An evaluation of pedagogically informed parameterised questions for self assessment

Self-assessment is a crucial component of learning. Learners can learn by asking themselves questions and attempting to answer them. However, creating effective questions is time-consuming because it may require considerable resources and the skill of critical thinking. Questions need careful construction to accurately represent the intended learning outcome and the subject matter involved. There are very few systems currently available which generate questions automatically, and these are confined to specific domains. This paper presents a system for automatically generating questions from a competency framework, based on a sound pedagogical and technological approach. This makes it possible to guide learners in developing questions for themselves, and to provide authoring templates which speed the creation of new questions for self-assessment. This novel design and implementation involves an ontological database that represents the intended learning outcome to be assessed across a number of dimensions, including level of cognitive ability and subject matter. The system generates a list of all the questions that are possible from a given learning outcome, which may then be used to test for understanding, and so could determine the degree to which learners actually acquire the desired knowledge. The way in which the system has been designed and evaluated is discussed, along with its educational benefits

An E-Learning Investigation into Learning Style Adaptivity

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Defining adaptation in a generic multi layer model : CAM: the GRAPPLE conceptual adaptation model

Authoring of Adaptive Hypermedia is a difficult and time consuming task. Reference models like LAOS and AHAM separate adaptation and content in different layers. Systems like AHA! offer graphical tools based on these models to allow authors to define adaptation without knowing any adaptation language. The adaptation that can be defined using such tools is still limited. Authoring systems like MOT are more flexible, but usability of adaptation specification is low. This paper proposes a more generic model which allows the adaptation to be defined in an arbitrary number of layers, where adaptation is expressed in terms of relationships between concepts. This model allows the creation of more powerful yet easier to use graphical authoring tools. This paper presents the structure of the Conceptual Adaptation Models used in adaptive applications created within the GRAPPLE adaptive learning environment, and their representation in a graphical authoring tool

Defining adaptation in a generic multi layer model : CAM: the GRAPPLE conceptual adaptation model

Authoring of Adaptive Hypermedia is a difficult and time consuming task. Reference models like LAOS and AHAM separate adaptation and content in different layers. Systems like AHA! offer graphical tools based on these models to allow authors to define adaptation without knowing any adaptation language. The adaptation that can be defined using such tools is still limited. Authoring systems like MOT are more flexible, but usability of adaptation specification is low. This paper proposes a more generic model which allows the adaptation to be defined in an arbitrary number of layers, where adaptation is expressed in terms of relationships between concepts. This model allows the creation of more powerful yet easier to use graphical authoring tools. This paper presents the structure of the Conceptual Adaptation Models used in adaptive applications created within the GRAPPLE adaptive learning environment, and their representation in a graphical authoring tool

Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Problem-Solving Knowledge Mining from Users’\ud Actions in an Intelligent Tutoring System

In an intelligent tutoring system (ITS), the domain expert should provide\ud relevant domain knowledge to the tutor so that it will be able to guide the\ud learner during problem solving. However, in several domains, this knowledge is\ud not predetermined and should be captured or learned from expert users as well as\ud intermediate and novice users. Our hypothesis is that, knowledge discovery (KD)\ud techniques can help to build this domain intelligence in ITS. This paper proposes\ud a framework to capture problem-solving knowledge using a promising approach\ud of data and knowledge discovery based on a combination of sequential pattern\ud mining and association rules discovery techniques. The framework has been implemented\ud and is used to discover new meta knowledge and rules in a given domain\ud which then extend domain knowledge and serve as problem space allowing\ud the intelligent tutoring system to guide learners in problem-solving situations.\ud Preliminary experiments have been conducted using the framework as an alternative\ud to a path-planning problem solver in CanadarmTutor

Towards a competency model for adaptive assessment to support lifelong learning

Adaptive assessment provides efficient and personalised routes to establishing the proficiencies of learners. We can envisage a future in which learners are able to maintain and expose their competency profile to multiple services, throughout their life, which will use the competency information in the model to personalise assessment. Current competency standards tend to over simplify the representation of competency and the knowledge domain. This paper presents a competency model for evaluating learned capability by considering achieved competencies to support adaptive assessment for lifelong learning. This model provides a multidimensional view of competencies and provides for interoperability between systems as the learner progresses through life. The proposed competency model is being developed and implemented in the JISC-funded Placement Learning and Assessment Toolkit (mPLAT) project at the University of Southampton. This project which takes a Service-Oriented approach will contribute to the JISC community by adding mobile assessment tools to the E-framework

Utility-Based Evaluation of Adaptive Systems

The variety of user-adaptive hypermedia systems available calls for methods of comparison. Layered evaluation techniques appear to be useful for this purpose. In this paper we present a utility-based evaluation approach that is based on these techniques. Issues that arise when putting utility-based evaluation into practice are dealt with. We also explain the need for interpretative user models and common sets of evaluation criteria for different domains