PERBEDAAN KEMAMPUAN KOGNITIF DAN KETERAMPILAN KOOPERATIF DALAM PEMBELAJARAN KOOPERATIF MODEL JIGSAW DAN COOPERATIV SCRIPT SISWA KELAS VIII MTS MUHAMMADIYAH 1 MALANG

Cooperative learning is a study which focused on cooperation among students to reach learning goal. Cooperative learning with Jigsaw Model and Cooperative Script were two cooperative learning model which had many characteristic similarity. The characteristic similarity became background of study. Statement of problems in the research was whether any different of cognitive ability and cooperative skill of students using cooperative learning with Jigsaw model and cooperative script. \ud The research had three purposes, the first was to find out whether any different of cognitive ability using jigsaw model and cooperative script. The second was to find out whether any different in students cooperative skill using Jigsaw model and cooperative script. The third was students perception to the cooperative learning. \ud The research used was quasi-experiment using two group research pretest and posttest design. Population were students of VIIIA, VIIIB, and VIIIC degree Islamic School (MTs) Muhammadiyah I Malang. Samples used were two class taken by random sampling technique. \ud They were VIIIB as cooperative learning jigsaw model class, VIIIC as cooperative learning cooperative script model class. Data collected were analyzed using normality and homogeneity test, data then analyzed by t-test differential test. \ud The research showed that there were difference among cognitive ability and cooperative skill of students taught by cooperative learning jigsaw model and cooperative script. It was proven from t-test with t-count = 2,16 > t-table = 2,04 for cognitive ability data. Cooperative skill data analysis found t-count 4,54>t-table = 2,04. Data analysis showed that cognitive ability and cooperative skill taught by jigsaw model was better than students taught by cooperative script. \ud Students responds about cooperative learning Jigsaw model with most answers were agree to the statement in the questionnaire with percentage 79%, and 75,27 % in cooperative script for agree answer

A cognitive hierarchy model of learning in networks

This paper proposes a method for estimating a hierarchical model of bounded rationality in games of learning in networks. A cognitive hierarchy comprises a set of cognitive types whose behavior ranges from random to substantively rational. SpeciÖcally, each cognitive type in the model corresponds to the number of periods in which economic agents process new information. Using experimental data, we estimate type distributions in a variety of task environments and show how estimated distributions depend on the structural properties of the environments. The estimation results identify signiÖcant levels of behavioral hetero-geneity in the experimental data and overall conÖrm comparative static conjectures on type distributions across task environments. Surprisingly, the model replicates the aggregate pat-terns of the behavior in the data quite well. Finally, we found that the dominant type in the data is closely related to Bayes-rational behavior

A Cognitive Model of the Learning Curve

This article provides a cognitive foundation of the parameters that regulate a model of the learning curve. Organizational learning and its actual occurrence are linked to the number of available categories and to the amount of information to be processed.Learning Curve, Organization of Production, Team Work

The Role of Consciousness in Memory

Conscious events interact with memory systems in learning, rehearsal and retrieval (Ebbinghaus 1885/1964; Tulving 1985). Here we present hypotheses that arise from the IDA computional model (Franklin, Kelemen and McCauley 1998; Franklin 2001b) of global workspace theory (Baars 1988, 2002). Our primary tool for this exploration is a flexible cognitive cycle employed by the IDA computational model and hypothesized to be a basic element of human cognitive processing. Since cognitive cycles are hypothesized to occur five to ten times a second and include interaction between conscious contents and several of the memory systems, they provide the means for an exceptionally fine-grained analysis of various cognitive tasks. We apply this tool to the small effect size of subliminal learning compared to supraliminal learning, to process dissociation, to implicit learning, to recognition vs. recall, and to the availability heuristic in recall. The IDA model elucidates the role of consciousness in the updating of perceptual memory, transient episodic memory, and procedural memory. In most cases, memory is hypothesized to interact with conscious events for its normal functioning. The methodology of the paper is unusual in that the hypotheses and explanations presented are derived from an empirically based, but broad and qualitative computational model of human cognition

LIDA: A Working Model of Cognition

In this paper we present the LIDA architecture as a working model of cognition. We argue that such working models are broad in scope and address real world problems in comparison to experimentally based models which focus on specific pieces of cognition. While experimentally based models are useful, we need a working model of cognition that integrates what we know from neuroscience, cognitive science and AI. The LIDA architecture provides such a working model. A LIDA based cognitive robot or software agent will be capable of multiple learning mechanisms. With artificial feelings and emotions as primary motivators and learning facilitators, such systems will ‘live’ through a developmental period during which they will learn in multiple ways to act in an effective, human-like manner in complex, dynamic, and unpredictable environments. We discuss the integration of the learning mechanisms into the existing IDA architecture as a working model of cognition

Eksperimentasi Model Pembelajaran Matematika Problem Posing Dengan Teknik Learning Cell Pada Materi Pokok Bangun Ruang Sisi Datar Ditinjau Dari Gaya Kognitif Siswa Pada Siswa SMP Kelas VIII Di Kabupaten Sukoharjo

This research was aimed at searching and finding: 1) the most effective mathematics learning model among three models, including problem posing model with learning cell technique, problem posing model without learning cell technique, and direct learning model, 2) more effective student's cognitive style of field independent and field dependent, 3) more effective student's cognitive style of field independent and field dependent on each model, and 4) the most effective mathematical learning model among three models, including problem posing model with learning cell technique, problem posing model without learning cell technique, and direct learning model on each student's cognitive style. This type of the research was a quasy-experimental research. The population was all students of grade VIII of state junior high school in Sukoharjo regency in 2013/2014. The size of the samples was 302 students consisted of 102 students in the first experimental group, 101 students in the second experimental group, and 99 students in control group. The data instruments used were documents of student's early achievement, cognitive style questionnaire, and mathematics achievement test. The data was analyzed using analysis of variance. The conclusions of the research were as follows. (1) Problem posing mathematics learning model with learning cell technique is more effective than problem posing model without learning cell technique; problem posing mathematics learning model with learning cell technique is more effective than direct learning model; and problem posing learning model without learning cell technique is more effective than direct learning model, (2) Students having field independent cognitive style have greater achievement than those having field dependent cognitive style, (3) Dealing with problem posing model with learning cell technique, students having field independent cognitive style and field dependent cognitive style have the same achievement; dealing with problem posing learning model without learning cell technique, students having field independent cognitive style have greater achievement than those having field dependent cognitive style; and dealing with direct learning model, students having field independent cognitive style have greater achievement than those having field dependent cognitive style, and (4) To students having field independent cognitive style, problem posing model with learning cell technique, problem posing model without learning cell technique, and direct learning model give the same student's achievement; to students having field dependent cognitive style, problem posing model with learning cell technique gives higher student's achievement than problem posing model without learning cell technique and direct learning model, and problem posing model without learning cell technique gives higher student's achievement than direct learning model

Exploring the concept of learning agility : a thesis presented in partial fulfilment of the requirements for the degree of Master of Business Studies in Human Resource Management at Massey University, Manawatu, New Zealand

Continuous learning and employee adaptation have become increasingly important within modern organisational environments categorised by volatility, uncertainty, complexity, and ambiguity. In turn, this has resulted in a growing body of literature supporting a construct known as learning agility. This study sought to determine the underlying psychological variables that support individual learning agility. In doing so, cognitive ability, personality, and emotional intelligence assessments distributed by OPRA Psychology Group were administered to a random sample of Scenic Hotel Group employees to obtain quantifiable data. Alongside this, a validated learning agility questionnaire was administered to participants and their managers to obtain a measure of each employee’s learning agility. Participants’ learning agility scores were then correlated with their personality, cognitive ability, and emotional intelligence assessment results. Results of this study indicate that learning agility is significantly positively correlated with overall cognitive ability. Furthermore, learning agility shows a significantly positive relationship with personality factors associated with openness to experience, extraversion, and the neuroticism sub-trait, tense-driven. As an outcome, this has provided for a tentative model of learning agility comprising of: 1. Cognitive ability 2. Learning mindset and behaviour 3. Contribution to the social learning environment This research adds to the current body of literature available into a construct known as a key determinant of employee performance and potential (Eichinger & Lombardo, 2000; McCauley, 2001). Furthermore, it provides the foundations for the development of a derived measure of learning agility that can be determined using existing psychometric assessments

A Psychogenetic Algorithm for Behavioral Sequence Learning

This work presents an original algorithmic model of some essential features of psychogenetic theory, as was proposed by J.Piaget. Specifically, we modeled some elements of cognitive structure learning in children from 0 to 4 months of life. We are in fact convinced that the study of well-established cognitive models of human learning can suggest new, interesting approaches to problem so far not satisfactorily solved in the field of machine learning. Further, we discussed the possible parallels between our model and subsymbolic machine learning and neuroscience. The model was implemented and tested in some simple experimental settings, with reference to the task of learning sensorimotor sequences

Neurons and symbols: a manifesto

We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty