2 Background: Knowledge and Learning
This first chapter on background literature discusses relevant concepts from the disciplines of Education and Learning Sciences. First, we introduce some relevant terminology, and the concepts of deep or meaningful learning. Then we discuss several research backed principles that have been shown to lead to meaningful learning. Next, we discuss how learning, sensemaking, and searching for information are related, and how modern technologies provide affordances for new forms of learning and knowledge work in the 21st century. We also discuss some concepts about individual differences of learners as well as techniques that can promote better learning. In the last section, we state what implications these findings have for shaping the longitudinal study in this dissertation.
2.1 Terminology
The Webster dictionary1 defines knowledge in two ways. The first definition is “the range of one’s information or understanding”. Vakkari (2016) says it is “the totality what a person knows, that is, a personal knowledge or belief system. It may include both justified, true beliefs and less justified, not so true beliefs, which the person more or less thinks hold true.” Webster’s second definition of knowledge is “the sum of what is known: the body of truth, information, and principles acquired by humankind”. We can regard this as universal knowledge.
Learning is a process, that leads to a change in (personal) knowledge, beliefs, behaviours, and attitudes (Ambrose et al., 2010). Thus, learning always aims to increase one’s personal knowledge, and can often draw from the body of universal knowledge. In some cases, the change in personal knowledge can also lead to change in universal knowledge, such as when new discoveries are made, or new philosophies are proposed. Human learning is an innate capacity. It is longitudinal and unfolds over time. Learning is lifelong and life-wide, and has a lasting impact on how humans think and act (Ambrose et al., 2010; Kalantzis & Cope, 2012). Learning can be informal or formal. Informal learning is the casual learning taking place in everyday life, and is incidental to the everyday life experience. Formal learning is the deliberate, conscious, systematic, and explicit acquiring of knowledge (Kalantzis & Cope, 2012).
Education is a form of formal learning. It is the systematic acquiring of knowledge. In today’s world, the institutions of education are formally constructed places (classrooms), times (of the day and of life) and social relations (teachers and students); for instance, schools, colleges, and universities. The scientific discipline of Education concerns itself with the systematic investigation of the ways in which humans know and learn. It is the science of “coming to know” (Kalantzis & Cope, 2012).
Figure 2.1: Deep learning (of the human kind) versus traditional (also often online) classroom practices. Compiled from Cope & Kalantzis (2017) and Sawyer (2005).
Pedagogy describes small sequences of learner activities that promote learning in educational settings (Kalantzis & Cope, 2012). Traditional approaches to (classroom) pedagogy, especially the didactic pedagogy, primarily involves a teacher telling, and a learner listening. The teacher is in command of the knowledge, and their mission is to transmit this knowledge to the learners, in a one-way flow. It is hoped that the learners will dutifully absorb the knowledge laid before them by the teacher. The balance of agency weighs heavily towards the teacher. “There is a special focus on long-term memory, or retention, measurable by the ritual of closed-book, summative examination” (Cope & Kalantzis, 2017).
Figure 2.2: Meaningful learning (aka deep learning) as explained by Novak (2010, fig. 5.3) (annotations our own).
Cognitive scientists had discovered that learners retain material better, and are able to generalize and apply it to a broader range of contexts, when they learn deep knowledge rather than surface knowledge, and when they learn how to use that knowledge in real-world social and practical settings (Sawyer, 2005). Deep learning 2 takes place when “the learner chooses conscientiously to integrate new knowledge to knowledge that the learner already possesses” and involves “substantive, non-arbitrary incorporations of concepts into cognitive structure” (Novak, 2002, p. 549) and may eventually lead to the development of transferable knowledge and skills. A parallel terminology for deep learning (Marton & Säaljö, 1976; Marton & Säljö, 1976) is meaningful learning (Ausubel et al., 1968; Novak, 2002), and they are often contrasted with surface learning or rote learning. Figure 2.1 discusses some more details on deep or meaningful learning, and the limitations of traditional classroom practices to promote deep learning. Figure 2.2 describes (using a concept map) how meaningful learning can be achieved and sustained, and our annotations highlight how Search-as-learning systems can foster the same.
2.2 Principles of Meaningful Learning
Ambrose et al. (2010) have proposed several principles of (student) learning that lead to creation of deeper knowledge in learners, and help educators understand why certain teaching approaches may help or hinder learning. These principles are based on research and literature from a range of disciplines in psychology, education, and anthropology, and the authors claim they are domain independent, experience independent, and cross-culturally relevant.
- Students’ prior knowledge can help or hinder learning.
- How students organize knowledge influences how they learn and apply what they know.
- Students’ motivation determines, directs, and sustains what they do to learn.
- Goal-directed practice coupled with targeted feedback enhances the quality of students’ learning.
- Students’ current level of development interacts with the social, emotional, and intellectual context around the student to impact learning.
- To become self-directed learners, students must learn to monitor and adjust their approaches to learning.
In line with the above, the US National Research Council identified several key principles about experts’ knowledge (National Research Council, 2000), that illustrate the outcome of successful learning:
Experts notice features and meaningful patterns of information that are not noticed by novices.
Experts have acquired a great deal of content knowledge that is organized in ways that reflect a deep understanding of their subject matter.
Experts’ knowledge cannot be reduced to sets of isolated facts or propositions but, instead, reflects contexts of applicability: that is, the knowledge is ‘conditionalized’ on a set of circumstances.
Experts are able to flexibly retrieve important aspects of their knowledge with little attentional effort.
Though experts know their disciplines thoroughly, this does not guarantee that they are able to teach others.
Experts have varying levels of flexibility in their approach to new situations.
The principles of learning illustrate that both the context of learning, and the individual differences of learners moderate the learning process. The findings about expert knowledge suggests that incorporating new information into existing knowledge structures in a meaningful manner is a key aspect of learning. We discuss these concepts in more detail in the following sections.
2.3 Meaningful Learning as Sensemaking
In this section, we discuss how meaningful learning can be further qualified using the concepts of sensemaking. Sensemaking3 is a process that occurs when learners connect their previously developed knowledge, ideas, abilities, and experiences together to address the uncertainty presented by a newly introduced phenomenon, problem, or piece of information (Next Generation Science Standards, 2021). A significant portion of learning is sensemaking, especially those which use recorded information or systematic discovery to learn concepts, ideas, theories, and facts in a domain (such as science or history) (P. Zhang & Soergel, 2014). The phrase “figure something out” is often synonymous with sensemaking. Sensemaking is generally about actively trying to figure out the way the world works, and/or exploring how to create or alter things to achieve desired goals (Next Generation Science Standards, 2021). (Dervin & Naumer, 2010) distinguish work on sensemaking in four fields: “Human Computer Interaction (HCI) (Russell’s sensemaking); Cognitive Systems Engineering (Klein’s sensemaking); Organizational Communication (Weick’s sensemaking; Kurtz and Snowden’s sense-making); and Library and Information Science (Dervin’s sense-making)”.
Many theories of learning and sensemaking revolve around the concept of fitting new information into an existing or adapted knowledge structure (P. Zhang & Soergel, 2014). The central idea is that knowledge is stored in human memory as structures or schemas, which comprise interconnected concepts and relationships. When new information is encountered or acquired, the learner or sensemaker needs to actively construct a revised or entirely new knowledge structure. Examples of some such theories include: the assimilation theory (theory of meaningful learning) (Ausubel et al., 1968; Ausubel, 2012; Novak, 2002; Novak, 2010); the schema theory (Rumelhart & Norman, 1981; Rumelhart & Ortony, 1977); and the generative learning theory (Grabowski, 1996; Wittrock, 1989); all of which have their foundations in the Piagetian concepts of assimilation and accommodation (Piaget, 1936).
Assimilation means addition of new information into an existing knowledge structure. A “synonym” (Vakkari, 2016) for assimilation is accretion, which is the gradual addition of factual information to an existing knowledge structure, without structural changes. Accretion does not change concepts and their relations in the structure, but may populate a concept with new instances or facts. Accommodation means modifying or changing existing knowledge structures, by adding or removing concepts and their connections in the knowledge structure. Accommodation is subdivided into tuning / weak-revision, and restructuring, based on the degree of structural changes (P. Zhang & Soergel, 2014). Tuning or weak revision does not include replacing concepts or connections between concepts in the structure, but tuning of the scope and meaning of concepts and their connections. This may include, for example, generalizing or specifying a concept. Restructuring means radically changing and replacing concepts and their connections in the existing knowledge structure, or creating of new structures. Such radical changes often take place when prior knowledge conflicts with new information. New structures are constructed either to reinterpret old information or to account for new information (Vakkari, 2016; P. Zhang & Soergel, 2014). A comparison of these types of conceptual changes can be found in (P. Zhang & Soergel, 2014 Table 3).
2.3.1 Concept Maps to enhance Sensemaking
As we saw in the previous section, deep learning / meaningful learning / sensemaking is a process in which new information is connected to a relevant area of a learner’s existing knowledge structure. However, the learner must choose to do this, and must actively seek a way to integrate the new information with existing relevant information in their cognitive structure (Ausubel et al., 1968; Novak, 2010). Learning facilitators (e.g., teachers) can encourage this choice by using the concept mapping technique.
A concept-map is a two-dimensional, hierarchical node-link diagram (a graph in Computer Science parlance) that depicts the structure of knowledge within a discipline, as viewed by a student, an instructor, or an expert in a field or sub-field. The map is composed of concept labels, each enclosed in a box (graph nodes); a series of labelled linking lines (labelled edges); and an inclusive, general-to-specific organization (Halttunen & Jarvelin, 2005). Concept-maps assess how well students see the ‘’big picture’’, and where there are knowledge-gaps and misconceptions. A mind map is a diagram similar to a concept map, comprising nodes and links between nodes. However, mind maps emerge from a single centre, and have a more hierarchical, tree like structure. Concept maps are more free-form, allowing multiple hubs and clusters. Also, mind-maps have unlabelled links, and are subjective to the creator. There are no “correct” relationships between nodes in a mind map. Figure shows the key features of a concept map, with the help of a concept map.
Concept maps are therefore, arguably the most suited mechanism to represent the cognitive knowledge structures, connections, and patterns in a learner’s mind. Conventional tests, such as multiple choice questions, are best at assessing students’ recall of facts and guessing skills. Their format treats information as distinct and separate items, rather than interconnected pieces of a bigger picture. Concept maps on the other hand, encourage learners to identify and make connections between concepts that they know, and concepts that are new to them. Concept maps have been used for over 50 years to provide a useful and visually appealing way of illustrating and assessing learners’ conceptual knowledge (Egusa et al., 2010, 2014a, 2014b, 2017; Halttunen & Jarvelin, 2005; Novak, 2010; Novak & Gowin, 1984).
Analysis of concept maps can reveal interesting patterns of learning and thinking. Some of these measures that have been used by (Halttunen & Jarvelin, 2005) are: addition, deletion, and differences in top-level concept-nodes; depths of hierarchy; and number of concepts that were ignored or changed fundamentally. In this regard, (Novak & Gowin, 1984) have presented well-established scoring schemes to evaluate concept-maps: 1 point is awarded for each correct relationship (i.e. concept–concept linkage); 5 points for each valid level of hierarchy; 10 points for each valid and significant cross-link; and 1 point for each example.
Having discussed how deep learning / meaningful learning / sensemaking involves creation of knowledge structures in the learner’s mind, and suitably adding new pieces of information in the knowledge structure, we now discuss how these processes are influenced in the 21st century with the presence of new media, digital technologies, and information retrieval systems.
2.4 ‘New’ Learning as Online Information Searching
Digital media technologies and e-learning ‘ecologies’ can enable new forms and models of learning, that are fundamentally different from the traditional classroom practices of didactic pedagogy (Cope & Kalantzis, 2017). Some key concepts associated with these forms of ‘new learning’ are described below. These concepts from the Educational Sciences domain tie back strongly to the issues, challenges, and research agenda being investigated by researchers in the Search as Learning and Information Retrieval domain (Section 1.1.
2.4.1 Active Knowledge Making
The Internet and new forms of media provide us the opportunity to create learning environments where learners are no longer mainly consumers of knowledge, but also modifiers, producers, and exchangers of knowledge. In active knowledge making, learners can, and often need to, find information on their own using online resources. They are not restricted to the textbook alone. The Internet is often a definitive resource for information on any given topic. A learner can search the web (to learn) at any time, from anywhere, on any web-enabled device.
As knowledge producers, learners search and analyze multiple sources with differing and contradictory perspectives, and develop their own observations and conclusions. In this process, they become researchers themselves and learn to collaborate with peers in knowledge production. Collaboration gives learners the opportunity to work with others as coauthors of knowledge, peer reviewers, and discussants to completed works. Because learners bring their own views, outlooks, and experiences, the knowledge artefact they create is often uniquely voiced instead of a templated “correct” response (Amina, 2017).
Learners become active knowledge producers (for instance, project-based learning, using multiple knowledge sources, and research based knowledge making), and not merely knowledge consumers (as exemplified in the ‘transmission’ pedagogies of traditional textbook learning or e-learning focused on video or e-textbook delivery). Active knowledge making practices underpin contemporary emphases on innovation, creativity and problem solving, which are quintessential ‘knowledge economy’ and ‘knowledge society’ attributes.— Cope & Kalantzis (2017)
2.4.2 Artefacts for Learning Assessment
Traditionally, the focus of learning outcomes has been long term memory. Students and learners were expected to remember a collection of facts, definitions, proofs, equations, and other associated details. For a significant amount of modern knowledge-work today, memory is actually less important. Information is so readily accessible now that it is no longer necessary to remember the information. Because of the technological phenomenon, the mass of information is available ubiquitously 4 to a learner (or a knowledge worker), in every moment of learning. Empirical details such as facts, definitions, proofs, or equations do not need to be remembered today, because they can always be looked up again (Amina, 2017; Cope & Kalantzis, 2017).
This creates an interesting shift in the focus of learning and knowledge work today: “if we are not going to measure and value long-term memory in education, what are we going to assess?” Cope & Kalantzis (2017) suggest that we assess the knowledge artefacts that learners produce. In active knowledge making, the final work 5 can be proof of the learning outcome and represent a learner’s ability to use the resources that are available (Amina, 2017). Measure of learning can be measure of information quality and information use in artefacts. This shows a shift in pedagogy and assessment and an increase in personalization and individualization of learning (Pea & Jacks, 2014). Memorizing the information on a topic is less important, compared to the writing, synthesizing, analyzing, and sensemaking of the available information that has been referenced in the work. This shifts the focus of assessment to the quality of the artefacts and the processes of their construction. Moreover, as technology increases the ability to capture detailed data from formal and informal learning activities, it can give us a new view of how learners progress in acquiring knowledge, skills, and attributes (DiCerbo & Behrens, 2014). Because learning is a continuous, longitudinal process, these advanced, technologically enhanced assessments are more useful in understanding the learning process and knowledge development (Amina, 2017).
Assessing open-ended artefacts does come with its challenges and limitations. First, assessing and grading artefacts requires the development of detailed qualitative coding guides (M. J. Wilson & Wilson, 2013). This process involves defining grading criteria and measuring inter-coder agreement to ensure that the coding guide is reliable. Prior studies have scored summaries along dimensions such as the inclusion of facts, relationships between facts, and evaluative statements (Lei et al., 2015; Roy et al., 2021; M. J. Wilson & Wilson, 2013). Second, the quality of responses may be difficult to compare across learners. Since this type of assessment imposes very few constraints on the learners’ responses, it may cause some learners to satisfice, and not convey everything that was learned. Additionally, writing skills are likely to vary across learners, and some may not be able to effectively articulate everything that was learnt.
2.4.3 ‘Information Search and Evaluation’ as and for Learning
Learning today is more about navigation, discernment, induction, and synthesis, and less about memory and deduction (Cope & Kalantzis, 2013). However, knowing the source, finding the source, and using the information critically is important to learn and know now more than ever before (Amina, 2017). Learners must know the social sources of knowledge and understand and correctly use quotations, paraphrases, remixes, links, citations, and the like in the works that they develop. Searching and sourcing from the web entails a process of developing and completing a work that inevitably makes learners knowledge producers, as long as they can navigate and critically discern the value of multiple sources. This is a skill that must be learned, as many sources of information are not valid, reliable, or authentic (McGrew et al., 2018; Wineburg & McGrew, 2016). Understanding the different sources and identifying the more reliable ones are essential for effective teaching and learning (McGrew et al., 2017; McGrew, 2021). This is a critical aspect because the inability to cite properly or to use reliable resources provides learners with misconstrued information and ideas (Amina, 2017; Breakstone et al., 2021; McGrew et al., 2017).
The Stanford History Education Group (SHEG) conceptualised the Civic Online Reasoning (COR) curriculum 6 to enable students to effectively search for and evaluate online information (Breakstone et al., 2018; Breakstone et al., 2021; McGrew, 2020). The curriculum centres on asking three questions of any digital content: (i) who is behind a piece of information? (ii) what is the evidence for a claim? (iii) what do other sources say? The curriculum has lessons and assessments for information evaluation skills such as lateral reading (Wineburg & McGrew, 2017), identifying news versus opinions, checking domain names, identifying sponsored content, evaluating evidence, and practising click restraint (McGrew & Glass, 2021). The lessons were developed and piloted by the Stanford History Education Group (McGrew et al., 2018; McGrew, 2020; McGrew & Glass, 2021). Taken together, these strategies will allow academics and students to better evaluate digital content, from the perspectives of professional fact checkers.
The purview of the Civic Online Reasoning curriculum is more targeted than the expansive fields of media and digital literacy 7, (which can embrace topics ranging from cyberbullying to identity theft). Civic Online Reasoning focuses squarely on how to sort fact from fiction online, a prerequisite for responsible civic engagement in the twenty-first century (Breakstone et al., 2021; Kahne et al., 2012; Mihailidis & Thevenin, 2013).
2.5 Promoting Better Learning
It is not the technology that makes a difference; it is the pedagogy.
— Cope & Kalantzis (2017)
Having discussed how meaningful learning takes place, and how it is influenced by the presence of digital media and the mass of information on the Internet, let us now look deeper into the learners as persons themselves. In this section, we discuss how different cognitive and metacognitive practices and aspects of learners can promote better learning. These phenomena have important implications for any digital systems that aim to foster learning.
2.5.1 Externalization and Articulation
The learning sciences have discovered that when learners externalize and articulate their developing knowledge, they learn more effectively (National Research Council, 2000). Best learning takes place when learners articulate their unformed and still developing understanding, and continue to articulate it throughout the process of learning. This phenomenon was first studied in the 1920s by Russian psychologist Lev Vygotsky. Articulating and learning go hand in hand, in a mutually reinforcing feedback loop. Often learners do not actually learn something until they start to articulate it. While thinking out loud, they learn more rapidly and deeply than while studying quietly (Sawyer, 2005). The learning sciences community is actively researching how to support students in their ongoing process of articulation, and which forms of articulation are the most beneficial to learning. Articulation is more effective if it is scaffolded – channelled so that certain kinds of knowledge are articulated, and in a certain form that is most likely to result in useful reflection (Sawyer, 2005). Students need help in articulating their developing understandings, as they do not yet know how to think about thinking, or talk about thinking; their knowledge state is anomalous (Belkin et al., 1982).
2.5.2 Metacognition and Reflection
Figure 2.3: Operational definitions and features of the metacognition components, adapted from Schraw & Dennison (1994) and Vancouver Island University (2021).
One of the reasons that articulation is so helpful to learning is that it promotes reflection or metacognition. Metacognition, commonly referred to as thinking about thinking, involves thinking at a higher level of abstraction, which in turn improves thinking and learning (Blanken-Webb, 2017). It is “the process of reflecting on and directing one’s own thinking” (National Research Council, 2000, p. 78), and involves thinking about the process of learning, and thinking about knowledge. This ties forward to the self-regulation that effective learners exhibit (Section 2.5.4). Effective learners are aware of their learning process, and can measure how efficiently they are learning as they study.
The literature on metacognition broadly identifies two fundamental components of metacognition: knowledge about cognition, and regulation of cognition. Knowledge about cognition includes three subprocesses that facilitate the reflective aspect of metacognition: declarative knowledge (knowledge about self and about strategies), procedural knowledge (knowledge about how to use strategies), and conditional knowledge (knowledge about when and why to use strategies). Regulation of cognition include a number of subprocesses that facilitate the control aspect of learning. Five component skills of regulation have been discussed extensively in the literature, including planning, information management strategies, comprehension monitoring, debugging strategies, and evaluation. The operational definitions of these components are described in Figure 2.3
Schraw & Dennison (1994) developed the Metacognitive Awareness Inventory (MAI) survey and a scoring guide to measure these self-reported components and subprocesses of metacognition. The original survey consists of 52 true/false questions (Appendix B.5), such as “I consider several alternatives to a problem before I answer”, “I understand my intellectual strengths and weaknesses”, “I have control over how well I learn”, and ”I change strategies when I fail to understand”. The instrument has been widely used in research, and has its reliability and validity measures available. Later, Terlecki & McMahon (2018) proposed a revised version of the MAI, using five-point Likert-scales, ranging from “I never do this” to “I do this always”. They argue that when measuring change in metacognition over time, the Likert-scale based ‘how often’ questions are more effective than dichotomous ‘Yes/No’ questions (Terlecki, 2020; Terlecki & McMahon, 2018).
2.5.3 Motivation
Figure 2.4: The motivation and self-determination continuum, as proposed by the Self-Determination Theory (SDT). Figure adapted from Ryan & Deci (2000a), Ryan & Deci (2000b), and Guyan (2013).
Motivation is the process that initiates, guides, and maintains goal-oriented behaviours (Cherry, 2020). The Self-Determination Theory (SDT) represents a broad framework for the study of human motivation and personality (Ryan & Deci, 2017). SDT differentiates the types of motivation based on the reasons that give rise to behaviour: intrinsic motivation and extrinsic motivation. Intrinsic motivation is engaging in a task or behaviour for the rewards inside the task or behaviour, such the pleasure, enjoyment and satisfaction that the behaviour provides. It is a stable form of motivation. Extrinsic motivation is engaging in a task or behaviour for the rewards outside the task or behaviour, such as receiving rewards, avoidance of punishment, gaining social approval, or achievement of a valued result. Extrinsic motivation is on a continuum from less stable to more stable, as illustrated in Figure 2.4. Extrinsic motivation does not last unless the rewards and punishments are explicitly visible (Deci & Ryan, 2013; Ryan & Deci, 2000b; Tahamtan, 2019).
Ryan (1982) proposed the Intrinsic Motivation Inventory (IMI) (Appendix B.3), a multidimensional questionnaire intended to assess participants’ subjective experience related to a target activity in laboratory experiments. The instrument assesses participants’ interest/enjoyment, perceived competence, effort, value/usefulness, felt pressure and tension, and perceived choice while performing a given activity, yielding six subscale scores. The interest/enjoyment subscale is considered the most indicative self-report measure of intrinsic motivation. The perceived choice and perceived competence concepts are theorized to be positive predictors of both self-report and behavioural measures of intrinsic motivation. The pressure/tension is theorized to be a negative predictor of intrinsic motivation. Effort is a separate variable that is relevant to some motivation questions, so it is used if it is relevant. The value/usefulness subscale is used to measure internalization, with the idea being that people internalize and become self-regulating with respect to activities that they experience as useful or valuable for themselves.
2.5.4 Self-regulation
Self-regulation is the ability to develop, implement, and flexibly maintain planned behaviour in order to achieve one’s goals. Self-regulation, and more broadly, self-direction, are critical to being an effective “lifelong” learner. Self-regulation becomes increasingly important at higher levels of education and in professional life, as people take on more complex tasks and greater responsibilities for their own learning. However, these metacognitive skills tend to fall outside the content area of most courses, and therefore, often neglected in instruction (Ambrose et al., 2010, p. 191). Building on the foundational work of Kanfer (1970b); Kanfer (1970a), Miller and Brown formulated a seven-step model of self-regulation (J. Brown, 1998; W. R. Miller & Brown, 1991). In this model, behavioural self-regulation may falter because of failure or deficits at any of these seven steps: (i) receiving relevant information, (ii) evaluating the information and comparing it to norms, (iiii) triggering change, (iv) searching for options, (v) formulating a plan, (vi) implementing the plan, and (vii) assessing the plan’s effectiveness (which recycles to steps (i) and (ii)). Although this model was developed specifically to study addictive behaviours, the self-regulatory processes it describes are meant to be general principles of behavioural self-control. J. M. Brown et al. (1999) developed the Self-Regulation Questionnaire (SRQ) (Appendix B.4) to assess these self-regulatory processes through self-report. The items were developed to mark each of the seven sub-processes of the W. R. Miller & Brown (1991) model, forming seven subscales of the SRQ. The 63-item scale elicits responses in the form of 5-point Likert scale, ranging from strongly disagree to strongly agree. Based on clinical and college samples, the authors tentatively recommend a score of 239 and above as high (intact) self-regulation capacity (top quartile), 214-238 as intermediate (moderate) self-regulation capacity (middle quartiles), and 213 and below as low (impaired) self-regulation capacity (bottom quartile).
2.5.4.1 Self-directed and Self-regulated Learning
As we saw in the previous sections, self-regulation, motivation, and metacognition are key concepts that moderate the learning process. These terms are couched in the concepts of self-regulated learning and self-directed learning.
Self-directed learning (SDL) is a “process in which individuals take the initiative, with or without the help from others, in diagnosing their learning needs, formulating goals, identifying human and material resources, choosing and implementing appropriate learning strategies, and evaluating learning outcomes”(Knowles, 1975, p. 18). Self-regulated learning (SRL) can be described as the degree to which students are “metacognitively, motivationally, and behaviourally active participants in their own learning process” (Zimmerman, 1989, p. 329).
Figure 2.5: Self-directed learning vs. self-regulated learning, as illustrated by Saks & Leijen (2014).
Often used interchangeably, self-directed learning (SDL) and self-regulated learning (SRL) have some important similarities and differences (Figure 2.5) (Saks & Leijen, 2014). SDL, originating from adult education, is a broader, macro-level construct, and is usually practised outside the traditional school environment. The self-directed learner is free to design their own learning environment, and free to plan and set their own learning goals. SRL, on the other hand, is a narrower, micro-level construct, originating from educational and cognitive psychology, and is mostly utilized in the school environment. Learners do not have as much freedom as in SDL. The instructor or facilitator often defines the learning task and the learning goals. Self-directed learning may include self-regulated learning, but the converse is not true (Jossberger et al., 2010; Loyens et al., 2008). In other words, “a self-directed learner is supposed to self-regulate, but a self-regulated learner may not self-direct” (Saks & Leijen, 2014). Despite their differences, SDL and SRL share key similarities (Saks & Leijen, 2014). First, both can be seen in two dimensions: (i) external to the learner, as a process or series of events, and (ii) internal to the learner, arising from the learner’s personality, aptitude, and individual differences. Second, both the learning processes have four key phases: (i) defining tasks, (ii) setting goals and planning, (iii) enacting strategies, and (iv) monitoring and reflecting. Third, both SDL and SRL require active participation, goal-directed behaviour, metacognition, and intrinsic motivation.
In summary, metacognition is monitoring and controlling what is in the learner’s head; self-regulation is monitoring and controlling how the learner interacts with their environment; self-regulated learning is the application of metacognition and self-regulation to learning (Mannion, 2020); and the whole learning process is sustained by motivation, which is desirable to be intrinsic.
2.6 Summary and Implications for this Dissertation
In this first chapter of the background literature review, we discussed (i) what is meaningful learning, a.k.a. deep learning, or sensemaking; (ii) how meaningful learning updates the learner’s cognitive knowledge structure; (iii) how the learning process is influenced by digital technologies, mass of information on the Internet, and IR systems; and (iv) what principles and practices learners and educators must realize and follow to promote meaningful learning. These findings are from the domains of Educational Sciences, Learning Sciences and Cognitive Sciences. We argue that these are important aspects to be considered when designing future IR or educational information systems that aim to combine and improve the searching and learning experience.
Guided by these findings, we made some important decision choices for the longitudinal study conducted in this dissertation. We aimed to situate learners in their context, and incorporate their individual differences using metacognition, motivation, and self-regulation characteristics. Additionally, we aimed to assess learning using artefacts and concept maps. We chose not use traditional tests like question-answers, and multiple choice assignments, since they are often not the preferred choice of knowledge-work output in real world scenarios.
In the next chapter, we look at relevant literature from the Information Sciences and Interactive Information Retrieval disciplines.