I am going to discuss how the models denis has just been talking about can be applied to educational practice. Some of this is quite speculative but bear with me….
CEN Launch, Oliver Hulme
Dr. Oliver Hulme Computational models and Education How computational models of development could apply to educational practice Education and Neuroscience workshop Oct 2008
Computational models and Education Computational modeling of cognitive development offers a novel framework for thinking about how a child does something (counts, reads, learns etc) Models make concrete predictions about developmental processes and therefore could be used to predict the outcome of educational interventions This provides opportunity for formally optimising educational interventions To delineate how, I will explore a (crude) comparison between medicine and educational neuroscience
Medical and Education? Commentators have highlighted the abstract similarity between medicine and education (Schlagger, Fischer etc) Biology Medical Interventions Educational Interventions Medicine Educational neuroscience
Medical and Education? Biology Medical Interventions Educational Interventions Given that Medicine has translated biological knowledge into real world interventions that work… How can Educational Neuroscience adapt this paradigm? Developmental modelling could be critical for this translation
Medical Paradigm Medicine predominantly intervenes through pharmacology Problem: Millions of candidate drugs but cannot clinically test all due to ethical and resource constraints Solution: Filter out poor drugs + clinically test few that are most likely to work Candidate drugs Lead compound Clinical trial Market
Drug discovery by screening Computationally model the potential efficacy of candidates to identify lead compounds (virtual high throughput screening) Pre-selects drugs for clinical testing Screening Candidates Lead compound
Educational Paradigm? Education intervenes through technology and teaching Problem: Millions of potential interventions but cannot test all due to ethical and resource constraints Solution: Filter out poor interventions + only empirically test select few that are most likely to work Candidates interventions Lead intervention Pedagogical trial Schools
Concrete examples? Suppose we are trying to optimise reading performance for phonological dyslexia through educational intervention Problem: The space of possible interventions is infinite How to define reading performance Solution: Test subspaces (i.e. a limited set of dimensions) Operationalise reading performance with an established index
For example Operationalise reading performance by word recognition accuracy Select subspace of interventions involving simultaneous presentation of graphemes and phonemes and select 3 variables to manipulate Even this subspace contains a large number of candidate interventions Intervention subspace Reward Repetition rate Age
Candidate Interventions? Screen: Use computational models of development to screen the potential efficacy of each intervention on word recognition accuracy lead interventions ( eg Predicted optimum is age 12, repetition rate = 10, reward = 3 hedons ) Models direct us to parts of intervention space that predict educational best results Screening Reward Repetition rate Age
Pedagogical trials Only empirically test the efficacy of the lead interventions in ecologically valid settings Select educational interventions based on evidence
Advantages of using models to screen Can explore educational interventions which may be unethical to pilot on humans Can explore how multiple variables interact to impact on educational performance Can test parts of intervention space that would be appear too stupid to test in real life and opens up possibility of unexpected results Can test effects of interventions on whole developmental trajectory not just a discrete timespans
Open questions Framework applies to any type of computational model. Are connectionist models suitable for this implementation? Given that connectionist models ‘are not intended to be neural models, but rather cognitive information processing models’ (Mareschal and Thomas 2007) The question is whether they are sufficiently grounded in biology to yield the accuracy required for predicting the outcomes of educational intervention
Problems… How to map from model to reality How generalisable are the abstract tasks the model performs Difficulty of mapping existing interventions onto model parameters and vice versa
Most developmental psychologists describe what children can do not how they they do it Knowing how a child does something, count, read, reason, offers the opportunity for principled intervention to improve or remediate that faculty. This would be the long term aim of educational neuroscience, having a physical theory of childrens development, and its interaction with educational interventions. Through these models one could facilitate optimal trajectories for each child.
Computational Modelling <ul><li>A standard in the physical sciences </li></ul><ul><li>They are tools for exploring causal mechanisms of development </li></ul><ul><li>Can track HOW learning mechanisms interact with the characteristics of the environment to produce observed behaviors </li></ul>Equally they would be tools for exploring the causal mechanisms of educational intervention and child development. This opens up the space of possible interventions and allows one more efficiently to identify subspaces which offer optimal developmental trajectories. How do the learning mechanisms interact with the characteristics of the educational environment to produce observable educational outcomes.
Computational Modelling <ul><li>Of course, all models involve approximations! </li></ul><ul><li>Making the right approximation depends on the nature of the target problem and the of our understanding of the problem. </li></ul>This involves collaboration between the psychologist, pedagogues, pedagogists, computational neuroscientists,
Building a model is NOT the same as building a baby! " The art of model building is the exclusion of real but irrelevant parts of the problem, and entails hazards for the builder and the reader. The builder may leave out something genuinely relevant; the reader, armed with too sophisticated an experimental probe… may take literally a schematized model whose main aim is to be a demonstration of possibility." The question is whether in designing complex interventions the accuracy of the models trajectory may depend on what is left out. Models are tools for reasoning
Connectionist Models <ul><li>Cognitive models loosely based on neural information processing </li></ul><ul><li>Develop internal representation as part of learning </li></ul><ul><li>Not tabula rasa systems. </li></ul>I want to relate the mechanisms of neural information processing to behaviors characteristic of cognition
An illustrative example… The What and Where of infant object-directed behaviors <ul><li>Some old stuff </li></ul><ul><li>Some new stuff </li></ul><ul><li>Some future stuff </li></ul>
Dr. Oliver Hulme Plan Emphasis is on interacting with the environment. Education is an environmental intervention and therefore these models are suited to testing different interventions without ethical concerns of experimenting with a child’s developmental trajectory.
Dr. Oliver Hulme Plan What are the parameters of an intervention? Reward schedule, magnitude, valence, frequency Optimal stage in developmental trajectory Longitudinal frequency? Personalised developmental models? Given a set of data pertaining to the infants pscyhology, set up a individualised model, against which one can test your interventions for a full blown individualised learning scheme Model full classrooms? Macroscopic forecast modelling of educational policy?
Dr. Oliver Hulme Plan How does it change now? What will it change in 5 years time? In 25 years time
Dr. Oliver Hulme Plan Review of Posners book by Bradley Schlaggar
Dr. Oliver Hulme Plan Kurt Fischer and others in the 1 st issue of MBE highlight the analogy between MBE and medicine, both using knowledge to inform practice. Medical applications of biological knowledge require independent empirical tests. Educational applications of biological knowledge also require independent empirical tests. Neuroscience and modelling of neuroscience data require judicious interpretation followed up by research that tests their application to the classroom