In the recent years, the globalization and massification of video education offer involved more and more eLearning scenarios within universities. This article refers to interactive video and proposes an overview of it. We analyze the background information, regarding the eLearning campus used in virtual universities around the world, the MOOC movement in the last year, and the related interactive video platforms in the (education) field. At the same time, we pay particular attention to technical aspects of the interactive video: defining concept, types of video metadata, media fragments and types of annotations, as primordial elements that bring interactivity. We tested some free and commercial interactive web application. We gathered all the ideas. We propose a framework for an interactive system web based on the main modules: video resource management (production, transcoding and storage), annotations, Linked Open Data, distribution medium, player interface, data analytics and recommendation system. On the way, we offer our findings, together with our recommendations for an annotation interface and player module. It is our idea for Politehnica University Timisoara, either as a standalone solution or a complement to actual virtual campus (http://cv.upt.ro) depending on future development plans and financial aspects.
Kreber's 1998 study shows that an individual's personality plays a major role in the educational process, and directly determines a student's predisposition to engage in a self-directed learning process. This is particularly the case with distance education, where motivation is a key factor. With the newly emerging field of interactive video in education, personality traits can be determined by various approaches. Filling out a personality form might be an unattractive method of profiling students. But with the case of interactive video, requiring by definition a voluntary user interaction, this information can be obtained by aligning interaction patterns to personality traits, using established psychological classification models. A key advantage is the non-intrusiveness of the process. We first define what interactive video is and briefly present what interactions are possible with the video content. We offer a quick example of interactive video in education. Based on the way users interact with a i-video material, we outline 5 types of interactive actions: General interest, Interface Interaction, Content Interaction, Social Interaction and Contributive Interaction. Further, we use these 5 categories of interaction to classify various types of users into psychological categories, using several personality models. A binary model first proposed by Carl Jung of extrovert/introvert is first discussed. We then discuss a four-category model of Guardian/Artisan/Idealist/Rational proposed by Keirsey in 1998, extendable to a 16-category model where each of the four primary personality traits are each further dissected in four subcategories. For each of the personality models used, we create a matrix of scores for all 5 types of interactions, using these interactions with the interactive video content to progressively determine a student's personality. This automatic profiling model is tested on several subjects. To verify accuracy of the automatic profiling model, the participants are then asked to complete forms for personality tests, their scores being compared to. Results are discussed and conclusions are drawn, highlighting the benefits of using automatic profiling mechanisms in interactive video learning applications.
This paper discusses the current developments in interactive video and its implications for online learning, and particularly to the new educational concept of MOOC - Massive Open Online Courses. The authors present an interactive video platform that can be easily used for generation of interactive videos. Interactivity is added to videos by temporal and spatial interactive multimedia annotations using media fragments, as well as decisions at the end of a video, via a special interface. The same interactive video platform allows the dynamic generation of a graph-type multiple path linear story, with information dependent on previous decisions and choices, similar to an educational experience where later courses depend on prerequisite information. Information depth is ensured via various types of annotations. The authors consider two main types of content annotations - basic annotations explaining fundamental concepts, and advanced annotations providing in-depth specialized information about a given topic. The dynamic of user interaction with these two types of annotations is used to determine the users level of knowledge about the course topic. All these aspects are then put in perspective against a MOOC environment, showing how interactive video can be used to enhance the learning process and to enhance the user experience. We also discuss the cost-effective means of adding value and interactivity to the video content, whether generated purposely to be interactive, or simply adding interaction to existing video content to enhance it. As a conclusion, the article proposes a theoretical and practical approach supported by existing examples, and draws future directions and guidelines for developing educational projects and applications using interactive video in a MOOC context.
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