process through project-based endeavors, interaction with external organizations, the utilization of digital technologies, and a concentration on addressing complex, socially significant problems. Concurrently, the authors observed the absence of a universally accepted methodology for implementing CBL. This phenomenon gives rise to substantial variations across educational programs, thereby complicating the objective assessment of the approach’s effectiveness.

Despite the growing popularity of CBL, existing research indicates that a number of unresolved methodological issues remain. Most studies focus on analyzing the impact of CBL on student motivation, teamwork development, and engagement in the educational process (Nichols et al., 2016; Johnson et al., 2015; Malmqvist et al., 2015; Piironen et al., 2009; Membrillo-Hernández et al., 2019; Gallagher & Savage, 2023). However, significantly less attention has been paid to the development of students’ methodological competence—which encompasses the ability to independently select research methods, justify strategies for solving complex engineering problems, critically evaluate the limitations of chosen approaches, and adapt one’s activities to the changing conditions of professional practice. Furthermore, the mechanisms for the pedagogical design of an educational environment that fosters the progressive development of such competencies have not been sufficiently explored. This highlights the need for more holistic models of instructional organization that combine the advantages of PBL and CBL with modern theories of complex learning design, such as the 4C/ID model.

2.3. The Four-Component Instructional Design (4C/ID) Model as a Framework for Designing Complex Learning

The development of problem-based and challenge-based learning has resulted in a significant shift towards a more authentic professional practice-oriented educational process. However, the efficacy of these methods is contingent upon the quality of the instructional design of the learning environment. The utilization of authentic engineering endeavors does not inherently ensure the cultivation of sophisticated professional competencies, particularly when the educational methodology does not ensure a systematic escalation in task complexity, a judicious allocation of the learning burden, and the incorporation of a multifaceted array of knowledge and competencies. The quest for a methodological framework to inform the design of such learning led to the emergence of the 4C/ID model. In the contemporary academic landscape, this pedagogical approach is widely regarded as a foundational model for the organization of professional training, particularly in the context of complex tasks (van Merriënboer, 1997; van Merriënboer et al., 2002, 2024).

The 4C/ID model was proposed by van Merriënboer (1997) as a universal instructional design system aimed at developing the complex cognitive skills required to solve professional problems in a dynamic environment. In contradistinction to conventional instructional models predicated on the sequential mastery of discrete pieces of knowledge and operations, the 4C/ID concept conceptualizes professional activity as an integrated system of interconnected actions. Furthermore, these actions must be developed under conditions that closely mirror real-world practice. The model’s fundamental objective is to cultivate the learner’s capacity for autonomous integration of knowledge, skills, and decision-making strategies when confronted with novel, previously unexplored tasks.

Central to the model are four interrelated components (van Merriënboer et al., 2024):

(1)

Educational tasks that ensure the formation of holistic professional activity;

(2)

Supporting information necessary to understand the principles of solving complex problems;

(3)

Procedural information provided directly in the process of performing the activity;

(4)

The practice of individual elements aimed at automating routine operations. Unlike traditional modular training, in which knowledge is acquired in isolation from each other, 4C/ID involves the simultaneous development of cognitive, practical and metacognitive components of professional competence through the implementation of complex tasks of increasing complexity.

Further development of the model is presented in (van Merriënboer et al., 2024). Here, the authors introduced a systematic methodology for designing educational programs to train specialists across various professional fields. The proposed ten-step algorithm encompassed the analysis of professional activities, the decomposition of competencies, the development of a sequence of learning tasks, the determination of pedagogical support mechanisms, and the construction of a learning outcome assessment system. This approach ensured the integrity of the educational program and alignment among learning objectives, course content, and expected professional outcomes. A crucial aspect of the model is its focus not on the transmission of isolated knowledge, but on fostering the ability to independently apply that knowledge in new contexts—an approach that aligns with the modern requirements of engineering education.

The theoretical foundation of the 4C/ID model is Cognitive Load Theory, originally developed by Sweller (1988). According to Sweller (1988), learning efficiency is constrained by the limited capacity of human working memory. Consequently, overly complex or poorly structured learning tasks significantly impair the quality of material acquisition (Sweller, 1988). Sweller (1988) demonstrated that when tackling novel tasks, a substantial portion of cognitive resources is consumed not by the formation of new knowledge, but by managing the excessive load resulting from an inefficient organization of learning activities. Therefore, successful learning requires the design of an educational environment that minimizes extraneous cognitive load while simultaneously stimulating the formation of long-term cognitive schemas.

These ideas were further developed in a joint study by van Merriënboer and Sweller (2005), in which the authors present cognitive load theory as a methodological framework for designing complex learning (van Merriënboer & Sweller, 2005). The authors substantiated the need to gradually increase the complexity of learning tasks, adapt the level of instructional support based on learners’ proficiency, and integrate various types of learning information into a unified system for developing professional competencies. van Merriënboer and Sweller (2005) emphasized that effective instruction should aim not only at knowledge transmission but also at developing mechanisms for transferring that knowledge to new professional situations.

Paas et al. (2003) made a significant contribution to the development of cognitive load theory. They proposed a modern classification of cognitive load comprising intrinsic, extraneous, and productive components (Paas et al., 2003). This concept made it possible to explain how the organization of the educational process affects the quality of complex knowledge acquisition. Specifically, Paas et al. (2003) demonstrated that a well-designed learning environment should simultaneously reduce extraneous cognitive load—caused by inefficient information presentation—and increase productive load, which is associated with the construction of robust cognitive structures. These principles are particularly important in the training of engineers, who must work with vast amounts of interconnected technical information and make decisions under conditions of high uncertainty.

Despite the widespread adoption of the 4C/ID model in engineering, medical, and professional education, existing research has primarily focused on the design of individual courses or the development of specific professional skills. Significantly less attention has been paid—by authors such as van Merriënboer (1997), van Merriënboer et al. (2002), van Merriënboer et al. (2024), Sweller (1988), and Paas et al. (2003)—to using this model to foster the methodological competence of future professionals. This competence encompasses the ability to independently select research methods, devise strategies for solving novel engineering problems, critically evaluate the applicability of various approaches, and adapt to the rapidly changing technological landscape of the IT sector. Furthermore, most published studies (van Merriënboer, 1997; van Merriënboer et al., 2002, 2024) have examined the 4C/ID model in isolation from modern Case-Based Learning (CBL) models. Yet, integrating the two offers the potential to combine the benefits of authentic professional challenges with an evidence-based approach to designing the development of complex competencies. This highlights a promising avenue for future research: the development of comprehensive educational models aimed at preparing engineers for the digital economy.

2.4. Engineering and IT Education as an Environment for Developing Competency-Based Learning Models

Engineering education, including in the Republic of Kazakhstan, was among the first fields of higher education to systematically replace the traditional lecture-based, reproductive model of instruction with active learning approaches. The increasing complexity of engineering work in Kazakhstan, growing interdisciplinarity, the digitalization of industry, and the need to train specialists capable of solving non-standard professional problems have driven a shift from knowledge transmission to the development of professional competencies.

In a comprehensive review synthesizing findings from numerous empirical studies in engineering education, Prince (2004) showed that active learning substantially improves students’ retention of material, academic performance, and engagement compared with traditional lectures. The effect is particularly pronounced when students engage in independent intellectual work and solve engineering problems collaboratively. Prince and Felder (2006) systematized a range of inductive teaching methods, namely problem-based, project-based, inquiry-based, and case-based learning. The authors emphasized that in all of these approaches, learning begins not with the presentation of theory but with the analysis of a practical problem whose solution requires students to acquire knowledge independently. Focusing on the design of engineering courses, Felder and Brent (2003) noted that modern curricula should develop not only foundational knowledge but also competencies in teamwork, communication, design, decision-making, and continuous professional self-development, and that course content should therefore be structured around professional tasks.

One of the best-known international models for modernizing engineering education is the CDIO (Conceive–Design–Implement–Operate) framework, developed under the leadership of Crawley et al. (2007). It is based on students progressively mastering the full life cycle of an engineering product, from conception and design to implementation and operation, which has led to its widespread adoption by engineering universities worldwide. Graham’s (2018) analytical report, prepared for the Massachusetts Institute of Technology, analyzed leading universities around the world and showed that the most successful engineering programs share several features: the integration of project-based work into the curriculum, close collaboration with industry, the use of interdisciplinary projects, and an emphasis on authentic engineering problems.

The theory of constructive alignment (Biggs & Tang, 2003) serves as an important link between the complex learning architecture provided by the 4C/ID model, the authenticity of CBL tasks, and the logic of assessment. According to this principle, intended learning outcomes, teaching and learning activities, and assessment criteria must be aligned at the same level of cognitive complexity. For the present study, this has a direct implication. The target competence is the ability to justify methodological choices in an ill-structured, open-ended professional environment, which is the defining feature of CBL. Therefore, neither the system of instructional support within 4C/ID nor the assessment criteria can be limited to checking the syntactic correctness of algorithms. Authentic cases serve simultaneously as an environment for skill integration and as a valid context for assessing the development of students’ pre-adaptive expertise.

Kolmos et al. (2009) showed that problem-based learning (PBL) is not a single educational technology but a family of diverse models. These models differ in the degree of student autonomy, the role of the teacher, the nature of the projects, and the organization of the learning process, while sharing a common focus on the active construction of knowledge through solving complex professional problems. Comparing problem-based and project-based learning, Mills and Treagust (2003) noted that both approaches are effective in engineering education but address different pedagogical goals. PBL develops analytical thinking and the skills of formulating and solving problems, whereas project-based learning emphasizes the creation of a final engineering product, project management, and the integration of knowledge across disciplines. In contemporary practice, the two approaches are increasingly used in combination.

Research over recent decades (Biggs & Tang, 2003; Crawley et al., 2007; Felder & Brent, 2003; Graham, 2018; Kolmos et al., 2009; Mills & Treagust, 2003; Prince, 2004; Prince & Felder, 2006) indicates a shift in engineering education toward a competency-based model of professional training. This model is characterized by active learning, project-based work, the integration of theory and practice, collaboration with industry, and the development of professional thinking. Engineering education became the main testing ground for PBL, CDIO, constructive alignment, and other models that were later widely adopted in IT education and in the training of specialists for the digital economy.

2.5. Development of Methodological Competence and Adaptive Expertise

The concept of adaptive expertise was first introduced by Hatano and Inagaki (1986), who distinguished between two types of professional mastery: routine expertise and adaptive expertise. A routine expert demonstrates high efficiency when performing tasks that are familiar to them; however, when confronted with operational conditions that deviate from the norm, these experts often encounter significant challenges. In contrast, an adaptive expert is capable not only of successfully applying existing knowledge but also of restructuring their problem-solving strategies. This enables the adaptive expert to formulate novel approaches and attain mastery over hitherto unexplored technologies. According to Hatano and Inagaki (1986), adaptive expertise has become an essential prerequisite for professional practice in today’s rapidly evolving technological landscape.

These notions were subsequently elaborated in the seminal study by Bransford et al. (2000), which centered on the principles of human learning. The authors demonstrated that a profound comprehension of material materializes not from rote memorization, but through the active construction of knowledge, continuous reflection, and the transfer of acquired experience to new situations. The organization of the learning process is of particular importance. Indeed, it is this very organization that fosters independent problem-solving, the discussion of alternative approaches, and the analysis of one’s own errors. Such an educational process has been shown to facilitate the development of flexible professional thinking and the capacity for lifelong learning.

Hilton and Pellegrino (2012) synthesized evidence on transferable 21st-century competencies, including critical thinking, complex problem-solving, creativity, collaboration, communication, and independent learning. The authors synthesized the findings of numerous international studies and identified a set of universal competencies in demand within the modern knowledge economy. In addition to subject-specific knowledge, critical thinking, complex problem-solving, creativity, collaboration, communication, and the ability to learn independently are essential. Hilton and Pellegrino (2012) regard these competencies as transferable, as they can be effectively applied across various professional contexts, regardless of the specific subject area.

The issue of knowledge transfer is analyzed in detail by Schwartz et al. (2000). The authors demonstrated that conventional teaching methods frequently enable only the expeditious retrieval of learned material, whereas contemporary professional practice necessitates the capacity to apply knowledge in novel contexts. Schwartz et al. (2000) propose distinguishing between the efficiency of initial knowledge acquisition and the capacity for innovative transfer. It is precisely educational approaches grounded in open-ended problem-solving and inquiry-based activities that foster the development of flexible cognitive strategies and successful knowledge transfer.

The concept of adaptive expertise was further developed in the context of teaching by Crawford et al. (2005). The authors delineated the adaptive specialist as a professional who possesses the capacity to perpetually assess their own practice, recalibrate work strategies to align with the circumstances, and adroitly employ novel instruments. Despite the fact that the study by Crawford et al. (2005) concentrated on the training of science educators, the proposed model of adaptive expertise is equally applicable to engineering education. In this field, the ability to rapidly master new technologies has emerged as a critical professional competency.

The development of higher-order cognitive skills is closely linked to the concept of higher-order thinking skills (HOTS). Brookhart (2010) defined HOTS as a set of intellectual activities comprising the analysis, evaluation, and synthesis of information, as well as argumentation, decision-making, and the resolution of non-standard problems. The author emphasized that these skills cannot be cultivated through the traditional rote reproduction of educational material; rather, they develop only through complex problem-solving, project-based work, inquiry, and professional interaction.

Consequently, research conducted over recent decades (Hatano & Inagaki, 1986; Bransford et al., 2000; Hilton & Pellegrino, 2012; Schwartz et al., 2000; Crawford et al., 2005; Brookhart, 2010; Bereiter & Scardamalia, 1993) has revealed a gradual shift in higher education goals from the transmission of subject-specific knowledge to the development of universal intellectual competencies. According to these authors, methodological competence, higher-order cognitive skills, and adaptive expertise are interconnected characteristics of the modern professional. These traits determine an individual’s ability to work effectively amidst technological uncertainty, rapidly master new professional domains, and collaborate successfully within interdisciplinary teams. Indeed, these qualities have emerged as the primary educational outcomes of implementing problem-based learning, challenge-based learning, the 4C/ID model, and other competency-oriented pedagogical approaches.

2.6. Industry 4.0–5.0 and the Transformation of Engineering Education: The Role of University-Industry Cooperation

The development of relevant educational models cannot be viewed in isolation from the global changes taking place in Kazakhstan’s industrial sector and knowledge economy. The concepts of Industry 4.0 and Industry 5.0 have had a considerable impact on the requirements for training engineering personnel in the country. The emphasis has transitioned from the mere replication of professional knowledge to the cultivation of the capacity to function in the context of high technological variability, digitalization, and interdisciplinary collaboration. Consequently, higher education is gradually transitioning toward competency-based learning models. These models are designed to foster a flexible professional mindset, adaptability, and a readiness for lifelong learning.

The term “Fourth Industrial Revolution” gained widespread currency following the publication of the work by Schwab (2017). The author demonstrated that digital technologies—such as artificial intelligence, the Internet of Things, big data, robotics, additive manufacturing, and cyber-physical systems—are radically transforming not only industry but society as a whole. The author posited that human competencies-particularly those that facilitate effective interaction with rapidly evolving technologies-have become the defining resource of the digital economy.

A comprehensive systems analysis of the opportunities and challenges associated with Industry 4.0 was presented by Xu et al. (2018). The researchers observed that the digital transformation of manufacturing is accompanied by evolving requirements for workforce qualifications. Consequently, in addition to fundamental professional training, specialists must possess competencies in critical thinking, data analysis, decision-making, interdisciplinary collaboration, and the rapid adoption of new technologies. This is of particular importance in the field of information technology. These requirements directly influence the content of engineering education and drive the implementation of active learning technologies.

One of the earliest academic publications to establish the conceptual foundations of Industry 4.0 was the work by Lasi et al. (2014). Lasi et al. (2014) conceptualized Industry 4.0 as the integration of manufacturing processes, information technologies, and intelligent control systems within a unified digital space. The new industrial paradigm is characterized by a high level of automation, real-time data exchange, and the utilization of cyber-physical systems. This underscores the imperative for cultivating a novel breed of specialist, one who possesses the capacity to function as a nexus of engineering, information technology, and management.

The practical principles for implementing Industry 4.0 systems were formulated by Hermann et al. (2016). The authors identified four fundamental principles: The four pillars of this framework are interoperability, information transparency, technical support, and decentralized decision-making. The implementation of these principles necessitates that engineers possess not only technical expertise but also the capacity to function in distributed digital environments, conduct analysis of substantial data sets, and make autonomous decisions within complex manufacturing systems.

As the digital industry has evolved, research focus has undergone a gradual shift from the technological aspect to the human role in intelligent manufacturing. Romero et al. (2016) were among the first to highlight this trend, proposing the “Operator 4.0” concept. The concept of the next-generation worker is characterized by the notion of an active participant who engages with intelligent machines, robotic systems, and artificial intelligence technologies. In this model, technology does not supplant the human worker; rather, it augments their professional capabilities. This shift necessitates the cultivation of cognitive, communicative, and adaptive competencies.

This notion underwent further elaboration with the advent of the Industry 5.0 concept. Nahavandi (2019) underscores that this novel industrial paradigm prioritizes not only the efficacy of digital technologies but also humanistic values. The human element, encompassing well-being, creative potential, and the capacity for responsible decision-making, assumes a central role. Consequently, while the primary focus of Industry 4.0 centered on the digitalization of production, Industry 5.0 conceptualizes technology as a means to augment human capabilities rather than to substitute for them.

An exhaustive examination of the evolution of the Industry 5.0 concept is provided in the study by Leng et al. (2022). The authors demonstrated that modern industry is evolving towards sustainability, smart manufacturing, environmental responsibility, and product personalization. In this professional context, the key characteristics required of engineering professionals include a capacity for innovation, systems thinking, interdisciplinarity, collaboration, and continuous professional development. Consequently, educational programs—including those in the Republic of Kazakhstan—must focus not only on mastering technologies but also on developing the universal competencies that ensure successful professional adaptation (Carayannis & Campbell, 2009).

The evolving landscape of professional training requirements is accompanied by a concurrent transformation in the interactions among universities, industry, and the state. One of the most prominent models of innovation-driven development is the Triple Helix concept, proposed by Etzkowitz and Leydesdorff (2000). Under this model, the university transcends its traditional educational role, becoming an active participant in the innovation economy. It collaborates with industrial enterprises and government institutions to generate new knowledge, technologies, and innovations. Specialist training is increasingly conducted in close alignment with real-world production challenges.

Building on this idea, Carayannis and Campbell (2009) proposed the concepts of Mode 3 and the Quadruple Helix. In this framework, the Triple Helix innovation system is augmented by the inclusion of society as an independent participant in the innovation process. Carayannis and Campbell (2009) emphasized that modern education must prepare specialists capable of working within open, networked innovation systems, interacting with diverse stakeholders, and effectively contributing to the creation of new knowledge.

A comprehensive examination of practical mechanisms for university-industry collaboration has been conducted (Perkmann et al., 2013). The authors demonstrated that the most effective forms of academic-industry interaction include the following: joint research projects, project-based learning, industrial internships, the involvement of industry representatives in curriculum development, and students solving real-world industrial problems. Such forms of collaboration have been shown to facilitate the successful development of professional competencies and bridge the gap between academic training and the demands of the modern labor market.

The evolution of Industry 4.0 and Industry 5.0 concepts has prompted a substantial re-evaluation of the objectives of engineering and IT education. In the contemporary professional landscape, individuals must demonstrate not only profound subject-matter expertise but also methodological competence, critical thinking skills, a capacity for innovation, adaptive expertise, and the ability to collaborate effectively within interdisciplinary teams. In this context, problem-based learning, challenge-based learning, the 4C/ID model, project-based approaches, and active university-industry collaboration emerge as complementary elements of a unified educational strategy for training the workforce required for the digital economy and the knowledge society.

3. Materials and Methods

3.1. Corporate-University Integration and the Formation of Preadaptive Competence

For the present study, the empirical dataset was compiled from aggregated data provided by a number of technology enterprises and IT companies in Kazakhstan. An analysis of corporate requirements has revealed a shift in focus from narrowly specialized skills, or “hard skills,” to pre-adaptive competence. This shift is characterized by the ability of specialists to proactively adapt mathematical and algorithmic frameworks to rapidly evolving technological paradigms.

The collaboration between Kazakhstani universities and the corporate sector has been instrumental in mitigating the discrepancy between academic theory and market demands. Partner companies functioned not only as clients seeking specific competencies but also as validators of real-world challenges, providing anonymized data and scenarios drawn from the design of complex distributed systems. This initiative laid the groundwork for the implementation of Problem-Based Learning (PBL) methodologies, with a focus on authentic engineering tasks.

The study was conducted within the institutional and educational context of Kazakhstan, where undergraduate IT professionals are trained through specialized degree programs in fields such as computer science, information systems, cybersecurity, and software engineering. The participants were third- and fourth-year students who had already completed foundational courses in systems engineering, algorithms, and information technology. This stage of study was selected because the approach required students to integrate previously acquired technical knowledge rather than develop programming skills from scratch.

It is important to note that the study sample was drawn from a distinctive transitional socioeconomic context. Higher education in the Republic of Kazakhstan operates against the backdrop of the accelerated digitalization of the national economy, particularly through government-led digitalization programs, and the active expansion of infrastructure by multinational IT corporations. This macroeconomic context creates high career expectations among students, who regard the IT sector as a primary avenue for upward social mobility. At the same time, the gap between academia’s emphasis on foundational theory and the stringent practical demands of outsourcing companies strongly motivates students to engage with authentic engineering challenges, making this sample particularly relevant for evaluating problem-oriented learning models.

3.2. Detailed Breakdown of Educational Use Cases—“Smart Campus” System

The development of the architecture and algorithmic framework for a “Smart Campus” system was selected as the overarching concept. The following case studies were utilized in the analysis.

Case 1.

The students were presented with a challenge regarding the fault tolerance and security of the “Smart Campus” network, which constitutes an element of critical information infrastructure (CII).

The primary task entailed the conceptualization of a data exchange architecture for campus IoT devices, with the consideration of prospective future requirements to migrate to post-quantum encryption protocols and ensure data protection against potential compromises.

The students’ role was as follows: the groups functioned as system architects. The students were tasked with the application of mathematical modeling methods to calculate network load, provide justification for their selection of optimization algorithms, and propose protocols for secure component interaction.

A prerequisite for a successful resolution was the modeling of an adversarial interaction between the CII protection system and a potential attacker. In order to accomplish this objective, students integrated the mathematical framework of bilinear games into the project. This approach formalized attack and defense strategies for the IoT infrastructure as a non-zero-sum matrix game, where identifying a saddle point determined the optimal balance between the computational costs of encryption and the probability of network node compromise.

Case 2.

The 4C/ID Model in Kazakhstan’s Universities.

Partner universities employed the Four-Component Instructional Design (4C/ID) model to design predictive analytics subsystems for the campus environment.

The core task involved developing an intelligent energy management system for classroom facilities amidst fluctuating external factors.

The 4C/ID implementation proceeded as follows: as complex tasks, students developed fuzzy logic-based controllers and applied evolutionary algorithms to optimize resource allocation. Supportive information included the study of game theory to model resource consumer behavior, while procedural information was provided through mentoring sessions with engineers from IT companies.

To objectively evaluate the effectiveness of the synthesized IoT-related algorithms, students had to move beyond basic performance indicators and implement domain-specific metrics, such as communication node energy consumption and data transmission latency. Students validated the performance of evolutionary algorithms (NSGA-III, MOEA/D) in finding Pareto-optimal solutions not only visually but also by applying Pareto-front quality metrics, such as Hypervolume (HV), Inverted Generational Distance (IGD), and Spacing (SP).

In the first case, the task formulation allowed infrastructure resilience to be considered not only in terms of cryptographic and computational metrics, but also in relation to security requirements, operational continuity, and the implications of engineering decisions for users of the campus digital environment. In the second case, the energy-consumption optimization task linked the effectiveness of algorithmic solutions to resource constraints and the operating conditions of facilities used for educational purposes. Thus, the connection with the human-centric principles of Industry 5.0 was reflected primarily in the formulation and context of the engineering challenges, rather than being treated as a separately measured outcome of the experiment.

In Case 2, the assessment focused on the students’ ability not merely to use software libraries but to mathematically justify the convergence of evolutionary algorithms (NSGA-III/MOEA-D), validating the quality of the trade-offs achieved between energy consumption and latency through the calculation of HV, IGD, and SP metrics.

3.3. Research Design and Mixed Assessment Methodology

The study employed a quasi-experimental pretest–posttest design involving experimental and control groups. Because the participants were drawn from pre-existing student groups rather than assigned individually at random, this design was intended to assess the effects of the pedagogical intervention under authentic educational conditions while accounting for the potential influence of institutional- and group-level factors. The quantitative component compared the learning outcomes of the experimental and control groups, whereas the qualitative component examined changes in students’ reflective approaches to methodological decision-making.

To assess the efficacy of the proposed didactic models, a mixed-methods exploratory design was utilized, integrating quantitative success metrics with a qualitative analysis of cognitive patterns.

The subsequent quantitative analysis phase assessed academic performance and the quality of the projects’ technical implementation. The metrics encompassed the computational complexity of the proposed algorithms and the accuracy of the optimization models.

The quantitative data were analyzed using descriptive statistics, the Shapiro–Wilk test to assess the distribution of scores, and an independent-samples Student’s t-test to compare the experimental and control groups. Cohen’s d was calculated to estimate the magnitude of the between-group difference.

The qualitative analysis focused on assessing students’ reflective readiness to update their methodological approaches when working under conditions of uncertainty. The toolkit encompassed the analysis of reflective essays through the application of thematic coding and semi-structured interviews. The evaluation assessed the prospective engineers’ capacity to substantiate their selection of mathematical frameworks. This included the rationale behind the implementation of fuzzy logic in a particular “Smart Campus” node as opposed to conventional routing methodologies. Additionally, the study gauged their aptitude to modify systems in accordance with evolving stakeholder demands.

The integration of quantitative code and architecture quality indicators with qualitative reflection metrics was found to have a synergistic effect on the training process for IT engineers when combining CBL and 4C/ID.

3.4. The Aim of the Pedagogical Experiment

To assess the impact of the proposed model for developing adaptive professional competence on the readiness of IT students to solve professional tasks amidst the digital transformation of the economy of the Republic of Kazakhstan.

To assess performance on each practical assignment (i.e., case study), every student submission was evaluated using a criterion-referenced analytical rubric on a scale from 0 to 100 points. The final score for each case study was calculated as the sum of the scores awarded across five predefined criteria specific to the engineering task. This score constituted an independent measure of case-study performance and did not incorporate the SDLRS results.

3.5. Research Base

The study sample consisted of 150 undergraduate students from four universities in Kazakhstan (see Table 1). Specifically, third- and fourth-year students from the following majors were included: Computer Science, Information Systems, Cybersecurity, and Software Engineering.

Table 1. Characteristics of study sample.

University	Number of Students
University A	40
University B	38
University C	36
University D	36
Total	150

3.6. Organization of the Study

The study is divided into three stages, as follows:

(1) During the baseline assessment conducted before the intervention, relevant initial characteristics of the students were recorded, including their prior professional preparation and readiness to undertake complex engineering tasks.

(2) The intervention stage. The experimental group implemented a model comprising personalized learning, AI tools, project-based learning, teamwork, adaptive learning pathways, and digital case studies (1 and 2).

The listed elements (personalized learning, AI tools, teamwork, and adaptive learning pathways) were not treated as separate pedagogical interventions that could be varied independently. Rather, they represented specific ways of implementing individual components of the CBL–4C/ID model. For example, individual learning pathways and personalization operationalize the principle of gradually reducing instructional support (scaffolding fading) built into 4C/ID, whereas teamwork and the use of AI tools reflect the authentic, stakeholder-oriented nature of CBL tasks.

The control group followed a traditional learning approach.

(3) The final assessment phase focused on analyzing the predefined outcomes of the engineering case study, as well as qualitative changes in students’ reflective approaches to methodological decision-making.

(4) The groups were divided as shown in Table 2.

Table 2. Distribution of participants between the control and experimental groups by university.

University	Control	Experimental	Total
University A	20	20	40
University B	19	19	38
University C	18	18	36
University D	18	18	36
Total	75	75	150

It should be noted that the control and experimental groups were formed from pre-existing student groups at the participating universities rather than through individual random assignment. Each condition comprised 75 students. Participants in the experimental group were taught using the CBL–4C/ID approach, whereas those in the control group received traditional instruction. Group allocation was determined before the pedagogical intervention began and remained unchanged throughout the study. Both the control and experimental conditions were represented at each of the four participating universities; therefore, the comparison was not based on assigning entire universities to different study conditions.

Allocation was not conducted at the university level. Each of the four universities included participants from both the control and experimental conditions. However, individual students were not randomized. Instead, pre-existing student groups served as the unit of allocation. This arrangement preserved the natural organization of the educational process and reduced the risk of contamination between the control and experimental groups through the transfer of elements of the pedagogical intervention.

3.7. Assessment Methodologies

Separate instruments were used to assess different dimensions of the learning outcomes, and their scores were not combined into a single composite measure.

The Self-Directed Learning Readiness Scale (SDLRS) and the DigComp competence framework were used solely as supplementary diagnostic instruments to characterize relevant aspects of students’ learning readiness and digital competence. Their scores were neither treated as primary outcome variables in the statistical comparisons reported in this article nor incorporated into the case-study scores. The quantitative findings presented in the Section 4 are limited to the independently assessed performance outcomes for the two engineering case studies.

To avoid any ambiguity in the interpretation of the findings, it should be emphasized that the measures of self-directed learning readiness and digital competence were not used to calculate the final scores for Case Study 1 or Case Study 2. These instruments were employed to provide supplementary information about the educational context and selected dimensions of competence development. By contrast, the primary quantitative outcomes of the pedagogical experiment reported in the Section were the independently assessed performance scores for the two complex engineering case studies. The M values presented in Table 3 refer exclusively to the mean final scores awarded for the respective engineering case studies using the 0–100-point analytical rubric; they do not represent aggregated or composite measures.

Table 3. Comparative analysis of the success of complex engineering case studies in control and experimental groups.

Indicator (Success Metric)	Control Group (CG) (n = 75) (M ± SD)	Experimental Group (EG) (n = 75) (M ± SD)	t-Test Value	Significance Level (p)	Effect Size (Cohen’s d)
Case Study 1: Ensuring the resilience of critical information infrastructure (CII)	70.33 ± 17.32	79.36 ± 18.64	3.07	0.003	0.50
Case Study 2: Intelligent Energy Management	71.12 ± 17.50	80.61 ± 18.52	3.23	0.002	0.53

To ensure the construct validity of the study, the variables under assessment were operationalized as follows.

The degree of methodological competence was evaluated by experts based on the final case study reports. The primary criterion was not so much the correctness of the final program code as the students’ ability to justify their choice of specific mathematical tools. For instance, students were expected to provide a reasoned transition from classical routing to fuzzy logic or select NSGA-III over MOEA/D based on Pareto front properties. In addition, students were expected to rigorously formalize problem boundary conditions, including the search for saddle points in bilinear games.

The consistency of the final scores assigned by the three independent experts was assessed using Cronbach’s alpha, calculated from the scores given by each of the three experts, with each expert’s ratings treated as a separate “item” of the scale. The obtained value (α = 0.84) indicates a high level of consistency in the final scores across the experts.

4. Results

Statistical analyses were conducted using IBM SPSS Statistics 27. The mean (M) and standard deviation (SD) were calculated to summarize the results. The normality of each quantitative variable was assessed using the Shapiro–Wilk test. Because no statistically significant departures from the assumption of normality were identified for the variables presented in Table 3, between-group differences were analyzed using an independent-samples Student’s t-test. Effect sizes were estimated using Cohen’s d. Neither nonparametric methods nor analysis of variance was used for the results reported in this study.

The following research results were obtained; see Table 3.

M values presented in Table 3 represent the average final scores for the corresponding engineering case studies. Each project submission was assessed by independent experts using a criterion-referenced analytical rubric scored on a scale from 0 to 100 points. The individual score underlying each reported mean was a stand-alone case-performance measure, calculated as the strict sum of scores across five equally weighted criteria, each worth a maximum of 20 points: (1) the mathematical validity of the selected algorithmic solution; (2) the efficiency and fault tolerance of the proposed architecture; (3) the quality of software code optimization; (4) the depth of reflection demonstrated in justifying the methodological approach; and (5) consideration of non-functional requirements, including security and energy efficiency. Scores from supplementary instruments, such as the SDLRS and DigComp, were used solely to characterize the educational profile of the sample and were not mathematically incorporated into the M values reported in the Results section.

The Shapiro–Wilk test revealed no statistically significant departures from normality in the distributions of the variables. This supported the use of an independent-samples Student’s t-test for between-group comparisons.

To complement the quantitative data, a thematic analysis was conducted on 45 reflective essays from students in the experimental group and 12 semi-structured interviews. The coding process identified three consistent themes characterizing the students’ cognitive changes:

“From syntax to meaning”: students noted a shift in focus from the question “How do I write the code?” to “Why is this specific algorithm applicable here rather than another one?” (n = 29).

“Tolerance for ambiguity”: the majority of respondents (n = 31) indicated that the absence of a single correct answer in the case studies initially caused discomfort; however, this subsequently significantly boosted their confidence in making engineering decisions based on incomplete data.

The analysis was conducted in several stages: the initial coding of statements and textual excerpts, the grouping of codes into thematic categories, the comparison of themes across the reflective essays and semi-structured interviews, and the final interpretation of the themes in relation to RQ2. The three themes identified did not represent isolated participant opinions; rather, they reflected consistent, recurring patterns in descriptions of changes in methodological decision-making, attitudes toward uncertainty, and perceptions of the professional relevance of the assignments.

Representative excerpts from the interview transcripts are provided to illustrate the patterns identified. Regarding the theme of “Tolerance of Uncertainty,” one student noted: “At first, the complete absence of a single correct answer in the post-quantum cryptography case was frustrating. We had always been taught to look for the ‘right’ algorithm. However, once we began modeling a non-zero-sum game, we came to understand that architecture always involves trade-offs” (Participant 14, experimental group). The following response was typical of the theme of “Industry Validation”: “Feedback from practicing engineers made us rewrite the logic of the fuzzy controller. We realized that what matters is not merely whether the algorithm converges on paper, but also its computational cost on real-world hardware.”

“Industry validation”: the involvement of mentors from IT companies transformed the students’ perception of the projects. Tasks were no longer viewed merely as “academic obligations” but rather as “legitimate professional practice” offering prospects for employment at IT companies in the Republic of Kazakhstan.

5. Discussion

The performance advantage observed in the first case study should be interpreted primarily as evidence of more successful performance on an open-ended engineering task rather than as a direct measure of adaptive competence itself. The experimental group achieved a higher mean score (79.36 ± 18.64) than the control group (70.33 ± 17.32), and the between-group difference was statistically significant (t = 3.07; p = 0.003; d = 0.50). This finding is consistent with research indicating that problem-oriented learning is particularly effective when students must apply their knowledge in situations that extend beyond previously practiced problem-solving algorithms (Dochy et al., 2003; Strobel & van Barneveld, 2009). Within the present pedagogical intervention, the CBL component provided an authentic, open-ended engineering challenge, whereas the 4C/ID structure supplied the instructional support required to manage the associated learning complexity. Thus, the observed difference in performance is consistent with the hypothesized mechanism of the approach, although it should not be interpreted as a direct psychometric assessment of adaptive competence.

A thorough examination of the findings from Case 1, which entailed the conceptualization of fault-tolerant network architecture while considering a prospective transition to post-quantum encryption protocols, has been conducted. The results of this examination have indicated that students in the experimental group exhibited a substantially more profound comprehension of the methodological framework (79.36 ± 18.64 points) in comparison to the control group (70.33 ± 17.32 points). This discrepancy was found to be statistically significant (t = 3.07, p = 0.003) with a medium effect size (d = 0.50). In accordance with the cognitive load perspective described by Paas et al. (2003), the conventional academic model cultivates “routine expertise,” at best. Conversely, the immersion of the experimental group students in the comprehensive “Smart Campus” system challenge fostered the development of patterns of “adaptive expertise.” This ability to adapt the mathematical framework of bilinear games to formalize attack and defense strategies for IoT infrastructure is evidenced by their success in achieving an optimal balance between the probability of compromise and computational costs.

The obtained effect size (d = 0.50) aligns with the meta-analytic findings of Strobel and van Barneveld (2009), which confirm the consistent superiority of problem-based approaches in tasks requiring knowledge transfer. Furthermore, the qualitative data complemented the findings of Gallagher and Savage (2023) by demonstrating that the integration of CBL and 4C/ID stimulates methodological metacognition—specifically, the students’ ability to critically evaluate the applicability limits of their own engineering solutions—rather than merely boosting motivation.

In addressing Case 2, which focused on developing a predictive analytics subsystem based on fuzzy logic and evolutionary algorithms, students in the experimental group also demonstrated significant superiority, with an average score of 80.61 ± 18.52 points, as compared to 71.12 ± 17.50 points for the control group (t = 3.23, p = 0.002, d = 0.53). The experimental group’s high performance on this task indicates that the students reached the higher cognitive levels of Bloom’s taxonomy. The following verbs are employed: “analyzing,” “evaluating,” and “creating.” The successful completion of the case necessitated the utilization of off-the-shelf software libraries by the engineers, in conjunction with the rigorous mathematical validation of trade-off solutions and the identification of the Pareto front. This process involved the mandatory calculation of hypervolume (HV) and inverted generational distance (IGD) metrics.

Particular attention should be given to how the humanistic principles of Industry 5.0—human-centricity and sustainability—were conceptualized within the otherwise highly technocratic tasks used in the experiment. In Case Study 1, which focused on post-quantum cryptography, technological reliability was not treated as an end in itself. Students were required to identify a mathematically grounded trade-off between maximum cryptographic security and a seamless user experience without performance degradation (human-centric usability). In Case Study 2, which addressed intelligent control, the application of evolutionary algorithms (NSGA-III and MOEA/D) was directly aligned with sustainable development objectives, namely environmental responsibility and minimizing the energy consumption of shared educational infrastructure. Thus, the proposed model teaches future engineers to view algorithmic technologies not in isolation, but as tools for creating sustainable, secure, and human-centric environments.

From the perspective of cognitive load theory (Sweller, 1988; van Merriënboer & Sweller, 2005), the radical increase in the complexity of the learning material–specifically, the introduction of cooperative games and NSGA-III/MOEA-D algorithms–generated a high intrinsic load in both groups. However, the effective separation of supportive and procedural information within the 4C/ID model made it possible to minimize extraneous load. This approach reduced the necessity for extensive searching for syntactic solutions and enabled the reallocation of working memory resources toward productive tasks, particularly the construction of robust mental schemas for systems engineering. The implementation of timely mentoring sessions functioned as cognitive “scaffolds,” effectively mitigating potential overload and facilitating students’ ability to prioritize strategic over tactical engineering aspects. This phenomenon is further substantiated by the absence of indications of mental deterioration observed in the reflective essays composed by students within the experimental group.

In the present study, the Quadruple Helix concept should be understood as an institutional framework for positioning the educational initiative within a broader innovation ecosystem rather than as an empirical variable that can be measured independently. Universities and industry partners formed the core of the collaborative structure, while the societal dimension was represented by human-centric objectives and outcomes designed to address user needs, such as the elements embedded in the Smart Campus projects. This distinction is important because the experiment assessed educational outcomes rather than the effects of the Quadruple Helix model itself.

Our findings extend the current understanding of the scope of applicability of the 4C/ID model (van Merriënboer et al., 2024; Paas et al., 2003). Unlike conventional studies in which the model has primarily been applied to reduce cognitive overload during the acquisition of highly prescribed procedures, our study demonstrated its effectiveness in fostering productive cognitive load. Mentoring sessions enabled students to avoid expending working-memory resources on searching for syntactic solutions and instead direct those resources toward methodological reflection and systems thinking. This finding supports the propositions advanced by Schwartz et al. (2000) concerning the mechanisms of innovative knowledge transfer.

6. Study Limitations and Future Directions

Despite the consistent positive outcomes, this study is not without its limitations. The experiment was conducted using a sample from four higher education institutions in Kazakhstan (n = 150) and assessed short-term effects within a single semester. The quasi-experimental design–specifically, the absence of randomization at the individual student level–permits the influence of institutional factors, thereby reducing internal validity in comparison to a randomized controlled trial.

Furthermore, the experimental intervention included not only CBL and 4C/ID components but also a number of accompanying elements, all implemented simultaneously within a single integrated model. Consequently, the observed effects should be attributed to the model as a whole rather than exclusively to CBL–4C/ID as such. In particular, the design used does not allow the unique contribution of each individual component to be statistically isolated. Future studies employing a dismantling design are needed to assess the relative contribution of each element of the model.

Since inter-institutional collaboration between universities and corporate universities was not a separately varied factor in this quasi-experimental design, the question of its synergistic effect (RQ3) was addressed at a conceptual and qualitative level rather than through independent statistical testing. Further research comparing conditions with different degrees of industry partner involvement would allow this effect to be estimated more precisely.

The degree of methodological competence was evaluated by experts based on project reports. Future research should incorporate standardized psychometric questionnaires to measure adaptive expertise. The absence of a delayed post-test administered 6–12 months later precludes conclusions regarding the long-term stability of the cognitive schemas developed. The cultural and educational context of Kazakhstan precludes the generalizability of the findings to other national higher education systems.

Subsequent research endeavors should prioritize longitudinal tracking of the professional trajectories of graduates working as system architects, DevOps engineers, and data scientists. Additionally, there is a need to adapt the proposed model for other engineering disciplines.

7. Conclusions

The theoretical-methodological analysis and experimental study have resulted in the following conclusions, which define the scientific novelty and practical significance of the work:

• A model for developing the methodological competence of future IT engineers was theoretically substantiated and empirically tested. The model integrates Challenge-Based Learning (CBL) with the Four-Component Instructional Design (4C/ID) model. The findings indicate that combining these approaches enhances students’ ability to solve complex engineering problems under conditions of uncertainty and to justify their selection and adaptation of methodological tools.
• The Smart Campus concept provided an authentic educational environment in which students addressed complex engineering challenges based on real-world industry situations. Under these conditions, the experimental group outperformed the control group on both case-study assignments. This finding suggests that combining authentic case studies with structured instructional support improved students’ ability to solve complex engineering problems effectively.
• Analysis of data collected from 150 undergraduate students at four universities in Kazakhstan revealed statistically significant differences between the experimental and control groups in their performance on the engineering case studies. The experimental group achieved higher mean scores on both the first case study (79.36 vs. 70.33; p = 0.003; Cohen’s d = 0.50) and the second case study (80.61 vs. 71.12; p = 0.002; Cohen’s d = 0.53). These findings indicate moderate between-group effects within the context of the present study.
• Partnerships between industry and universities created important contextual conditions for implementing the proposed instructional model. The involvement of industry partners as mentors and providers of authentic engineering challenges enhanced the professional relevance of the educational environment and supported student engagement in solving complex systems-engineering problems. In this study, the Quadruple Helix concept serves as an institutional and conceptual framework rather than as an independently measured explanatory variable.
• The methodological guidelines developed in this study provide a structured foundation for the further adaptation of complex engineering learning tasks and instructional support systems in IT education. The findings support continued efforts to refine and evaluate these guidelines within computer science, software engineering, and cybersecurity programs in Kazakhstan. Their transferability to the national higher education systems of other countries should be examined through inter-university, cross-cultural, and longitudinal research before large-scale implementation can be recommended.
• The study demonstrated that integrating Industry 5.0 principles into engineering education requires a reconsideration of the metrics used to evaluate algorithmic success. Moving beyond exclusively technocratic indicators, such as speed and throughput, toward human-centric and sustainable solutions—such as optimizing the energy consumption of shared educational resources and balancing security with usability for campus users—is a fundamental prerequisite for developing social responsibility among future IT engineers.