Model Of Skill Learning Research Articles

Presenting the educational policy model of skill learning for first year high school students: with a mixed approach

Background and Aim: Students can better understand what they have learned and prepare to build a better future. The present research was conducted to provide a model of educational policy for skill learning of first-year high school students. Methods: In terms of the applied goal, this research was exploratory mixed (qualitative-quantitative) in terms of data, and in terms of nature, in the qualitative dimension, it was a systemic (paradigm) foundation data, and in the quantitative dimension, it was a cross-sectional survey. The statistical population of the qualitative part included university professors and experts who had papers in the field of skill training, and in the quantitative part, it included all the teachers of the first year of secondary education in the central province who had a master's degree and above in the field of educational sciences, numbering 500 people. The sampling method in the qualitative part is purposeful and theoretical, and with the number of 15 experts, the theoretical saturation was reached. In the quantitative part of the sample size, 217 people were selected based on Morgan's volume determination formula. In the current research, after the open and axial coding of the measurement tool, it was sent in the form of a set form and sent to experts for selective coding and validation. Based on this, a researcher-made questionnaire was designed and distributed among the randomly selected statistical sample. Then the collected data were analyzed using descriptive and inferential statistics. Results: As a result, 5 dimensions, 20 components and 118 indicators were finalized for the educational policy model of students' skill learning. Conclusion: After the final approval and prioritization of the experts, the dimensions, components and indicators of the model were drawn, and the experts again validated the said model.

Skill learning in making and experiencing artworks: technologies that transform detached intellectuals into bodily engaged actors

Rietveld discusses three skills that artistic practices have to offer to makers of technologies for embedding such technologies in society. An ecological dynamics model of skill learning empowers the embedding of Rietveld’s described skills in three social domains: technology makers, artworkers, and visitors of such artworks.

Covert skill learning in a cortical-basal ganglia circuit

We learn complex skills such as speech and dance through a gradual process of trial and error. Cortical-basal ganglia circuits have an important yet unresolved function in this trial-and-error skill learning; influential 'actor-critic' models propose that basal ganglia circuits generate a variety of behaviours during training and learn to implement the successful behaviours in their repertoire. Here we show that the anterior forebrain pathway (AFP), a cortical-basal ganglia circuit, contributes to skill learning even when it does not contribute to such 'exploratory' variation in behavioural performance during training. Blocking the output of the AFP while training Bengalese finches to modify their songs prevented the gradual improvement that normally occurs in this complex skill during training. However, unblocking the output of the AFP after training caused an immediate transition from naive performance to excellent performance, indicating that the AFP covertly gained the ability to implement learned skill performance without contributing to skill practice. In contrast, inactivating the output nucleus of the AFP during training completely prevented learning, indicating that learning requires activity within the AFP during training. Our results suggest a revised model of skill learning: basal ganglia circuits can monitor the consequences of behavioural variation produced by other brain regions and then direct those brain regions to implement more successful behaviours. The ability of the AFP to identify successful performances generated by other brain regions indicates that basal ganglia circuits receive a detailed efference copy of premotor activity in those regions. The capacity of the AFP to implement successful performances that were initially produced by other brain regions indicates precise functional connections between basal ganglia circuits and the motor regions that directly control performance.

Modeling of mesencephalic locomotor region for Nao humanoid robot

PurposeThe purpose of this paper is to model a motor region named the mesencephalic locomotors region (MLR) which is located in the end part of the brain and first part of the spinal cord. This model will be used for a Nao soccer player humanoid robot. It consists of three main parts: High Level Decision Unit (HLDU), MLR‐Learner and the CPG layer. The authors focus on a special type of decision making named curvilinear walking.Design/methodology/approachThe authors' model is based on stimulation of some programmable central pattern generators (PCPGs) to generate curvilinear bipedal walking patterns. PCPGs are made from adaptive Hopfs oscillators. High level decision, i.e. curvilinear bipedal walking, will be formulated as a policy gradient learning problem over some free parameters of the robot CPG controller.FindingsThe paper provides a basic model for generating different types of motions in humanoid robots using only simple stimulation of a CPG layer. A suitable and fast curvilinear walk has been achieved on a Nao humanoid robot, which is similar to human ordinary walking. This model can be extended and used in other types of humanoid.Research limitations/implicationsThe authors' work is limited to a special type of biped locomotion. Different types of other motions are encouraged to be tested and evaluated by this model.Practical implicationsThe paper introduces a bio‐inspired model of skill learning for humanoid robots. It is used for curvilinear bipedal walking pattern, which is a beneficial movement in soccer‐playing Nao robots in Robocup competitions.Originality/valueThe paper uses a new biological motor concept in artificial humanoid robots, which is the mesencephalic locomotor region.

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

This work presents an integrated model of skill learning that takes into account both implicit and explicit processes and both action-centered and non-action-centered knowledge. The existing distinction between procedural and declarative knowledge unnecessarily confounds these two aspects (action-centeredness and accessibility), and can be made clearer through separating the two aspects. The model is used to simulate human data in a letter counting task. The work shows how the data in this task may be captured using either action-centered knowledge alone or both action-centered and non-action-centered knowledge, though the combined approach produced a better fit. The results demonstrate the difference between these approaches and provide a new perspective on skill learning.

The interaction of implicit learning, explicit hypothesis testing learning and implicit-to-explicit knowledge extraction

To further explore the interaction between the implicit and explicit learning processes in skill acquisition (which have been tackled before, e.g. in [Sun, R., Merrill, E., & Peterson, T. (2001). From implicit skill to explicit knowledge: A bottom-up model of skill learning. Cognitive Science, 25(2), 203-244; Sun, R., Slusarz, P., & Terry, C. (2005). The interaction of the explicit and the implicit in skill learning: A dual-process approach. Psychological Review, 112(1), 159-192]), this paper explores details of the interaction of different learning modes: implicit learning, explicit hypothesis testing learning, and implicit-to-explicit knowledge extraction. Contrary to the common tendency in the literature to study each type of learning in isolation, this paper highlights the interaction among them and various effects of the interaction on learning, including the synergy effect. This work advocates an integrated model of skill learning that takes into account both implicit and explicit learning processes; moreover, it also uniquely embodies a bottom-up (implicit-to-explicit) learning approach in addition to other types of learning. The paper shows that this model accounts for various effects in the human behavioural data from the psychological experiments with the process control task, in addition to accounting for other data in other psychological experiments (which has been reported elsewhere). The paper shows that to account for these effects, implicit learning, bottom-up implicit-to-explicit extraction and explicit hypothesis testing learning are all needed.

The Interaction of the Explicit and the Implicit in Skill Learning: A Dual-Process Approach.

This article explicates the interaction between implicit and explicit processes in skill learning, in contrast to the tendency of researchers to study each type in isolation. It highlights various effects of the interaction on learning (including synergy effects). The authors argue for an integrated model of skill learning that takes into account both implicit and explicit processes. Moreover, they argue for a bottom-up approach (first learning implicit knowledge and then explicit knowledge) in the integrated model. A variety of qualitative data can be accounted for by the approach. A computational model, CLARION, is then used to simulate a range of quantitative data. The results demonstrate the plausibility of the model, which provides a new perspective on skill learning.

Top-down versus bottom-up learning in cognitive skill acquisition

This paper explores the interaction between implicit and explicit processes during skill learning, in terms of top-down learning (that is, learning that goes from explicit to implicit knowledge) versus bottom-up learning (that is, learning that goes from implicit to explicit knowledge). Instead of studying each type of knowledge (implicit or explicit) in isolation, we stress the interaction between the two types, especially in terms of one type giving rise to the other, and its effects on learning. The work presents an integrated model of skill learning that takes into account both implicit and explicit processes and both top-down and bottom-up learning. We examine and simulate human data in the Tower of Hanoi task. The paper shows how the quantitative data in this task may be captured using either top-down or bottom-up approaches, although top-down learning is a more apt explanation of the human data currently available. These results illustrate the two different directions of learning (top-down versus bottom-up), and thereby provide a new perspective on skill learning.

From implicit skills to explicit knowledge: a bottom-up model of skill learning

This paper presents a skill learning model Clarion. Different from existing models of mostly high-level skill learning that use a top-down approach (that is, turning declarative knowledge into procedural knowledge through practice), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Our model is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line reactive learning. It adopts a two-level dual-representation framework (Sun, 1995), with a combination of localist and distributed representation. We compare the model with human data in a minefield navigation task, demonstrating some match between the model and human data in several respects.

From implicit skills to explicit knowledge: a bottom‐up model of skill learning

AbstractThis paper presents a skill learning model CLARION. Different from existing models of mostly high‐level skill learning that use a top‐down approach (that is, turning declarative knowledge into procedural knowledge through practice), we adopt a bottom‐up approach toward low‐level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Our model is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on‐line reactive learning. It adopts a two‐level dual‐representation framework (Sun, 1995), with a combination of localist and distributed representation. We compare the model with human data in a minefield navigation task, demonstrating some match between the model and human data in several respects.

Comments on Rod Ellis's "The Structural Syllabus and Second Language Acquisition". Implicit Knowledge, Second Language Learning, and Syllabus Construction

a In his TESOL Quarterly article (Vol. 27, No. 1, Spring 1993), Rod Ellis attempts to make a case for the role of a structural syllabus as a means to promote gradual mastery of implicit second language knowledge. Ellis proposes a two-dimensional model of skill learning which is similar to the analysis control model proposed by Bialystok (1982). However, the invocation of a distinction between implicit and explicit knowledge to replace one of Bialystok's constructs, the dimension of analysis, leads to confusion and theoretical incoherence because it is not clear how Ellis's definitions of implicit and explicit knowledge relate to similar distinctions in cognitive psychology and L2 research (e.g., Hulstijn, 1990; Logan, 1988; McLaughlin, 1987; Reber, 1989; Shiffrin & Schneider, 1977). No empirical evidence from this research is adduced to support assertions Ellis makes about implicit knowledge which are fundamental to his proposals for pedagogy. In particular, it is unclear how Ellis motivates claims about the nature of the interface between implicit and explicit knowledge, and the form of implicit knowledge. In his model of second language acquisition processes, Ellis replaces Bialystok's (1982) +/analyzed distinction with a distinction between two of knowledge, explicit and implicit. For Bialystok, this dimension represents a continuum of analysis, with early language being toward the unanalyzed end of the scale and analyzed language characterizing the later, more developed system. For Ellis this is a dichotomy between interfaced, but distinct implicit and explicit types of knowledge. Bialystok's dimension of control is referred to as the to procedural (Anderson, 1983) dimension by Ellis. Language development is characterized as the process whereby explicit declarative

Training spatial abilities: Effects of practice on rotation and synthesis tasks

Three studies are reported. In the first study, changes in performance on four spatial tests were examined for eighty-three subjects who attempted 1,200 three-dimensional rotation problems. Gains in accuracy in solving these problems averaged over 1 SD from the first to the last block of trials. Gains on the reference tests were also substantial, ranging from over 1 SD on a speeded rotation test to approximately .5 SD on a more difficult form-board test. In the second study, fifty subjects were administered the same spatial tests, but without practice on rotation problems. The large gains in Study 1 appeared to be attributable to practice on the tests. In the third study, 385 subjects attempted 144 mental synthesis problems. Subjects were randomly assigned to a feedback or to a no-feedback condition. Females in the feedback condition showed the greatest reduction in errors from the first to the second half of the test. However, improvements were noted only for a specific type of foil. Results of all three studies are discussed in terms of Anderson's (1983) model of skill learning and fact acquisition.