Neuroscience and educational practice – A critical assessment from the perspective of philosophy of science

Matta, Corrado

doi:10.1080/00131857.2020.1773801

Cited by 5 publications

(8 citation statements)

References 38 publications

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“…This component is also in line with Schoenfeld's theory of mathematics learning, which describes the process of mathematics learning as a mental discipline that trains the cognitive (mind) to turn students into trained thinkers [31]. [27]. Corrado Matta [27] argue that the process of mathematics learning is a process of integration and mechanism of cognitive activity in general and a deep cognitive process.…”

Section: Mathematics Learning Modelsupporting

confidence: 56%

“…[27]. Corrado Matta [27] argue that the process of mathematics learning is a process of integration and mechanism of cognitive activity in general and a deep cognitive process. Mayer [12] introduces a model of mathematics learning and problem-solving strategies to act as a moderator for learning mastery through the following four components described in Table 2.…”

Section: Mathematics Learning Modelmentioning

confidence: 99%

“…According to Alghafri and Ismail [23], neuroscience mechanisms play an important role during mathematics learning i.e., influencing students' emotions, actions and thinking coordination. There is a knowledge gap between neuroscience and its impact on students' learning abilities [26], [27]. Hohnen and Murphy [20] pointed out that the failures of educators in producing effective interventions are due to not knowing the role, nature and potential of certain components and parts of the brain involved in learning.…”

Section: Introductionmentioning

confidence: 99%

“…Hohnen and Murphy [20] pointed out that the failures of educators in producing effective interventions are due to not knowing the role, nature and potential of certain components and parts of the brain involved in learning. In practice, there is still a lack of research in the field of neuroscience of learning [27]. Neuroscience studies are more focused on brain growth problems, autism spectrum disorder (ASD) and also learning problems such as dyslexia, anxiety, number literacy problems and so on [25].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Neuro-Fuzzy Inference System(ANFIS) Formulation to Predict Students' Neuroscience Mechanistic: A Concept of an Intelligent Model to Enhance Mathematics Learning Ability

Bakar¹,

Ghani²,

Abdullah³

et al. 2022

TEM Journal

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Students' mathematics learning ability should always be assessed, predicted and given appropriate interventions. However, due to lack of exposure and knowledge to mechanisms of neuroscience and Adaptive neuro-fuzzy inference system (ANFIS), both elements are not optimally applied in educational measurement and evaluation settings. Therefore, based on the findings of neuroscience through the AGES model and the ANFIS formulation as well as the mathematics learning model, this paper will discuss the development of a conceptual model for predicting students' neuroscience mechanistic. The significance of this model is to reveal students' mathematical learning ability and analyze the causes of weaknesses or attributes that affect learning.

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Section: Mathematics Learning Modelsupporting

confidence: 56%

Section: Mathematics Learning Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Neuro-Fuzzy Inference System(ANFIS) Formulation to Predict Students' Neuroscience Mechanistic: A Concept of an Intelligent Model to Enhance Mathematics Learning Ability

Bakar¹,

Ghani²,

Abdullah³

et al. 2022

TEM Journal

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show abstract

“…In conclusion, the solution to this problem involves a mechanistic approach rooted in neuroscience that considers both the external and internal aspects of the student, as discussed in studies by [41][42][43][44][45]. This opinion is based on factors such as emotion, motivation, readiness, belief, cognitive and metacognitive processes and is related to the constructs of neuroscience.…”

Section: Neuroscience and Problem-solving Viewsmentioning

confidence: 99%

An Intelligent Mathematics Problem-Solving Tutoring System Framework

Mohamad Ariffin Abu Bakar,

Ahmad Termimi Ab Ghani,

Mohd Lazim Abdullah

2024

Int. J. Onl. Eng.

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This study proposed a novel framework for redesigning problem-solving activities in an intelligent tutoring system (ITS) called the intelligent neural-mechanistic mathematics problem- solving tutoring system (IN-MP-STS). This concept paper presents a new approach to ITS by incorporating elements of neuroscience mechanisms as a learning strategy that focuses on optimizing the brain’s ability through neural mechanisms. It also introduces fuzzy neural networks (FNNs) as a tool for modulating assessment and analyzing outcomes. This framework offers an alternative perspective on delivery methods and learning approaches in the ITS module. By effectively integrating neuroscience mechanistic elements such as motivation, activation, regulation, execution, memorization, and interactivities, deep learning can be achieved, leading to improved student competence. This framework also proposes an adaptive assessment component based on FNNs, which will enhance the measurement and feedback modules in the system. It is necessary to modify the way that ITS and soft computing methods, such as the study of neural networks (NNs), are combined to make learning measurement and assessment more transparent. This innovation has not been fully disclosed, so researchers are encouraged to further test the concepts presented to assess their alignment with the existing system and ethical considerations. This framework enhances the conceptual research findings of FNNs and incorporates neuroscience-based strategies into architecture and autonomous problem-solving skills within an ITS model. It also offers references for the development of problem-solving learning. IN-MP-STS has the potential to significantly enhance students’ competencies and abilities, thereby fostering the development of more comprehensive, holistic, and sustainable ITS. This approach also has the potential to enrich the existing literature on the sustainability of neural networks.

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Educational neuromyths and instructional practices: The case of inclusive education teachers in Hong Kong

Tsang,

Francis,

Pavlidou

2024

Trends in Neuroscience and Education

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Neuroscience and educational practice – A critical assessment from the perspective of philosophy of science

Cited by 5 publications

References 38 publications

Adaptive Neuro-Fuzzy Inference System(ANFIS) Formulation to Predict Students' Neuroscience Mechanistic: A Concept of an Intelligent Model to Enhance Mathematics Learning Ability

Adaptive Neuro-Fuzzy Inference System(ANFIS) Formulation to Predict Students' Neuroscience Mechanistic: A Concept of an Intelligent Model to Enhance Mathematics Learning Ability

An Intelligent Mathematics Problem-Solving Tutoring System Framework

Educational neuromyths and instructional practices: The case of inclusive education teachers in Hong Kong

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