Heuristic Evaluation for Serious Immersive Games and M-instruction

© Springer International Publishing Switzerland 2016. Two fast growing areas for technology-enhanced learning are serious games and mobile instruction (M-instruction or M-Learning). Serious games are ones that are meant to be more than just entertainment. They have a serious use to educate or promote other types of activity. Immersive Games frequently involve many players interacting in a shared rich and complex-perhaps web-based-mixed reality world, where their circumstances will be multi and varied. Their reality may be augmented and often self-composed, as in a user-defined avatar in a virtual world. M-instruction and M-Learning is learning on the move; much of modern computer use is via smart devices, pads, and laptops. People use these devices all over the place and thus it is a natural extension to want to use these devices where they are to learn. This presents a problem if we wish to evaluate the effectiveness of the pedagogic media they are using. We have no way of knowing their situation, circumstance, education background and motivation, or potentially of the customisation of the final software they are using. Getting to the end user itself may also be problematic; these are learning environments that people will dip into at opportune moments. If access to the end user is hard because of location and user self-personalisation, then one solution is to look at the software before it goes out. Heuristic Evaluation allows us to get User Interface (UI) and User Experience (UX) experts to reflect on the software before it is deployed. The effective use of heuristic evaluation with pedagogical software [1] is extended here, with existing Heuristics Evaluation Methods that make the technique applicable to Serious Immersive Games and mobile instruction (M-instruction). We also consider how existing Heuristic Methods may be adopted. The result represents a new way of making this methodology applicable to this new developing area of learning technology

Meeting of the MINDS: an information retrieval research agenda

Since its inception in the late 1950s, the field of Information Retrieval (IR) has developed tools that help people find, organize, and analyze information. The key early influences on the field are well-known. Among them are H. P. Luhn's pioneering work, the development of the vector space retrieval model by Salton and his students, Cleverdon's development of the Cranfield experimental methodology, Spärck Jones' development of idf, and a series of probabilistic retrieval models by Robertson and Croft. Until the development of the WorldWideWeb (Web), IR was of greatest interest to professional information analysts such as librarians, intelligence analysts, the legal community, and the pharmaceutical industry

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Satellite-enabled interactive education: scenarios and systems architectures

There are specific sectors of the economy that can benefit from satellite-based tele-education. Areas, such as maritime and agriculture, share common needs for both broadband connectivity at remote geographical areas that cannot otherwise be covered, and for innovative content for tele-education purposes. Furthermore, each area has special requirements with regard to the type of content to be delivered. In this paper we propose a set of architectural designs and case scenarios that will realise such interactive end-to-end education systems based on satellite communications. Services requirements in this setting are also identified and discussed

Detecting Sarcasm in Multimodal Social Platforms

Sarcasm is a peculiar form of sentiment expression, where the surface sentiment differs from the implied sentiment. The detection of sarcasm in social media platforms has been applied in the past mainly to textual utterances where lexical indicators (such as interjections and intensifiers), linguistic markers, and contextual information (such as user profiles, or past conversations) were used to detect the sarcastic tone. However, modern social media platforms allow to create multimodal messages where audiovisual content is integrated with the text, making the analysis of a mode in isolation partial. In our work, we first study the relationship between the textual and visual aspects in multimodal posts from three major social media platforms, i.e., Instagram, Tumblr and Twitter, and we run a crowdsourcing task to quantify the extent to which images are perceived as necessary by human annotators. Moreover, we propose two different computational frameworks to detect sarcasm that integrate the textual and visual modalities. The first approach exploits visual semantics trained on an external dataset, and concatenates the semantics features with state-of-the-art textual features. The second method adapts a visual neural network initialized with parameters trained on ImageNet to multimodal sarcastic posts. Results show the positive effect of combining modalities for the detection of sarcasm across platforms and methods.Comment: 10 pages, 3 figures, final version published in the Proceedings of ACM Multimedia 201