Examination of the Nonlinear Dynamic Systems Associated with Science Student Cognition While Engaging in Science Information Processing

Lamb, Richard; Cavagnetto, Andy; Akmal, Tariq

doi:10.1007/s10763-014-9593-2

Cited by 16 publications

(4 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of these tools in education to investigate cognitive actions and their underlying neurological mechanisms more holistically and directly in educational and classroom environments is sorely needed [2]. However, current investigations and research in cognitive science typically address the role of one of the attributes using decontextualized tasks in isolated environments such as a laboratory [3]. Of specific interest is how to investigate the interactions of the student within the complex social environment of the classroom in students with extensive support needs in the general education setting [4,5].…”

Section: Opinionmentioning

confidence: 99%

Artificial Intelligence and Sensor Technologies the Future of Special Education for Students with Intellectual and Developmental Disabilities

Lamb,

Choi,

Owens

2023

GJIDD

View full text Add to dashboard Cite

Learning environments such as classrooms and online systems for students with developmental or intellectual disabilities are typically dynamic, multisensory, and make use of top-down attention and working memory mechanisms to promote sense making by the student. This is specifically true when the student with intellectual disabilities is mainstreamed into a general education classroom. The complex interactions between the classroom, the student, and the content creates serious difficulty in assessing a student’s understanding, particularly for students who are non-verbal or have deficits in communication. However, the last five years have ushered a revolution in computational power, brain mapping, wearable sensors use, large scale data collection, generative artificial intelligence, and physiological signal processing techniques e.g., the 4th industrial revolution

show abstract

Section: Opinionmentioning

confidence: 99%

Artificial Intelligence and Sensor Technologies the Future of Special Education for Students with Intellectual and Developmental Disabilities

Lamb,

Choi,

Owens

2023

GJIDD

View full text Add to dashboard Cite

show abstract

“…ere has been some promising research in that regard: including the importance of emotions in learning (Immordino-Yang, 2015); situating EN in a sociocultural context (Hall, Curtin and Rutherford, 2013); recognizing that learning is undertaken by embodied learners (Fugate, Macrine and Cipriano, 2019;Shapiro and Stolz, 2019); acknowledging that learners are enactively embedded in their surroundings (Gallagher and Allen, 2018); and modelling the brain as a dynamic open system (Ashourvan et al, 2019). e latter has been used, for example, to think di erently about music learning (Schiavio et al, 2017), moral development (Sankey and Kim, 2016) and science study (Lamb, Cavagnetto and Akmal, 2014). ese more holistic, emergent, contextual and dynamic approaches to understanding the learner -and modelling learning -has promise for neuroscience research supporting human ourishing.…”

Section: Does En Have Amentioning

confidence: 99%

Contexts of educational neuroscience

Joldersma¹,

Herwegen²

2022

Reimagining Education: The International Science and Evidence Based Education Assessment

View full text Add to dashboard Cite

E ducational neuroscience (EN) is a complex research eld with a bidirectional relationship between neuroscience and education. The chapter examines the social, cultural, political, ideological and conceptual contexts of EN. One conceptual context for (educational) neuroscience is self-understanding of neuroscience, as a frame for research agendas and dissemination, including a model of explanation (reductionism versus holism) and a model of learning and the learner. When the aim of education is human ourishing, it is important to understand learners in their psychosocial, socioeconomic and sociocultural contexts with the goal of holistic well-being. These contexts shape both the research eld itself and its direct or indirect uses in/as educational practices. The chapter explores the challenges for EN, including those relating to complexity of learning and of translation to the classroom, research and inclusion, and its ethical implications.

show abstract

“…One example of a MOEA that provides a means to optimize STAC-M is NeuroEvolution of Augmented Topologies. Source code is available for download [23]. Some additional examples of evolutionary algorithms are the multiobjective firefly algorithm and the multiobjective flower pollination algorithm.…”

Section: Multiobjective Evolutionary Algorithmsmentioning

confidence: 99%

Computational Modeling of Teaching and Learning through Application of Evolutionary Algorithms

Lamb

Premo

2015

Computation

Self Cite

View full text Add to dashboard Cite

Within the mind, there are a myriad of ideas that make sense within the bounds of everyday experience, but are not reflective of how the world actually exists; this is particularly true in the domain of science. Classroom learning with teacher explanation are a bridge through which these naive understandings can be brought in line with scientific reality. The purpose of this paper is to examine how the application of a Multiobjective Evolutionary Algorithm (MOEA) can work in concert with an existing computational-model to effectively model critical-thinking in the science classroom. An evolutionary algorithm is an algorithm that iteratively optimizes machine learning based computational models. The research question is, does the application of an evolutionary algorithm provide a means to optimize the Student Task and Cognition Model (STAC-M) and does the optimized model sufficiently represent and predict teaching and learning outcomes in the science classroom? Within this computational study, the authors outline and simulate the effect of teaching on the ability of a "virtual" student to solve a Piagetian task. Using the Student Task and Cognition Model (STAC-M) a computational model of student cognitive processing in science class developed in 2013, the authors complete a computational experiment which examines the role of cognitive retraining on student learning.

show abstract

Examination of the Nonlinear Dynamic Systems Associated with Science Student Cognition While Engaging in Science Information Processing

Cited by 16 publications

References 47 publications

Artificial Intelligence and Sensor Technologies the Future of Special Education for Students with Intellectual and Developmental Disabilities

Artificial Intelligence and Sensor Technologies the Future of Special Education for Students with Intellectual and Developmental Disabilities

Contexts of educational neuroscience

Computational Modeling of Teaching and Learning through Application of Evolutionary Algorithms

Contact Info

Product

Resources

About