An incremental meta-cognitive-based scaffolding fuzzy neural network

The idea of meta-cognitive learning has enriched the landscape of evolving systems, because it emulates three fundamental aspects of human learning: what-to-learn; how-to-learn; and when-to-learn. However, existing meta-cognitive algorithms still exclude Scaffolding theory, which can realize a plug-and-play classifier. Consequently, these algorithms require laborious pre- and/or post-training processes to be carried out in addition to the main training process. This paper introduces a novel meta-cognitive algorithm termed GENERIC-Classifier (gClass), where the how-to-learn part constitutes a synergy of Scaffolding Theory - a tutoring theory that fosters the ability to sort out complex learning tasks, and Schema Theory - a learning theory of knowledge acquisition by humans. The what-to-learn aspect adopts an online active learning concept by virtue of an extended conflict and ignorance method, making gClass an incremental semi-supervised classifier, whereas the when-to-learn component makes use of the standard sample reserved strategy. A generalized version of the Takagi-Sugeno Kang (TSK) fuzzy system is devised to serve as the cognitive constituent. That is, the rule premise is underpinned by multivariate Gaussian functions, while the rule consequent employs a subset of the non-linear Chebyshev polynomial. Thorough empirical studies, confirmed by their corresponding statistical tests, have numerically validated the efficacy of gClass, which delivers better classification rates than state-of-the-art classifiers while having less complexity.

Subject independent human action recognition using spatio-depth information and meta-cognitive RBF network

In this paper, we present a machine learning approach for subject independent human action recognition using depth camera, emphasizing the importance of depth in recognition of actions. The proposed approach uses the flow information of all 3 dimensions to classify an action. In our approach, we have obtained the 2-D optical flow and used it along with the depth image to obtain the depth flow (Z motion vectors). The obtained flow captures the dynamics of the actions in space time. Feature vectors are obtained by averaging the 3-D motion over a grid laid over the silhouette in a hierarchical fashion. These hierarchical fine to coarse windows capture the motion dynamics of the object at various scales. The extracted features are used to train a Meta-cognitive Radial Basis Function Network (McRBFN) that uses a Projection Based Learning (PBL) algorithm, referred to as PBL-McRBFN, henceforth. PBL-McRBFN begins with zero hidden neurons and builds the network based on the best human learning strategy, namely, self-regulated learning in a meta-cognitive environment. When a sample is used for learning, PBLMcRBFN uses the sample overlapping conditions, and a projection based learning algorithm to estimate the parameters of the network. The performance of PBL-McRBFN is compared to that of a Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers with representation of every person and action in the training and testing datasets. Performance study shows that PBL-McRBFN outperforms these classifiers in recognizing actions in 3-D. Further, a subject-independent study is conducted by leave-one-subject-out strategy and its generalization performance is tested. It is observed from the subject-independent study that McRBFN is capable of generalizing actions accurately. The performance of the proposed approach is benchmarked with Video Analytics Lab (VAL) dataset and Berkeley Multimodal Human Action Database (MHAD). (C) 2013 Elsevier Ltd. All rights reserved.

Database Independent Human Emotion Recognition with Meta-Cognitive Neuro-Fuzzy Inference System

Facial emotions are the most expressive way to display emotions. Many algorithms have been proposed which employ a particular set of people (usually a database) to both train and test their model. This paper focuses on the challenging task of database independent emotion recognition, which is a generalized case of subject-independent emotion recognition. The emotion recognition system employed in this work is a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). McFIS has two components, a neuro-fuzzy inference system, which is the cognitive component and a self-regulatory learning mechanism, which is the meta-cognitive component. The meta-cognitive component, monitors the knowledge in the neuro-fuzzy inference system and decides on what-to-learn, when-to-learn and how-to-learn the training samples, efficiently. For each sample, the McFIS decides whether to delete the sample without being learnt, use it to add/prune or update the network parameter or reserve it for future use. This helps the network avoid over-training and as a result improve its generalization performance over untrained databases. In this study, we extract pixel based emotion features from well-known (Japanese Female Facial Expression) JAFFE and (Taiwanese Female Expression Image) TFEID database. Two sets of experiment are conducted. First, we study the individual performance of both databases on McFIS based on 5-fold cross validation study. Next, in order to study the generalization performance, McFIS trained on JAFFE database is tested on TFEID and vice-versa. The performance The performance comparison in both experiments against SVNI classifier gives promising results.

Action Recognition from Motion Capture Data using Meta-cognitive RBF Network Classifier

Action recognition plays an important role in various applications, including smart homes and personal assistive robotics. In this paper, we propose an algorithm for recognizing human actions using motion capture action data. Motion capture data provides accurate three dimensional positions of joints which constitute the human skeleton. We model the movement of the skeletal joints temporally in order to classify the action. The skeleton in each frame of an action sequence is represented as a 129 dimensional vector, of which each component is a 31) angle made by each joint with a fixed point on the skeleton. Finally, the video is represented as a histogram over a codebook obtained from all action sequences. Along with this, the temporal variance of the skeletal joints is used as additional feature. The actions are classified using Meta-Cognitive Radial Basis Function Network (McRBFN) and its Projection Based Learning (PBL) algorithm. We achieve over 97% recognition accuracy on the widely used Berkeley Multimodal Human Action Database (MHAD).

Human action recognition in H.264/AVC compressed domain using meta-cognitive radial basis function network

In this paper, we propose a H.264/AVC compressed domain human action recognition system with projection based metacognitive learning classifier (PBL-McRBFN). The features are extracted from the quantization parameters and the motion vectors of the compressed video stream for a time window and used as input to the classifier. Since compressed domain analysis is done with noisy, sparse compression parameters, it is a huge challenge to achieve performance comparable to pixel domain analysis. On the positive side, compressed domain allows rapid analysis of videos compared to pixel level analysis. The classification results are analyzed for different values of Group of Pictures (GOP) parameter, time window including full videos. The functional relationship between the features and action labels are established using PBL-McRBFN with a cognitive and meta-cognitive component. The cognitive component is a radial basis function, while the meta-cognitive component employs self-regulation to achieve better performance in subject independent action recognition task. The proposed approach is faster and shows comparable performance with respect to the state-of-the-art pixel domain counterparts. It employs partial decoding, which rules out the complexity of full decoding, and minimizes computational load and memory usage. This results in reduced hardware utilization and increased speed of classification. The results are compared with two benchmark datasets and show more than 90% accuracy using the PBL-McRBFN. The performance for various GOP parameters and group of frames are obtained with twenty random trials and compared with other well-known classifiers in machine learning literature. (C) 2015 Elsevier B.V. All rights reserved.

Examining the Learning Effects of Segmented Model Demonstrations on the Motor & Cognitive Learning of the Basketball Jump Shot

The purpose of the present experiment was to determine whether learning is optimized when providing the opportunity to observe either segments, or the whole basketball jump shot. Participants performed 50 jump-shots from the free throw line during acquisition, and returned one day later for a 10 shot retention test and a memory recall test of the jump-shot technique. Shot accuracy was assessed on a 5-point scale and technique assessed on a 7-point scale. The number of components recalled correctly by participants assessed mental representation. Retention results showed superior shot technique and recall success for those participants provided control over the frequency and type of modelled information compared to participants not provided control. Furthermore, participants in the self-condition utilized the part-model information more frequently than whole-model information highlighting the effectiveness of providing the learner control over viewing multiple segments of a skill compared to only watching the whole model.

An experimental base to cognitive learning: its application in the education of young hearing-impaired children

This paper reviews a program of study for language development using a cognitive approach.

The impact of pre -training interventions on cognitive, skill, and affective learning outcomes: A meta -analytic examination of four decades of research

Technological advancements and the ever-evolving demands of a global marketplace may have changed the way in which training is designed, implemented, and even managed, but the ultimate goal of organizational training programs remains the same: to facilitate learning of a knowledge, skill, or other outcome that will yield improvement in employee performance on the job and within the organization (Colquitt, LePine, & Noe, 2000; Tannenbaum & Yukl, 1992). Studies of organizational training have suggested medium to large effect sizes for the impact of training on employee learning (e.g., Arthur, Bennett, Edens, & Bell, 2003; Burke & Day, 1986). However, learning may be differentially affected by such factors as the (1) level and type of preparation provided prior to training, (2) targeted learning outcome, (3) training methods employed, and (4) content and goals of training (e.g., Baldwin & Ford, 1988). A variety of pre-training interventions have been identified as having the potential to enhance learning from training and practice (Cannon-Bowers, Rhodenizer, Salas, & Bowers, 1998). Numerous individual studies have been conducted examining the impact of one or more of these pre-training interventions on learning. ^ I conducted a meta-analytic examination of the effect of these pre-training interventions on cognitive, skill, and affective learning. Results compiled from 359 independent studies (total N = 37,038) reveal consistent positive effects for the role of pre-training interventions in enhancing learning. In most cases, the provision of a pre-training intervention explained approximately 5–10% of the variance in learning, and in some cases, explained up to 40–50% of variance in learning. Overall attentional advice and meta-cognitive strategies (as compared with advance organizers, goal orientation, and preparatory information) seem to result in the most consistent learning gains. Discussion focuses on the most beneficial match between an intervention and the learning outcome of interest, the most effective format of these interventions, and the most appropriate circumstances under which these interventions should be utilized. Also highlighted are the implications of these results for practice, as well as propositions for important avenues for future research. ^

Instructional strategies integrating cognitive style construct : a meta-knowledge processing model

The overarching goal of this dissertation was to evaluate the contextual components of instructional strategies for the acquisition of complex programming concepts. A meta-knowledge processing model is proposed, on the basis of the research findings, thereby facilitating the selection of media treatment for electronic courseware. When implemented, this model extends the work of Smith (1998), as a front-end methodology, for his glass-box interpreter called Bradman, for teaching novice programmers. Technology now provides the means to produce individualized instructional packages with relative ease. Multimedia and Web courseware development accentuate a highly graphical (or visual) approach to instructional formats. Typically, little consideration is given to the effectiveness of screen-based visual stimuli, and curiously, students are expected to be visually literate, despite the complexity of human-computer interaction. Visual literacy is much harder for some people to acquire than for others! (see Chapter Four: Conditions-of-the-Learner) An innovative research programme was devised to investigate the interactive effect of instructional strategies, enhanced with text-plus-textual metaphors or text-plus-graphical metaphors, and cognitive style, on the acquisition of a special category of abstract (process) programming concept. This type of concept was chosen to focus on the role of analogic knowledge involved in computer programming. The results are discussed within the context of the internal/external exchange process, drawing on Ritchey's (1980) concepts of within-item and between-item encoding elaborations. The methodology developed for the doctoral project integrates earlier research knowledge in a novel, interdisciplinary, conceptual framework, including: from instructional science in the USA, for the concept learning models; British cognitive psychology and human memory research, for defining the cognitive style construct; and Australian educational research, to provide the measurement tools for instructional outcomes. The experimental design consisted of a screening test to determine cognitive style, a pretest to determine prior domain knowledge in abstract programming knowledge elements, the instruction period, and a post-test to measure improved performance. This research design provides a three-level discovery process to articulate: 1) the fusion of strategic knowledge required by the novice learner for dealing with contexts within instructional strategies 2) acquisition of knowledge using measurable instructional outcome and learner characteristics 3) knowledge of the innate environmental factors which influence the instructional outcomes This research has successfully identified the interactive effect of instructional strategy, within an individual's cognitive style construct, in their acquisition of complex programming concepts. However, the significance of the three-level discovery process lies in the scope of the methodology to inform the design of a meta-knowledge processing model for instructional science. Firstly, the British cognitive style testing procedure, is a low cost, user friendly, computer application that effectively measures an individual's position on the two cognitive style continua (Riding & Cheema,1991). Secondly, the QUEST Interactive Test Analysis System (Izard,1995), allows for a probabilistic determination of an individual's knowledge level, relative to other participants, and relative to test-item difficulties. Test-items can be related to skill levels, and consequently, can be used by instructional scientists to measure knowledge acquisition. Finally, an Effect Size Analysis (Cohen,1977) allows for a direct comparison between treatment groups, giving a statistical measurement of how large an effect the independent variables have on the dependent outcomes. Combined with QUEST's hierarchical positioning of participants, this tool can assist in identifying preferred learning conditions for the evaluation of treatment groups. By combining these three assessment analysis tools into instructional research, a computerized learning shell, customised for individuals' cognitive constructs can be created (McKay & Garner,1999). While this approach has widespread application, individual researchers/trainers would nonetheless, need to validate with an extensive pilot study programme (McKay,1999a; McKay,1999b), the interactive effects within their specific learning domain. Furthermore, the instructional material does not need to be limited to a textual/graphical comparison, but could be applied to any two or more instructional treatments of any kind. For instance: a structured versus exploratory strategy. The possibilities and combinations are believed to be endless, provided the focus is maintained on linking of the front-end identification of cognitive style with an improved performance outcome. My in-depth analysis provides a better understanding of the interactive effects of the cognitive style construct and instructional format on the acquisition of abstract concepts, involving spatial relations and logical reasoning. In providing the basis for a meta-knowledge processing model, this research is expected to be of interest to educators, cognitive psychologists, communications engineers and computer scientists specialising in computer-human interactions.

Greater cognitive effort for better learning : tailoring an instructional design for learners with different levels of knowledge and motivation

The capacity limitation of working memory is a widely recognised determinant of human learning. A cognitive load exceeding the capacity hampers learning. Cognitive load can be controlled by tailoring an instructional design to levels of learner prior knowledge. However, such as design does not necessarily motivate to use the available capacity for better learning. The present review examines literatures on the effects of instructional design, motivation, emotional state, and expertise level on cognitive load and cognitive effort, which ultimately affect working memory performance and learning. This examination suggests further studies on the effects of motivation and negative emotional states on the use of working memory. Prospective findings would help better explain and predict individual differences in the use of working memory for cognitive learning and task performance.

Metaphor and Genre: An Approach to improve the learning process of English for Aeronautics

This study suggests a theoretical framework for improving the teaching/ learning process of English employed in the Aeronautical discourse that brings together cognitive learning strategies, Genre Analysis and the Contemporary theory of Metaphor (Lakoff and Johnson 1980; Lakoff 1993). It maintains that cognitive strategies such as imagery, deduction, inference and grouping can be enhanced by means of metaphor and genre awareness in the context of content based approach to language learning. A list of image metaphors and conceptual metaphors which comes from the terminological database METACITEC is provided. The metaphorical terms from the area of Aeronautics have been taken from specialised dictionaries and have been categorised according to the conceptual metaphors they respond to, by establishing the source domains and the target domains, as well as the semantic networks found. This information makes reference to the internal mappings underlying the discourse of aeronautics reflected in five aviation accident case studies which are related to accident reports from the National Transportation Safety Board (NTSB) and provides an important source for designing language teaching tasks. La Lingüística Cognitiva y el Análisis del Género han contribuido a la mejora de la enseñanza de segundas lenguas y, en particular, al desarrollo de la competencia lingüística de los alumnos de inglés para fines específicos. Este trabajo pretende perfeccionar los procesos de enseñanza y el aprendizaje del lenguaje empleado en el discurso aeronáutico por medio de la práctica de estrategias cognitivas y prestando atención a la Teoría del análisis del género y a la Teoría contemporánea de la metáfora (Lakoff y Johnson 1980; Lakoff 1993). Con el propósito de crear recursos didácticos en los que se apliquen estrategias metafóricas, se ha elaborado un listado de metáforas de imagen y de metáforas conceptuales proveniente de la base de datos terminológica META-CITEC. Estos términos se han clasificado de acuerdo con las metáforas conceptuales y de imagen existentes en esta área de conocimiento. Para la enseñanza de este lenguaje de especialidad, se proponen las correspondencias y las proyecciones entre el dominio origen y el dominio meta que se han hallado en los informes de accidentes aéreos tomados de la Junta federal de la Seguridad en el Transporte (NTSB)

Cooperative or collaborative learning: Is there a difference in university students’ perceptions?

The hypothesis that the same educational objective, raised as cooperative or collaborative learning in university teaching does not affect students’ perceptions of the learning model, leads this study. It analyses the reflections of two students groups of engineering that shared the same educational goals implemented through two different methodological active learning strategies: Simulation as cooperative learning strategy and Problem-based Learning as a collaborative one. The different number of participants per group (eighty-five and sixty-five, respectively) as well as the use of two active learning strategies, either collaborative or cooperative, did not show differences in the results from a qualitative perspective.

Floating on a sea of talk: Reading comprehension through speaking and listening

Talk is the foundation for thought and understanding, and the key to literacy learning. Research demonstrates that powerful meta-cognitive strategies can be taught to help students self-monitor their comprehension when reading print and digital texts. This paper provides a repertoire of motivating speaking and listening tips to develop the meta-cognitive thinking of students in the elementary years.