Когнитивная нагрузка и успеваемость студентов: роль последовательности учебных заданий и педагогической поддержки тема диссертации и автореферата по ВАК РФ 00.00.00, кандидат наук Горбунова Анна Юрьевна

• Горбунова Анна Юрьевна
• кандидат наук
• 2025

Оглавление диссертации кандидат наук Горбунова Анна Юрьевна

TABLE OF CONTENTS

DEFINITIONS

INTRODUCTION

CHAPTER 1: THEORETICAL FRAMEWORK: COGNITIVE LOAD THEORY, INSTRUCTIONAL SEQUENCING AND SUPPORT

1.1 Cognitive load theory

1.1.1 Main assumptions of the cognitive load theory

1.1.2 Cognitive load effects. Worked examples effect

1.1.3 Weaknesses of cognitive load theory

1.2 Instructional sequencing and support

1.2.1 Instructional sequencing

1.2.2 Instructional support

1.3. Inductive and deductive methods framework

1.3.1 Deductive teaching methods

1.3.2 Inductive teaching methods

Conclusion of the chapter

CHAPTER 2: EMPIRICAL RESEARCH ON COGNITIVE LOAD AND INSTRUCTIONAL SEQUENCES

2.1 Research of the inductive and deductive sequences

2.2 Research on worked examples and instructional support

Conclusion of the chapter

CONCLUSION

REFERENCES

APPENDICES

Appendix 1. Sequencing problem solving and instruction

Appendix 2. Items of the cognitive load measurement tool (Leppink et al., 2013)

Appendix 3. Learning materials for experiment №1

Appendix 4. Learning materials for experiment №2

Appendix 5. Practical recommendations of the implementation of inquiry-based learning

Appendix 6. Russian translation of the thesis

Введение диссертации (часть автореферата) на тему «Когнитивная нагрузка и успеваемость студентов: роль последовательности учебных заданий и педагогической поддержки»

INTRODUCTION

Relevance of the Research Topic. The growing demand for high-quality education and the need to adapt teaching practices to the rapid advancement of technology and the challenges of digital transformation have had a profound impact on modern educational systems. In the context of automation and digitalization, the development of students' problem-solving skills has become particularly important, calling for a reconsideration of traditional instructional methods and the adoption of more effective teaching strategies.

From this perspective, instructional design plays a pivotal role. While international research often focuses on the practical aspects of designing educational environments and materials, Russian educational theory and practice continue to emphasize the traditions of didactics (Chernobay & Koreshnikova, 2021). In this study, instructional design is regarded as equivalent to didactics, as both fields address learning objectives, content, methods, and formats. Research in this domain enhances our understanding of various instructional approaches and supports their adaptation to current challenges, particularly those posed by digital learning environments that demand tailored methods and tools (Osmolovskaya, 2020). As a result, these developments help enhance the quality of education and align it with broader strategic goals for educational advancement.

Inquiry-based learning is a pedagogical approach that prioritizes active student engagement in the process of exploring and solving complex tasks (Klarin, 2016). It encompasses exploratory behavior directed toward acquiring and assimilating new information from external sources, which is considered a universal feature across various types of activity (Poddyakov, 2006). One of the fundamental principles of the educational theory is the recognition of learner activity as the central element of the learning process. This idea has been further developed both in international frameworks (problem-based learning, inquiry-based learning, productive failure) and within the Russian scientific tradition, primarily through A.N. Leontiev's activity theory (1975). Problem situations, tasks, or assignments are defined as those that require cognitive processing and mental effort (Kovalevskaya et al., 2010). Such tasks are recognized as a means of fostering students' cognitive engagement and deepening their learning (Makhmutov, 2016). Although the importance of problem-solving for effective learning is widely acknowledged, there is limited consensus on the optimal methods for teaching it (Van Merrienboer, 2013). Furthermore, despite a substantial body of research on problem-solving skills (Hmelo-Silver et al., 2007; Leppink et al., 2014), ongoing debates persist among scholars and practitioners regarding the most effective teaching approach (de Jong et al., 2024; Sweller et al., 2024).

Problem-solving rarely stands alone; rather, it is typically embedded within broader instructional processes that incorporate a variety of teaching methods. The structuring and sequencing of these

activities may involve presenting problem-solving tasks prior to direct instruction. Such inquiry-based approaches have become increasingly popular among educators and are frequently used to introduce new courses or topics, based on the belief that they foster greater student engagement and motivation (de Jong et al., 2024). However, other researchers argue that direct instruction is particularly critical for novice learners, as it aligns more closely with the limitations of human cognitive architecture and may therefore lead to more effective learning outcomes (Sweller et al., 2024). Cognitive load theory offers a robust theoretical framework for examining how different instructional methods influence learners' capacity to process and retain new information, and how instructional design can be leveraged to minimize unnecessary cognitive load (Sweller, 1988).

In search of efficient and effective ways to teach students new information, researchers sometimes resort to extreme thinking and thrive to find evidence in support of their ideas while underestimating other approaches. Advocates of inquiry-based learning argue that direct instruction can be incorporated within inquiry-based methods while still allowing students autonomy and the opportunity for exploration and discovery (de Jong et al., 2024). However, cognitive load theory suggests that instructors should ensure that students' initial engagement with problem-solving tasks is effective and efficient (X. Chen et al., 2019). There are no universal teaching methods and instructional designers and researchers should aim at understanding under what conditions and for what learner characteristics some methods may be more effective than others. Moreover, with the growing expertise of learners these methods should change in order to meet their evolving needs at the same time optimizing cognitive load imposed during learning through promoting deeper processing of information.

Thus, this study is relevant from both scientific and practical perspectives, addressing the demands of modern education for more effective learning strategies. Theoretically, it advances the understanding of the cognitive mechanisms underlying problem-based learning and fills gaps in research on the impact of instructional design factors, such as problem-solving sequencing and support on cognitive load in different instructional settings. From a didactic perspective, it refines instructional process organization principles within information-rich learning environments. In terms of instructional design, this study aims to develop methodological solutions that optimize teaching, reduce excessive cognitive load, and enhance the effectiveness of educational programs designed to address modern challenges and incorporate innovative instructional practices.

Development of the research topic. Over the past decades, interest in instructional design has significantly increased in both international and Russian educational research. Globally, it is recognized as a key area within educational technology and evidence-based teaching, supported by numerous empirical studies and publications (Chernobay et al., 2022). One of the most influential theories shaping instructional design is cognitive load theory, developed by John Sweller (Sweller, 1988). Cognitive load theory focuses on optimizing learning processes by managing cognitive load (Sweller et al., 2019). In

addition, research on inquiry-based learning (de Jong et al., 2024) provides a theoretical foundation, though many aspects of its interaction with cognitive load remain contested. Questions regarding the optimal management of students' cognitive resources, the role of instructional sequencing, and the impact of worked examples (Makhmutov, 2016) remain central to research but require further investigation.

Russian educational and psychological research has contributed significantly to the study of cognitive processes. Vygotsky's cultural-historical theory (Vygotsky, 1978) emphasizes the role of social interaction and cultural tools in learning, which is essential for understanding cognitive resource distribution. Galperin's theory of stepwise formation of mental actions describes how learners transition from external material actions to automated mental operations, highlighting the importance of guiding frameworks for learning (Galperin, 2002). Talyzina (1998) further developed these ideas, investigating gradual skill acquisition and knowledge automation. A significant contribution to the understanding of cognitive activity was made by the theory of developmental learning proposed by D.B. Elkonin and V.V. Davydov (Davydov, 1996), which emphasizes the central role of theoretical thinking and learning activity as the leading form of engagement that fosters development.

Russian scholars have also contributed to the development of problem-based learning as an instructional approach that promotes deep knowledge acquisition and cognitive skill development. I.Ya. Lerner and M.N. Skatkin identified problem-based learning as a crucial stage in active knowledge construction (Makhmutov, 2016). I.Ya. Lerner emphasized that problem solving should permeate the learning process, integrating with non-problem-based methods, while A.V. Brushlinsky linked it to broader cognitive development (Kovalevskaya et al., 2010) and emphasized the role of purposeful and personally meaningful activity in the process of problem solving (Klarin, 2016). M.I. Makhmutov (2016) argued that problem-based learning should unify instruction and character development to cultivate independent thinking. A.M. Matyushkin and M.N. Skatkin noted that the core of problem-based learning lies in working with contradictions, which drives students toward solutions (Kovalevskaya et al., 2010).

Notwithstanding the fact that the term "cognitive load" was not used in its modern sense within Russian pedagogical and psychological science, the theories described above formulated principles that closely align with some ideas of cognitive load theory. For example, concepts such as the necessity of a clear orienting basis for action, the gradual increase of task complexity, the organization of learning activities in accordance with students' cognitive abilities, and the notion of a learning task as a tool for developing theoretical thinking (as proposed by L.S. Vygotsky, P.Ya. Galperin, D.B. Elkonin, and V.V. Davydov) resonate with current understandings of different types of cognitive load, scaffolding, and the management of learning conditions to enhance learning. In this context, Leontiev's activity theory is of particular importance, as it views learning as the organization of purposeful and motivated activity by the learner, aimed at mastering new modes of action. Likewise, within the framework of problem-based

learning (M.I. Makhmutov, A.M. Matyushkin), scholars explored how to structure complex learning situations and provide instructional support, bringing these approaches into close alignment with contemporary perspectives on instructional design and problem-solving support.

Although several Russian theoretical approaches align closely with the key principles of cognitive load theory, there remains a notable gap in contemporary Russian educational research when it comes to integrating these ideas into the framework of modern instructional design. In particular, there is a shortage of empirical studies focused on developing original instructional design models and adapting international research findings to the specific context of Russian education (Chernobay et al., 2022). Moreover, contemporary Russian studies on cognitive load theory remain limited and are often framed within psychological rather than educational research (Ledneva & Kovalev, 2021). Public discourse on cognitive load theory is often oversimplified (Efimova, 2022), reflecting the rise of online education, where the need for effective instructional design has intensified. Consequently, there is an urgent demand for instructional designers capable of developing evidence-based online learning materials.

International research, in turn, places significant emphasis on cognitive load issues, examining them through the lens of task structure and the impact of various instructional strategies. The process of making multiple attempts to move from the given state to the goal state is called means-end analysis that requires a lot of cognitive resources (Sweller et al., 2019). Problems also differ in their types, researchers distinguish well-structured problems when the goal and the actions to reach that goal are clear, as they have only one acceptable solution; and ill-structured problems when the problem-solver does not know the goal or how to reach it, most of the times it has multiple solutions (Jonassen, 2000).

One way to manage cognitive load is to create chunks of instructional content and present it with the help of different activities, such as explicit instruction, supportive information and problem-solving tasks. Consequently, the manner in which information is organized, structured, and presented can impact the cognitive demands placed on learners due to the impact it has on the number of interacting elements. Instructional sequencing allows instructors to reduce element interactivity by finding a balance between instruction itself and different practical assignments (Costley et al., 2023). Previous research focuses on two major approaches: explicit instruction first (traditional) and problem-solving first (inquiry) (de Jong et al., 2024; Gorbunova et al., 2023; Sweller et al., 2024).

Explicit instruction first means systematic and thorough explanation of concepts and modeling of procedures before challenging learners to apply those concepts or procedures (Sweller et al., 2024) and it is also the most traditional way to transfer knowledge from one person to another. Therefore, provided that instruction respects the main principles of human cognitive architecture, this instructional method is considered to be more effective and efficient (P. A. Kirschner et al., 2006). Less guided instruction featuring discovery and inquiry first is therefore expected to hamper learning because it

involves means-end analysis and a significant amount of cognitive resources is spent in vain. However, inquiry learning has gained popularity among researchers and practitioners (Zhang et al., 2022) and is assumed to have a positive impact on conceptual knowledge (de Jong et al., 2024), motivation (Glogger-Frey et al., 2015) and understanding of the material during subsequent instructional phases (Loibl & Rummel, 2014). Cognitive load researchers agree that explicit instruction should always be accompanied with practical assignments (Sweller et al., 2024), but the most debatable assumption concerns the placement of problem-solving tasks, and the level of support needed as well as learners' characteristics, educational situations when it might be properly used, and teachers' proficiency and ability to strictly implement the requirements that come along with the instructional method.

Furthermore, previous research states that some sequences might require more mental effort than others even with the same content being presented (Gerjets et al., 2004). It might be the matter of providing a specific type of support, as several studies have shown that supported problem solving may help to decrease cognitive load and promote learning through the process of schema construction. There is a crucial difference between supported and unsupported problem solving, i.e. support during problem solving may help learners to link given states to possible solutions (McLaren et al., 2014), and a problemsolving first sequence may lead to greater performance in problem-solving (X. Chen et al., 2020).

Some problem-solving first instructional sequences attempt to provide additional support in order to decrease cognitive burden on learners (O. Chen et al., 2020). One of such instructional strategies is productive failure which introduces learners to problem-solving before providing instruction, inviting initial attempts that are likely to result in failure, however this failure might act as a catalyst for learning and knowledge acquisition (Sinha & Kapur, 2021). Another widely applied instructional strategy is problem-based learning and one of its distinct features is built-in support during problem-solving as well as other types of support, such as social support (Wijnia et al., 2019).

Overall, the integration of appropriate instructional support mechanisms during problem-solving is essential, and worked examples, by virtue of their structure and function, are well equipped to fulfill the objective of supporting problem-solving (O. Chen, Retnowati, et al., 2023). Research on worked examples has shown that novice learners learn more from studying worked examples than from solving the equivalent unsupported problems (Atkinson et al., 2000; F. Paas & van Gog, 2006; Renkl et al., 2009; Sweller et al., 2011). The use of worked examples while solving a problem reduces element interactivity when solution steps of examples are revealed in isolated segments, allowing learners to process information more efficiently (F. Paas et al., 2003). Such step-by-step delivery of solution steps allows for the formation of schemas that are created as a result of visualizing how to solve a problem (O. Chen & Kalyuga, 2020). Another feature of worked examples is that it helps to close the gap between the problem state and the goal state through the demonstration of solution steps (F. Paas & van Gog,

2006), and frees cognitive resources and reduces mental effort when solving problems with clearly stated goals (Wittwer & Renkl, 2010). Previous research shows that worked examples are particularly effective for novice learners (Van Gog et al., 2008). Thus, worked examples can be seen as a potential form of problem-solving support in online learning environments that may enhance online learning.

While the fields of cognitive load, instructional sequence and support, as well as worked examples have been extensively studied and discussed in previous literature, there remain several issues and areas of research that have received less attention. These issues include the research approaches to instructional sequencing, particularly problem-solving first sequences and its compatibility with the principles of cognitive load theory, as well as how they can be customized and tailored to the needs and characteristics of different learner populations. Another issue is the extent to which different forms of support are most effective in reducing cognitive load and promoting learning in different instructional contexts as well as how worked examples can be effectively integrated into different instructional sequences.

Thus, the need for further research is driven by the necessity to develop and justify a conceptual framework for examining different instructional sequences, considering both perspectives, as well as optimal strategies for implementing worked examples. The practical application of such a framework will enhance the effectiveness of the educational process and minimize working memory overload, which, in turn, will have a positive impact on students' academic performance.

This study aims to address the contradictions that arise in the learning process within the framework of cognitive load theory. On the one hand, the optimal use of working memory resources requires a structured approach; on the other hand, existing inquiry-based approaches view self-directed knowledge construction as an effective way for students to acquire knowledge. However, under high cognitive load conditions, such exploratory learning may become ineffective without appropriate instructional support.

This contradiction is further complicated by the abundance of theoretical studies and conflicting empirical evidence, which highlight the relative effectiveness of different instructional sequences across a wide range of learning contexts and student characteristics (O. Chen, Retnowati et al., 2023; Sinha & Kapur, 2021; Sweller, 2024; van Harsel et al., 2020). At the core of discussions surrounding problemsolving support and the use of worked examples lies a fundamental tension between providing instructional guidance to reduce cognitive load and encouraging learner autonomy.

Additionally, there is a discrepancy between the theoretical understanding of the need to reduce cognitive load and its practical implementation in education. While excessive cognitive load is known to impair learning effectiveness, problem-based and inquiry-based instruction often fails to sufficiently account for cognitive load management strategies. In this context, worked examples serve as a critical instructional support tool, helping to structure the learning process, reduce uncertainty, and alleviate

working memory load for students.

Research problem. Despite ongoing debates in the fields of educational science and instructional design regarding the effectiveness of inquiry-based learning approaches (de Jong et al., 2024; Loibl et al., 2017) and cognitive load-based instructional strategies (O. Chen & Kalyuga, 2020; Sweller et al., 2019; Van Gog et al., 2008), there is currently no clear consensus on the most effective way to integrate problem-solving tasks into instructional sequences to minimize cognitive load while maximizing learning outcomes.

Advocates of different approaches: cognitive load theory (Sweller et al., 2024) and inquiry-based learning (de Jong et al., 2024) offer contrasting perspectives on their respective benefits and limitations, each providing compelling theoretical arguments supported by empirical research. While studies have demonstrated that worked examples effectively reduce cognitive load and enhance learning (Atkinson et al., 2000; O. Chen, Retnowati et al., 2023; Paas & van Gog, 2006), the specific mechanisms underlying their supportive function within different instructional sequences remain a topic of discussion. Further research is needed to determine how best to integrate and align worked examples with problem-solving activities (Costley et al., 2024) across various instructional sequences. Additionally, there is a persistent demand for effective teaching methods and instructional materials that are designed to enhance learning. Cognitive load theory provides educators with a framework for optimizing instructional design by developing learning materials that align with human cognitive processing and facilitate efficient information acquisition.

Thus, the central research problem of this study is the need to develop and justify a conceptual framework for analyzing different instructional sequences, incorporating perspectives from both cognitive load theory and inquiry-based learning. This study seeks to identify the relationships between instructional sequencing, cognitive load, and students' academic performance, while also exploring the role of worked examples as instructional support in this process. The key research question that arises is: how can inquiry-based learning approaches and worked examples be justified from the perspective of cognitive load theory to optimize cognitive load and enhance students' academic performance?

The object of the study is the enhancement of learning effectiveness through cognitive load management in the process of problem-solving.

The subject of the study is the impact of instructional sequencing and instructional support on improving learning effectiveness through cognitive load management in the problem-solving process.

The aim of this dissertation is to provide a theoretical rationale and empirical validation of the impact of instructional sequencing and worked examples, as a form of instructional support, on students' cognitive load and academic performance during problem-solving tasks.

To achieve this goal, the following research objectives were formulated: 1. Develop and justify a conceptual framework that defines key characteristics of instructional

sequences, examines the relationship between instructional sequencing and cognitive load levels, and demonstrates that inductive methods can align with cognitive load theory when appropriate instructional support is integrated.

2. Assess the relationship between instructional sequencing, cognitive load, and academic performance, as well as evaluate the effectiveness of different sequences when worked examples are used as instructional support.

3. Investigate how worked examples, as a form of problem-solving support, contribute to optimizing learning by minimizing extraneous cognitive load.

4. Synthesize theoretical and empirical findings to develop methodological recommendations for the use of inductive instructional sequences and instructional support in the form of worked examples in educational practice.

Research hypothesis: inductive and deductive instructional sequences, when classified within a conceptual framework that accounts for the type and style of initial activity as well as the level of instructional support, have distinct impacts on students' cognitive load and academic performance. Inductive sequences can be consistent with the principles of cognitive load theory when they incorporate instructional support, such as worked examples, which can also be effectively integrated into deductive learning approaches.

Based on the research hypothesis, the following research questions were formulated:

1. How can different instructional sequences be classified, and what characteristics make inquiry-based approaches compatible with the principles of cognitive load theory?

2. How do various instructional sequences influence different types of cognitive load, and what impact do they have on students' academic performance?

3. How effective are worked examples as instructional support in reducing cognitive load and improving academic performance across different instructional sequencing approaches?

The methodological and theoretical foundation of the study is based both on contemporary concepts of cognitive psychology and instructional design, as well as on the principles of the activity-based approach. This study is primarily grounded in cognitive load theory (Sweller et al., 2019), which provides a scientific understanding of working memory limitations and information processing in learners. The application of cognitive load theory in this research ensures a theoretically grounded selection of methods and research procedures. Additionally, this study incorporates inquiry-based learning approaches (de Jong et al., 2024), including problem-based learning (Berkel et al., 2010) and the productive failure approach (Sinha & Kapur, 2021). These theoretical perspectives enabled the categorization of instructional sequences into inductive and deductive models, facilitating an analysis of the key characteristics of inquiry-based learning that align with cognitive load theory principles. Furthermore, the integration of the worked-example approach (Renkl, 2014) allowed for a structured

examination of how instructional support, in the form of worked examples, helps organize the learning process and contributes to the optimal distribution of cognitive resources.

In this study, cognitive load is not viewed as an isolated psychological construct, but rather as a variable that can be deliberately influenced through instructional design. Instructional sequencing and worked examples are considered specific didactic tools aligned with key principles of didactics: coherence, progression, and accessibility (I.M. Osmolovskaya); as well as with the theoretical foundations of the step-by-step formation of mental actions (P.Ya. Galperin, N.F. Talyzina). Accordingly, this research is grounded in didactics as a field of pedagogy focused on the design of instructional content, structure, and methods, while also drawing on the principles of instructional design that account for the cognitive constraints and capacities of learners.

Research methods. The study employs both general scientific and specialized research methods, encompassing theoretical and empirical approaches (Baiborodova & Chernyavskaya, 2014). These methods include: literature analysis and conceptual classification of instructional sequences, enabling the identification of key differences between inductive and deductive approaches; empirical research, involving data collection and analysis through experimental methods; survey methods, including questionnaires designed to measure cognitive load and tests assessing academic performance; statistical methods, such as ANOVA, regression analysis, and correlation analysis, used for processing empirical data and identifying relationships between key variables. The comprehensive approach combines theoretical analysis and empirical investigation, which provides a robust framework for testing the research hypothesis.

Research sample and data collection. The empirical data for this study were collected at the National Research University Higher School of Economics during the 2021-2022 and 2022-2023 academic years. The first experiment involved a sample of 254 participants, who were randomly assigned to three groups, while the second experiment included 87 students, randomly distributed into two groups.

Research stages. The study was conducted in three stages:

1. Development of the conceptualframeworkfor inductive and deductive sequences - a comprehensive literature review on Cognitive Load Theory and an analysis of various instructional sequences, focusing on the type of instructional support provided during problem-solving. This stage involved the theoretical development of a framework for inductive and deductive instructional sequences, identifying the characteristics of different sequences that contribute to reducing cognitive load in problem-solving.

2. Investigation of direct instruction first vs. problem-solving first approaches - an experimental study (n = 254) examining the impact of three instructional sequences: (1) direct instruction first, (2) supported problem-solving first, and (3) unsupported problem-solving. The study measured the

effects of these sequences on cognitive load (intrinsic, extraneous, and germane) and academic performance.

3. Investigation of worked examples as instructional support for problem-solving - a second experimental study (n = 87) comparing two instructional sequences: (1) an inductive sequence with worked examples and (2) a deductive sequence with worked examples. The study aimed to assess the role of worked examples in instructional support and their impact on cognitive load management and learning effectiveness.

Scientific novelty of study:

- A conceptual framework has been developed and theoretically substantiated for classifying instructional sequences. This framework distinguishes between inductive and deductive approaches based on the type and style of initial learning activity and the degree of instructional support, while linking these dimensions to cognitive processing mechanisms in working memory, thereby expanding the theoretical foundation for designing effective instructional strategies.

- The assumptions of cognitive load theory have been further refined in relation to inductive instructional strategies. The study demonstrates that when instructional support, specifically in the form of worked examples, is incorporated, inductive sequences can align with the core principles of the theory.

- Empirical research revealed significant differences in students' cognitive load and academic performance during problem-solving depending on the instructional sequence type and the presence of worked examples as support. These findings provide a more nuanced understanding of how instruction influences learning, thus extending the theoretical propositions of cognitive load theory.

- The study provides empirical evidence that the integration of worked examples into both inductive and deductive instructional sequences contributes to the reduction of extraneous cognitive load and improves student performance. This broadens both the theoretical and practical understanding of instructional support within the framework of cognitive load theory, emphasizing its flexibility and effectiveness across varying instructional approaches, thereby making a substantial contribution to teaching methodology and the enhancement of educational quality.

Theoretical contribution. This study contributes to the field by clarifying and extending the theoretical foundations of learning, particularly in relation to learners' cognitive constraints and resources, through the perspective of cognitive load theory. It integrates insights from information processing mechanisms, the structure of instructional sequences, and instructional support in the context of problem-solving.

- The development of a conceptual framework for inductive and deductive instructional sequences offers a useful theoretical tool for analyzing educational strategies. It can be used to explore how variations in content delivery and instructional structure influence cognitive load and learning

outcomes in problem-solving settings.

- The findings of this study refine the boundaries of the applicability of inductive learning strategies within the scope of cognitive load theory. The results demonstrate that when instructional support in the form of worked examples is provided, inquiry-based learning can align with the principles of cognitive load theory.

- The framework represents a significant contribution to the ongoing scientific debate on the role of problem-solving within instructional sequencing (Sweller, van Merrienboer, Kirschner, de Jong, etc) and the place of inquiry-based learning in educational practice. The empirical data and cognitive load analysis derived from various instructional methods and sequences provide insights into the most effective strategies for integrating inductive and deductive instructional approaches.

- The study contextualizes and extends key ideas from Russian educational theory, including the theory of step-by-step formation of mental actions (P.Ya. Galperin), developmental learning (D.B. Elkonin, V.V. Davydov), and problem-based learning (M.I. Makhmutov), aligning them with contemporary cognitive frameworks and instructional design principles, thereby reinforcing the link between psychology and education.

- The findings confirm the effectiveness of worked examples as an instructional support tool, reinforcing problem-solving processes and reducing cognitive load. These results are also valuable for advancing instructional design methodologies and refining teaching strategies (O. Chen, Retnowati et al., 2023). Previous research suggested that presenting worked examples before problem-solving was more effective than studying them after problem-solving (Van Gog et al., 2008). However, this study demonstrates that worked examples improve learning efficiency in both instructional sequences, as they lower cognitive load and enhance knowledge acquisition.

- This research contributes to the theory of instructional design, expanding the understanding of how various instructional sequencing strategies influence learning outcomes. The empirical data obtained align with previous theoretical findings regarding the stability of intrinsic cognitive load across instructional methods (Sweller, 2024), the negative impact of extraneous cognitive load on learning outcomes (O. Chen, Paas et al., 2023; Krieglstein et al., 2023), and the ambiguous nature of germane cognitive load, which can be interpreted as both mental effort directed at managing intrinsic load (Kalyuga, 2011; Klepsch & Seufert, 2020) and deeper conceptual understanding (Gorbunova et al., 2024).

- This study contributes to the development of research methodology in the field of instructional design within the Russian context, including the initial adaptation and use of the translated cognitive load measurement instrument (Leppink et al., 2013). This creates a foundation for further research and the refinement of instruments in accordance with international standards.

Practical contribution. The study contributes to advancing instructional design as a field

dedicated to developing effective educational strategies and improving teaching practices.

- This study provides experimental evidence on the impact of instructional sequencing (inductive and deductive approaches) on cognitive load levels and students' academic performance in problemsolving. The findings contribute to the development of more effective teaching strategies, helping students to acquire knowledge with reduced cognitive strain and improved learning outcomes. Furthermore, the results highlight the importance of instructional support in developing students' ability to manage cognitive resources, which not only enhances academic performance but also mitigates potential challenges in problem-solving.

- Practical methodological recommendations have been developed for incorporating worked examples into instructional sequences as a form of instructional support. By following these recommendations, educators can design and adapt worked examples to fit specific subject areas, improving students' understanding and retention of information. This approach is particularly beneficial in ill-structured domains, such as law, where learners often struggle with conceptual knowledge acquisition.

- The study underscores the limitations of inquiry-based learning methods, emphasizing the need to consider learner characteristics, instructional goals, and learning conditions when designing problem-solving activities. The findings reinforce the necessity of adhering to clear methodological guidelines to ensure the effective application of inquiry-based approaches in various educational contexts.

Statements for defense.

1. The developed conceptual framework defines essential characteristics of instructional sequencing, such as the type and style of initial learning activities, enabling the classification of sequences into inductive and deductive approaches. It also establishes the relationship between instructional sequencing and students' cognitive load levels, demonstrating that inductive methods can be effectively implemented in educational practice when instructional support is provided during problem-solving which makes them compatible with the principles of cognitive load theory.

2. Instructional sequencing significantly influences both extraneous and germane cognitive load, as well as students' academic performance in the following ways:

o The deductive sequence leads to higher academic performance.

o The inductive approach with problem-solving support results in the highest levels of germane cognitive load, promoting meaningful learning.

o The inductive approach without instructional support produces the highest extraneous cognitive load and the lowest academic performance, indicating potential cognitive overload.

3. Worked examples, when used as problem-solving support, play a critical role in reducing extraneous cognitive load and enhancing learning efficiency. Their implementation improves knowledge acquisition in both inductive and deductive instructional sequences, reinforcing the effectiveness of

structured guidance in learning.

Validity and reliability of the research findings.

The validity of the research findings is ensured through theoretical justification, methodological consistency, the use of valid and reliable methods, and the statistical analysis and replicability of experimental results. The theoretical foundation of the study is based on contemporary concepts and theories, while the research methods were selected to align with the study's objectives and research questions. The empirical component of the study was conducted through a series of experiments with quantitative data analysis. Cognitive load was assessed using a translated version of the instrument developed by Leppink et al. (2013), which has been widely validated in international research. The use of this tool allows for the alignment of the obtained data with international findings and promotes the comparability of research approaches. To confirm the validity of the results, statistical data analysis methods were applied, including significance testing and correlation analysis, allowing for an objective evaluation of identified relationships. The replicability of the findings is ensured by a rigorous methodological procedure and the use of standardized techniques for assessing cognitive load and academic performance.

Presentation of research findings.

The research findings were discussed and validated through presentations at various academic events and conferences. The key conclusions and empirical data were presented at international conferences focusing on cognitive load, instructional design, and educational technology, including:

• The European Conference on Educational Research (ECER), Yerevan, Armenia (2022)

• XV Annual International Conference on Education and New Learning Technologies, Palma de

Mallorca, Spain (2023)

• XV International Cognitive Load Theory Conference, Montpellier, France (2023)

At these conferences, modern approaches to cognitive load management and instructional process organization were actively discussed.

At the national level, the study was presented at:

• XIII International Conference of Higher Education Researchers, Moscow, Russia (2022)

• IX International Forum on Teacher Education, Kazan, Russia (2023)

These events provided an opportunity to receive expert feedback and recommendations from Russian specialists in education and cognitive science. Additionally, the research findings were tested in applied research seminars held at the Institute of Education, National Research University Higher School of Economics (HSE University), where discussions focused on the implementation of worked examples and instructional sequencing in educational practice. The results of this study have been published in peer-reviewed academic journals indexed in leading scientific databases, confirming their scholarly significance and adherence to academic standards.

Structure of the thesis.

The structure of the dissertation follows the logical framework of scientific research. It consists of an introduction, two main chapters, a conclusion, definitions, a reference list comprising 164 sources, and five appendices. The total length of the dissertation is 116 pages, including six tables and five figures.

Заключение диссертации по теме «Другие cпециальности», Горбунова Анна Юрьевна

ЗАКЛЮЧЕНИЕ

Настоящее исследование позволило достичь поставленной цели - теоретически обосновать и эмпирически проверить влияние учебной последовательности и алгоритмов решения задач как средства педагогической поддержки на когнитивную нагрузку и успеваемость студентов. Все исследовательские вопросы получили последовательные и доказательные ответы в рамках решения задач исследования и обоснования положений, выносимых на защиту, каждый из которых нашел отражение в соответствующих теоретических и эмпирических результатах.

Тем самым, логическая связь между задачами и положениями, выносимыми на защиту, обеспечивает доказательность полученных выводов и внутреннюю согласованность исследования.

В ходе работы была сформулирована и эмпирически подтверждена гипотеза о том, что индуктивные методы обучения могут соответствовать положениям теории когнитивной нагрузки при наличии продуманного педагогического дизайна, включающего достаточную поддержку. Это подчеркивает важность осознанного проектирования образовательных программ, учитывающего не только тип учебной последовательности, но и способы организации педагогической поддержки. В соответствии с поставленными во введении задачами исследования были получены значимые теоретические и эмпирические результаты.

Анализ литературы выявил существующие дискуссии о преимуществах различных стратегий обучения, включая прямое обучение и проблемное обучение. Полученные результаты показали, что эффективность индуктивных методов возрастает при наличии алгоритмов решения задач, которые служат инструментом структурирования познавательной деятельности и оптимизируют перераспределение когнитивных ресурсов обучающихся. Включение алгоритмов решения задач в учебный процесс не только снижает внешнюю когнитивную нагрузку, но и способствует лучшему усвоению материала, что подчеркивает необходимость интеграции таких инструментов в педагогический дизайн образовательных программ. Таким образом, исследование подтвердило, что методы педагогического дизайна, ориентированные на управление когнитивной нагрузкой, могут быть эффективны и соответствуют дидактическим принципам. Полученные результаты также согласуются с ключевыми положениями отечественной педагогики. Алгоритмы решения задач можно рассматривать как форму педагогической поддержки, обеспечивающую переход обучающегося в зону ближайшего развития, где выполнение действия становится возможным благодаря внешней опоре. Такая поддержка структурирует учебную деятельность, выполняя функцию ориентировочной основы действия и направляя внимание на существенные элементы задачи. Это соответствует логике развивающего обучения, в котором обучение строится на решении учебных задач и формировании понятийного мышления, а не на простом усвоении готовых ответов.

Ответ на первый исследовательский вопрос был получен в ходе реализации первой задачи, в результате которой была предложена концепция индуктивных и дедуктивных учебных последовательностей, основанная на типе и уровне педагогической поддержки, а также стилистике начальной активности. Теоретически обосновано, что индуктивные методы обучения, включающие поддержку в виде алгоритмов решения задач, могут успешно сочетаться с теорией когнитивной нагрузки и способствовать более эффективному обучению, в то время как отсутствие поддержки является несовместимым с принципами теории, приводит к увеличению

внешней когнитивной нагрузки и затруднению обучения. Это послужило основой для первого положения, показывающего, что индуктивные методы могут соответствовать теории когнитивной нагрузки при условии включения педагогической поддержки.

Второй исследовательский вопрос был охвачен в рамках второй задачи, которая реализована через серию эмпирических исследований. Экспериментальные результаты подтвердили, что студенты, обучавшиеся по дедуктивной последовательности, показали наиболее высокие результаты в тестах, однако индуктивная последовательность с поддержкой продемонстрировала значительные преимущества в формировании релевантной когнитивной нагрузки и развитии навыков аргументации, особенно в юридическом контексте. Это подчеркивает важность гибких образовательных стратегий, сочетающих разные методы обучения в зависимости от учебных целей. Результаты легли в основу второго положения, демонстрирующего различия между дедуктивными и индуктивными подходами по показателям внешней и релевантной нагрузки, а также успеваемости.

Третий исследовательский вопрос отражён в третьей задаче. Полученные эмпирические данные показали, что алгоритмы решения задач являются универсальным инструментом, который можно использовать не только в STEM-дисциплинах, но и в гуманитарных науках, таких как юриспруденция. Они помогают обучающимся лучше структурировать информацию, выстраивать аргументацию и применять профессиональную терминологию, что делает их важным элементом дидактических стратегий, ориентированных на развитие профессиональных компетенций. Эти результаты подтверждают, что методы педагогического дизайна, направленные на управление когнитивной нагрузкой, могут быть адаптированы для различных образовательных направлений, что делает их перспективными для дальнейшего изучения и внедрения в образовательную практику. Данные результаты представлены в третьем положении, подтверждающем, что алгоритмы снижают внешнюю нагрузку и повышают учебные результаты вне зависимости от типа последовательности.

Четвёртая задача, связанная с формулированием практических рекомендаций, обеспечила прикладной результат исследования и основана на ответах на все три исследовательских вопроса, тем самым интегрируя выводы в рекомендации для педагогического дизайна.

Несмотря на полученные результаты, исследование имеет ряд ограничений, связанных с особенностями экспериментального дизайна и выборкой обучающихся. В частности, исследования проводились в контролируемых условиях, что может ограничивать их применение в естественной образовательной среде. Еще одним важным аспектом является то, что участниками исследования были исключительно студенты юридического направления одного вуза - Национального исследовательского университета "Высшая школа экономики" (НИУ ВШЭ), обладающие высоким академическим уровнем подготовки, что может ограничивать

возможность обобщения полученных результатов на другие возрастные группы и области знаний. Кроме того, в фокусе исследования находился один вид педагогической поддержки -алгоритмы решения задач, тогда как другие стратегии (например, коллективная работа, встроенная поддержка) также могут оказывать влияние на когнитивную нагрузку и требуют дальнейшего изучения. Помимо этого, в исследовании когнитивная нагрузка рассматривалась преимущественно в рамках рациональной модели обучающегося как субъекта переработки информации. Эмоциональные, личностные, мотивационные и ценностные аспекты деятельности обучающихся, которые также могут влиять на восприятие учебного материала и когнитивные процессы, не включались в анализ и требуют отдельного внимания в будущих исследованиях.

Еще одним возможным ограничением исследования является отсутствие учета времени, которое участники затрачивали на каждый этап учебного процесса. Хотя в одном из эмпирических исследований использовалась среда самостоятельного обучения, а в другом устанавливались конкретные временные рамки для каждой активности, все же остается вероятность, что отдельные участники могли потратить на каждое задание больше или меньше времени, чем было отведено, что потенциально могло повлиять на их общие результаты обучения и надежность измерения успеваемости. Наконец, предложенные в исследовании рекомендации по выбору индуктивных и дедуктивных методов обучения не являются исчерпывающими. Они охватывают только четыре широкие категории методов преподавания, что может не учитывать все разнообразие конкретных педагогических стратегий, используемых в образовательной практике.

Перспективные направления будущих исследований связаны с дальнейшей адаптацией и всесторонней валидацией инструментов измерения когнитивной нагрузки в различных образовательных контекстах, что позволит повысить точность и надёжность оценки когнитивных процессов, а также обеспечить их соответствие как отечественным, так и международным стандартам эмпирических исследований.

Дальнейшие шаги в развитии исследовательской области включают изучение комбинированных стратегий, сочетающих индуктивные и дедуктивные методы. Такая комбинация может помочь учитывать индивидуальные потребности разных обучающихся и оптимизировать результаты обучения за счет разнообразия учебных стратегий, поддерживающих различные стили и этапы обучения. Например, курс может начинаться с индуктивного подхода для активизации предшествующих знаний и вовлечения обучающихся, а затем переходить к дедуктивному подходу, обеспечивающему необходимую структуру и руководство для более глубокого изучения материала.

Кроме того, сбор дополнительных эмпирических данных в поддержку индуктивно-дедуктивной концептуальной рамки может усилить теоретические основания данных

педагогических выводов, а также помочь в дальнейшей проверке и уточнении индуктивно-дедуктивной концепции как эффективного инструмента для совершенствования педагогического дизайна и образовательной практики. Проверка гипотез о пояснительном и познавательном обучении в различных учебных последовательностях может дать ценные эмпирические доказательства, подтверждающие полезность индуктивно-дедуктивной рамки. Также расширение эмпирической базы за счет исследования их применения в различных образовательных дисциплинах и контекстах может повысить обобщаемость и применимость полученных результатов.

Особое внимание следует уделить изучению влияния различных учебных последовательностей на мотивацию и вовлеченность обучающихся, поскольку эти факторы оказывают значительное влияние на качество усвоения знаний. Полученные результаты подчеркивают необходимость дальнейшей интеграции теоретических основ педагогического дизайна с практическими дидактическими решениями, что позволит совершенствовать образовательные методики и разрабатывать новые подходы к управлению когнитивной нагрузкой.

Согласно теории самоопределения (Deci & Ryan, 1985), автономия является ключевым фактором, влияющим на внутреннюю мотивацию и вовлечённость в обучение. Будущие исследования могли бы изучить, различаются ли восприятия обучающимися своей автономии в зависимости от использования индуктивных или дедуктивных учебных последовательностей с различной степенью педагогической поддержки. Анализ взаимодействия между ощущением автономии, когнитивной нагрузкой и учебными результатами может дать ценные рекомендации для проектирования образовательной среды, которая не только оптимизирует когнитивную обработку информации, но и способствует развитию мотивационных и эмоциональных аспектов обучения.

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