157 research outputs found
Automatic authoring in the LAOS AHS authoring model
No full textIn this paper, we extend the automatic authoring techniques that can be built based on the LAOS model, a five-layer AHS authoring model. As the LAOS model itself is fairly complex, although information-rich, an adaptive hypermedia author needs a lot of system support to be able to populate all its levels with the corresponding information. Therefore, such automatic authoring techniques, which are actually automatic transformation (and interpretation) rules between the different layers of the model, have been designed. These automatic rules represent, in the area of adaptive systems, designer-goal oriented adaptation techniques. They should represent the goal of the designer that is authoring the hypermedia (such as the pedagogical goal in educational adaptive hypermedia). Therefore, this paper represents yet another step towards an adaptive hypermedia (or adaptive course) that ‘writes itself’. The focus here is on automatic transformation between the domain and a newly introduced goal and constraints model, to show that the effort of introducing this new layer can be minimal
Evaluation of interoperability of adaptive hypermedia systems : testing the MOT to WHURLE conversion in a classroom setting
No full textThe creation process of adaptive hypermedia is rarely evaluated. Moreover, conversion between different adaptive hypermedia systems has barely been proposed, yet alone tested in realistic settings. This paper presents the evaluation of the interoperability of two adaptive (educational) hypermedia systems, MOT and WHURLE, the one serving as authoring system, and the other as delivery system. The evaluation is performed with the help of a class of thirty-one students enrolled in the fourth year of the "Politehnica" Unversity of Bucharest, who were taking a one-week intensive course on Adaptive Hypermedia. This paper describes and interprets our first experiments of the "write once, deliver many" paradigm of adaptive hypermedia creation
Writing MOT, Reading AHA! Converting between an authoring and a delivery system for adaptive educational hypermedia
No full textThis paper reports about the recent advances towards establishing a common platform for adaptive educational hypermedia (AEH) authoring. We present the conversion from MOT, a dedicated authoring system, to AHA! used in this context as delivery system for AEH. Moreover, we describe two new representation languages that emerged in the process: a common format for defining the static material, CAF, and an extended adaptation language for the description of the dynamic behaviour, LAG. Finally, some evaluations are shown and conclusions are drawn
A layered approach towards domain authoring support
No full textThis paper presents an approach to authoring support for Web courseware based on a layered ontological paradigm. The ontology-based layers in the courseware authoring architecture serve as a basis for formal semantics and reasoning support in performing generic authoring tasks. This approach represents an extension of our knowledge classification and indexing mechanism from a previously developed system, AIMS, aimed at supporting students while completing learning tasks in a Web-based learning/training environment. We propose the addition of two vertical layers in the system architecture, Author assisting layer and Operational layer, with the role of facilitating the creation of the ontological layers (Course ontology and Domain ontology) and of the educational metadata layer. Here we focus on the domain ontology creation process, together with the support that the additional layers can provide within this process. We exemplify our method by presenting a set of generic tasks related to concept-based domain authoring and their ontological support
An AI-Based Feedback Visualisation System for Speech Training
No full textThis paper proposes providing automatic feedback to support public speech training. For the first time, speech feedback is provided on a visual dashboard including not only the transcription and pitch information, but also emotion information. A method is proposed to perform emotion classification using state-of-the-art convolutional neural networks (CNNs). Moreover, this approach can be used for speech analysis purposes. A case study exploring pitch in Japanese speech is presented in this paper
The three layers of adaptation granularity
No full textIn spite of the interest in AHS, there are not as many applications as could be expected. We have previously pinpointed the problem to rely on the difficulty of AHS authoring. Adaptive features that have been successfully introduced and implemented until now are often too fine grained, and an author easily loses the overview. This paper introduces a three-layer model and classification method for adaptive techniques: direct adaptation rules, adaptation language and adaptation strategies. The benefits of this model are twofold: on one hand, the granulation level of authoring of adaptive hypermedia can be precisely established, and authors therefore can work at the most suitable level for them. On the other hand, this is a step towards standardization of adaptive techniques, especially by grouping them into a higher-level adaptation language or strategies. In this way, not only adaptive hypermedia authoring, but also adaptive techniques exchange between adaptive applications can be enabled
A Good Classifier is Not Enough: A XAI Approach for Urgent Instructor-Intervention Models in MOOCs
No full textDeciding upon instructor intervention based on learners’ comments that need an urgent response in MOOC environments is a known challenge. The best solutions proposed used automatic machine learning (ML) models to predict the urgency. These are ‘black-box’-es, with results opaque to humans. EXplainable artificial intelligence (XAI) is aiming to understand these, to enhance trust in artificial intelligence (AI)-based decision-making. We propose to apply XAI techniques to interpret a MOOC intervention model, by analysing learner comments. We show how pairing a good predictor with XAI results and especially colour-coded visualisation could be used to support instructors making decisions on urgent intervention
Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums
No full textMassive Open Online Courses (MOOCs) have become a popular choice for e-learning thanks to their great flexibility. However, due to large numbers of learners and their diverse backgrounds, it is taxing to offer real-time support. Learners may post their feelings of confusion and struggle in the respective MOOC forums, but with the large volume of posts and high workloads for MOOC instructors, it is unlikely that the instructors can identify all learners requiring intervention. This problem has been studied as a Natural Language Processing (NLP) problem recently, and is known to be challenging, due to the imbalance of the data and the complex nature of the task. In this paper, we explore for the first time Bayesian deep learning on learner-based text posts with two methods: Monte Carlo Dropout and Variational Inference, as a new solution to assessing the need of instructor interventions for a learner’s post. We compare models based on our proposed methods with probabilistic modelling to its baseline non-Bayesian models under similar circumstances, for different cases of applying prediction. The results suggest that Bayesian deep learning offers a critical uncertainty measure that is not supplied by traditional neural networks. This adds more explainability, trust and robustness to AI, which is crucial in education-based applications. Additionally, it can achieve similar or better performance compared to non-probabilistic neural networks, as well as grant lower variance
A Survey of Collaborative Reinforcement Learning: Interactive Methods and Design Patterns
No full textRecently, methods enabling humans and Artificial Intelligent (AI) agents to collaborate towards improving the efficiency of Reinforcement Learning - also called Collaborative Reinforcement Learning (CRL) - have been receiving increasing attention. In this paper, we provide a long-term, in-depth survey, investigating human-AI collaborative methods based on both interactive reinforcement learning algorithms and human-AI collaborative frameworks, between 2011 and 2020. We elucidate and discuss synergistic analysis methods of both the growth of the field and the state-of-the-art; we suggest novel technical directions and new collaboration design ideas. Specifically, we provide a new CRL classification taxonomy, as a systematic modelling tool for selecting and improving new CRL designs. Furthermore, we propose generic CRL challenges providing the research community with a guide towards effective implementation of human-AI collaboration. The aim is to empower researchers to develop more efficient and natural human-AI collaborative methods that could utilise the different strengths of humans and AI
Comparative analysis of adaptation in adaptive educational hypermedia and IMS-learning design
No full textCurrently, Adaptive Educational Hypermedia (AEH) and IMS Learning Design (IMS-LD) are separate research areas, with little shared knowledge between them. Their goal, however, is the same: to design, author and implement the best possible learning experience for the learner. This paper addresses the issue of differences and similarities between AEH and IMS-LD with regard to knowledge representation and adaptation and investigates, generically, as well as for the specific case of the Layered AHS Authoring-Model and Operators (LAOS) framework, how these paradigms can benefit from each other
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