Issues in the Design of an Environment to Support the Learning of Mathematical Generalisation

2009

HIS

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.

Section: Exploratory Learning For Mathematical Generalisationmentioning

confidence: 99%

Hybrid model for learner modelling and feedback prioritisation in exploratory learning

2009

HIS

“…We start by presenting the first version of the system, called ShapeBuilder [73,20], based on which the learner model v0 was developed. The user interface of the system is presented in Figure 5 and includes an The affordances of the system with the information available from the interaction with the interface are listed in Table 2.…”

Section: Initial Design: Learner Model V0mentioning

confidence: 99%

Participatory Learner Modelling Design: A methodology for iterative learner models development

2015

Information Sciences

Learner models are built to offer personalised solutions related to learning. They are often developed in parallel to the development of adaptive learning systems and thus, linked to the system's development.The adaptive learning systems literature reports numerous accounts of learner model development, but there are no reports on the methodological aspects of developing learner models and the relation between the development of the learner model component and the rest of the system. This paper presents the Participatory Learner Modelling Design methodology, which outlines the steps for learner model development and their relation to the development of the system. The methodology is illustrated with a case study of an adaptive educational system.

“…Moreover, the knowledge about a task evolves over time -students may discover different ways of approaching the task, rendering the knowledge base suboptimal for generating proper feedback, despite the initially good coverage. To address this, we employ a mechanism for adapting the knowledge base in the context of eXpresser [5], an ELE for mathematical generalisation.…”

Section: Introductionmentioning

confidence: 99%

“…2 Mathematical Generalisation with eXpresser eXpresser [5] is an ELE for the domain of mathematical generalisation. It is designed for classroom use and targets pupils of 11 to 14 year-olds.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Modelling of Users’ Strategies in Exploratory Learning Using Case-Based Reasoning

Gutierrez-Santos

Knowledge-Based and Intelligent Information and Engineering Systems

2010

In exploratory learning environments, learners can use different strategies to solve a problem. To the designer or teacher, however, not all these strategies are known in advance and, even if they were, introducing them in the knowledge base would involve considerable time and effort. In previous work, we have proposed a case-based knowledge representation, modelling the learners behaviour when constructing/exploring models through simple cases and sequences of cases, called strategies. In this paper, we enhance this approach with adaptive mechanisms for expanding the knowledge base. These mechanisms allow to identify and store inefficient cases, i.e. cases that pose additional difficulty to students in their learning process, and to gradually enrich the knowledge base by detecting and adding new strategies.