A Learning Environment for Promoting Structured Algebraic Thinking in Children

Gutiérrez, Sergio; Mavrikis, Manolis; Pearce, Darren

doi:10.1109/icalt.2008.281

Cited by 4 publications

(6 citation statements)

References 13 publications

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“…4 are correct symmetrical "elegant" solutions, but trials with pupils have shown that not all of them use this type of approach [17,18]. Some pupils surround the pond in a non-systematic manner and with variable degrees of symmetry.…”

Section: Namementioning

confidence: 97%

“…To illustrate the operation of the CBR component and its role to support learner modelling we use an example from ShapeBuilder, a task called "pond tiling", which is common in the English school curriculum and expects learners to produce a general expression for finding out how many tiles are required for surrounding any rectangular pond [17]. The high level learning objective in the Domain Model is to acquire the ability to perform structural reasoning [18].…”

Section: Scenario-based Validation Of the Cbr Componentmentioning

confidence: 99%

“…To address this problem the ShapeBuilder system [17,50], which is an ELE developed in the context of the Mi-Gen project, 1 aims to facilitate the correspondence between the models, patterns and structures (visual representations) that the learners build, on one hand, and 1 Funded by the ESRC/EPSRC Teaching and Learning Research Programme (Technology Enhanced Learning); Award no: RES-139-25-0381. ); http://www.tlrp.org/tel/migen/.…”

Section: Exploratory Learning For Mathematical Generalisationmentioning

confidence: 99%

“…(a) the Expression Toolbar; (b) the Shape List; (c) the overall ShapeBuilder interface (the gridded area is the interaction canvas); (d) the Expression Palette. The figure is reproduced from[17].…”

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confidence: 99%

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Hybrid model for learner modelling and feedback prioritisation in exploratory learning

Cocea

Magoulas

2009

HIS

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Individual and/or hybrid AI techniques are often used in learning environments for well-structured domains to perform learner diagnosis, create and update a learner model and provide support at individual or group level. This paper presents a conceptual model that employs a synergistic approach based on Case-Based Reasoning (CBR) and Multicriteria Decision Making (MDM) components for learner modelling and feedback generation during exploration in an ill-defined domain of mathematical generalisation. The CBR component is used to diagnose what students are doing on the basis of simple and composite cases; simple cases represent parts of the models that the learners could possibly construct during an exploratory learning activity, while composite cases, which are assembled from simple cases, correspond to strategies that learners may adopt to construct their models. Similarity measures are used to identify how close/far are the learners from solutions pre-specified and stored in the knowledge base. This information is then fed into the MDM component that is responsible for prioritising types of feedback depending on the context. The operation of the two components and the effectiveness of the synergistic approach are validated through user scenarios in the context of an exploratory learning environment for mathematical generalisation.

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Section: Namementioning

confidence: 97%

Section: Scenario-based Validation Of the Cbr Componentmentioning

confidence: 99%

Section: Exploratory Learning For Mathematical Generalisationmentioning

confidence: 99%

mentioning

confidence: 99%

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Hybrid model for learner modelling and feedback prioritisation in exploratory learning

Cocea

Magoulas

2009

HIS

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“…In response to this event, the MessagesRestlet asks the DataModel to notify all connected clients that its representation has now changed 11 . The client's server is subsequently notified 12 and requests an update to the appropriate ResourceList using the ResourceListManager 13,14 . At this stage, the client's ResourceList has not been updated; all that has happened is that the server has notified the ResourceList that it must re-check with the server to find out the latest state of its data resource.…”

Section: Implementation Overviewmentioning

confidence: 99%

The Conceptual and Architectural Design of a System Supporting Exploratory Learning of Mathematics Generalisation

Pearce

Poulovassilis

2009

Lecture Notes in Computer Science

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Abstract. The MiGen project is designing and developing an intelligent, exploratory environment to support 11-14-year-old students in their learning of mathematical generalisation. Deployed within the classroom, the system will also provide tools to assist teachers in monitoring students' activities and progress. This paper describes the conceptual and architectural design of the system, and gives a detailed technical explanation of a working proof-of-concept prototype of the architecture, motivating in particular the technologies and approaches chosen to implement the necessary functionality given the context of the project. We also discuss how the prototype will be used as a basis for developing the first full version of the MiGen system, in the context of ongoing knowledge acquisition and analysis within the project's iterative, stakeholder-centred design, development and testing methodology.

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From Mini Rover Programs to Algebraic Expressions

Demo

2010

2010 10th IEEE International Conference on Advanced Learning Technologies

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A Learning Environment for Promoting Structured Algebraic Thinking in Children

Cited by 4 publications

References 13 publications

Hybrid model for learner modelling and feedback prioritisation in exploratory learning

Hybrid model for learner modelling and feedback prioritisation in exploratory learning

The Conceptual and Architectural Design of a System Supporting Exploratory Learning of Mathematics Generalisation

From Mini Rover Programs to Algebraic Expressions

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