A Bottom-Up Model of Skill Learning

9 - Knowledge Acquisition Via Bottom-up Learning

<p>Previous research has shown that incidental exposure to second-language collocations in reading texts can produce gains in declarative collocational knowledge. However, there is little evidence that incidental exposure leads to the acquisition of procedural collocational knowledge. One key study that investigated these two areas was Sonbul and Schmitt (2013). They found that advanced non-native speakers gained substantial declarative knowledge of low-frequency technical collocations after three exposures in two incidental reading conditions in one treatment session; typographic enhancement of the collocations produced more correct answers than no enhancement. However, the researchers found no evidence of procedural collocational knowledge in a primed lexical decision task. Experiment 1 in this thesis was a conceptual replication and extension of Sonbul and Schmitt (2013). In a counter-balanced learning and experimental condition, 62 advanced adult ESL speakers were exposed to nine occurrences of 15 low-frequency technical collocations in 500-word texts in three sessions on two consecutive days. (input flooding). Three incidental learning treatments were implemented: reading-only (typographically unenhanced), bolding-only and bolding-plus-glossing. Collocational knowledge was assessed in two tests of declarative knowledge: a cued-recall (gapfill) test and a form-recognition (multiple-choice) test. Procedural collocational knowledge was operationalised as a priming effect in a primed lexical decision task. The results of the immediate cued-recall and form-recognition post-tests corroborate Sonbul and Schmitt’s findings: multiple encounters with the collocations produced substantial declarative collocational knowledge, and more declarative knowledge was produced through exposure to typographically-enhanced collocations (with and without glosses) than to typographically-unenhanced collocations. Procedural knowledge was found, but, unexpectedly, only in the reading-only treatment. Experiment 2 focused on non-technical lexical (verb + noun) collocations and grammatical (preposition + noun) collocations. Two incidental learning conditions were used: bolding and no-bolding. Seventy-eight intermediate-to-upper-intermediate-level adult native speakers of Chinese were exposed to six occurrences of each of 48 non-technical English collocations as they read twelve 170-word treatment texts on two consecutive days. The immediate post-test session comprised a gapfill (cued-recall) test, for measuring declarative knowledge, and a self-paced reading task, for measuring procedural knowledge. The results show an increase in declarative knowledge in both learning conditions. Bolding produced more declarative knowledge of preposition + noun collocations than no bolding; however, bolding was no more effective than no bolding for verb + noun collocations. In the self-paced reading task, the absence of bolding of verb + noun collocations led to a tendency towards the development of procedural knowledge, but this was not the case for the typographically enhanced verb + noun collocations. For preposition + noun collocations (both unbolded and bolded) no evidence of procedural knowledge was found. The findings of both experiments indicate that input flooding of collocations in incidental learning conditions can produce declarative collocational knowledge, and that typographic enhancement may lead to more declarative knowledge than lack of typographic enhancement. Repeated exposure to typographically-unenhanced collocations in reading contexts may produce procedural knowledge of collocations more quickly than exposure to typographically-enhanced collocations. These findings indicate that declarative and procedural knowledge of MWUs are dissociated in the sense that they follow different developmental paths. In a teaching context, I predict that the knowledge of collocations may be acquired incidentally through the use of texts such as graded readers which contain multiple instances of collocations.</p>

In addition to content knowledge, it is necessary to teach learning skills. Using relevant learning skills assures better learning outcomes and abilities for the whole life course. Due to a packed curriculum or teachers having insufficient skills, student learning skills are often neglected. The aim of this study is to find out whether it is possible to support the development of declarative knowledge in learning skills in teachers through procedural knowledge acquired via practical teaching activities. A quasi-experimental study using semi-structured pre- and post-intervention interviews and six-month intervention activities was conducted with 14 teachers. The procedural knowledge of the teachers was assessed based on their practical experience with the intervention activities. Their declarative knowledge was assessed based on pre- and post-intervention interview reflections. The findings revealed that procedural knowledge, being predominantly teacher centred, mostly appears through experience and reflections on the teaching practice that guides them. It also appeared that the intervention focusing on advancing procedural knowledge among teachers advanced their declarative knowledge in terms of metacognition, revealing new aspects of motivation and sense-giving for their learning. As follow-up research, we plan to continue studying conditional knowledge in teachers in order to identify situations where this is applied.

It appears that there are resemblances in the organization of memory and the visual system, although the functions of these faculties differ considerably. In this article, the principles behind this organization are discussed. One important principle regards the distinction between declarative and procedural knowledge, between knowing that and knowing how. Declarative knowledge is considered here not as an alternative kind of knowledge, as is usually the case in theories of memory, but as part of procedural knowledge. In our view this leads to another approach with respect to the distinction. Declarative knowledge has occupied more attention in (cognitive) psychological research than can be justified on the basis of the importance of procedural knowledge for behavior. We also discuss the question whether there are other brain faculties that reflect the same organizational characteristics. We conclude with some speculations about the consequent role of consciousness in such a tentative model.

Knowledge Representation Methods for Dairy Decision Support Systems

To determine whether youth athletes with an “average” (regional), “high” (sub-elite), and “very high” (elite) level of performance differ with respect to their self-assessed tactical skills, 191 youth field hockey players (mean age 15.5 years, s = 1.6) completed the Tactical Skills Inventory for Sports (TACSIS) with scales for declarative (“knowing what to do”) and procedural (“doing it”) knowledge. Multivariate analyses of covariance with age as covariate showed that elite and sub-elite players outscored regional players on all tactical skills (P < 0.05), whereas elite players had better scores than sub-elite players on “positioning and deciding” (P < 0.05) only. The sex of the athletes had no influence on the scores (P > 0.05). With increasing level of performance, scores on declarative and procedural knowledge were higher. Close to expert performance, declarative knowledge no longer differentiated between elite and sub-elite players (P > 0.05), in contrast to an aspect of procedural knowledge (i.e. positioning and deciding), where elite players outscored sub-elite players (P < 0.05). These results may have implications for the development of talented athletes.

Capturing human data in a letter counting task: Accessibility and action-centeredness in representing cognitive skills

Introduction Reinforcement learning formalizes the concept of learning from interactions.1 Broadly, reinforcement learning focuses on a setting in which an agent (decision maker) sequentially interacts with an environment that is partially unknown to them. At each stage, the agent takes an action and receives a reward. The objective of the agent is to maximize rewards accumulated in the long run. There are many situations in health care where decisions are made sequentially for which reinforcement learning approaches could prove useful for decision making. Throughout this article, we consider treatment prescription as an archetypical example to connect reinforcement learning concepts to a health care setting. In this setting, the care provider, the prescribed treatment, and the patients can be viewed as the agent, the action, and the environment, respectively, as depicted in Figure 1.Figure 1: Sequential treatment of AKI or CKD complications modeled as a reinforcement learning problem.Background In this section, with the objective of making reinforcement learning literature more accessible to a clinical audience, we briefly introduce related fundamental concepts and approaches. We refer the interested reader to Sutton and Barto1 for a comprehensive introduction to reinforcement learning. Markov Decision Processes Markov decision processes (MDPs) are a formalism of the sequential decision-making problem that has been central to the theoretical and practical advancements of reinforcement learning. In each stage of an MDP, the agent observes the state of the environment and takes an action, which, in turn, results in a change of the state. This change of state is assumed to be probabilistic with the next state being determined only by the preceding state, the chosen action, and the transition probability. The agent also receives a reward that is a function of the taken action, the preceding state, and the subsequent state. In an MDP, the objective of the agent is to maximize the return defined as the reward accumulated over a time horizon. In some applications, it is common to consider the horizon to be infinite, in which case the future rewards are discounted by a factor smaller than one. The selection of action by the agent on the basis of the observed state is known as the policy. More formally, a policy is a probabilistic mapping from states to each possible action. Because the policy and the reward are a function of the state, it is critical to estimate the utility of being in a certain state. More specifically, the value function is defined as the expected return starting from a given state under the chosen policy. Under this formalism, the objective of the agent is to find the optimal policy that maximizes the value function for all states. Reinforcement Learning Methods Action-value methods are a class of reinforcement learning methods in which the actions are chosen on the basis of the estimation of their long-term value. A prominent example of an action-value method is Q-learning in which the agent iteratively takes actions with the highest estimated values and updates the action-state value function on the basis of new observations. Policy gradient methods are another class of reinforcement learning methods that seek to optimize the policy directly instead of choosing actions on the basis of their respective estimated value. Such methods could be advantageous in health care applications that entail a large number of possible actions, e.g., when recommending a wide range of drug dosages or treatment options. Clinical Applications Reinforcement learning frameworks and methods are broadly applicable to clinical settings in which decisions are made sequentially. A prominent clinical application of reinforcement learning is for treatment recommendation, which has been studied across a variety of diseases and treatments including radiation and chemotherapy for cancer, brain stimulation for epilepsy, and treatment strategies for sepsis.2–5 In such treatment recommendation settings, a policy is commonly known as a dynamic treatment regime. There are various other clinical applications of reinforcement learning including diagnosis, medical imaging, and decision support tools (see refs. 2–5 and the references therein). Reinforcement Learning in Nephrology Although there have been recent applications of machine learning in nephrology,6,7 to the best of the authors' knowledge, the application of reinforcement learning to nephrology has been primarily limited to optimizing the erythropoietin dosage in hemodialysis patients.8,9 However, there are other settings where reinforcement learning has the potential to improve patient care in nephrology. For example, reinforcement learning methods can be adopted in the treatment of the complications of AKI or CKD (Figure 1). In this problem, the state models the conditions of the patient (e.g., vital signs, laboratory test results including urine and blood tests, and urine output measurements). The action refers to the treatment options (e.g., the dosage of medications such as sodium polystyrene sulfonate, and hemodialysis). The reward models the improvement in patient conditions. Similarly, reinforcement learning can help automate and optimize the dosage of immunosuppressive drugs in kidney transplants. Challenges and Opportunities Despite the success of reinforcement learning in several simplified clinical settings, their large-scale application to patient care faces several open challenges. The complexity of human biology complicates modeling clinical decision making as a reinforcement learning problem. The state space in such settings is often enormous, which could make a purely computational approach infeasible. Moreover, modeling all potential objectives a priori as a reward function may not be feasible. To overcome these challenges and realize the potential of reinforcement learning, clinical insight can play a pivotal role. More specifically, restricting the state space to only include highly relevant clinical variables could greatly reduce the computational complexity. Furthermore, using inverse reinforcement learning,2 relevant reward functions can be learned from retrospective studies assuming the optimality of clinical decisions. Another critical challenge is addressing moral and ethical concerns. It is imperative to ensure that reinforcement learning methods do not cause harm to the patient. To this end, there exists a need for a thorough validation of such methods before their use in patient care. Hence, there is a need to go beyond retrospective studies that have been used for the proof of concept of most existing reinforcement learning methods in health care applications.2,3 The lessons learned from the success of reinforcement learning in other application areas (e.g., self-driving cars) can help navigate the path to realizing its potential in health care. Accessible open-source simulation environments that enable researchers to compare various approaches are essential to the field of reinforcement learning. OpenAI Gym is currently the leading toolkit containing a wide range of simulated environments, e.g., surgical robotics.10 The development of high-quality and reliable simulation environments for nephrology and other health care applications can facilitate the development and validation of reinforcement learning methods beyond limited retrospective studies. The adoption of methods validated in such simulation environments in actual clinical settings will require clinicians' oversight. Similar to how self-driving cars require a human driver to ensure collision avoidance, clinicians' oversight is critical to ensure the safety of the patients, especially in the early stages of the adoption of reinforcement learning methods. The data from clinicians' decisions (e.g., overruling the automated treatment recommendation) can be used to improve the reliability of autonomous systems over time and reduce the burden of clinicians' oversight.

Background and Study Aim. Tactical knowledge, both declarative and procedural, is an essential component of cognitive development in youth soccer. The aim of this study is to describe and analyze the declarative and procedural tactical knowledge of youth football players from a professional club in southern Chile. Material and Methods. The Soccer Offensive Tactical Knowledge Test and Procedural Tactical Knowledge Test were the instruments used in the study to assess declarative tactical knowledge (game principles and decision making) and procedural knowledge (execution based on the tactical strategy in a 3x3 reduced-field). Assessments of functional differences could be evaluated due to the representation of players from various positions in the sample. Results. The results indicated superior performance in off-ball procedural skills (JSB: 4.19 ± 1.64) but poor performance in off-ball marking (MJSB: 3.56 ± 2.09), illustrating limitations in defensive anticipation and positional reading. Defenders performed better than midfielders and forwards in all procedural dimensions (p &lt; 0.05), indicating an internalization of tactics in a functional way according to their positional roles. At the declarative level, operational principles–based knowledge among the participants was found to be relatively higher (8.57 ± 2.63), while decision making was found to be lower in this study (5.94 ± 2.28), indicating a deviation from theory to practical execution. No significant differences were found by position in this type of knowledge. Conclusions. The finding of a partial dissociation between declarative and procedural learning, as confirmed earlier in related work, has implications for the methodological design of coaching at the U13 and U14 levels. It indicates why emergent-based methodologies that support the interweaving of tactical thought and action to enable adjusted and effective learning in grassroots football are urgently needed.

The potential of Virtual Reality (VR) in enhancing learning and training is being widely explored. The relationship of immersion, as one of the core technical features of VR, with knowledge types has not been fully explored. This study aims to investigate how VR immersion levels (high vs. low) affect the acquisition of declarative and procedural knowledge, as well as related cognitive and affective factors. A 2 × 2 mixed design was adopted, with 64 college students who had no VR experience and no background in professional medical knowledge being randomly assigned to either a high-immersion group (using HTC Vive Pro headsets) or a low-immersion group (using desktop monitors). Participants completed learning tasks on thyroid and related diseases (declarative knowledge) and cardiopulmonary resuscitation (procedural knowledge), followed by knowledge tests and self-report questionnaires to measure presence, motivation, self-efficacy, cognitive load, and emotional states. Results showed that high immersion significantly improved learning outcomes for both types of knowledge with large effect sizes. In both knowledge domains, high immersion also enhanced presence, intrinsic motivation, self-efficacy, and positive emotions. However, cognitive load was reduced only for declarative knowledge, and no significant effects were observed for self-regulation. These findings highlight the differential impact of VR immersion on knowledge acquisition and provide insights for optimizing VR-based educational interventions.

Professional handball coaches management of players' situated understanding during official games

‘High-quality talk’ is a fundamental principle of many approaches to teaching grammar. However, relatively few studies have attempted to characterize this talk with attention to the ways in which classroom dialogue might engender metalinguistic thinking. This paper explores how the concepts of procedural and declarative metalinguistic knowledge may be applied to classroom discourse in order to identify the problems and potentials of grammatical ‘Metatalk’. The data is drawn from observations of functionally-oriented grammar lessons in 17 classrooms across England. This opportunity sample was drawn from the intervention group of a randomized control trial, all of whom were working within a pedagogical model which embeds attention to grammar as a resource for meaning-making within the teaching of writing. Given the impact of effective teacher-student dialogue on student learning, studies such as this are valuable for illuminating how classroom talk operates within the teaching of grammar for writing. The findings particularly reveal the role of teacher-guided talk during collaborative writing activities in facilitating bi-directional transfer between declarative and procedural metalinguistic knowledge.

In this study, we investigated the effects of students’ participation in a collaborative, project-based engineering design course on their domain knowledge, interests, and strategic processing. Participants were 70 college seniors working in teams on a design project of their choosing. Their declarative, procedural, and principled knowledge, along with their domain interest and their interest in select roles within that domain were tested at the outset of the semester and at its conclusions. Findings indicated that this course contributed to a rise in students’ declarative knowledge, but not their procedural or principled knowledge of engineering design. Further, there was no significant change in students’ personal interest in the domain over the semester, and their role interests were not associated with their demonstrated knowledge in the field at posttest. Implications for the perceived effectiveness of learning environments consisting of peer collaboration and relevant problems on students’ academic development are considered.

Skilled behaviour in real-world contexts often relies on a combination of both declarative and procedural learning. However, precisely how declarative and procedural knowledge interact is not yet fully understood. Previous findings have shown that procedural and declarative learning may interact or compete at the systems level during encoding, consolidation, and retrieval, but beyond this, it is not known whether declarative and procedural representations themselves interact. The goal of the current study is to investigate whether procedural and declarative knowledge can contribute simultaneously to categorization response selection behavior. We designed a stimulus set in which information learned by each system sometimes supports different responses, and created trials in the test phase that are designed to maximize such divergence. Participants were instructed to use a completely diagnostic, verbalizable, shape-based rule to categorize exemplars, receiving feedback after each trial. However, unbeknownst to participants, the categories also differed probabilistically in their color distributions. Participants used both color (learned procedurally) and shape (learned declaratively) to categorize exemplars, responding more quickly when both sources indicated the same category judgement, and more slowly when they conflicted. Debriefing confirmed that most participants were unaware of the color distributions. These results show simultaneous trial-level contributions from both declarative and procedural memory systems. Our findings represent a novel form of interaction between the two systems and have implications for domains beyond the laboratory, such as decision-making and classroom instruction.

This paper describes a methodology for applying expert systems to the task of executing process plans as a control mechanism for the factory floor. This methodology includes an architecture for representing both declarative and procedural knowledge. Declarative knowledge describes the state of the factory floor and the process plan operations that can be performed on parts on the factory floor (e.g., machining and assembly operations). Declarative knowledge is represented using both facts and schemata. Procedural knowledge identifies when and how process plan operations are to be executed. Procedural knowledge is represented using data-driven rules with an asynchronous event handling capability. Rules that determine when process plan operations can be performed embody largely domain-independent scheduling knowledge. Rules that determine how to execute process plan operations require domain-dependent knowledge about particular operations. Both of these types of rules also require information about the current state of the factory floor. A context graph mechanism is included in the methodology to represent temporal precedence constraints among process plan operations (i.e., the structure of a context graph indicates process plan operation precedences). Contexts are also used to represent a part configuration relative to a process plan operation. A prototype expert system based on the methodology presented in this paper has been developed using the Automated Reasoning Tool (ART).