Cognitive Learning Model Research Articles

Impact of Learning Model Based on Cognitive Conflict toward Student’s Conceptual Understanding

The problems that often occur in the learning of physics is a matter of misconception and low understanding of the concept. Misconceptions do not only happen to students, but also happen to college students and teachers. The existing learning model has not had much impact on improving conceptual understanding and remedial efforts of student misconception. This study aims to see the impact of cognitive-based learning model in improving conceptual understanding and remediating student misconceptions. The research method used is Design / Develop Research. The product developed is a cognitive conflict-based learning model along with its components. This article reports on product design results, validity tests, and practicality test. The study resulted in the design of cognitive conflict-based learning model with 4 learning syntaxes, namely (1) preconception activation, (2) presentation of cognitive conflict, (3) discovery of concepts & equations, (4) Reflection. The results of validity tests by some experts on aspects of content, didactic, appearance or language, indicate very valid criteria. Product trial results also show a very practical product to use. Based on pretest and posttest results, cognitive conflict-based learning models have a good impact on improving conceptual understanding and remediating misconceptions, especially in high-ability students.

Standards for smart education \u2013 towards a development framework

Smart learning environments (SLEs) utilize a range of digital technologies in supporting learning, education and training; they also provide a prominent signpost for how future learning environments might be shaped. Thus, while innovation proceeds, SLEs are receiving growing attention from the research community, outputs from which are discussed in this paper. Likewise, this broad application of educational digital technologies is also the remit of standardization in an ISO committee, also discussed in this paper. These two communities share a common interest in, conceptualizing this emerging domain with the aim to identifying direction to further development. In doing so, terminology issues arise along with key questions such as, ‘how is smart learning different from traditional learning?’ Presenting a bigger challenge is the question, ‘how can standardization work be best scoped in today's innovation-rich, networked, cloud-based and data-driven learning environments?’ In responding, this conceptual paper seeks to identify candidate constructs and approaches that might lead to stable, coherent and exhaustive understanding of smart learning environments, thereby providing standards development for learning, education and training a needed direction. Based on reviews of pioneering work within smart learning, smart education and smart learning environments we highlight two models, a cognitive smart learning model and a smartness level model. These models are evaluated against current standardization challenges in the field of learning, education and training to form the basis for a development platform for new standards in this area.

A cognitive learning model in distance education of higher education institutions based on chaos optimization in big data environment

In the era of big data, the traditional distance learning method cannot effectively distinguish the weight of each course attribute, ignoring the recognition process of knowledge cognition law in higher education institutions. In order to better study the learning motivation, learning effect, and cognitive process of distance learners, a chaos optimization cognitive learning model based on chaos optimization and big data analysis is designed in this paper. The proposed model takes into account the learners’ learning motivation, learning task demands, and the change rate of cognitive rules and transforms the learning process of distance learning into a multi-objective optimization problem. The experimental results show that the proposed model can effectively improve the teaching quality of distance education courses in higher education institutions, and the model is scalable and compatible.

The Role of Engagement in Learning From Active and Incidental News Exposure on Social Media

The growing reliance on social media as news platforms may lead to more passive news consumption but also offers greater potential for engaging in news. This study investigates the role of engagement with news content on Facebook and Twitter between news exposure and current events knowledge. An online survey (N = 400) tests the relationships between social media news seeking, incidental exposure to news on social media, engagement in shared news content, cognitive elaboration, and current events knowledge. The results show that both active seeking of and incidental exposure to news on both sites are linked to engagement, which is linked to greater cognitive elaboration about the content. Furthermore, engagement mediates the relationship between both types of news exposure and cognitive elaboration. However, engagement and elaboration are not related to knowledge. These results indicate that the key role of social media in news content is not knowledge gain but the ability to engage users who may be passively receiving news on these sites. This study extends the cognitive mediation model of learning from the news in the context of current social media, with updated news consumption norms such as engagement with news on these sites, and incidental news exposure.

Rethinking models of professional learning as tools: a conceptual analysis to inform research and practice

One approach to designing, researching or evaluating professional learning experiences is to use models of learning processes. Here we analyse and critique five significant contemporary analytical models: three variations on path models, proposed by Guskey, by Desimone and by Clarke and Hollingsworth; a model using a systemic conceptualisation of learning by Opfer and Pedder; and a cognitive learning model by Evans. To do this, we develop and illustrate an analytical framework focused on model components, purposes, scope, explicit and implicit theories of learning and change processes, agency and philosophical underpinnings. We identify similarities, differences, inconsistencies and limitations in the models. This provides the basis for reconceptualising models as tools to be deployed alongside other relevant constructs and thus the analytical framework can support a more informed selection of theoretical models by researchers and practitioners.

Research on the Design of Parallel Hypernetworks Model Based on Hadoop Cloud Computing Platform

The evolutionary hypernetwork is a kind of cognitive learning model inspired by biological networks and the hypernetwork model has been successfully applied in plenty of fields. But the hypernetwork model may lead to a huge amount of hyperedges in the process of high dimensional data, which is the bottleneck of the single machine hardware implementation. In order to solve this problem, this paper presents the parallel model of hypernetwork computation under the Hadoop cloud computing platform, which changes single machine algorithm to multiple machines. Empirical studies on the acute leukemia dataset result demonstrate that the proposed hypernetwork classifier of parallel evolutionary hypernetwork model based on the Hadoop cloud computing platform has higher classification accuracy rate and good scalability. This is the main advantage of the parallel evolutionary hypernetwork model.

Cognitive learning approach for travel demand modeling: estimation results

Abstract: The paper reports progress in the development of an agent-based model of cognitive learning, which simulates spatial perception updating in connection with daily travel behavior based on the principle of Bayesian perception updating. This model is embedded in a multi agent-based model of activity-travel scheduling and choice behavior. The aim of this paper is to empirically estimate the proposed model using data on individuals’ landmark recognition in a field survey. The main findings of the study show that the model fits the data satisfactorily and results are reasonable. The comparison between the proposed Bayesian model and a more basic binary logit model shows that the model improves when prior probabilities are taken into account, which provides evidence for the proposed Bayesian model.

The Cognitive Development Learning Model and Its Application in Autonomous Navigation of Mobile Robot

In terms of the navigation problem of the mobile robot occurring in unknown environment, based on the cognitive and developmental mechanism of the biology, a cognitive development model for the autonomous learning navigation of the mobile robot is designed. Through autonomous insertion and deletion of neuron nodes in the neural network, the neural network with a dynamically adjusted structure is designed and the biological characteristics of living beings are imitated, thereby obtaining a network scale that matches the application requirements. The thermodynamic process is used to simulate the asymptotic properties of the animals. The cognitive learning algorithm is designed from the Monte Carlo method, the Metropolis algorithm and the annealing algorithm, and it is theoretically proved convergent. Based on the formation of the cognitive development process, the autonomous navigation activities of the mobile robot is under study. The experimental results show that, with the help of the cognitive development model, the robot automatically acquires knowledge and accumulates experience from the environment through cognition and development like animals. And the robot gradually forms, develops and improves the autonomous navigation skills in a self-organized way.

Examining teaching based on errors in mathematics amongst pupils with learning disabilities

Teaching mathematics while learning from students' mistakes, errors and misconceptions, is most important for meaningful learning. This study was based on intervention programs prepared by preservice teachers. It aimed to examine their knowledge of assessment of errors in mathematics amongst pupils with learning disabilities, and their use as a basis for didactically adapted teaching. The research methodology was qualitative. Three pairs of preservice teachers (a total of six students) studying special education participated. Content analysis was performed using the Individualized Education Program and according to Hiebert and Lefevre's cognitive learning model. The research findings support and indicate the importance of coordinating an analysis of the pupil's errors and the choice of a didactically adapted teaching strategy, in order to achieve a more effective focus on the mathematical difficulties and the misconceptions, and thus to contribute to improving the therapeutic effectiveness and realize the student's abilities to the maximum. The current study is thus innovative in examining the assessment of the errors in mathematics made by students with learning disabilities, not only in regards to the disability in cognitive functioning and difficulties in mathematical functioning, but also in regards to the use of conceptual and procedural mathematical knowledge by the preservice teachers and the teacher.

Neural signatures of experience-based improvements in deterministic decision-making

Feedback about our choices is a crucial part of how we gather information and learn from our environment. It provides key information about decision experiences that can be used to optimize future choices. However, our understanding of the processes through which feedback translates into improved decision-making is lacking. Using neuroimaging (fMRI) and cognitive models of decision-making and learning, we examined the influence of feedback on multiple aspects of decision processes across learning. Subjects learned correct choices to a set of 50 word pairs across eight repetitions of a concurrent discrimination task. Behavioral measures were then analyzed with both a drift-diffusion model and a reinforcement learning model. Parameter values from each were then used as fMRI regressors to identify regions whose activity fluctuates with specific cognitive processes described by the models. The patterns of intersecting neural effects across models support two main inferences about the influence of feedback on decision-making. First, frontal, anterior insular, fusiform, and caudate nucleus regions behave like performance monitors, reflecting errors in performance predictions that signal the need for changes in control over decision-making. Second, temporoparietal, supplementary motor, and putamen regions behave like mnemonic storage sites, reflecting differences in learned item values that inform optimal decision choices. As information about optimal choices is accrued, these neural systems dynamically adjust, likely shifting the burden of decision processing from controlled performance monitoring to bottom-up, stimulus-driven choice selection. Collectively, the results provide a detailed perspective on the fundamental ability to use past experiences to improve future decisions.

Reinforcement learning analysis for a minimum time balance problem

Reinforcement learning was developed to solve complex learning control problems, where only a minimal amount of a priori knowledge exists about the system dynamics. It has also been used as a model of cognitive learning in humans and applied to systems, such as pole balancing and humanoid robots, to study embodied cognition. However, closed-form analysis of the value function learning based on a higher-order unstable test problem dynamics has been rarely considered. In this paper, firstly, a second-order, unstable balance test problem is used to investigate issues associated with the value function parameter convergence and rate of convergence. In particular, the convergence of the minimum time value function is analysed, where the minimum time optimal control policy is assumed known. It is shown that the temporal difference error introduces a null space associated with the experiment termination basis function during the simulation. As this effect occurs due to termination or any kind of switching in control signal, this null space appears in temporal differences (TD) error for more general higher-order systems. Secondly, the rate of parameter convergence is analysed and it is shown that residual gradient algorithm converges faster than TD(0) for this particular test problem. Thirdly, impact of the finite horizon on both the value function and control policy learning has been analysed in case of unknown control policy and added random exploration noise.

Listening with Your Gut: The Role of Intuition Boundary Judgments

ABSTRACTBoundary dilemmas are an important aspect of clinical social work. The author presents a case example to demonstrate the use of clinical intuition to arrive at a judgment in one such dilemma. The analytic literature suggests that intuition is best understood as unconscious communication. Cognitive research has offered two alternative models of intuition: the heuristics model and the learning model. The case presented illustrates that perhaps the psychoanalytic understanding of intuition as unconscious communication and the cognitive learning model operated in tandem to help the therapist arrive at a decision that served the client and treatment goals in this particular case.

Ontology Update in the Cognitive Model of Ontology Learning

Ontology has been used in many hot-spot fields, but most ontology construction methods are semiautomatic, and the construction process of ontology is still a tedious and painstaking task. In this paper, a kind of cognitive models is presented for ontology learning which can simulate human being’s learning from world. In this model, the cognitive strategies are applied with the constrained axioms. Ontology update is a key step when the new knowledge adds into the existing ontology and conflict with old knowledge in the process of ontology learning. This proposal designs and validates the method of ontology update based on the axiomatic cognitive model, which include the ontology update postulates, axioms and operations of the learning model. It is proved that these operators subject to the established axiom system.

A Quantisation of Cognitive Learning Process by Computer Graphics-Games: Towards More Efficient Learning Models

With the latest developments in computer technologies and artificial intelligence (AI) techniques, more opportunities of cognitive data acquisition and stimulation via game-based systems have become available for computer scientists and psychologists. This may lead to more efficient cognitive learning model developments to be used in different fields of cognitive psychology than in the past. The increasing popularity of computer games among a broad range of age groups leads scientists and experts to seek game domain solutions to cognitive based learning abnormalities, especially for younger age groups and children. One of the major advantages of computer graphics and using game-based techniques over the traditional face-to-face therapies is that individuals, especially children immerse in the game’s virtual environment and consequently feel more open to share their cognitive behavioural characteristics naturally. The aim of this work is to investigate the effects of graphical agents on cognitive behaviours to generate more efficient cognitive models.

Self-regulated learning and students’ metacognitive feelings in physical education

The aim of this study was to examine students’ self-regulated learning and metacognitive feelings regarding a sport skill in elementary physical education. Participants were 88 fifth-and sixth-grade students who were assigned to four groups and practised basketball shooting at different levels of self-regulated learning (i.e. observation, emulation, self-control, and self-regulation). Students were pre- and post-tested in basketball shooting performance and their knowledge regarding technical aspects of the basketball shooting. They also reported their metacognitive feelings of effort, difficulty, and correctness regarding basketball shooting before and after the practice. Results showed that students who experienced both observational learning and emulative practice (i.e. practice with social feedback) improved their knowledge regarding technical aspects of the basketball shooting. However, no differences were found among groups in basketball shooting performance. Moreover, students’ post-test basketball shooting performance was negatively correlated with students’ feelings of difficulty and positively correlated with students’ feelings of correctness. Results are discussed with reference to social cognitive models of self-regulated learning. The role of metacogitive feelings in the process of self-regulated learning of a sport skill is also discussed.

Effect of Internet-Based Cognitive Apprenticeship Model (i-CAM) on Statistics Learning among Postgraduate Students.

Because students’ ability to use statistics, which is mathematical in nature, is one of the concerns of educators, embedding within an e-learning system the pedagogical characteristics of learning is ‘value added’ because it facilitates the conventional method of learning mathematics. Many researchers emphasize the effectiveness of cognitive apprenticeship in learning and problem solving in the workplace. In a cognitive apprenticeship learning model, skills are learned within a community of practitioners through observation of modelling and then practice plus coaching. This study utilized an internet-based Cognitive Apprenticeship Model (i-CAM) in three phases and evaluated its effectiveness for improving statistics problem-solving performance among postgraduate students. The results showed that, when compared to the conventional mathematics learning model, the i-CAM could significantly promote students’ problem-solving performance at the end of each phase. In addition, the combination of the differences in students' test scores were considered to be statistically significant after controlling for the pre-test scores. The findings conveyed in this paper confirmed the considerable value of i-CAM in the improvement of statistics learning for non-specialized postgraduate students.

Chaotic dynamics and convergence analysis of temporal difference algorithms with bang‐bang control

SummaryReinforcement learning is a powerful tool used to obtain optimal control solutions for complex and difficult sequential decision making problems where only a minimal amount of a priori knowledge exists about the system dynamics. As such, it has also been used as a model of cognitive learning in humans and applied to systems, such as humanoid robots, to study embodied cognition. In this paper, a different approach is taken where a simple test problem is used to investigate issues associated with the value function's representation and parametric convergence. In particular, the terminal convergence problem is analyzed with a known optimal control policy where the aim is to accurately learn the value function. For certain initial conditions, the value function is explicitly calculated and it is shown to have a polynomial form. It is parameterized by terms that are functions of the unknown plant's parameters and the value function's discount factor, and their convergence properties are analyzed. It is shown that the temporal difference error introduces a null space associated with the finite horizon basis function during the experiment. The learning problem is only non‐singular when the experiment termination is handled correctly and a number of (equivalent) solutions are described. Finally, it is demonstrated that, in general, the test problem's dynamics are chaotic for random initial states and this causes digital offset in the value function learning. The offset is calculated, and a dead zone is defined to switch off learning in the chaotic region. Copyright © 2015 John Wiley & Sons, Ltd.

A Computational Cognitive Model of User Applying Creativity Technique in Creativity Support Systems

Abstract Numerous creativity techniques have been purposed and applied in creativity support system. Because most creativity techniques are used informally and hardly represented formally in computer, it becomes very difficult to build the computational cognitive model of user applying those techniques. However the model is necessary for creativity support systems to detect or predict the change of user's cognitive state in time and make some adaption to avoid inhibiting creativity of user. In this paper we introduce extension creative idea generation method which has the characteristics of formalization and systematization. The method can be represented by extension rules which provide the precondition to build computational cognitive model of user in creativity support systems. The computational cognitive model of user learning in applying extension creative idea generation method is presented through experiments. The experimental results show how and when the user will develop the application skill of creativity technique and inhibit his creativity.

Assessing Professional Vision in Teacher Candidates

In this study, we present an approach to validating the video-based Observer Extended Research Tool, which empirically captures prospective teachers’ professional vision in a standardized yet contextualized way. We extended the original Observer tool with the aim of providing a reliable, efficient measure for subpopulations in different consecutive phases of teacher education (university and induction phase). Therefore, we expand the measure to include a broader spectrum of knowledge about effective teaching by drawing on a cognitive process-oriented teaching and learning model while at the same time the number of test items is shortened to ensure a economically manageable assessment tool. In the validation study, we tested the extent to which the extension meets the criteria of context validity, reliability, and sensitiveness for different subpopulations. The participants were 317 preservice teachers and teacher candidates who worked with the Observer Extended Research Tool. Measurement quality was investigated using methods of item response theory. Our results confirm that the Observer Extended Research Tool provides a reliable measure of description, explanation, and prediction as aspects of professional vision within and across different subpopulations in teacher education.

Clinical Nursing Reasoning in Nursing Practice: A Cognitive Learning Model based on a Think Aloud Methodology

Clinical Nursing Reasoning in Nursing Practice: A Cognitive Learning Model based on a Think Aloud Methodology