Meta-Learning: A Nine-Layer Model Based on Metacognition and Smart Technologies

Abstract

The international organizations of education have already pointed out that the way students learn, what they learn, and the skills needed, will be radically transformed in the coming years. Smart technologies are ready to come into play, changing the conditions of learning, providing opportunities for transformative learning experiences, and promising more conscious, self-directed and self-motivated learning. Meta-learning refers to a set of mental meta-processes by which learners consciously create and manage personal models of learning. Meta-learning entails a cluster of meta-skills that are progressively and hierarchically transformed, ensuring the transition to the highest levels of understanding termed meta-comprehension. The current article aims to investigate the concept of meta-learning and describe the meta-levels of learning through the lens of metacognition. In addition, the potential of smart technologies to provide fertile ground for the implementation of meta-learning training strategies is examined. The results of this article provide a new meta-learning theoretical framework supported by smart devices capable of supporting future meta-learners or, more accurately, meta-thinkers, to transcend the usual states of knowing and move to the next meta-levels of human intelligence.

1. Introduction

According to Aristotle, the desire to learn is a natural tendency in human beings. However, students often complain that the way they learn does not sufficiently motivate them to take control of learning [1]. At the same time, international organizations of education give warnings that learners, to a significant extent, seem to be unprepared to deal with the challenges that the future brings, as they lack the meta-skills needed to become the changemakers of future society [2].

Modern societies are on the eve of world-historical events that will inevitably lead to a transformation in learning [3,4]. What we learn, how we learn, and the skills we need to succeed, will be an issue of radical change and extensive debate [5]. Artificial intelligence (AI) has broad implications for education and training institutions, aiming to empower innovative learning and equip lifelong learners with the skills needed to lead both work, and society [3,4]. Smart technologies refer to artifacts that demonstrate some properties of ubiquitous computing, including artificial intelligence, the internet of things, and wearable technologies in the form of an accessory such as glasses, a backpack, or even clothes. Smart technologies provide learners with the unique opportunity of free access to knowledge, from everywhere, at any time. Recent studies recognize that smart technologies provide several opportunities for the design of smart and innovative learning environments with the potential to support self-directed and self-regulated learning [6,7].

Future education will provide learners with limitless access to information. On the one hand, this will create unique opportunities, as learning will become self-directed, adapted to a student’s pace of learning, and their needs and goals. On the other hand, in a world inundated with data, major challenges arise, demonstrating an urgent need for learners to be equipped with skills and strategies that will allow them to manage big data more critically, maximizing learning outcomes [8].

In this context, pedagogy and learning theories acquire a special role in supporting future learning, especially with the involvement of smart technologies. However, less attention has been given to the development of smart pedagogies and theories which satisfy the requirements of future learning within smart-learning environments [8].

The concept of metacognition was first used by Flavell to describe learners’ awareness of their cognition and cognitive functions [9]. Metacognition involves reflective skills as well as the ability to self-regulate mental processes [10]. Nelson and Narens’ metacognitive theoretical framework first introduced the idea of meta-levels, describing the interaction of object-level and meta-level in self-monitoring and self-control processes [11]. Later studies have also attempted to further analyze the layered approach of metacognition [12]. In recent years, the concept of metacognition has received new emphasis because of the urgent need to prepare self-regulated learners capable of using digital technologies as self-learning tools [13]. As a result, an intense research interest in creating learning environments that support autonomous, self-directed, and self-regulated learning has arisen.

Given the rapid technological and social changes that will decisively affect the face of learning in the coming years, giving learning a metacognitive character, we aim to present a nine-layered pyramid model of meta-learning based on metacognition and smart technologies. The hierarchy implies that learning occurs progressively as humans ascend step by step to the meta-levels of self-learning. The meta-learning model is based on the principles of metacognition, including other building elements such as emotional intelligence, spiritual intelligence, mindfulness, higher mental abilities, meta-motivations, and consciousness. The nine-layered pyramid model of meta-learning supported by smart technologies aims to contribute to the discussion regarding the role of learning theories in the digital era. It also highlights the decisive role of metacognition in training learners’ ability to take control of their learning with the aid of smart learning technologies.

The proposed 9-layered meta-learning model brings together several special characteristics. It is a metacognitive learning taxonomy, as each meta-level of learning mostly depends on metacognitive processes and skills that progressively evolve. This model also differs because it recognizes parameters such as emotional intelligence, spirituality, mindfulness, consciousness and sub-consciousness parameters (i.e., conscious and unconscious processing) that affect learning. In addition, it is constructed to be applied not only in conventional learning environments. On the contrary, the model is structured in a way that permits blending with smart devices intending to support future smart meta-learning environments.

2. Materials and Methods

This article aims to renew the debate about the theory of learning in the digital era, introducing the concept of meta-learning. Specifically, we present a nine-layered pyramid model of meta-learning based on the principles of metacognition and smart technologies. In addition, we present a brief review of representative research concerning the role of smart technologies in training meta-learning skills.

The first section is devoted to the theoretical background that paved the way for establishing the idea of the meta-learning framework. We started by looking for well-recognized learning theories, models and taxonomies to shed light on existing approaches regarding the nature of learning, the role of learners, and how metacognition is involved in learning procedures. In addition, we summarized recent neuroscientific research regarding the neurophysiological processes that demonstrate how the human brain learns.

In the second section, we initially clarify the building elements of the nine-layered meta-learning model, emphasizing the role of metacognition. In the second subsection, we describe step-by-step the meta-levels of learning. For this objective, we investigated evidence-based research regarding the meta-cognitive procedures and meta-skills required at each level, taking into consideration the role of technology.

The last part of this study concerns the potential of smart technologies in supporting meta-learning training. We investigated the effectiveness of smart technologies, including artificial intelligence, with a special focus on intelligent agents and learning assistants, smart wearable technology, smart mobile learning technologies, virtual immersive technologies, and robotics.

3. Theoretical Background

3.1. Learning Theories and Taxonomies

Learning definitions vary widely across fields, owing partly to the many methodologies employed to measure its occurrence. Behaviorism defines learning as the acquisition of new behaviors. According to the cognitive learning theory, learning can be defined as a set of internal mental processes by which humans receive, decode, store and retrieve information. Constructivism approaches learning as a search for meaning and understanding [14].

Bloom et al.’s taxonomy [15] is a combination of three hierarchical models by which the learning objectives are classified according to their complexity and specificity. The taxonomy recognizes three interrelated and overlapping learning domains, namely the cognitive, affective and psychomotor domains. In the cognitive domain, for instance, the model proposed that learning takes place in the following six stages: (a) knowledge, (b) comprehension, (c) application, (d) analysis (e) synthesis and (f) evaluation.

Anderson and Krathwohl (2001) [16] proposed a revised two-dimensional model based on Bloom’s taxonomy. One distinction between this approach and Bloom’s taxonomy was that creativity was then at the top of the learning pyramid. The second dimension of the model concerned the knowledge to be learned, which included four categories (factual, conceptual, procedural and metacognitive knowledge), giving special emphasis to metacognition.

Marzano et al. [17] described a two-dimensional hierarchical learning taxonomy that consisted of six levels of information processing matched with three domains of knowledge. Each level indicated a specific type of thinking which required the development of various skills related to knowledge retrieval, comprehension, analysis, knowledge utilization, metacognition, and self-system. The second dimension was the knowledge dimension, which contained six types of thinking within the following three different domains termed information (facts), mental procedures, and psychomotor procedures [18].

Biggs et al. [19] developed a new, layered taxonomy based on the learning outcomes in terms of their complexity. The structure of the observed learning outcome, known as the SOLO taxonomy, consisted of the following five hierarchical stages of understanding: (a) pre-structural (b) uni-structural, (c) multi-structural, (d) relational and (e) extended abstract level.

Gagne [20] introduced a hierarchical learning taxonomy based on the degree of complexity of the mental processes involved. The model was structured in eight layers: (a) signal learning, (b) stimulus-response learning, (c) chaining, (d) verbal association, (e) discrimination learning, (f) concept learning, (g) rule learning and (h) problem-solving.

The theories and taxonomies have numerous similarities but also discrepancies. For instance, most theories represent learning as a continuum of discrete levels (i.e., Bloom, Gagne, Biggs and Marzano), assuming people gradually acquire new and more complex skills after systematic practice. Most theories also outline the special role of several cognitive functions such as attention and memory. What is worth pointing out is that the abovementioned theories either implicitly or explicitly recognize that learning occurs progressively and hierarchically, and that metacognition plays a significant role in successful learning. However, theories such as Gagne’s model aim to help the teacher to develop effective instructional strategies and apply assessment methods. The abovementioned theories put less emphasis on self-directed learning and recognize a quite limited role for metacognitive procedures.

3.2. How the Brain Learns: The Neuroscientific Approach

Recent neuroscientific research provides evidence that learning never stops, not even when sleeping [21]. On the contrary, it is a dynamic process of development and remodeling of the brain architecture through the growth of new neurons and stronger connections. In the learning brain, neurogenesis and neuroplasticity phenomena take place, especially when humans systematically apply “learning to learn” practices [22].

People learn more efficiently when learning is active and self-directed. New studies reveal that rhythmic or oscillatory activity in the brain has great potential for offering novel insights into self-directed learning [23]. Neurons interact via electrical signals known as brain waves. Brain waves seem to play a crucial role in explicit and implicit learning. Studies have also shown that learners who apply strategies to self-regulate brain activity, can better manipulate the mental abilities that boost learning. Studies have also revealed that certain brain oscillations predict specific learning outcomes [24,25]. For instance, alpha oscillations are associated with increased attention, creative thinking and better learning outcomes [26]. This knowledge is of great importance, provided that future learning technologies will use brain-computer interfaces and other technologies to help learners train new skills by measuring and providing feedback according to the data collected [27].

Numerous studies have demonstrated that human physiological processes play a crucial role in learning. Hormones are the chemical regulators of various physiological processes which have a major influence on cognitive and metacognitive functions as well as mood levels [28,29]. Hormones and neurotransmitters released during and after stressful situations are recognized as important modulators of human learning and memory capacity [30,31]. For instance, an extreme elevation of the stress hormones (i.e., cortisol) minimizes learners’ ability to take control of the learning procedures. In addition, abnormal alterations have been associated with learning difficulties [32,33]. Human physiology plays an important role in learners’ mood stability and positivity, which in turn accelerates the speed of learning and motivates students to learn [34,35,36].

International organizations that systematically work on education policies point out that the face of learning will radically change in the coming years. The way people learn, what they learn and the skills needed will be a matter of extensive research. For instance, the students will be expected to take a highly dynamic role, to be more independent and self-directed learners. It is not accidental that among the skills most needed for the 21st century will be metacognitive skills. Furthermore, learning will be a continuous process. Even adult workers as well as older people would be essential to learn how to acquire new skills as a requirement to survive, adapt in a changing world, succeed and improve well-being opportunities [2]. Furthermore, digital technologies (immersive technologies, brain-computer interfaces), and distance education will transform the face of learning from preschool to higher education, including vocational education [37]. At the same time, a growing number of neuroscientific studies have presented new evidence about the factors that may affect human cognition and provide education with a new perspective on how learning may occur [38,39]. In this context of constant change in various areas of human life, a new debate has already begun regarding the future of learning in the digital era, the new role of learners in future societies, the efficacy of existing learning approaches, and the urgent need for new proposals, new learning frameworks, and pedagogies [40].

4. A 9-Layered Meta-Learning Pyramid Model Based on Metacognition

4.1. The Building Elements of the Meta-Learning Model

Metacognition is defined as the set of regulatory meta-abilities and meta-skills that learners consciously apply to regulate cognitive and psychophysiological operations, resulting in optimal learning outcomes. Metacognition includes learners’ meta-ability to monitor, regulate and adapt their internal cognitive processes, identify the difference between functional and dysfunctional states of mind, and consciously select those states that awaken the full range of their learning potential. Metacognition shows the consciousness learners have about their abilities, skills, and strategies as well as the flexibility to strategically utilize their mental powers to achieve higher goals. Metacognition provides learners with the unique ability to have supervision over learning, to seek the reasons, to wonder about the meaning of knowledge, and search for self-understanding [12,41].

Metacognition is structured on eight distinct but complementary pillars (Figure 1) [12,41,42]:

• Metacognitive Knowledge: Learners’ ability to construct knowledge, particularly regarding their cognitive functions, making meta-representations of that knowledge. It also requires an understanding of cognition, its operations as well as its hierarchical organization. It also necessitates a deep understanding of the nature of acquiring meta-abilities and meta-skills that permit “learning to learn” operations.
• Applied metaknowledge: The individual should apply metacognitive knowledge and strategically employ their mental tools according to the degrees of freedom defined by a task, problem or situation. Applied meta-knowledge comes after experience, reflection, and systematic practice in real-world situations. It also implies consciousness over personal strengths and weaknesses. Finally, it entails the ability to transfer existing knowledge to novel contexts.
• Self-observation: Real-time monitoring of the external (exteroception) and internal processes (introspection) during learning. It is a kind of internal attention and control that lights up the sources of knowledge. Self-observation constitutes a higher meta-ability which develops progressively after systematic training.
• Self-regulation: The capacity to manage cognition, to repair any observable disturbance, which interrupts the proper functioning of the cognitive and psychophysiological functions that in turn affect learning.
• Adaptability: Individuals’ ability to modify their mental and emotional functioning as well as their learning behavior in line with personal goals and demands of learning.
• Recognition: Learners’ ability to recognize and be aware of their mental and emotional states and understand how they affect learning. It also implies the ability to recognize others’ states of mind as a means of learning from others.
• Discrimination: Filtering and strategically selecting what is vital or unnecessary in a learning situation in terms of information and knowledge, distinguishing the known from the unknown in each problem, and the helpful from unhelpful variables in learning.
• Mnemosyne: The state of awakening implies the voluntary maintenance of a state of relaxed awareness and readiness to achieve peak performance. It also symbolizes the internalized knowledge that motivates and drives learners toward independence, mindfulness and academic achievement.

Higher Mental/Emotional Abilities are considered an essential part of the proposed framework since learning is now recognized as a dynamic process that aims to cultivate learners’ talents, boost excellence, and prepare charismatic innovators [43]. Higher mental abilities engage cognitive functions such as attention, working memory, mental imagery, and attention [44]. Higher mental abilities are also associated with a set of complex thinking processes such as problem-solving, and analytical and critical thinking [45]. Many researchers identify higher cognitive abilities with executive functions that include a range of control and self-regulation skills such as the ability of inhibition and attentional regulation [46]. In the emotional domain, as a higher emotional ability, we consider the ability not only to perceive but most importantly to regulate emotions [47].

Emotional Intelligence plays a crucial role in the meta-learning model. Emotions indicate the readiness to learn. Self-management of emotions can determine to a large extent the learning outcomes [41,48]. For instance, positivity can boost creativity and innovation in learning, while negative emotions predict academic underachievement [49].

Spiritual Intelligence is an essential element of the meta-learning pyramid. Spirituality helps learners to avoid attachment to phenomena and drives them to seek meanings, reasons and true knowledge. Nowadays, humans have limitless access to knowledge. Spiritual intelligence can help learners to make moral decisions about the use of knowledge, to better deal with difficulties and to promote learning through the cultivation of humility, kindness and generosity. Such traits and skills are vital for the development of healthy citizens and societies [50,51,52,53].

Consciousness: Future education is called upon to prepare students to enter a conscious society where people can learn, think and react fast, accurately and wisely [12,54,55]. Learning also is consciousness-state dependent [12,41,56].

Sub-conscious learning: For years, learning revolved around training the conscious part of the mind. Recent studies, however, call this causal stance of conscious will into question, demonstrating that decisions and actions are initiated even though we are unconscious of the goals to be attained, or their motivating effect on our behavior [57,58]. For instance, stimuli that are presented beyond awareness (i.e., subliminal visual cues) can affect a person’s motivational states, which in turn have a significant impact on human learning behaviors [59]. Subliminal training interventions (i.e., masked stimuli, subliminal priming) expose subjects to visual or/and auditory stimuli under the threshold of conscious perception. Such training methods have shown improvements to cognitive operations, self-regulation skills, and academic achievement [60,61,62]. Other studies show that when subjects are in a state of reduced awareness, they are more able to learn and accept new knowledge [63]. Modern research shows that higher mental abilities such as creativity involve mechanisms that are not necessarily conscious. Sub-conscious mechanisms are also responsible for unlearning or relearning new skills and habits. Immersive technologies, intelligent tutoring systems, and digital games are considered promising tools for training future learners through the use of techniques that train subjects at a non-conscious level [64,65,66]. As a consequence, the subconscious is recognized as an important variable in the meta-learning framework supported by smart technologies [67,68,69,70,71,72].

Mindfulness: Being mindful leads learners to have greater control over learning procedures, to better deal with stressful events, and to avoid forming mindsets that unnecessarily limit their learning potential. Mindfulness is considered a form of metacognition as it trains self-awareness, emotional regulation, and attentional control. Contemplative practices in education cultivate positive learning habits, enhance self-regulation skills, increase perceptual awareness, and reduce self-bias. In addition, such practices support the neural networks of self-awareness [42,49,73,74,75,76,77].

4.2. The 9-Layered Model of Meta-Learning

4.2.1. Senses and Interest Stimulation

Interest and curiosity are considered the inherent driving forces that stimulate and feed the desire for knowing and learning. Interest drives attention toward stimuli, motivates goal setting, and determines learners’ readiness to start ascending the meta-levels of learning [78]. In the state of interest, cognitive and affective qualities intertwine to make learning less effortful, self-regulated, and enjoyable, while at the same time an enduring predisposition for re-engagement is progressively cultivated [79]. When learners’ interest is triggered, higher cognitive abilities such as attention and memory operate more smoothly, ensuring the maintenance and better processing of sensory stimuli [12,80]. The fourth industrial revolution requires continuous learning, upskilling, and reskilling, especially in the domains of science, technology, engineering, and mathematics (STEM). However, students’ low interest, with the focus on social groups such as women, people with disabilities and minorities, explains why future education should start by cultivating students’ enduring interest in cutting-edge knowledge and technologies [81]. Interest progressively develops. Thus, at this stage of learning, learners mostly depend on external support, until the time they would be stable and conscious of what their real interests are [78]. Thus, meta-learning environments should be appropriately designed to stimulate learners’ attention by providing activities characterized by novelty, surprise, and ambiguity. The connection of academic subjects with existing interests, the awakening of positive emotions, and immediate needs can also serve this goal. Problem-based learning, group work, and the personalization of content and context can be beneficial too [78,79].

4.2.2. Data Research and Collection

Searching is considered the doorway to self-directed learning [82]. The next step in the learning-to-learn pyramid concerns the transformation of interest and curiosity into data-seeking behaviors. Data can be defined as discrete and objective observations, facts, or symbols without meaning, for as long as they remain unprocessed and unstructured [83]. The research reveals that students are often less skilled in data-research skills, they face difficulties in making research designs, collecting and analyzing data, which inevitably leads to later academic underachievement [84]. In the era of big data, learners should be able to deal with a vast amount of digital and non-digital data as a means to lay the groundwork for learning, solving problems and making successful decisions [85]. Data-driven learning is usually utilized in foreign language learning to help students to learn from data, taking the role of the researcher in guided discovery tasks. Learning from authentic data promote self-regulated learning raises awareness skills and promotes learners’ agency, autonomy and motivation for learning [86]. Learning environments should provide opportunities for data searching with the support of digital technologies. Meta-learners should make surveys and online searches in databases, to use statistical methods and tools [85,87].

4.2.3. Information Organization and Interconnection

The challenge in the next meta-level of the learning hierarchy concerns the transformation of data into meaningful and purposeful information. The term information is usually defined in terms of data that have been processed to be understood, and be purposeful [83]. Learning at this meta-level involves a set of meta-strategic manipulations applied by learners to extract meanings from datasets in the form of information, organize information, and construct information clouds [12]. In cognitive psychology, the theory of information processing outlines the importance of mechanisms by which an individual’s brain records, stores, and retrieves information [88]. Meta-learners are required to develop various information processing meta-skills including association and organization skills to understand relations between elements and construct taxonomies [83,89]. Even more importantly, information manipulation requires meta-learners to be equipped with memory control meta-skills and meta-strategies [88,90]. In this context, learning should take place with the use of information-rich learning environments engaging hybrid methodologies with the blending of digital technologies with meta-learning strategies [90,91].

4.2.4. Knowledge Structuring and Creation

Experience, along with reflection and deeper information processing, triggers the start of mental procedures that contribute to knowledge creation [92]. Moving from information to knowledge, learners begin to identify patterns and rules between data and information, structuring, in this way, the first image of knowledge [12]. Learners with well-structured knowledge can understand their surroundings, make interconnections between new and existing knowledge, predict and deliberately make judgments during the learning procedure [93]. Thus, the meta-level of knowledge creation involves higher mental skills including analysis, and reflection upon and synthesis of multiple sources of information [83]. Memory once again plays a crucial role in conceptualizing knowledge, in other words, in understanding concepts, principles, theories, models and classifications [94]. In the 21st century, knowledge creation is considered more as a matter of community rather than as a personal challenge. Learners should be able to produce collective knowledge by working together on authentic problems, with real ideas in open workspaces. Knowledge-building environments with the aid of digital technologies could play a significant role in transforming schools into knowledge-creating organizations [95].

4.2.5. Specialization Development and Filtering Sets of Knowledge

Expertise is characterized by superior performance in a particular domain of knowledge, making learning even more independent, autonomous and self-regulated. In scientific research, for instance, it is associated with the ability to identify the boundaries of knowledge by determining for instance how theories contradict each other, and why some laws are not always applicable in solving complex problems and explaining phenomena. As experts, we can also characterize people with exceptional cognitive skills provided that expert skills, although automated, are to some extent under human control and constitute the outcome of systematic practice. Mnemonic skills resulting from well-trained memory control abilities can be characterized as a form of expertise. Speed learning, including fast reading, listening or calculation skills, is also considered an example of expertise [12,41,54,90,96]. Experts’ performance has been proven to be superior to novices because experts are more flexible in navigating smaller problem spaces and identifying the cases where knowledge is applicable or inapplicable under the constraints of a problem [96]. Filtering skills describe the meta-ability to be selective and determine the factors that guarantee achievement. Meta-learners discern differences between cognitive and emotional situations and choose the most helpful, positive, and supportive for their targets, success, fulfillment and personal development [41,97]. Faculty mentors, disciplinary networks and knowledge-restructuring techniques can significantly support specialization development [97].

4.2.6. Self-Actualized Learning

Optimal learning necessitates that meta-learners, in addition to the basic physiological needs, aim to satisfy a higher growth need known as self-actualization. Self-actualization can be described as the learners’ internal drive to expand their understanding and achieve their full potential in learning [98]. In the meta-learning pyramid, self-actualization represents those meta-abilities that maximize learning potential and denotes the transition from interest and curiosity to a love of learning and knowing [12,50,98,99]. Self-actualized meta-learners appreciate the goods of learning, seek deeper meaning in learning, and wonder about the ethical issues concerning the use of the acquired knowledge. Meta-learning is also characterized by a more accurate perception of reality because meta-learners abandon imposed knowledge, and mental obstacles (i.e., procrastination), or overlearned dysfunctional beliefs and, instead, use critical tools to achieve reasoning autonomy [12,50]. Self-actualization makes learning empathetic. By this, we mean that learners embrace the diversity of knowledge in different societies and cultures and use knowledge to make sustainable decisions that promote well-being [100]. Among the most important goals in meta-learning environments, either traditional or digital, will be the creation of learning experiences that promote self-actualization. Meta-learners should seek learning experiences that provide opportunities for perceiving knowledge from different perspectives with the aid of reflective technologies such as virtual reality and artificial intelligence. Smart technologies integrate data from various circumstances of life, so they can support learners to develop better awareness about the ways they can reach their full potential in learning [67,99].

4.2.7. Learning to Learn from the Unknown, Acceptance of Universal Laws

As learners, we often assume that we have all the potential knowledge, and we cultivate the subjective belief that this is true. At the same time, we often deny any alternative knowledge that contradicts that which is previously established [12,41]. However, the history of science has shown that the most significant developments usually come after a conflict between new and existing theoretical insights and usually appear to defy common sense. This occurred when Max Planck addressed a difficulty in the statistical properties of radiation, which led him to the development of quantum mechanics. It also happened when Einstein examined questions about observers traveling at the speed of light, which led to the theory of relativity [101].

4.2.8. Transcendence Learning

Learners become meta-learners, and more accurately meta-thinkers, the moment they can transcend the current state of knowledge and move to the next level of comprehension [12,41]. The crucial thing for learners is not to follow a path but to discover or develop a passion for learning that will constantly motivate them to bushwhack a path of their own, even through uncertain terrain. That will encourage meta-learners to transcend their limitations and stereotypes, use all the available mental capacity and find their path to holistic learning [102]. This only happens when learners find a transcendental purpose and hitch their wagon to something larger than themselves. Only then can they discover their true potential and realize the role that they will play in writing the next great chapter in future societies. At this meta-level, self-oriented motives for learning—such as the desire to have an interesting or enjoyable career—are not enough. On the contrary, learning requires dedication and a desire to follow a higher purpose. This is why transcendental learners such as scientists can work on a ‘boring’ task with unreduced interest and dedication [103].

4.2.9. Unification of Knowledge

Scientists frequently use the phrase “theory of everything” to denote the ultimate theory of the cosmos. As stated, a set of equations is enough to describe all phenomena that have been observed, or that will ever be observed. It is the current version of the ancient Greeks’ reductionist ideal, a viewpoint of the natural world that has been effective in bettering the condition of humanity and remains the central paradigm in modern science. [104]. A theory of everything, final theory, ultimate theory, unified field theory, or master theory is a hypothetical, unitary, all-encompassing, coherent theoretical framework of physics that fully explains and connects all parts of the world [101,104] (Figure 2).

5. Smart Meta-Learning Technologies

Artificial Intelligence: Artificial Intelligence is gaining an increasingly important role in meta-learning procedures. The customization as well as the personalization of learning content according to the learners’ needs, reinforce and gradually internalize learners’ motivations, enhance curiosity, and, in turn, help meta-learners to make fine-tuned efforts to achieve ever-higher goals. Most importantly, they provide feedback, which is a key element in reflection processes [54,105,106].

AI is directed towards experiential learning, which is the foundation of knowledge creation. In addition to enriching experiences, AI contributes to the uptake and retention of information, enabling memory control abilities to find fertile ground. AI can also support higher forms of learning, providing access to global knowledge [105,106].

AI can provide support to learners’ by recognizing their strengths and weaknesses during learning. As a result, AI learning environments are aligned with the principles of metacognition, training learners to deal with personal weaknesses as learning opportunities. Most important, AI helps learners to master existing skills and abilities [105,106,107].

Most importantly, AI eliminates the barriers that people with learning difficulties, physical impairments or various forms of discrimination (i.e., women) face, ensuring equal access to learning opportunities [105,108,109]. Thus, the concept of meta-learning with the contribution of AI embraces all social groups with the overriding objective of preparing meta-learners equipped with leadership skills and collective consciousness [109].

Intelligent Agents: Teachable agents constitute a learning technology based on the “learning by teaching” metacognitive strategy. Learners take the role of the teacher intending to teach their virtual agents new concepts [110]. Okita et al. [111] examined whether the use of teachable agents combined with recursive feedback could improve students’ meta-learning abilities. The results revealed that students showed greater abilities to use critical thinking in problem-solving tasks. Students also improved their understanding of their knowledge by observing, understanding and modifying their agent’s performance. In another study conducted by Matsuda et al. [112], students utilized an online learning environment for algebra equations intending to teach an agent how to solve problems, while the system provided students with metacognitive scaffolding on how to teach. It was found that students’ mastery in problem-solving was increased. Chin et al. [110] also found that teachable agents can help students develop hierarchical reasoning skills.

Chatbots/AI Learning Assistants: The Chatbot system is one of the most common AI technologies used to enhance learning activities. Chatbots are conversational or interactive agents that take the role of teacher, mentor or assistant and respond to users in real-time [113]. Working with conversational agents, learners are gradually taught to explain themselves, including the reasons for their positions, thus enhancing knowledge acquisition and retention. As virtual teaching assistants, they have been found to encourage self-reflection abilities through conversations and interactions, providing smart feedback according to learners’ strengths and weaknesses. Studies have also shown that chatbots, as counselors, recognize users’ psychological needs and provide constant support to lower learners’ stress, encouraging them to see failures as learning opportunities [114,115,116].

Smart Wearable Technologies: Wearable devices are electronic and mobile devices, or computers with a wireless communications functionality that are integrated into gadgets, accessories, or clothing, that may be worn on the human body, or even invasive versions such as microchips or smart tattoos. Wearables offer a variety of characteristics, such as biofeedback or other sensory physiological functions [117]. Smart wearable technologies have the potential to support learning by bringing sensor data and other physiological parameters into the play of the proposed meta-learning pedagogy [118,119].

Meta-learning requires learners’ awareness of their mental and emotional state. Smartwatches provide learners with appropriate feedback and support regarding their mental readiness and the ways to achieve a state of optimal learning. In this context, they assist the user in making optimal judgments and adopting self-regulating behaviors and adaptive strategies during the learning process. In addition, smartwatches play a crucial role in establishing positive habits and functional thinking, as well as positive behavioral patterns [120,121]. Smartwatches constitute training tools for improving higher mental abilities such as memory control abilities, understanding new knowledge, and applying this knowledge in new situations [121].

Smart headbands are devices that measure the user’s brain waves using electroencephalography sensors and incorporate the collected data in smartphones or virtual worlds to help learners be aware of their mental and emotional state, the levels of their attention and anxiety, and generally the readiness of being effectively engaged in learning procedures [122,123,124]. Brain reader wearables, combined with other technologies such as virtual reality, can provide learners with the opportunity to train attention but also to utilize attention as a meta-learning tool [125]. Amores et al. [125], using virtual reality and real-time brain activity sensing, helped subjects to voluntarily use their attention as a means to make changes in a 3D environment and control superpowers such as levitation and telekinesis.

Smart Mobile Learning Technologies: Mobile learning (M-learning) is recognized as an area of rapid development with predictions, placing it at the top of future learning technologies [91]. M-learning technologies aim to support user’s learning through the use of wireless internet and mobile devices, including mobile phones, personal digital assistants (PDAs), smartphones, and tablet PCs [126]. Smart mobile learning technologies provide a set of features (i.e., ubiquity, flexibility, portability) that encourages learners to become knowledge creators. Mobile devices provide learners with the unique opportunity to learn anytime and anywhere, providing unlimited access to learning resources. Mobile learning (M-learning) encourages learners to direct and regulate their learning. Last but not least, m-learning encourages openness to creative ideas and active construction of new concepts within different learning spaces [127].

Virtual Immersive Technologies: The term ‘immersive technologies’ incorporates the technologies of virtual reality (VR), augmented reality (AR) and extended reality (XR). VR and AR are two closely related innovations that have been applied in learning by blending machine learning and other AI techniques to enhance the user experience [3].

Self can decisively influence behavior, being both a barrier and a catalyst to meta-learning procedures. VR in this context can be seen as a vector of behavioral and emotional change. It has the added value to generate transformative experiences which in turn modify the individuals’ worldview. Alterations in inner experience result from restructuring, altering and replacing a human’s self-consciousness. VR presence and emotional engagement help individuals to change perspective and be engaged in reflective observation [128]. This process could be considered a driving force for upgrading meta-learning abilities.

Studies have already shown that the implementation of psychological techniques within immersive virtual environments can improve higher mental abilities such as executive functions (i.e., cognitive flexibility, inhibition control), mental imagery, and creative problem-solving [36,37,38,39,40,41,67,68,69,70,71,72]. VR Neurolinguistic programming techniques aim to motivate behavioral change through the implementation of practices such as positive visualizations, role modeling and role-playing within virtual environments with the support of virtual characters known as avatars [67,68,69,70,71,72,129,130].

Meta-learning is all about an act of transcendence. Learners become meta-learners the moment they can transcend the current state of knowledge and move to the next level of comprehension. VR hypnosis techniques intend to induce a state of semi-consciousness known as a trance state. Learners are exposed to simulations of various situations (i.e., stressful situations) while at the same time they are guided through hypnotic suggestions. In this state, they are more able to deal with well-established beliefs and dysfunctional memories that waste cognitive resources and prevent meta-learners from taking appropriate conscious decisions. Thus, VR subconscious training maximizes learning outcomes and reduces the resistance derived from conscious control processes, providing a powerful tool for unlearning and behavior modification [68,69].

VR trains numerous meta-abilities and meta-skills such as self-observation, self-regulation and adaptability, providing a fertile field for the practice of metacognition, engaging both the pathways of effortful and effortless self-regulation. This means that virtual reality can be implemented not only for acquiring new knowledge but also as a powerful tool for unlearning and relearning [67,68,69,70,71,72,90].

Robotics and 3D Printing: Robotics and 3D printing promote a series of self-learning skills. Learners develop a deeper understanding of complex concepts by applying theoretical knowledge to real situations. They are encouraged to take the initiative, set goals, identify learning resources, and select and evaluate their metacognitive strategies. They also improve skills such as creative thinking, ingenuity and innovative thinking. Robotic and 3D printing activities help learners to ask questions and seek and identify the reasons behind the problems. Most importantly, learners develop not only self-consciousness skills but also collective consciousness as they are progressively motivated to learn with the intention to advance science and find solutions for sustainability and global well-being [131].

6. Discussion

In the current study, the concept of meta-learning was presented to describe the progressive process of transition to the nine meta-levels of conscious and self-directed learning. A nine-layer pyramid model of meta-learning was presented, structured on the principles of metacognition with the contribution of smart technologies. The meta-learning model marked out the metacognitive procedures, including the meta-skills and meta-abilities required to advance a learner’s state of knowledge, learning and intelligence.

The proposed model aligns in several aspects with well-recognized theoretical models such as those described in previous sections (see Section 3.1). Some similarities include the common focus on the continuum of discrete levels, through which learning can pass through (i.e., Bloom’s taxonomy). In addition, the proposed model puts a special emphasis on the special role of attention and memory in the regulation of learning procedures. This model takes the common view that learning is a matter of systematic practice. Some of the innovations of the proposed model that differentiate it from the previous ones are the following: the nine-layer model of meta-learning is fully based on metacognition, in contrast to previous theories that recognized a significant but limited role for metacognitive processes. Some of the theories presented (in Section 3.1) mainly aim to help teachers develop effective instructional strategies and assessment methods (i.e., Gagne’s model). The nine-layered model aims mainly to support self-directed learning without this meaning that it is not addressed to teachers as well. In addition, the meta-learning model is adapted to the skills required in the 21st century and the learning technologies that will play an important role in the field of education in the coming years.

Future learning environments, especially digital ones, will play a very important role in future education [132,133,134]. The proposed model aimed to present a theoretical framework designed to encourage students to manifest and progressively develop their inherent willingness to learn with a better understanding and management of the mechanisms that promote learning.

Smart technologies and AI were found to be promising tools for equipping lifelong learners with the metacognitive skills needed to be inclusive, satisfied, and successful in future society. Specifically, smart technologies were found to train learners to have a better awareness of how learning occurs, to better control the mental and emotional states that affect learning, to recognize personal strengths and weaknesses, and to master skills such as complex problem-solving, creative, and critical thinking. Most importantly, smart technologies are designed to support learners’ needs, encouraging them to internalize motivations which is the driving force of meaningful and metacognitive learning.

It is worth noting that this framework is in line with studies that propose that smart technologies should offer learners the facilitating structure and tools that encourage them to make maximum use of their intelligence. It is not the smart technologies that we expect to be doing the goal setting, the planning, and the general self-management [7]. On the contrary, the main objective of meta-learning is to use smart learning technologies to support learners in developing metacognitive skills, such as self-observation, self-regulation, and flexibility. Smart meta-learning environments, according to this study, are those environments that, either explicitly or implicitly, provide training for helping learners to develop a wide range of meta-skills needed to become thinkers, innovators, responsible citizens, and mindful, future-ready minds.

Although the 9-layered meta-learning model supported by smart technologies focuses on the processes by which people achieve wiser learning, it has the potential to support both students and teachers, as well as all those involved in the design and implementation of educational or lifelong training programs. However, for learners and educators to harness the meta-learning opportunities of smart technologies, they must develop an awareness of their potential and affordances.

The nine-layer pyramid model of meta-learning based on metacognition and smart technologies can be seen as a model of self-directed and lifelong learning, as it covers a wide range of meta-skills people need to acquire from school years up to older ages. It also recognizes several parameters that play a role in equipping learners with meta-skills in various domains such as the physical, intellectual, socio-emotional and even spiritual.

The main goal of this paper was to present a new theoretical framework of meta-learning that emerged from extensive research in the existing literature. We tried to gather representative studies by looking for well-established learning theories, studying future learning technologies, seeking the new demands in learning, and seeking the skills that students are expected to develop in the coming years. Studying this material, we tried, as much as possible, to unify the existing knowledge in a theoretical framework that, contrary to previous ones, recognizes the learner as the protagonist, the creator, and the regulator of learning procedures. According to our knowledge, this is the first learning framework that is for the most part based on metacognition principles. Therefore, our primary ambition was to contribute to the debate about the future of learning by proposing a theoretical framework. Our future research will focus on examining the feasibility of the proposed framework by conducting relevant experimental studies.

The proposed framework can be used to support the design of smart meta-learning environments. Although this model focuses on the nine meta-levels of learning, it can be used to direct the implementation of appropriate teaching strategies. Future research could examine the feasibility of the 9-layered pyramid model of meta-learning within educational contexts recruiting learners with different characteristics (i.e., age, people with disabilities) with the use of smart technologies [135,136].

7. Conclusions

The current study presented a nine-layered meta-learning pyramid model based on metacognition and smart technologies intending to help future learners to transcend the usual state of knowing and gradually move to the next meta-levels of human intelligence, termed meta-intelligence. This process requires the development of higher motives (meta-motivations) [12], which is aptly described in self-determination theory by Deci and Ryan as the process of internalization of motivations [137]. The proposed model examined and integrated the knowledge derived from well-established theories of learning, theories of metacognition, spiritual and emotional intelligence, mindfulness and theories of learning. The smart meta-learning pyramid was based on the urgent need to reskill and upskill future learners with those meta-skills needed to become the real drivers of change in future schools and society. In addition, the model integrated smart technologies as an essential part of meta-learning. Indeed, the investigation of evidence-based research demonstrated that smart technologies have the potential to support meta-learning processes by providing, for instance, smart feedback which motivates reflection and adaptation or providing transformative experiences inducing the states of consciousness that promote deep and meaningful learning. Our study proposed that optimal meta-learning training requires the appropriate blending of meta-learning strategies supported by smart technologies.