Augmented reality to promote collaborative and autonomous learning in higher education

A character network is a graph extracted from a narrative in which vertices represent characters and edges correspond to interactions between them. A number of narrative-related problems can be addressed automatically through the analysis of character networks, such as summarization, classification, or role detection. Character networks are particularly relevant when considering works of fiction (e.g., novels, plays, movies, TV series), as their exploitation allows developing information retrieval and recommendation systems. However, works of fiction possess specific properties that make these tasks harder. This survey aims at presenting and organizing the scientific literature related to the extraction of character networks from works of fiction, as well as their analysis. We first describe the extraction process in a generic way and explain how its constituting steps are implemented in practice, depending on the medium of the narrative, the goal of the network analysis, and other factors. We then review the descriptive tools used to characterize character networks, with a focus on the way they are interpreted in this context. We illustrate the relevance of character networks by also providing a review of applications derived from their analysis. Finally, we identify the limitations of the existing approaches and the most promising perspectives.

Extraction and Analysis of Fictional Character Networks: A Survey

The latest evaluation indicators of digital literacy are applied to college students to evaluate their level of digital literacy. Evaluation areas applied in this study are roughly classified into ...

A study on the effect of digital literacy on information use behavior

A character network is a graph extracted from a narrative, in which vertices represent characters and edges correspond to interactions between them. A number of narrative-related problems can be addressed automatically through the analysis of character networks, such as summarization, classification, or role detection. Character networks are particularly relevant when considering works of fictions (e.g. novels, plays, movies, TV series), as their exploitation allows developing information retrieval and recommendation systems. However, works of fiction possess specific properties making these tasks harder. This survey aims at presenting and organizing the scientific literature related to the extraction of character networks from works of fiction, as well as their analysis. We first describe the extraction process in a generic way, and explain how its constituting steps are implemented in practice, depending on the medium of the narrative, the goal of the network analysis, and other factors. We then review the descriptive tools used to characterize character networks, with a focus on the way they are interpreted in this context. We illustrate the relevance of character networks by also providing a review of applications derived from their analysis. Finally, we identify the limitations of the existing approaches, and the most promising perspectives.

Abstract The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk stratification, and individualised approaches to patient management. Such opportunities are particularly relevant for the management of older patients, a group that is characterised by complex multimorbidity patterns and significant interindividual variability in homeostatic capacity, organ function, and response to treatment. Clinical tools that utilise machine learning algorithms to determine the optimal choice of treatment are slowly gaining the necessary approval from governing bodies and being implemented into healthcare, with significant implications for virtually all medical disciplines during the next phase of digital medicine. Beyond obtaining regulatory approval, a crucial element in implementing these tools is the trust and support of the people that use them. In this context, an increased understanding by clinicians of artificial intelligence and machine learning algorithms provides an appreciation of the possible benefits, risks, and uncertainties, and improves the chances for successful adoption. This review provides a broad taxonomy of machine learning algorithms, followed by a more detailed description of each algorithm class, their purpose and capabilities, and examples of their applications, particularly in geriatric medicine. Additional focus is given on the clinical implications and challenges involved in relying on devices with reduced interpretability and the progress made in counteracting the latter via the development of explainable machine learning. 

A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future.

Stories in digital content (e.g., movies) are usually developed using many kinds of relationships among the characters. In order to efficiently manage such contents, we want to exploit a social network (called Character-net) extracted from the stories. Since scripts are composed of several elements (i.e., scene headings, character names, dialogs, actions, etc.), we focus on analyzing interactions (e.g., dialog) among the characters to build such a social network. Most importantly, these relationships between minor and major characters can be abstracted and clustered into similar scenes. Thereby, in this paper, we propose a novel method that can cluster characters using their emotional similarity. If a minor character has a similar emotion vector tothe main character, then the minor character can be classified as a tritagonist who helps the main character. Conversely, this minor character may be clustered into another group and denoted as an antagonist. Additionally, we show the efficiency of our proposed method by experiment in this paper.

Emotion-based character clustering for managing story-based contents: a cinemetric analysis

Story-based contents (e.g., novel, movies, and computer games) have been dynamically transformed into various media. In this environment, the contents are not complete in themselves, but closely connected with each other. Also, they are not simply transformed form a medium to other media, but expanding their stories. It is called as a transmedia storytelling, and a group of contents following it is called as a transmedia ecosystem. Since the contents are highly connected in terms of the story in the transmedia ecosystem, the existing content analysis methods are hard to extract relationships between the contents. Therefore, a proper content analysis method is needed with considering expansions of the story. The aim of this work is to understand how (and why) such contents are transformed by i) defining the main features of the transmedia storytelling and ii) building the taxonomy among the transmedia patterns. More importantly, computational transmedia ecosystem is designed to process a large number of the contents, and to support high understandability of the complex transmedia patterns.

A computational model of transmedia ecosystem for story-based contents

http://koreascience.or.kr:80/article/JAKO201432441779939.pdf

Movie Box-office Analysis using Social Big Data

As augmented reality technologies offer superior senses of presence and immersion to learners through layers of virtual information over the physical world, much attention has been drawn to this learning medium that creates a new learning environment driven by experiences. And, existing AR-based learning content has just focused on interactions among users and on three-dimensional imaging techniques while the collaborative element has not been taken into account fully. Against the backdrop, a rule-based interactive storytelling element has been introduced to the system that offers a variety of contents at a user’s choice so as to draw attention and interests. To verify the effectiveness of such learning system, an experiment was conducted with primary school students who went through a performance assessment of ‘Growing Peach Tree’. The experiment proved the learning system effective with a high score of 80.4, which is equivalent to that of debate-based learning.

Storytelling of Collaborative Learning System on Augmented Reality

Word emotion analysis is the basic step that recognizes emotions. Emotion words that express emotion on dialogs are classified into two classes such as direct and potential emotion word. Direct emotion word can represent clearly emotion and potential emotion word may represent specific emotion depending on context. Potential emotion word unlike direct emotion word is hardly extracted and identified. In this paper, we propose the method that extracts and identifies potential emotion words based on WordNet as well as direct emotion words. Potential emotion word can be extracted by measuring lexical affinity. Then, we consider the sense distance in order to minimize variation of meaning. In addition, we suggest the maximum sense distance that limits searching space and can extract the best potential emotion words.

Potential Emotion Word in Movie Dialog

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