Reflections on the Ecolab and the Zone of Proximal Development

In 1999 we reported a study that explored the way that Vygotsky’s Zone of Proximal Development could be used to inform the design of an Interactive Learning Environment called the Ecolab. Two aspects of this work have subsequently been used for further research. Firstly, there is the interpretation of the ZPD and its associated theory that was used to operationalize the ZPD so that it could be implemented in software. This interpretation has informed further research about how one can model context and its impact on learning, which has produced a design framework that has been successfully applied across a range of educational settings. Secondly, there is the Ecolab software itself. The software has been adapted into a variety of versions that have supported explorations into how to scaffold learners’ metacognition, how to scaffold learners’ motivation and the implications of a learner’s goal orientation upon their use of the software. The findings from these studies have informed our understanding of learner scaffolding and have produced consistent results to demonstrate the importance of providing learners with appropriately challenging tasks and flexible support. Vygotsky’s work is as relevant now as it was in 1999: it still has an important role to play in the development of educational software

Structured computer-based training in the interpretation of neuroradiological images

Computer-based systems may be able to address a recognised need throughout the medical profession for a more structured approach to training. We describe a combined training system for neuroradiology, the MR Tutor that differs from previous approaches to computer-assisted training in radiology in that it provides case-based tuition whereby the system and user communicate in terms of a well-founded Image Description Language. The system implements a novel method of visualisation and interaction with a library of fully described cases utilising statistical models of similarity, typicality and disease categorisation of cases. We describe the rationale, knowledge representation and design of the system, and provide a formative evaluation of its usability and effectiveness

Short-term efficacy and usage recommendations for a large-scale implementation of the math-whizz tutor

This paper adds to the evidence of the efficacy of intelligent tutoring systems (ITS) in mathematics learning by evaluating a large-scale intervention at the state of Aguascalientes, Mexico. We report the results of a quasi-experimental study, addressing at the same a particular request of the decision-makers responsible for the rollout to provide, from early stages of the intervention, independent evidence of the efficacy of Math-Whizz Tutor beyond its internal metrics, and recommendations in terms of the expected weekly usage levels to guide the blended learning approach

Empowering educators to be AI-ready

In this paper. we present the concept of AI Readiness, along with a framework for developing AI Readiness training. ‘AI Readiness’ can be framed as a contextualised way of helping people to understand AI, in particular, AI that is data driven. The nature of AI Readiness training is not the same as merely learning about AI. Rather, AI Readiness recognises the diversity of the professions, workplaces and sectors for whom AI has a potential impact. For example, AI Readiness for lawyers may be based on the same principles as AI Readiness for Educators. However, the details will be contextualised differently. AI Readiness recognises that such contextualisation is not an option: it is essential due to the multiple intricacies, sensitivities and variations between different sectors and their settings, which all impact on the application of AI. To embrace such contextualisation, AI Readiness needs to be an active, participatory training process and aims to empower people to be more able to leverage AI to meet their needs. The text that follows focusses on AI Readiness within the Education and Training sector and starts with a discussion of the current state of AI within education and training, and the need for AI Readiness. We then problematize the concept of AI Readiness, why AI Readiness is needed, and what it means. We expand upon the nature of AI Readiness through a discussion of the difference between human and Artificial Intelligence, before presenting a 7-step framework for helping people to become AI Ready. Finally, we use an example of AI Readiness in action within Higher Education to exemplify AI Readiness

Up and down the number line: modelling collaboration in contrasting school and home environments

This paper is concerned with user modelling issues such as adaptive educational environments, adaptive information retrieval, and support for collaboration. The HomeWork project is examining the use of learner modelling strategies within both school and home environments for young children aged 5 – 7 years. The learning experience within the home context can vary considerably from school especially for very young learners, and this project focuses on the use of modelling which can take into account the informality and potentially contrasting learning styles experienced within the home and school

On how Unsupervised Machine Learning Can Shape Minds: a Brief Overview

This paper briefly examines the relationship between unsupervised machine learning models, the learning affordances that such models offer, and the mental models of those who use them. We consider the unsupervised models as learning affordances. We use a case study involving unsupervised modelling via commonly used methods such as clustering, to argue that unsupervised models can be used as learning affordances, bychanging participants’ mental models, precisely because the models are unsupervised, and thus potentially lead to learning from unexpected or inexplicit patterns

Relationships: computational thinking, pedagogy of programming, and Bloom’s Taxonomy

This study explores the relationship between computational thinking, teaching programming, and Bloom’s Taxonomy. Data is collected from teachers, academics, and professionals, purposively selected because of their knowledge of the topics of problem solving, computational thinking, or the teaching of programming. This data is analysed following a grounded theory approach. A computational thinking taxonomy is developed. The relationships between cognitive processes, the pedagogy of programming, and the perceived levels of difficulty of computational thinking skills are illustrated by a model. Specifically, a definition for computational thinking is presented. The skills identified are mapped to Bloom’s Taxonomy: Cognitive Domain. This mapping concentrates computational skills at the application, analysis, synthesis, and evaluation levels. Analysis of the data indicates that abstraction of functionality is less difficult than abstraction of data, but both are perceived as difficult. The most difficult computational thinking skill is reported as decomposition. This ordering of difficulty for learners is a reversal of the cognitive complexity predicted by Bloom’s model. The plausibility of this inconsistency is explored. The taxonomy, model, and the other results of this study may be used by educators to focus learning onto the computational thinking skills acquired by the learners, while using programming as a tool. They may also be employed in the design of curriculum subjects, such as ICT, computing, or computer science. <br/

A methodology for the capture and analysis of hybrid data: a case study of program debugging

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