M. A. Tadlaoui, Rommel N. Carvalho, Mohamed Khaldi
{"title":"Managing the Learner Model With Multi-Entity Bayesian Networks in Adaptive Hypermedia Systems","authors":"M. A. Tadlaoui, Rommel N. Carvalho, Mohamed Khaldi","doi":"10.4018/978-1-5225-9031-6.CH005","DOIUrl":"https://doi.org/10.4018/978-1-5225-9031-6.CH005","url":null,"abstract":"Modeling the learner in adaptive systems involves different information. There are several methods to manage the learner model. They do not handle the uncertainty in the dynamic modeling of the learner. The main hypothesis of this chapter is the management of the learner model based on multi-entity Bayesian networks. This chapter focuses on modeling the learner model in a dynamic and probabilistic way. The authors propose in this work the use of the notion of fragments and m-theory to lead to a Bayesian multi-entity network. The use of this Bayesian method can handle the whole course of a learner as well as all of its shares in an adaptive educational hypermedia.","PeriodicalId":384539,"journal":{"name":"Cognitive Computing in Technology-Enhanced Learning","volume":"143 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131849349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Modern Health Management With Cognitive Computing and Big Data Analytics","authors":"Mamata Rath","doi":"10.4018/978-1-5225-9031-6.CH010","DOIUrl":"https://doi.org/10.4018/978-1-5225-9031-6.CH010","url":null,"abstract":"Big data analytics is an refined advancement for fusion of large data sets that include a collection of data elements to expose hidden prototype, undetected associations, showcase business logic, client inclinations, and other helpful business information. Big data analytics involves challenging techniques to mine and extract relevant data that includes the actions of penetrating a database, effectively mining the data, querying and inspecting data committed to enhance the technical execution of various task segments. The capacity to synthesize a lot of data can enable an association to manage impressive data that can influence the business. In this way, the primary goal of big data analytics is to help business relationship to have enhanced comprehension of data and, subsequently, settle on proficient and educated decisions.","PeriodicalId":384539,"journal":{"name":"Cognitive Computing in Technology-Enhanced Learning","volume":"477 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113995640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Eye Tracking Applications for E-Learning Purposes","authors":"Ismail El Haddioui","doi":"10.4018/978-1-5225-9031-6.CH007","DOIUrl":"https://doi.org/10.4018/978-1-5225-9031-6.CH007","url":null,"abstract":"E-learning has become a fundamental part of child education, higher education, and corporate training. In the design of adaptive e-learning environments, it is important to track and analyze learner behavior and preferences, and this is possible by recording their eye movements. Eye tracking is a technology developed to monitor eye movements allowing us to analyze the recorded gaze data. The main goal of this chapter is to determine the potential of eye tracking in the field of e-learning and the various applications of eye movement analysis for e-learning platforms. Results can be used to design an adaptive e-learning environment able to collect, analyze, and understand learner online behavior, preferences, and needs, and then offer an educational content adapted to each learner's needs by generating new customized learning situations.","PeriodicalId":384539,"journal":{"name":"Cognitive Computing in Technology-Enhanced Learning","volume":"160 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114091422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Paraskevi Papadopoulou, Kwok Tai Chui, L. Daniela, Miltiadis Demetrios Lytras
{"title":"Virtual and Augmented Reality in Medical Education and Training","authors":"Paraskevi Papadopoulou, Kwok Tai Chui, L. Daniela, Miltiadis Demetrios Lytras","doi":"10.4018/978-1-5225-9031-6.CH006","DOIUrl":"https://doi.org/10.4018/978-1-5225-9031-6.CH006","url":null,"abstract":"Virtual and Augmented Reality (VR & AR) with its various computer-based virtual simulations and teaching aids have already begun to transform the medical education and training. The use of virtual labs and anatomy lessons including the use of Virtual Learning Environments (VLEs) as in the delivery of lectures and surgery operations are explored. The purpose of this chapter is to promote the role of VR & AR in the context of medical education as an innovative, effective, and cost-reasonable solution for the provision of better and faster practical training. This chapter overall investigates and explores the potential of VLEs in terms of the necessary concepts and principles that allow students to develop a more direct and meaningful experiential understanding of the learning goals and outcomes of courses and of the practical and transferable skills required. A business model related to cloud active learning in medical education and training is proposed in line with the idea of an Open Agora of Virtual Reality Learning Services.","PeriodicalId":384539,"journal":{"name":"Cognitive Computing in Technology-Enhanced Learning","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114944987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Exploration of Automated Image Processing Techniques in the Study of Scientific Argumentation","authors":"Bo Pei, Henglv Zhao, Wanli Xing, Hee-Sun Lee","doi":"10.4018/978-1-5225-9031-6.CH008","DOIUrl":"https://doi.org/10.4018/978-1-5225-9031-6.CH008","url":null,"abstract":"Scientific argumentation is an epistemic practice where scientific theories are proposed, refined, and refuted, and also a language-based practice where evidence is provided in support of claims. This chapter explores how techniques of computerized image processing can help researchers to identify relationships between features of images and the quality of written artifacts used in scientific argumentation. In this chapter, secondary school students worked in an interactive simulation model and made claims about whether rain water was trapped underground. Automated image processing was employed to precisely quantify several image features relevant to the students' claims. Chi-square tests and independent samples t-tests were used to determine the relationships between the extracted features and the argumentation. The results revealed that the presence of a line on a student's snapshot had a significant effect on that student's claim and explanation scores and the starting and endpoints of the students' lines significantly influenced their explanation scores, but not their claim scores.","PeriodicalId":384539,"journal":{"name":"Cognitive Computing in Technology-Enhanced Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130109235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Principal Component Analysis on the Students' Perception of a Cognitive Assistant for Content Reinforcement in Higher Education","authors":"L. Medeiros, Marilene Garcia","doi":"10.4018/978-1-5225-9031-6.CH004","DOIUrl":"https://doi.org/10.4018/978-1-5225-9031-6.CH004","url":null,"abstract":"The study in this chapter presents training by highly ontology-oriented tutoring host (THOTH), a cognitive assistant applied to students of higher education. It was developed to provide a reinforcement of contents, aiming to reach a high level of interactivity between users and interfaces. THOTH is based on the theoretical assumption that knowledge is organized in the form of ontologies constructed in the ORAV model in regard to the Ausubel's meaningful learning. THOTH processes the required objects of the ontology in order to facilitate the formulation of standard questions based on the attributes. After one session, students gave its perceptions in a Likert-scale questionnaire with 13 questions. A principal component analysis was performed with 35 questionnaires revealing eight different categories of grouped questions, ranging from the degree of functionality in the learning process to featuring how users were accepting the conversations. The evaluation of the categories is explained quantitatively, highlighting relationships between the elements of each category of study.","PeriodicalId":384539,"journal":{"name":"Cognitive Computing in Technology-Enhanced Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124358406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Badreddine Sandid, Mohammed Amine Boughalem, Mohamed Khaldi
{"title":"Fostering Online Interactions Between Learners","authors":"Badreddine Sandid, Mohammed Amine Boughalem, Mohamed Khaldi","doi":"10.4018/978-1-5225-9031-6.CH003","DOIUrl":"https://doi.org/10.4018/978-1-5225-9031-6.CH003","url":null,"abstract":"Learning has abandoned its conventional and traditional knowledge acquisition style; it started breaking the limited information channels, flowing in all possible directions, while seeking and absorbing the intelligence of its choice. All due to technology that offers more possibilities in various fields, it makes it possible to fill this void created by these learning styles. Computer-supported collaborative learning (CSCL) is an educational approach in which learning is based on social interaction between learners through the internet. Sharing, the construction of knowledge, and skills development are part of the characteristics of this type of learning while pulling a better profit from technology. The CSCL approach can be implemented in online learning environments. This chapter provides an innovative pedagogical scenario that aims to promote online interactions between learners in a hybrid learning device based on CSCL approach.","PeriodicalId":384539,"journal":{"name":"Cognitive Computing in Technology-Enhanced Learning","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116280202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Enhancing the Credibility of the Decision-Making Journey Through Serious Games Learning Analytics","authors":"Louis Doru Havriliuc, Gianita Bleoju, A. Căpățînă","doi":"10.4018/978-1-5225-9031-6.CH002","DOIUrl":"https://doi.org/10.4018/978-1-5225-9031-6.CH002","url":null,"abstract":"This chapter proposes a specific learning system and a combined method to devise the learning analytics component as an envisioned solution to inform the development of other digital learning resources which are built for meeting specific, predefined learning objectives. The authors acknowledge the critical challenges of distinguishing between decision making and decision taking on the outcomes of learning. The ambition is to assess learning performance by means of unstructured interviews with participants using a digital marketing simulation game and how this has helped them attain job success working on real digital marketing projects. The authors expect that the consequences of decisions taken on real or realistic business conditions provided by a simulated learning environment should enrich the learning experience with insights (influences, constructs, and variables), unstructured knowledge representation, and rule-based decisions that learners will utilize and be alert and react to in real markets.","PeriodicalId":384539,"journal":{"name":"Cognitive Computing in Technology-Enhanced Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128472432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Evaluation of Mobile Apps for Chinese Language Learning","authors":"Liva Leja","doi":"10.4018/978-1-5225-9031-6.CH009","DOIUrl":"https://doi.org/10.4018/978-1-5225-9031-6.CH009","url":null,"abstract":"More and more people around the world choose to learn Chinese as a foreign language. However, due to its specifics, people often encounter difficulties in learning it. As a result of technological development, there are a number of useful tools that can improve the learning process, including various learning mobile applications (apps). Considering the huge amount of language learning applications and the lack of a unified evaluation system, it is possible to get lost in the options offered. The purpose of this chapter was to create criteria for evaluating Chinese language learning mobile apps that would help users with finding effective apps for learning Chinese as a foreign language.","PeriodicalId":384539,"journal":{"name":"Cognitive Computing in Technology-Enhanced Learning","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126187689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Meenakshi Tripathi, Saatvik Shah, Prashant Bahal, H. Sharma, Ritika Gupta
{"title":"Smart MM","authors":"Meenakshi Tripathi, Saatvik Shah, Prashant Bahal, H. Sharma, Ritika Gupta","doi":"10.4018/978-1-5225-9031-6.CH011","DOIUrl":"https://doi.org/10.4018/978-1-5225-9031-6.CH011","url":null,"abstract":"Rapid advancements have been made in the field of artificial intelligence in recent years. This has resulted in its adoption in various technologies from medicine to search engines. Existing media management systems have however not yet fully leveraged the power of artificial intelligence (AI) to give users enhanced information apart from basic media metadata. This chapter proposes a smart movie management system which works majorly offline and uses AI to deliver optimum information to the users on four vital tasks. These tasks are multilevel phrase level review polarity, plot and review keywords, a content-based recommendation system, and an emotion recognition system. The complete system works in near-real time with a user-friendly presentation to maximize a user's information gain.","PeriodicalId":384539,"journal":{"name":"Cognitive Computing in Technology-Enhanced Learning","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127095848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}