Commentary on Kurt VanLehn's "The behaviour of tutoring systems"

The paper by Kurt VanLehn has been designed for two audiences: one who does not know very much about ITSs and the other who does. The paper has much to offer both audiences. For the former it gives an indication of the educational performance and complexity that ITSs are now able to offer in a way that focuses on the pedagogy and not on the underlying mechanisms. For the latter audience it offers a way of thinking about the nature of ITSs that helps to unify the field and demonstrates common themes across a range of research programmes. The paper is interesting and useful to read and makes many wise points about ITSs and their development. This commentary on the paper is divided into two parts. The first deals with the basic ontology set out in VanLehn's paper and offers some comments on it. The second goes through the paper sequentially and makes some comments on individual sections and subsections (but not all of them)

Construction and abstraction: contrasting methods of supporting model building in learning science

No description supplie

Modelling human teaching tactics and strategies for tutoring systems

One of the promises of ITSs and ILEs is that they will teach and assist learning in an intelligent manner. Historically this has tended to mean concentrating on the interface, on the representation of the domain and on the representation of the student’s knowledge. So systems have attempted to provide students with reifications both of what is to be learned and of the learning process, as well as optimally sequencing and adjusting activities, problems and feedback to best help them learn that domain. We now have embodied (and disembodied) teaching agents and computer-based peers, and the field demonstrates a much greater interest in metacognition and in collaborative activities and tools to support that collaboration. Nevertheless the issue of the teaching competence of ITSs and ILEs is still important, as well as the more specific question as to whether systems can and should mimic human teachers. Indeed increasing interest in embodied agents has thrown the spotlight back on how such agents should behave with respect to learners. In the mid 1980s Ohlsson and others offered critiques of ITSs and ILEs in terms of the limited range and adaptability of their teaching actions as compared to the wealth of tactics and strategies employed by human expert teachers. So are we in any better position in modelling teaching than we were in the 80s? Are these criticisms still as valid today as they were then? This paper reviews progress in understanding certain aspects of human expert teaching and in developing tutoring systems that implement those human teaching strategies and tactics. It concentrates particularly on how systems have dealt with student answers and how they have dealt with motivational issues, referring particularly to work carried out at Sussex: for example, on responding effectively to the student’s motivational state, on contingent and Vygotskian inspired teaching strategies and on the plausibility problem. This latter is concerned with whether tactics that are effectively applied by human teachers can be as effective when embodied in machine teachers

Using a simulated student to repair difficulties in collaborative learning

We describe the use of a simulated student in a synchronous but distributed collaborative learning environment in the domain of programming. The role of the simulated student is to detect and repair difficulties in collaborative learning amongst the human students, for example when a human student is too passive or when the students start chatting about off-topic conversations. The simulated student intervenes by posting messages in the shared "chat" window, just like the human students and was believed to be another human student by them. The paper describes the rules by which the simulated student operates and briefly outlines an evaluation of the system with university first year programming students. The system proved to be successful both in detecting a range of difficulties and in intervening effectively

Resource reuse in ie-TV

The convergence of communications and information technology within education, as well as more widely, means that concepts developed within ITS & AIED are now applicable to a wider range of wired, and more interestingly 'wireless', technologies. In [1] we outlined the educational rationale of a Broadband User Model (BbUM) that would support the individualisation of the interactions, both between the technology and a user and between collaborating users, for a system able to deliver a variety of resources in a range of media, including interactive TV. At the heart of any such system there needs to be a database of resources from which the user, the educational designer or the system itself, including the user model, can select. Some of these resources will be items that were developed for other purposes, such as self-contained TV programmes, books or simulation programs. Others will be resources developed with such a system in mind. In either case the use and reuse of these resources depends on careful tagging at a level of granularity that enables them to be used both in their entire original form as well as in parts. For example, imagine that a TV programme is being indexed and that it consists of a number of items, originally in a chronological sequence. The tagging might indicate that one item is analagous to another or generalises it. Labelling the items makes explicit some of the implicit pedagogic relationships that underpin the design of the original programme. This enables the possibility of recomposing the TV programme in some other sequence that reflects a different overall pedagogical structure to the original. Moreover, each item is also tagged in terms of its position in some domain scheme. A prototype system has been implemented that employed a database searchable in a variety of ways, including the keywords matched against video/TV captions and/or automatically transcribed speech. Metadata included such fields as ID, title, ownership, media type, format, and duration. Content categorisation included topic, target user group, and interactivity. Form categorisation included problem, concept, description, and explanation or example

Computer discrimination between diseases of the brain based on MR image features

No description supplie

Learning interaction patterns using diagrams varying in level and type of interactivity

An experiment was conducted to investigate the differences between learners when using computer based learning environments (CBLEs) that incorporated different levels of interactivity in diagrams. Four CBLEs were created with combinations of the following two interactivity properties: (a) the possibility to rotate the whole diagram (b) the possibility to move individual elements of the diagram in order to apprehend the relationships between them. We present and discuss the qualitative findings from the study in terms of the learners’ interaction patterns and their relevance for the understanding of performance scores. This supports our previous quantitative analysis showing an interaction between cognitive abilities and interactivity. Based on our findings we reflect on the possibilities to inform CBLEs with relevant information regarding learners’ cognitive abilities and representational preferences

Pair programming and the re-appropriation of individual tools for collaborative software development

Although pair programming is becoming more prevalent in software development, and a number of reports have been written about it [10] [13], few have addressed the manner in which pairing actually takes place [12]. Even fewer consider the methods used to manage issues such as role change or the communication of complex issues. This paper highlights the way resources designed for individuals are re-appropriated and augmented by pair programmers to facilitate collaboration. It also illustrates that pair verbalisations can augment the benefits of the collocated team, providing examples from ethnographic studies of pair programmers 'in the wild'

Learning with E's: putting technology in its place

The topic of e-learning is the focus of much current interest within education and industry. to decrease the use of computers within education made by Cordes and. Miller (2000), for example, we need to avoid the temptation to pay too much attention to the technology and too little attention to the learners, teachers and their context. The future design challenge we face is the development of interactive educational content that enables learners to bridge the gap between the operational and conceptual levels of their interactive experience and engage with the concepts of the discipline being studied. The technology challenge is building a platform for the delivery of this content through existing and emerging technology and in multiple contexts. The theoretical challenge and potential pedagogical benefits lay in the development of a central pedagogical framework. In order to address these challenges we need to reflect on our progress to date, to assess the evidence for the effectiveness of e-learning and to identify what works and why

Expertise, motivation and teaching in learning companion systems

This paper describes work carried out to explore the role of a learning companion as a teachable student of the human student. A LCS for Binary Boolean Algebra has been developed to explore the hypothesis that a learning companion with less expertise than the human student would be beneficial if the student taught it. The system implemented two companions with different expertise and two types of motivational conditions. An empirical evaluation was conducted. Although significant differential learning gains between the experimental conditions were not observed, differences in learner behaviour between these conditions were. In particular students in the motivated condition with a weak companion taught it many more times than in the other experimental conditions and in general worked harder. Finally, the experiment also suggested that learning companions might be confusing for students if they try to resemble human behaviour, i.e. if they do not perform exactly as they are told