Kurt Squire · 2009
Drawing on a critical review of existing game-based learning literature, a content analysis and review of game-based learning products, and interviews with game-based learning designers, it shows how digital and video games are emerging as a new model for situated learning environments. This chapter argues that games problematize contemporary work in e-learning, which focuses on supplying, organizing, and repackaging content, offering new models that put primacy on experience. It uses comparative case study techniques to build a framework for game-based learning. The games establish challenges and goals for learners to meet and establish seductive identities and capacities for players. The chapter focuses on one of the many implications for instructional designers, the need to embrace aesthetics and compelling learning experiences as more than just superfluous, which is integral to effective training. Controlled Vocabulary Terms e-Learning; Game-based learning; Instructional design
Cited by 5
Αλέξανδρος Κλεφτοδήμος · 2016
H εστίαση αυτής της διατριβής είναι στην ανάλυση δεδομένων και εξόρυξη γνώσης από εκπαιδευτικά βίντεο καθώς και στην δημιουργία διαδραστικών περιβαλλόντων βιντεο-μάθησης που ενσωματώνουν δυνατότητες ανάλυσης δεδομένων. Η ανάλυση δεδομένων και συμπεριφορών από τη χρήση εκπαιδευτικών βίντεο είναι ένας χώρος έρευνας στη τομή των ερευνητικών πεδίων της ανάλυσης δεδομένων \& εξόρυξης γνώσης από εκπαιδευτικά περιβάλλοντα και της μάθησης με την χρήση εκπαιδευτικών βίντεο. Η ανάλυση δεδομένων που προέρχονται από την χρήση εκπαιδευτικών βίντεο έχει ως στόχο την κατανόηση των συμπεριφορών των εκπαιδευόμενων που παρακολουθούν εκπαιδευτικά βίντεο κάτω από διάφορες εκπαιδευτικές συνθήκες. Η προσέγγιση που ακολουθείται για την κατανόηση των συμπεριφορών αυτών βασίζεται στην ανάλυση δεδομένων με την χρήση στατιστικών μεθόδων, μεθόδων εξόρυξης γνώσης και οπτικοποιήσεων. Στο πρώτο μέρος της διατριβής παρουσιάζεται ένα πλαίσιο (framework) για την καταγραφή, την παρακολούθηση και την ανάλυση των συμπεριφορών εκπαιδευομένων που κάνουν χρήση εκπαιδευτικών βίντεο. Το πλαίσιο αυτό αποτελείται από έναν μηχανισμό σύλληψης δεδομένων παρακολούθησης και αλληλεπίδρασης, ένα κατάλληλο μοντέλο βάσης δεδομένων για την αποθήκευση των δεδομένων αυτών, καθώς και μια σειρά από οπτικοποιήσεις και δείκτες μέτρησης της δραστηριότητας των εκπαιδευομένων. Το πλαίσιο λαμβάνει επίσης υπόψη το γεγονός ότι τα βίντεο σήμερα μπορεί να έχουν διαδραστικά χαρακτηριστικά. Το πλαίσιο χρησιμοποιήθηκε κάτω από συγκεκριμένες εκπαιδευτικές συνθήκες σε δύο μελέτες περίπτωσης και εξάχθηκαν συμπεράσματα γύρω από τις συμπεριφορές των εκπαιδευομένων χρησιμοποιώντας τις οπτικοποιήσεις και τους δείκτες μέτρησης δραστηριότητας του πλαισίου καθώς και μεθόδους επεξεργασίας δεδομένων, στατιστικής ανάλυσης και εξόρυξης γνώσης. Κατά τη διάρκεια της έρευνας που πραγματοποιήθηκε στα πλαίσια της διατριβής υπήρχαν αξιοσημείωτες τάσεις που λάμβαναν χώρα στον τομέα των διαδραστικών βίντεο καθώς μια σειρά από διαδικτυακές πλατφόρμες που επιτρέπουν στους εκπαιδευτικούς να προσθέτουν διάφορες διαδραστικές δυνατότητες σε εκπαιδευτικά βίντεο έκαναν την εμφάνισή τους. Στην διατριβή γίνεται μια προσπάθεια να ληφθούν υπόψη οι τάσεις αυτές και να διερευνηθούν τρόποι για την ανάπτυξη διαδραστικών περιβαλλόντων βιντεο-μάθησης τα οποία ενσωματώνουν τις βασικές αρχές του πλαισίου ανάλυσης δεδομένων που παρουσιάζεται στο πρώτο μέρος της διατριβής. Πιο συγκεκριμένα στο δεύτερο μέρος αυτής της διατριβής παρουσιάζουμε έναν οδικό χάρτη για τη δημιουργία διαδραστικών περιβαλλόντων βιντεο-μάθησης, που υποστηρίζουν την ανάλυση δεδομένων παρακολούθησης-αλληλεπίδρασης, με την χρήση τεχνολογιών ανοιχτού λογισμικού και ανοιχτών διαδικτυακών πηγών. Περιβάλλοντα που δημιουργήθηκαν χρησιμοποιώντας τις αρχές του οδικού χάρτη χρησιμοποιήθηκαν σε τρεις διαφορετικές μελέτες περίπτωσης και εξάχθηκαν συμπεράσματα γύρω από τις συμπεριφορές των εκπαιδευομένων. Για την ανάλυση των συμπεριφορών χρησιμοποιήθηκαν και πάλι τα εργαλεία του πλαισίου (οπτικοποιήσεις και δείκτες μέτρησης δραστηριότητας) μαζί με στατιστικές μεθόδους και μεθόδους εξόρυξης γνώσης.
Cited by 0Open access
Ron Wilkinson · TESL Canada Journal · 1984
Cited by 9Open access
Shulong Yan, Emily Baxter · eLearn · 2018
What role should video play in online learning? That is the question that learning designers and teaching faculty face more and more when designing online instruction. In this paper we will share what we learned about students' learning experience in a video-based online course. We will propose a new perspective for looking at video as an instructional tool, based on our findings using a qualitative research method.
Cited by 1
Ilaria Torre, Ilenia Galluccio, Mauro Coccoli · CINECA IRIS Institutial Research Information System (University of Genoa) · 2022
Multimedia content and video-based learning are expected to take a central role in the post-pandemic world. Thus, providing new advanced interfaces and services that further exploit their potential becomes of paramount importance. A challenging area deals with developing intelligent visual interfaces that integrate the knowledge extracted from multimedia materials into educational applications. In this respect, we designed a web-based video player that is aimed to support video consumption by exploiting the knowledge extracted from the video in terms of concepts explained in the video and prerequisite relations between them. This knowledge is used to augment the video lesson through visual feedback methods. Specifically, in this paper we investigate the use of two types of visual feedback, i.e. an augmented transcript and a dynamic concept map (map of concept’s flow), to improve video comprehension in the first-watch learning context. Our preliminary findings suggest that both the methods help the learner to focus on the relevant concepts and their related contents. The augmented transcript has an higher impact on immediate comprehension compared to the map of concepts’ flow, even though the latter is expected to be more powerful to support other tasks such as exploration and in-depth analysis of the concepts in the video.
Cited by 10Open access
Mathai, Mareeta · Scholar Commons (Santa Clara University) · 2024
The task of video prediction is to generate unseen future video frames based on the past ones. It is an emerging, yet challenging task due to its inherent uncertainty and complex spatiotemporal dynamics. The ability to predict and anticipate future events from video prediction has applications in various prediction systems like self-driving cars, weather forecasting, traffic flow prediction, video compression etc. Due to the success of deep learning in the computer vision field, several deep learning Artificial Intelligence (AI) architectures such as convolutional neural networks (CNNs), long short-term memory (LSTMs), convolutional LSTMS (ConvLSTMs) and transformers have been explored to improve prediction accuracy. The internal representation, mainly the spatial correlations and temporal dynamics of the video, is learned and used to predict the next frames in deep learning-based video prediction. Several state-of-the-art deep learning methods have achieved superior video prediction accuracy at the expense of huge computational cost. In the light of recent wide popularity of Green AI which aims for efficient environment friendly solutions alongside accuracy, this research concentrates on efficient methods for video prediction. Such methods are suitable for memory-constrained and computation resource-limited platforms, such as mobile and embedded devices. We focus on CNN/LSTM methods and transformer-based architectures with fewer parameters for our lightweight efficient environment-friendly video prediction techniques. We conducted experimental studies on popular video prediction datasets and compared to existing methods, our proposed methods achieved competitive frame prediction accuracy with significantly reduced model size, trainable parameters, and computational complexity.
Cited by 0
Neil McLeod Hamilton, Gorry Fairhurst, Steven Pavett et al. · Journal of Audiovisual Media in Medicine · 2001
As a relevant exemplar of MPEG-1 and MPEG-2 digital video use, a multimedia computer-assisted learning (CAL) application dealing with Critical Communication Issues in medicine was developed. The application allowed the student to navigate through a series of high-quality digital video and audio clips that were delivered in near real-time from an Intranet server. This paper gives a brief background to the MPEG-2 video compression system and discusses the use of digital video in a CAL environment.
Cited by 0
Xiaolan Jiang · SOKENDAI (Graduate University for Advanced Studies) · 2020
Cited by 0Open access
Michail N. Giannakos, Konstantinos Chorianopoulos, Marco Ronchetti et al. · 2013
The International Workshop on Analytics on Video-based Learning (WAVe2013) aims to connect research efforts on Video-based Learning with Learning Analytics to create visionary ideas and foster synergies between the two fields. The main objective of WAVe is to build a research community around the topical area of Analytics on video-based learning. In particular, WAVe aims to develop a critical discussion about the next generation of analytics employed on video learning tools, the form of these analytics and the way they can be analyzed in order to help us to better understand and improve the value of video-based learning. WAVe is based on the rationale that combining and analyzing learners' interactions with other available data obtained from learners, new avenues for research on video-based learning have emerged.
Cited by 30
Kang Da Ji, Helmut Hlavacs · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · 2022
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Rare & Special e-Zone (The Hong Kong University of Science and Technology) · 2016
b1590244 HKUST Electronic Theses Learning-based video super-resolution by Xiong Lei thesis 2016 ix, 1, 38 pages : illustrations ; 30 cm The goal of video super-resolution (VSR) is to generate the high-resolution (HR) sequence based on the low-resolution (LR) input.…Read more ›
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Yang Wang · OSF Preprints (OSF Preprints) · 2025
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Karim Hesham Shaker Ibrahim · Advances in linguistics and communication studies · 2018
Video/digital games have grown into sophisticated, realistic, and engaging problem-solving virtual worlds that have their own literacy practices, affinity spaces, and online virtual communities. As a result, various studies have examined theirs to promote L2 learning and literacy. The findings of these studies suggest that digital games can promote multilingual communication, L2 vocabulary development, and situated L2 use. However, promising these findings, to-date little is known about the specific dynamics of gameplay that can facilitate L2 learning. To address this gap in the literature, this chapter will draw on interdisciplinary research on digital gaming from literacy studies, games' studies, and narratology to account for the L2 learning potentials of digital games. To explain their L2 learning potentials, the chapter will conceptualize digital games as dynamic texts, affinity spaces, and semiotic ecologies, and discuss the implications of each conceptualization for game-based L2 learning and teaching.
Cited by 1
Tae-Hyun Oh, Ronnachai Jaroensri, Chang-Il Kim et al. · Lecture notes in computer science · 2018
Cited by 13Open access
Lei Xiong · 2016
b1590244 HKUST Electronic Theses Learning-based video super-resolution by Xiong Lei thesis 2016 ix, 1, 38 pages : illustrations ; 30 cm The goal of video super-resolution (VSR) is to generate the high-resolution (HR) sequence based on the low-resolution (LR) input.…Read more ›
Cited by 0
Deny Sutrisno, B Buhari · Journal of Physics Conference Series · 2021
Abstract Blended Learning is a learning that combines face-to-face and online learning. In nursing practice courses, learning media about basic skills action procedures in nursing that can be accessed online is needed. Thus, it is necessary to design an instructional media containing action instructions for basic skills in nursing. The purpose of this research was to make video-based learning media as a guide to nursing procedures and to find out the results of the video-based learning media test. There were eight (8) stages in designing instructional media, namely: (1) writing video scripts, (2) taking video images, (3) editing videos, (4) validating, (5) revising, (6) uploading videos, (7) presenting videos to students, and (8) evaluating. This research was expected to produce teaching materials in the form of instructional video to support the lecturing process. The result of this study showed that the videos are very worthy.
Cited by 1Open access
Niels Seidel, Markus Hermes · 2024
This paper introduces a novel video learning environment designed for synchronous collaborative learning, a departure from the usual focus on asynchronous activities in such settings. The environment facilitates both loosely coupled and tightly coupled collaboration, encompassing video playback and annotation. It also provides a communication channel for dyads or small groups via parallel video or audio. To enhance collaboration efficiency and reduce non-learning coordination tasks, the environment integrates informal and formal CSCL scripts, specifically tailored for video-based learning. A multi-method user study involving three dyads validated the effectiveness of script-based collaboration and the extensive utilization of video-based resources in learning processes.
Cited by 0
Gandhi, Rikin, Pillai, Aishwarya · CGSPace A Repository of Agricultural Research Outputs (Consultative Group for International Agricultural Research) · 2015
Rikin Gandhi and Aishwarya Pillai explain how video-based learning can encourage communities to be co-creators of knowledge and not just passive recipients
Cited by 0Open access
Jinjin Chen, Wenyao Gan, Hengsheng Zhang et al. · 2021
Distortion of real video is affected by many factors. Many existing old videos n have the problem of low definition. However, most video enhancement based on paired learning is trained for specific degradation problem, lack of the ability to enhance real low-definition video with unknown distortion. In this paper, we propose a joint video enhancement algorithm based on unpaired learning, which uses a high-definition video to enhance a low-definition video with similar contents. In order to train the network, we also build three pairs of unpaired video datasets with chimpanzee, city night view and military figures as contents. In experiments, we compare our method with enhancement algorithm based on paired learning and other unpaired framework and find that our method achieves a higher performance,
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Levent Karacan · Hacettepe University Institutional Repository (hacettepe.edu.tr) · 2019
Image and video editing encompasses the wide range of image operations to give desired visual effects to a given image or video either for improving various visual properties such as color, contrast, luminance or better emphasizing some aspects of scenes such as an objects, background, activity, attribute, emotion etc. Popular graphical tools(e.g. Adobe Photoshop, GIMP) that provide rich image operations can be utilized to achieve the desired visual effects, however users need to be familiar with image processing methods and have skills to overcome challenging low-level operations on images and videos. Therefore, easy and efficient image and video editing methods are needed for casual users to manipulate visual contents with high-level interactions such as natural languages. On the other hand, it is expected that the processes will be imperceptibly flawless on the image, in other words, photorealism should not be degraded. Recently, data-driven or learning based new works which try to meet those expectations have been proposed for various image and video editing problems. In this thesis, we propose learning-based methods for a number of image and video editing problems which are alpha matting, visual attribute manipulation and language-based video manipulation following recent trends and developments. Our methods produce competitive or better results against state-of-the-art methods on benchmark datasets quantitatively and qualitatively while providing simple high-level interactions such as natural language and visual attributes. Besides, our visual attribute manipulation method is the first high-level photo editing approach to enable continuous control on transient attributes of natural landscapes in the literature. \n \nFor alpha matting, we present a new sampling-based alpha matting approach for the accurate estimation of foreground and background layers of an image. Previous sampling-based methods typically rely on certain heuristics in collecting representative samples from known regions, and thus their performance deteriorates if the underlying assumptions are not satisfied. To alleviate this, we take an entirely new approach and formulate sampling as a sparse subset selection problem where we propose to pick a small set of candidate samples that best explains the unknown pixels. Moreover, we describe a new dissimilarity measure for comparing two samples which is based on KL-divergence between the distributions of features extracted in the vicinity of the samples. The proposed framework is general and could be easily extended to video matting by additionally taking temporal information into account in the sampling process. Evaluation on standard benchmark datasets for image and video matting demonstrates that our approach provides more competitive results compared to the state-of-the-art methods. \n \nFor visual attribute manipulation, we explore building a two-stage framework for enabling users to directly manipulate high-level attributes of a natural scene. The key to our approach is a deep generative network which can hallucinate images of a scene as if they were taken at a different season (e.g. during winter), weather condition (e.g. in a cloudy day) or time of the day (e.g. at sunset). Once the scene is hallucinated with the given attributes, the corresponding look is then transferred to the input image while preserving the semantic details intact, giving a photo-realistic manipulation result. As the proposed framework hallucinates what the scene will look like, it does not require any reference style image as commonly utilized in most of the appearance or style transfer approaches. Moreover, it allows to simultaneously manipulate a given scene according to a diverse set of transient attributes within a single model, eliminating the need of training multiple networks per each translation task. Our comprehensive set of qualitative and quantitative results demonstrate the effectiveness of our approach against the competing methods. \n \nIn our last work, we introduce a new task of manipulating person videos with natural language, which aims to perform local and semantic edits on a video clip of an individual to automatically change their outfit based on a description of target look. To this end, we first collect a new video dataset containing full-body images of different persons wearing different types of clothes and their textual descriptions. The nature of our problem allows for better utilization of multi-view information and we exploit this property and design a new language-guided video editing model. Our architecture is composed of two subnetworks trained simultaneously: a network for constructing a concise representation of the person from multiple observations (representation network), and another network that benefits from the extracted internal representation for performing the manipulation according to the target description (translation network). Our qualitative and quantitative evaluations demonstrate that our proposed approach significantly outperforms existing frame-wise methods, producing temporally coherent and semantically more meaningful results.
Cited by 0Open access