14,844 research outputs found

    How Domain Differences Impact the Mode Structure of Expert Tutoring Dialogue

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    Whitney Layne Cade granted permission for the digitization of her paper. It was submitted by CDWhile human-to-human dialogue in tutoring sessions has received considerable attention in the last 25 years, there exists a paucity of work examining the pedagogical and motivational strategies of expert human tutors. An established trend in the tutorial dialogue community is to study tutorial dialogues in a very fine-grained manner, at the level of the speech act or dialogue move. The present work offers a coding scheme that examines larger, pedagogically distinct phases as the unit of analysis, referred to as “modes”, which exist in expert tutoring and provide the context needed to understand patterns of dialogue moves. The eight modes identified by this coding scheme are the Introduction, Lecture, Modeling, Scaffolding, Fading, Highlighting, Off Topic, and Conclusion mode, and each mode was reliably identified at or above the .8 kappa level. After determining how often modes occur and the amount of dialogue devoted to them in expert tutoring sessions, differences between the domains of math and science were investigated. Significant variance between the domains was revealed using this largergrained coding scheme, particularly in how Lecture and Scaffolding are used in expert tutoring. While these two modes tend to dominate most tutorial dialogue in this sample regardless of domain, the differences in their frequency and the amount of dialogue devoted to each mode suggest diverse tutoring goals associated with each domain. Other subtle differences in mode distributions draw attention both to the complexities of expert tutoring and the danger of generalizing tutorial structures across domains.This honors paper was approved by Dr. Natalie Person, Dr. Chris Wetzel, and Dr. Andrew Olne

    Enhancing simulation education with intelligent tutoring systems

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    The demand for education in the area of simulation is in the increase. This paper describes how education in the field of simulation can take advantage of the virtues of intelligent tutoring with respect to enhancing the educational process. For this purpose, this paper gives an overview of what constitutes the objectives and the content of a comprehensive course in discrete event simulation. The architecture of an intelligent tutoring system is presented and it is discussed how these sophisticated learning aids offer individualised student guidance and support within a learning environment. The paper then introduces a prototype intelligent tutoring system, the simulation tutor, and suggests how the system might be developed to enhance education in simulation

    Modelling human teaching tactics and strategies for tutoring systems

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    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

    Deeper Understanding of Tutorial Dialogues and Student Assessment

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    Bloom (1984) reported two standard deviation improvement with human tutoring which inspired many researchers to develop Intelligent Tutoring Systems (ITSs) that are as effective as human tutoring. However, recent studies suggest that the 2-sigma result was misleading and that current ITSs are as good as human tutors. Nevertheless, we can think of 2 standard deviations as the benchmark for tutoring effectiveness of ideal expert tutors. In the case of ITSs, there is still the possibility that ITSs could be better than humans.One way to improve the ITSs would be identifying, understanding, and then successfully implementing effective tutorial strategies that lead to learning gains. Another step towards improving the effectiveness of ITSs is an accurate assessment of student responses. However, evaluating student answers in tutorial dialogues is challenging. The student answers often refer to the entities in the previous dialogue turns and problem description. Therefore, the student answers should be evaluated by taking dialogue context into account. Moreover, the system should explain which parts of the student answer are correct and which are incorrect. Such explanation capability allows the ITSs to provide targeted feedback to help students reflect upon and correct their knowledge deficits. Furthermore, targeted feedback increases learners\u27 engagement, enabling them to persist in solving the instructional task at hand on their own. In this dissertation, we describe our approach to discover and understand effective tutorial strategies employed by effective human tutors while interacting with learners. We also present various approaches to automatically assess students\u27 contributions using general methods that we developed for semantic analysis of short texts. We explain our work using generic semantic similarity approaches to evaluate the semantic similarity between individual learner contributions and ideal answers provided by experts for target instructional tasks. We also describe our method to assess student performance based on tutorial dialogue context, accounting for linguistic phenomena such as ellipsis and pronouns. We then propose an approach to provide an explanatory capability for assessing student responses. Finally, we recommend a novel method based on concept maps for jointly evaluating and interpreting the correctness of student responses

    Analyzing collaborative learning processes automatically

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    In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

    Heavy Hero or Digital Dummy? Multimodal Player–Avatar Relations in Final Fantasy 7

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    This article analyses the player-avatar relation in Final Fantasy 7, drawing on multimodality theory to analyse textual structures both in the game and in the discourse of player-interviews and fan writing. It argues that the avatar is a two-part structure, partly designed in conventional narrative terms as a protagonist of popular narrative, and partly as a vehicle for interactive game-play. The former structure is replete with the traditions and designs of Japanese popular narrative, oral formulaic narrative and contemporary superhero narratives; and is presented to the player as an offer act – a declarative narrative statement. The latter is a construct of evolving attributes and economies characteristic of roleplaying games; and is presented to the player as a demand act – a rule-based command. Though these two functions separate out in the grammar of player and fan discourse, it is their integration which provides the pleasure of gameplay and narrative engagement

    Chapter 9: Quality Assurance

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    The OTiS (Online Teaching in Scotland) programme, run by the now defunct Scotcit programme, ran an International e-Workshop on Developing Online Tutoring Skills which was held between 8–12 May 2000. It was organised by Heriot–Watt University, Edinburgh and The Robert Gordon University, Aberdeen, UK. Out of this workshop came the seminal Online Tutoring E-Book, a generic primer on e-learning pedagogy and methodology, full of practical implementation guidelines. Although the Scotcit programme ended some years ago, the E-Book has been copied to the SONET site as a series of PDF files, which are now available via the ALT Open Access Repository. The editor, Carol Higgison, is currently working in e-learning at the University of Bradford (see her staff profile) and is the Chair of the Association for Learning Technology (ALT)

    The development of a rich multimedia training environment for crisis management: using emotional affect to enhance learning

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    PANDORA is an EU FP7-funded project developing a novel training and learning environment for Gold Commanders, individuals who carry executive responsibility for the services and facilities identified as strategically critical e.g. Police, Fire, in crisis management strategic planning situations. A key part of the work for this project is considering the emotional and behavioural state of the trainees, and the creation of more realistic, and thereby stressful, representations of multimedia information to impact on the decision-making of those trainees. Existing training models are predominantly paper-based, table-top exercises, which require an exercise of imagination on the part of the trainees to consider not only the various aspects of a crisis situation but also the impacts of interventions, and remediating actions in the event of the failure of an intervention. However, existing computing models and tools are focused on supporting tactical and operational activities in crisis management, not strategic. Therefore, the PANDORA system will provide a rich multimedia information environment, to provide trainees with the detailed information they require to develop strategic plans to deal with a crisis scenario, and will then provide information on the impacts of the implementation of those plans and provide the opportunity for the trainees to revise and remediate those plans. Since this activity is invariably multi-agency, the training environment must support group-based strategic planning activities and trainees will occupy specific roles within the crisis scenario. The system will also provide a range of non-playing characters (NPC) representing domain experts, high-level controllers (e.g. politicians, ministers), low-level controllers (tactical and operational commanders), and missing trainee roles, to ensure a fully populated scenario can be realised in each instantiation. Within the environment, the emotional and behavioural state of the trainees will be monitored, and interventions, in the form of environmental information controls and mechanisms impacting on the stress levels and decisionmaking capabilities of the trainees, will be used to personalise the training environment. This approach enables a richer and more realistic representation of the crisis scenario to be enacted, leading to better strategic plans and providing trainees with structured feedback on their performance under stress

    The Tutor's Role

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    This chapter addresses three questions about being an effective online tutor: 1. Why do we still think that online tutoring can principally draw its basis from face-to-face group processes and dynamics or traditional pedagogy? 2. Does the literature tell us anything more than we would make as an intelligent guess? 3. Do we really know what an ‘effective’ online tutor would be doing? The OTiS participants have gone some way to answering these questions, through the presentation and discussion of their own online tutoring experiences. Literature in this area is still limited, and suffers from the need for timeliness of publication to be useful. Intelligent guesses are all very well, but much better as a source of information for online tutors are the reflections and documented experiences of practitioners. These experiences reveal that face-to-face pedagogy has some elements to offer the online tutor, but that there are key differences and there is a need to examine the processes and dynamics of online learning to inform online tutoring
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