Authoring a Web‐enhanced interface for a new language‐learning environment

This paper presents conceptual considerations underpinning a design process set up to develop an applicable and usable interface as well as defining parameters for a new and versatile Computer Assisted Language Learning (CALL) environment. Based on a multidisciplinary expertise combining Human Computer Interaction (HCI), Web‐based Java programming, CALL authoring and language teaching expertise, it strives to generate new CALL‐enhanced curriculum developments in language learning. The originality of the approach rests on its design rationale established on the strength of previously identified student requirements and authoring needs identifying inherent design weaknesses and interactive limitations of existing hypermedia CALL applications (Hémard, 1998). At the student level, the emphasis is placed on three important design decisions related to the design of the interface, student interaction and usability. Thus, particular attention is given to design considerations focusing on the need to (a) develop a readily recognizable, professionally robust and intuitive interface, (b) provide a student‐controlled navigational space based on a mixed learning environment approach, and (c) promote a flexible, network‐based, access mode reconciling classroom with open access exploitations. At the author level, design considerations are essentially orientated towards adaptability and flexibility with the integration of authoring facilities, requiring no specific authoring skills, to cater for and support the need for a flexible approach adaptable to specific language‐learning environments. This paper elaborates on these conceptual considerations within the design process with particular emphasis on the adopted principled methodology and resulting design decisions and solutions

An integrated approach for analysing and assessing the performance of virtual learning groups

Collaborative distance learning involves a variety of elements and factors that have to be considered and measured in order to analyse and assess group and individual performance more effectively and objectively. This paper presents an approach that integrates qualitative, social network analysis (SNA) and quantitative techniques for evaluating online collaborative learning interactions. Integration of various different data sources, tools and techniques provides a more complete and robust framework for group modelling and guarantees a more efficient evaluation of group effectiveness and individual competence. Our research relies on the analysis of a real, long-term, complex collaborative experience, which is initially evaluated in terms of principled criteria and a basic qualitative process. At the end of the experience, the coded student interactions are further analysed through the SNA technique to assess participatory aspects, identify the most effective groups and the most prominent actors. Finally, the approach is contrasted and completed through a statistical technique which sheds more light on the results obtained that far. The proposal draws a well-founded line toward the development of a principled framework for the monitoring and analysis of group interaction and group scaffolding which can be considered a major issue towards the actual application of the CSCL proposals to real classrooms.Peer ReviewedPostprint (author's final draft

Adversarially Robust Distillation

Knowledge distillation is effective for producing small, high-performance neural networks for classification, but these small networks are vulnerable to adversarial attacks. This paper studies how adversarial robustness transfers from teacher to student during knowledge distillation. We find that a large amount of robustness may be inherited by the student even when distilled on only clean images. Second, we introduce Adversarially Robust Distillation (ARD) for distilling robustness onto student networks. In addition to producing small models with high test accuracy like conventional distillation, ARD also passes the superior robustness of large networks onto the student. In our experiments, we find that ARD student models decisively outperform adversarially trained networks of identical architecture in terms of robust accuracy, surpassing state-of-the-art methods on standard robustness benchmarks. Finally, we adapt recent fast adversarial training methods to ARD for accelerated robust distillation.Comment: Accepted to AAAI Conference on Artificial Intelligence, 202