The Role of Self-directed Learning in Promoting Deep Learning Processes: A Systematic Literature Review

Self-Directed Learning

The concept of Self-Directed Learning (SDL) originated from research into adult learning in the 1960s, and subsequently, the scope of SDL research has expanded to encompass all ages and educational levels. The initial focus was on recognizing adults’ conscious self-direction in learning and explaining how they learn (Merriam and Baumgartner, 2020). Houle (1961), through in-depth interviews with adult learners, categorized self-directed learning among adults into goal-oriented, learning-oriented, and activity-oriented types based on the motivation for participating in learning, providing a foundational framework for subsequent scholars’ research on adult learning. In 1966, Tough first introduced the concept of “self-directed learning,” whose connotation was further developed and expanded by Knowles (Tough, 1966). In 1975, Knowles published a seminal work on SDL, which is considered the most cited publication on SDL (Knowles, 1975).

Since Knowles (1975), there has been a considerable effort to define and describe SDL. Currently, scholars tend to understand the essence of “Self-Directed Learning” from two perspectives: a process-oriented view and a personality trait view. Among them, the typical “process-oriented” perspective includes: Knowles posits that children are learners with a higher dependence, while adults are independent learners. This implies that adult learning does not rely predominantly on external guidance like that of children; instead, they can self-regulate, completing their learning based on self-direction. Knowles views individuals as autonomous and independent, which is both a prerequisite and foundation for adults’ self-directed learning. Long (2000) sees self-directed learning not just as an outcome, but as a process where the learner is responsible for initiating, planning, implementing, and monitoring their learning. Additionally, many scholars from a psychological standpoint regard self-directed learning as a personality trait or inclination, a “personality trait view”: Wiley (1983) defines “Self-Directed Learning Readiness (SDLR)” as “the degree to which an individual possesses the attitude, abilities, and personality characteristics necessary for self-directed learning.” Lounsbury (2009) and others argue that there is a logical relationship between self-directed learning and personality traits.

This study adopts the definition by Knowles (1975), positing that self-directed learning is a process in which learners, based on actual conditions, identify certain learning needs and, by learning objectives, formulate relevant learning plans. Subsequently, they autonomously implement these plans and ultimately evaluate the learning outcomes.

Current research on Self-Directed Learning (SDL) involves multiple fields, such as lifelong learning, intrinsic motivation, online learning, blended learning, and deep learning. The growing domains in SDL research highlight its significant position in educational research and also indicate that educational researchers are increasingly recognizing the importance of cultivating learners’ self-directed learning capabilities. For example, research has demonstrated that SDL is a key characteristic of the capacity for lifelong learning and plays a crucial role in students’ academic and personal growth (Salleh et al., 2019). SDL not only promotes deep learning but also shows a significant correlation with academic achievement (Altinpulluk et al., 2023). This learning approach relies on students’ intrinsic motivation, self-efficacy, and willingness to face challenges. These factors work together to form the attitude and ability for self-directed learning in learners. Studies have shown that learners with high self-directed learning skills are more likely to become proactive self-regulators, exhibiting significant differences from lower achievers in terms of learning strategies, learning awareness, preparation, time, resource utilization, and peer support (van der Graaf et al., 2022). Research has confirmed a moderate positive correlation between self-directed learning and intrinsic motivation, indicating that learners’ intrinsic motivation is an important factor supporting self-directed learning (Altinpulluk et al., 2023). Educational practices, such as flipped classrooms, can effectively enhance students’ enthusiasm and self-directed learning capabilities (Hsieh & Maritz, 2023). Furthermore, learning styles impact students’ academic performance and the time invested in self-directed learning, with a variety of instructional guidance options provided by teachers further promoting certain aspects of students’ autonomous learning (Ganji et al., 2022; Hsieh & Maritz, 2023). Additionally, pedagogical and instructional design methods have potential impacts on fostering the development of students’ self-directed learning abilities in blended learning environments (Adinda & Mohib, 2020). Research indicates that SDL plays a key role in the deep processing of knowledge and is correlated with learning outcomes and academic achievements (Lai et al., 2024). Furthermore, creative learning outcomes can be supported through self-directed learning, and the attitudes and methods of self-directed learning positively predict online learning engagement. This positive relationship is mediated by the perceived value of learning goals, illustrating how self-directed learning fosters deeper levels of learning engagement (Sun et al., 2023). With the advancement of educational technology, research on SDL has also begun to focus on new trends in learning, such as Massive Open Online Courses (MOOCs) and home learning during the COVID-19 pandemic (Alhammadi, 2021; Altinpulluk et al., 2023). These studies not only explore the connection between SDL and global educational reforms but also emphasize the role of personal interest and self-regulation in different types of autonomous technology activities. Notably, personal interest is a significant predictor across all types of technological activities, while self-regulation primarily predicts instructional-oriented, information-oriented, and social-oriented activities (Lai et al., 2024). However, excessive cognitive load, especially in cases where cognitive abilities are not yet fully developed, may lead only to superficial changes in knowledge rather than deep learning (Butcher & Sumner, 2011). Therefore, educators need to consider this when designing and implementing self-directed learning activities to ensure that the activities can stimulate students’ interest without exceeding their cognitive load.

In summary, self-directed learning is a key factor in student success, promoting deep learning and academic achievement. However, which factors facilitate deep learning in the self-directed learning process, and how students experience and achieve the deep learning process require further study.

Deep learning

Deep learning, as a significant topic in the field of education, has seen its understanding and definition evolve through various stages over time. From early studies to modern theoretical explorations, scholars from multiple perspectives have defined and interpreted deep learning, creating a rich series of theoretical frameworks and viewpoints. In 1976, Ference Marton and Roger Säljö first introduced the concepts of Surface Learning and Deep Learning. Through experimental research on students reading academic articles, they differentiated between two learning approaches: Surface Learning focuses on the superficial information and rote memorization of texts, whereas Deep Learning emphasizes understanding the deep meaning of texts, questioning the author’s viewpoints, and connecting with one’s knowledge and experience (Marton, F., & Säljö, R., 1976). Scholars gradually expanded on this concept. For example, John Biggs built on Marton’s work to propose that deep learning involves high-level or active cognitive processing (Biggs, 1979). In “7 Powerful Strategies for Deep Learning,” Eric Jensen and LeAnn Nickelsen further highlighted the multi-level processing in deep learning and the transformation in learners’ thoughts, behaviors, or control during the learning process (Jensen, E., & Nickelsen, L., 2008). In China, research on deep learning began around 2005. Li Jiahao and others defined deep learning as the process of understanding new ideas and facts critically and integrating them into the existing cognitive structure (He Ling, & Li Jiahao, 2005). Subsequently, scholars domestically and internationally conducted deeper research on deep learning, exploring and defining it from various angles, including learning methods, processes, and outcomes. Modern research on deep learning not only focuses on the depth of the learning process, emphasizing the transition from superficial memorization to deep understanding and creation, but also values the learning outcomes, namely the development of learners’ problem-solving capabilities, metacognitive abilities, and creative thinking skills (Weng et al., 2023). Scholars like Julian Hermida and the National Research Council (NRC) in the United States emphasize deep learning as a continuous process of knowledge construction, aiding learners in solving practical difficulties and issues in social interactions (Hermida, J., 2014; Council, N.R., 2013). Canadian scholar Michael Fullan and others have proposed a new pedagogical perspective, suggesting that deep learning involves new types of learning partnerships between students and teachers, digital tools and resources, and deep learning tasks (Fullan, M., & Langworthy, M., 2014).

This study posits that deep learning is a learning process driven by intrinsic motivation, critically integrating and deeply processing information from multiple sources. It aims to foster the development of higher-order cognitive abilities—including application, analysis, evaluation, and synthesis capabilities (derived from Bloom’s taxonomy)—to achieve problem-solving and knowledge transfer. Furthermore, deep learning also emphasizes cultivating learners’ core competencies, such as collaboration, communication, autonomous learning, perseverance in learning, critical thinking, and innovative thinking, to meet the complex demands of future society. Through this process, learners can profoundly understand the content and effectively apply knowledge in new contexts, thereby promoting comprehensive personal development on multiple dimensions.

Current research on deep learning in the educational field primarily focuses on the study of learning strategies, the application of technologies and tools, and aspects such as student engagement and motivation (Aguiar-Castillo et al., 2021; Alhammadi, 2021; Fouché, 2024; Pereira & Wahi, 2021). For instance, some studies are dedicated to exploring and validating which learning strategies can effectively promote deep learning, such as critical thinking, problem-solving abilities, and reflective learning (Nhat & Le, 2023; Weng et al., 2023). Additionally, with the application of technology in education, researching how to use digital tools and online platforms to promote deep learning has become a hotspot. This includes exploring how blended learning, flipped classroom models, and gamified learning can support the deep learning process (An & Qu, 2021; Pereira & Wahi, 2021).

There are also studies dedicated to how to increase student engagement and intrinsic motivation during the deep learning process. This involves how to design learning activities that stimulate students’ curiosity and desire to explore, as well as how to enhance students’ self-efficacy and satisfaction with learning through autonomous learning strategies (Aguiar-Castillo et al., 2021; Pereira & Wahi, 2021). As the educational community deepens its understanding of student autonomy and deep learning, an increasing number of studies emphasize the critical role of autonomous learning in promoting deep learning. Research is also increasingly highlighting the integration of theory and practice, as well as the combination of knowledge from different disciplines (Parpala et al., 2022; Segú Odriozola, 2023). Based on current research, the integration of deep learning and self-directed learning (SDL) remains relatively unexplored. Although deep learning is considered a key approach to fostering students’ comprehensive skill development, its potential when combined with SDL has not been fully investigated. Self-directed learning emphasizes learner initiative and autonomy, aligning with the active participation emphasized in deep learning. Deep learning also emphasizes promoting students’ active exploration of knowledge and fostering students’ autonomy in learning. Therefore, focusing research on how to effectively integrate and apply these two modes of learning to optimize educational strategies and improve learning outcomes is both feasible and necessary. Future research on deep learning needs to pay more attention to how the principles of deep learning can be combined with self-directed learning strategies. In this way, educators can better meet students’ individualized and deep learning needs, promoting comprehensive student development.

Self-Determination Theory (SDT)

Self-Determination Theory (SDT) was originally proposed by Deci and Ryan. SDT emphasizes three fundamental psychological needs: Autonomy, Competence, and Relatedness (Deci & Ryan, 1985). SDT posits that if these three basic psychological needs are not met, an individual cannot grow and progress. The three core psychological needs of Self-Determination Theory provide an important perspective for understanding and researching Self-Directed Learning (SDL) and deep learning. Guided by SDT theory, research can better understand why students’ self-directed learning behavior can effectively promote deep learning. When students exhibit strong autonomy in their learning, it is often accompanied by strong intrinsic motivation, leading them to more actively engage in the learning process, explore knowledge, and deepen their understanding and application of knowledge. At the same time, the satisfaction of competence increases students’ sense of achievement during the learning process, enhancing their willingness to continue exploring and solving complex problems. The fulfillment of relatedness needs strengthens the connection and interaction between students and their teachers and peers, creating a supportive social environment for deep learning.

Metacognitive theory

Metacognitive theory focuses on individuals’ cognition, monitoring, and regulation of their learning processes (Flavell, J. H., 1976). It comprises three main components: metacognitive knowledge, metacognitive monitoring, and metacognitive experiences (Li Hongyu & Yin Hongxin, 2004). Metacognitive abilities enable learners to effectively plan, monitor, and evaluate their cognitive processes, thus better controlling the learning process. This study analyzes how autonomous learning activities promote the development of students’ metacognitive abilities, thereby supporting deep learning, under the guidance of metacognitive theory. By exploring how autonomous learning strategies (such as goal setting, self-monitoring, reflection, etc.) help students enhance their cognition and regulation of their learning processes, the research reveals the specific impact of these strategies on facilitating deep learning.

Self-Determination Theory (SDT) and Metacognitive Theory jointly provide strong theoretical support for this study. SDT analyzes the impact of psychological need satisfaction on the motivation for deep learning, while Metacognitive Theory explores how self-monitoring and regulation of the learning process promote deep learning abilities. Together, they offer theoretical and methodological guidance for examining the facilitative role of self-directed learning approaches in the deep learning process.

Sample acquisition

To effectively obtain the target literature for this study, a precise title search was conducted in the Web of Science database using keywords such as “Self-Directed Learning,” “Self-Regulated Learning,” “Independent Learning,” “Deep/Deeper Learning,” “Deep Approach,” “Deep Processing,” “Deep Strategies,” and “Deep Learner.” The literature search was limited to publications between January 1, 2020, and March 8, 2024, with the final search conducted on March 19, 2024, yielding a total of 265,092 articles (Deep Learning n=253,980, Self-Directed Learning n=2,112). The Web of Science database was chosen for this literature review because it is a renowned academic resource database that includes high-quality, peer-reviewed journal articles, conference papers, and more from various fields. It is widely recognized as an essential academic resource for the sciences, social sciences, arts, and humanities.

Sample selection criteria

To ensure the accuracy and reliability of the literature analysis results and to precisely present the interrelationship between Self-Directed Learning and Deep Learning, as well as the research situation regarding the facilitative role of Self-Directed Learning on Deep Learning, based on the research questions, this study established literature inclusion/exclusion criteria for the initially retrieved 265,092 documents as shown in Table 1. The first six criteria in Table 1 are common standards for selecting literature in systematic reviews, the seventh criterion restricts the research scope to higher education, the eighth criterion specifies the quality of the literature, excluding documents lacking a research question, rigorous research process, and clear research methods; the ninth criterion aims to focus the literature’s research topic on the study of Self-Directed Learning and Deep Learning, excluding research literature whose focus does not align.

Literature screening process

This study follows the methodology of a systematic literature review and meta-analysis, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for literature selection. We can visit the link (https://doi.org/10.6084/m9.figshare.26054176) for a complete list of reports. The screening process for the initial retrieval of 265,092 articles is as follows:

This left 1,978 articles (SDL n=469; Deep Learning n=1,509) for abstract and full-text review. Subsequently, a secondary search was conducted using a snowballing technique, resulting in 87 additional relevant papers (SDL n=38; Deep Learning n=49). After full-text review, 952 papers were excluded for being unrelated, non-educational, or non-empirical (SDL n=462; Deep Learning n=490).

The remaining 113 papers (SDL n=45; Deep Learning n=68) were then scrutinized for research questions, methodology, and conclusions, leading to the exclusion of 74 papers with unclear research questions, methodologies, or conclusions (SDL n=31; Deep Learning n=43). Ultimately, 39 papers were included in the final analysis (SDL n=14; Deep Learning n=25). The specific screening process is illustrated in Figures 1 and 2 (The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram can also be viewed via the link: https://doi.org/10.6084/m9.figshare.26114413 (Rui, 2024c), with the number of included papers detailed in Table 2.

Figure 1. The PRISMA flowchart for Self-directed learning Figure.

Figure 2. The PRISMA flowchart for Deep learning.

Our research team consists of three members. The article collection process was conducted by one member, and the collected articles were independently evaluated for quality by two experts in the team using the Joanna Briggs Institute (JBI) checklist. Based on the review results, all reviewers unanimously agreed that 39 articles met the minimum quality standards required for this study, and any discrepancies in evaluation judgments were resolved.

The data items included the article title, authors, publication year, host country, abstract, research topics, research methods (qualitative, quantitative, mixed methods, etc.), participant characteristics (age, educational background, etc.), study outcomes, sample size, research tools, research findings, contributions to deep learning research, contributions to self-directed learning research, student learning experiences, learning strategies, and deep learning outcomes. Through a thorough reading of the articles, the researchers manually organized the aforementioned information and summarized the data from the 39 articles into a series of tables. After a series of meetings and discussions, the three authors revised and refined the table data to minimize potential data bias in the research process.

Data analysis process

The data analysis process of this study went through three stages: key information summary, dimensional analysis, and content analysis.

The first phase involved summarizing the key information of the literature. The method of the content summary was utilized to distill the main content, research themes, findings, and contributions of the literature, allowing for a quick understanding of the core content and research focus, thereby laying the foundation for subsequent in-depth analysis. During this stage, the following tables were formed: Tables 3, 4, 5, 6, 7, 8. The exact details of the table can be found in the extended data (Rui, 2024b).

The second phase involved a dimensional analysis of the literature. Using thematic analysis, key concepts and themes were extracted and identified from the literature to explore what kind of relationship exists between SDL and deep learning, how SDL facilitates deep learning, and what are the key factors and conditions in SDL that promote deep learning. A deeper understanding of the interaction and influence mechanisms between SDL and deep learning was achieved through the categorization and comparison of key concepts and themes appearing in the literature. During this stage, the following tables were formed: Tables 9, 10. The exact details of the table can be found in the extended data (Rui, 2024b).

The third phase is content analysis. During the content analysis phase, the focus is on exploring how students experience and achieve deep learning within Self-Directed Learning. The analysis at this stage aims to deeply understand students’ learning experiences, the learning strategies they adopt, and how these strategies facilitate the achievement of deep learning. Content analysis, through a detailed examination of case studies, empirical research findings, and theoretical discussions in the literature, distills key experiences and processes that contribute to deep learning. During this stage, the following tables were formed: Tables 11, 12. The exact details of the table can be found in the Figshare system. We can access the link (https://doi.org/10.6084/m9.figshare.26012824) to get the analyze process data.

What is the interrelationship between Self-Directed Learning and Deep Learning?

Key Processes in Self-Directed Learning Strategies and Environments that Facilitate Deep Learning

Self-directed learning emphasizes the learner’s active participation and self-management abilities, which directly support key processes in deep learning, such as active exploration, critical thinking, and the ability to integrate new knowledge with existing knowledge (Long, H. B., 2000). For example, in online learning environments, students facilitate deep understanding and application of knowledge by autonomously selecting learning resources, setting their learning objectives, and managing their time (Sun et al., 2023). Research indicates that an individual’s interest in learning topics and the tendency to pursue personal interests through learning are key drivers for engaging students in self-directed learning (Lai et al., 2024). Interest and intrinsic motivation, as integral components of self-directed learning, are equally crucial to the occurrence of deep learning. Interest and intrinsic motivation can stimulate students’ curiosity and desire to explore, thereby facilitating the learning of complex concepts (Litzinger et al., 2005). Additionally, reflective learning activities within a self-directed learning environment, such as learning logs and self-assessment, can encourage students to deeply reflect on their learning processes and outcomes (Stephen & Rockinson-Szapkiw, 2021). This reflection not only helps students identify and correct misconceptions in their learning but also promotes a deep understanding of knowledge and the formation of long-term memory. Research indicates that environments supporting self-regulated learning behaviors are particularly important for deep learning (Evenhouse et al., 2023). For example, the blended model of autonomous learning and teacher-guided learning provided by blended learning environments, as well as the features of online course platforms that support students’ autonomous learning, are effective conditions for facilitating deep learning (Onah et al., 2020). During the self-directed learning process, the effective use of technology, such as online resources and Learning Management Systems (LMS), enhances the flexibility and convenience of accessing information, supports students in deeply exploring knowledge based on personal interests and needs, and further facilitates the occurrence of deep learning (Fanshawe & Barton, 2023).

Deep Learning, in Turn, Promotes the Practice and Development of SDL

The deep understanding and application of knowledge within the deep learning process can ignite learners’ curiosity and thirst for knowledge, thereby enhancing their interest in exploring new knowledge. This sustained learning motivation is a key factor for the success of SDL (Hsieh & Maritz, 2023). Deep learning aids learners in building a more solid and systematic knowledge structure by fostering critical thinking, problem-solving abilities, and the application of knowledge (Weng et al., 2023). This efficient learning process can enhance an individual’s self-directed learning capabilities, enabling learners to more effectively plan, monitor, and adjust their learning pathways (Ma, 2022). Reflective and evaluative activities in the deep learning process promote a profound understanding of one’s learning process, including recognizing one’s learning style, strengths, and areas for improvement (Nhat & Le, 2023). This self-reflection and evaluation capability is a core component of SDL, essential for learners to autonomously adjust their learning strategies (Fanshawe & Barton, 2023). Through engaging in deep learning activities, learners can more flexibly adapt to complex and uncertain learning situations, selecting appropriate learning resources and strategies. This adaptability and flexibility are necessary conditions for implementing effective SDL (Evenhouse et al., 2023; Onah et al., 2020; Stephen & Rockinson-Szapkiw, 2021).

In summary, deep learning is not only a result of SDL but also provides strong support for the implementation of SDL. Through deep learning, learners can cultivate a more proactive, reflective, and collaborative learning attitude, thereby further strengthening and optimizing the process of self-directed learning.

The potential connection between Self-Directed Learning (SDL) and Deep Learning

The reciprocal relationship between Self-Directed Learning (SDL) and Deep Learning indeed forms the core of their interaction, but there are also other potential connections between the two. These connections are not limited to direct mutual promotion but also include the wider educational context and the impact on personal development levels.

Common promoters of cognitive development: Both SDL and deep learning are committed to advancing learners’ cognitive development, especially in promoting higher-order thinking skills such as critical thinking, analysis, and evaluation. The development of these skills aids learners in thinking and making decisions more independently and effectively when faced with diverse and complex problems (Altinpulluk et al., 2023; Hsieh & Maritz, 2023; Nhat & Le, 2023; Saqr, Matcha, Jovanovic, et al., 2023).

The foundation of lifelong learning: Existing research has demonstrated that both SDL and deep learning are key components of lifelong learning. In today’s era, where knowledge is updated at an increasingly rapid pace, individuals must continually learn new knowledge and skills to adapt to changes in the times. SDL emphasizes the autonomy and initiative of learning, while deep learning values the quality and durability of learning. Combined, they provide a solid foundation for lifelong learning (Alhammadi, 2021; Geitz et al., 2023; Salleh et al., 2019; Saqr, Matcha, Jovanovic, et al., 2023).

The interplay of affect and motivation: The sense of achievement and sustained interest in exploring learning content generated during the deep learning process can significantly enhance learners’ intrinsic motivation. Intrinsic motivation not only drives learners to engage in deeper learning processes, but it is also crucial for the sustainability and effectiveness of SDL. Additionally, the enhancement of self-efficacy in SDL, in turn, strengthens learners’ motivation to engage in deep learning (Altinpulluk et al., 2023; Hsieh & Maritz, 2023; Lai et al., 2024; Nhat & Le, 2023), which not only improves the effectiveness of learning but also increases its sustainability.

What factors or conditions best facilitate the occurrence of deep learning during self-directed learning?

To address this issue, the study first conducted a thematic analysis of the literature, identifying key concepts and themes from the documents, and summarizing the essential information in Table 9. Specific information for Table 9 can be found in the attachment. Based on the information in the table, the study further merged related themes, reducing redundant information while improving conceptual clarity to more effectively identify key factors. At this stage, Table 10 was generated: Analysis of Analysis table of key factors for the self-directed learning environment to promote deep learning. Table 10 displays the key factors that promote deep learning in the process of self-directed learning, including two main categories: internal factors and external conditions. Internal factors include self-regulation and self-directed learning abilities, interest and motivation, metacognitive skills, as well as reflection and evaluation. External conditions involve the effective use of technology, interactive learning environments, innovative teaching models, and situational engagement and gamification. Each key factor will be analyzed next.

Internal factors

Self-regulation and self-directed learning abilities: Self-regulation and self-directed learning abilities are widely regarded as key elements for deep learning, capable of effectively facilitating the occurrence of deep learning among students. For example, the paper by Stephen & Rockinson-Szapkiw (2021) enhances students’ self-regulation and self-direction abilities through the design and implementation of a first-semester seminar course, particularly emphasizing the role of reflective activities (such as learning logs). Through continuous participation and reflection on the learning process, it promotes a deep understanding and application of knowledge. Similarly, the paper by Onah et al. (2020), by embedding MOOC platforms, promotes autonomous learning and self-regulated learning skills among undergraduate students, demonstrating how self-directed learning can facilitate deep learning within the context of integrating traditional and modern educational backgrounds.

Interest and motivation: Individual interest and intrinsic motivation, as significant factors driving deep learning, have a positive effect on students’ deep learning. For example, (Lai et al., 2024) explored how individual interest and self-regulation interact and affect the process of autonomously using technology for language learning, highlighting the critical role of individual interest and intrinsic motivation in promoting deep learning.

Metacognitive skills: Students’ metacognitive awareness is considered a key factor for deep learning. Metacognitive abilities, including metacognitive monitoring and regulation, enable students to monitor and reflect on their learning processes, identify learning obstacles, and effectively adjust their learning strategies (Moonen-Van Loon et al., 2022). Onah et al. (2020) facilitated students’ autonomous learning and self-regulated learning skills through the use of blended learning environments and MOOC platforms, reflecting the role of metacognitive skills in planning, executing, and evaluating learning strategies. Hua & Wang, (2024) research on the use of Learning Management Systems (LMS) supported students in self-monitoring during the learning process and allowed students to assess their learning progress, emphasizing the importance of metacognitive skills in enhancing deep learning and autonomous learning abilities.

Reflection and evaluation: Reflective learning activities such as learning logs and self-assessment play a very important role in deep learning. These methods help students reflect on their learning processes and the learning strategies applied, constituting a key component of deep learning (Stephen & Rockinson-Szapkiw, 2021). Students developed self-regulation and self-directed abilities in the process of completing reflective activities in learning logs, promoting a deep understanding and application of knowledge (Fanshawe & Barton, 2023). Research utilizing text mining technology to assist with feedback interpretation helped students self-assess and formulate new learning goals and strategies, emphasizing the role of reflection and evaluation in deep learning.

External factors

Effective use of technology: Research has demonstrated that technology plays a positive partial mediating role between autonomous learning and lifelong learning (Salleh et al., 2019). Interaction, sharing, and collaborative activities conducted through social networks can facilitate communication among students, knowledge sharing, and deep understanding (Salleh et al., 2019). In the current educational context, technological proficiency and the effective use of online resources are considered key to promoting deep learning. (Lai et al., 2024) The interaction between individual interest and self-regulation in the process of using technology for language learning demonstrates how technology supports personalized and deep learning. Autonomous informal learning using online resources highlights the role of technology in providing a learning environment with choices and freedom. (Salleh et al., 2019) the study proves that the effective use of technology can promote students’ deep learning.

Interactive learning environments: Research has shown that diverse and interactive learning environments (such as flipped teaching) can effectively increase student engagement and motivation for learning. Students’ perceptions of the learning environment, including the accessibility of resources, the supportiveness of the platform, and interaction with teachers and peers, are crucial to deep learning (Geitz et al., 2023; Thompson & Lake, 2023; Evenhouse et al., 2023). The blended learning environment provides students with diverse learning resources and supports self-regulated learning behaviors, offering the necessary conditions and opportunities for deep learning to occur, and enabling learners to engage in in-depth study. The introduction of interactive learning environments strengthens this process by promoting deeper levels of thinking, understanding, and application, thus more effectively facilitating the occurrence of deep learning (Salleh et al., 2019).

Innovative teaching models: Innovative teaching encompasses the adoption of innovative instructional designs and curricular structures, such as Project-Based Learning (PBL) and problem-solving, to provide authentic and challenging learning experiences. These methods encourage students to actively explore and apply knowledge, fostering the development of critical thinking and innovative capabilities. The study by Tuononen et al., (2023) highlights the effectiveness of innovative teaching in promoting deep learning through case-based instruction that provides authentic learning contexts, encourages active exploration and application of knowledge, develops higher-order thinking skills, and facilitates the internalization and transfer of knowledge. The study by Geitz et al., (2023) discusses how Design-Based Education (DBE) in foundational education, by providing a learning environment aligned with vocational fields and emphasizing the integration of practice and theory, enables students to engage in deep learning while solving real-world problems and developing key 21st-century skills.

Situational engagement and gamification: Contextual engagement and gamification effectively enhance the enjoyment and interactivity of learning, significantly increasing students’ emotional and behavioral engagement. Through the enhancement of student participation, they promote deeper learning and improved learning outcomes. These strategies, by creating engaging learning environments and practical opportunities, enhance students’ motivation to learn and sense of participation, thereby facilitating deep learning and the effective application of knowledge. Additionally, research by Salleh et al., (2019) demonstrates that informal autonomous learning utilizing online resources emphasizes that an individual’s interest in the learning topic is the primary driver of deep learning and emotional engagement. Research by Altinpulluk et al., (2023) has demonstrated that there is a positive relationship between self-directed learning and levels of intrinsic motivation within MOOC environments. The study also highlights the role of contextual engagement and gamification strategies in enhancing motivation and deep learning.

In the process of self-directed learning, these internal factors and external conditions work together through various mechanisms to facilitate the occurrence of deep learning, such as enhancing learning motivation, providing abundant learning resources, promoting communication and cooperation among students, and developing critical thinking and problem-solving skills. Taking these factors into account, it is possible to create a rich and diverse learning environment that is conducive to deep learning for students.

How do students experience and achieve deep learning within the process of self-directed learning?

Before answering how students experience and achieve deep learning within the process of self-directed learning, a content analysis of the literature is first conducted to summarize and categorize specific experiences of students during self-directed learning and how these experiences facilitate the achievement of deep learning. This includes the deep learning strategies adopted, and how specific learning experiences promote a deep understanding and application of knowledge. Detailed content is shown in Table 11: Table of Students’ Self-Directed Learning Experiences and Strategies for Achieving Deep Learning. Further content analysis of the literature is then conducted to delve deeper into the outcomes of deep learning achieved through the strategies or activities implemented during the process of self-directed learning. This analysis explores how learning strategies or activities specifically impact students’ outcomes in deep learning, including changes at the cognitive, emotional, and behavioral levels. Detailed content is shown in Table 12: Table of Achievements of Deep Learning in the Process of Students’ Self-Directed Learning.

Through thematic analysis of the content of the tables, it was discovered that students achieve deep learning during the process of self-directed learning through a series of strategies and activities. These can be summarized as reflection and self-regulation, enhancement of intrinsic and extrinsic motivation, utilization of technology and social media, Problem-Based Learning (PBL), and interdisciplinary collaboration, as well as the cultivation of metacognitive awareness.

Reflection and self-regulation

Reflection and self-regulation are key mechanisms for achieving deep learning in the process of self-directed learning. By writing learning logs and engaging in reflective writing activities, students can not only examine and evaluate their learning approaches, understanding processes, and their effectiveness but also identify and overcome obstacles in learning. Reflection and self-regulation are key mechanisms for achieving deep learning in the process of self-directed learning. By writing learning logs and engaging in reflective writing activities, students can not only examine and evaluate their learning approaches, understanding processes, and effectiveness but also identify and overcome obstacles in learning. Continuous self-monitoring and adjustment in learning not only directly affect the depth and efficiency of learning but also facilitate the development of students into independent and self-driven learners. Research by Stephen & Rockinson-Szapkiw (2021) indicates that engaging in high-impact educational practices, such as learning logs and reflective writing, can significantly enhance students’ self-regulation skills and promote a deeper understanding and application of knowledge. The study by Fanshawe & Barton (2023)found that supporting doctoral students’ self-directed learning through the use of a Learning Management System (LMS) revealed that regular reflection and self-monitoring activities help deepen students’ understanding of academic research and can assist in enhancing their academic writing and research skills. Current research underscores the importance of self-regulated learning abilities for deep learning. Through the integrated use of reflective learning activities such as learning logs and reflective writing, along with the cultivation of self-regulated learning abilities, students can achieve a deeper understanding and application of knowledge in the process of self-directed learning. The development of these strategies and skills is beneficial not only for current learning tasks but also lays a solid foundation for students’ lifelong learning.

Enhancement of intrinsic and extrinsic motivation

In the process of self-directed learning, the enhancement of both intrinsic and extrinsic learning motivation is crucial for promoting deep learning. The enhancement of learning motivation encourages students to participate more actively in learning and to explore knowledge more proactively, thus achieving a deeper understanding and application of knowledge. The study by Hsieh & Maritz (2023) detailed how flipped classroom instruction enhances students’ active participation in class, thereby improving their intrinsic motivation and self-directed learning abilities. Additionally, this model supports students in adjusting their learning pace according to their speed, aiding in the development of their self-regulated learning capabilities and lifelong learning habits. The study by Aguiar-Castillo et al. (2021) shows that by integrating gamification elements into the learning process in higher education, students are encouraged to explore the unknown. By solving challenges and puzzles within games, students’ curiosity and desire to explore are sparked, promoting active learning. In the process of exploration, students not only acquire new knowledge but also learn how to learn, cultivating critical thinking abilities and the development of higher-order skills.

Utilization of technology and social media

The utilization of social media platforms and online learning resources provides students with opportunities for interaction and collaboration. These platforms enable students to access a wide variety of learning materials. Digital learning methods not only greatly expand students’ knowledge and perspectives but also, through participation in online discussion groups and collaborative learning activities, allow students to share viewpoints, exchange ideas, and critically examine issues from different angles. Gradually, students develop the ability to deeply understand complex concepts and skills. Furthermore, this learning model also stimulates students’ critical thinking skills, enabling them to effectively discern and select when confronted with information. The study by Salleh et al. (2019) explores how social networking sites act as tools for self-directed learning, facilitating both individual and collective learning. It enhances learners’ control over their learning processes while also increasing the interactivity and cooperativeness of learning. The paper by Alhammadi (2021) analyzes how, during the COVID-19 pandemic, the quality of learning was maintained and enhanced through the use of online learning tools and platforms, especially social media. In a remote learning environment, the effective utilization of technology and social media plays a crucial role in maintaining student engagement, facilitating communication and collaboration, and supporting deep learning.

Problem-Based Learning (PBL) and Interdisciplinary collaboration

Students, by applying and integrating interdisciplinary knowledge in multidisciplinary team collaborations to solve real-world problems, not only enhance their problem-solving and analytical abilities but also deepen their understanding of knowledge. Through the practical application of knowledge to solve specific problems, students can learn and understand disciplinary knowledge more deeply, thereby achieving deep learning. The study by Geitz et al. (2023) emphasizes the importance of Problem-Based Learning (PBL) and multidisciplinary collaboration in Design-Based Education (DBE) environments. DBE environments, by integrating real-world problems and multidisciplinary knowledge, foster deep learning among students, showing significant advantages, particularly in promoting students’ innovative and critical thinking. The study by Saqr, Matcha, Jovanovic, et al. (2023) indicates that effective learning strategies, especially when applied in PBL environments, are crucial for promoting deep learning. By transferring and applying these strategies across different learning contexts, students are better able to adapt to PBL environments, facilitating the integration and application of interdisciplinary knowledge.

Cultivation of metacognitive awareness

Through metacognitive activities, students learn how to effectively plan their learning processes, including setting clear learning objectives and selecting appropriate learning resources, monitoring their learning progress, and evaluating the effectiveness of their learning outcomes and strategies, thereby managing and guiding their learning more effectively. The cultivation of metacognitive awareness is a core component of deep learning because it involves students’ cognition and regulation of their learning process, including setting goals, choosing strategies, monitoring progress, and reflecting on learning outcomes. Through metacognitive activities, students are not only able to identify and utilize effective learning strategies but can also adjust their learning methods when faced with challenges, ensuring the achievement of learning objectives. The study by Tuononen et al. (2023) explores the role of metacognitive awareness in promoting deep learning. The research found that by cultivating students’ metacognitive awareness, their learning outcomes can be significantly improved, as students learn how to effectively plan, monitor, and regulate their learning processes. The study by van der Graaf et al. (2022) emphasizes the importance of metacognitive activities in self-regulated learning. By implementing cognitive and metacognitive activities to monitor the learning process, students can better achieve their learning objectives, demonstrating how metacognitive activities support the key processes of deep learning. Therefore, the implementation of cognitive and metacognitive activities not only helps students effectively monitor and regulate their learning process but is also a key strategy in driving them toward achieving deep learning objectives, significantly enhancing the quality and effectiveness of learning.

Educational practice recommendations

Teaching strategies and methods

Reinforce reflection and self-regulation mechanisms: Incorporating regular reflective and self-assessment activities into course design, such as learning logs, project reflection reports, and self-assessment questionnaires, can help students engage in more effective self-regulation and reflection during self-directed learning, thereby promoting a deep understanding and application of knowledge.

Enhance students’ intrinsic and extrinsic motivation: Teachers can adopt various teaching strategies based on the characteristics of the teaching content to motivate students’ extrinsic motivation while ensuring that learning activities are connected to students’ personal interests and career goals to enhance intrinsic motivation. The enhancement of motivation helps increase students’ enthusiasm and curiosity for learning, which is crucial for deep learning.

Promote Problem-Based Learning (PBL) and interdisciplinary collaboration: Teachers can design PBL projects centered around real-world challenges, encouraging students to engage in interdisciplinary collaboration and autonomously seek solutions, providing resources and support when necessary, to cultivate students’ self-directed learning awareness and deep learning abilities.

Cultivate metacognitive awareness: Teachers can instruct students on how to effectively plan, execute, monitor, and adjust their learning through relevant courses and activities. Students are encouraged to apply these skills to enhance the effectiveness of self-directed learning and deep learning.

Adopt innovative teaching and assessment methods: Develop and implement innovative teaching methods and assessment strategies, such as project-based learning, contextual learning, and flipped classrooms, to promote students’ deep learning. The design of assessment methods should consider students’ self-directed learning experiences and deep learning outcomes, ensuring that assessment methods accurately reflect students’ learning progress and achievements.

Learning Environment and Support

Effectively utilize technology and social media: Actively integrate technological tools and social media platforms to support the autonomy, collaboration, and interactivity of learning. Provide training and guidance to help students effectively use technological tools for information retrieval, knowledge sharing, and collaborative learning.

Create supportive and interactive learning environments: Build an open, supportive, and interactive learning environment that encourages effective communication and collaboration among students and between students and teachers. Utilize online discussion boards, peer reviews, and group projects to foster this interaction.

By implementing the above recommendations, educators can more effectively support students in achieving deep learning during the process of self-directed learning, promote students’ personal and professional development, and lay a solid foundation for students’ lifelong learning journey.

Ethics and consent

Ethical approval and consent were not required.