Vol 13, No 10 (2018) International Journal of Emerging Technologies in Learning (iJET)

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HOy traemos a este espacio el último número el Vol 13, No 10 (2018) del International Journal of Emerging Technologies in Learning (iJET) This interdisciplinary journal aims to focus on the exchange of relevant trends and research results as well as the presentation of practical experiences gained while developing and testing elements of technology enhanced learning. So it aims to bridge the gap between pure academic research journals and more practical publications. So it covers the full range from research, application development to experience reports and product descriptions. Readers don't have to pay any fee. Vol 13, No 10 (2018) Table of Contents Papers Innovative English Classroom Teaching Based on Online Computer Technology in Rural Middle and Primary Schools Application of Brain Neural Network in Personalized English Education System Songlin Yang, Min Zhang A Generic Tool for Generating and Assessing Problems Automatically using Spreadsheets Maria Assumpció Rafart Serra, Andrea Bikfalvi, Josep Soler Masó, Jordi Poch Garcia A High Security Distance Education Platform Infrastructure Based on Private Cloud Jingtai Ran, Kepeng Hou, Kegang Li, Niya Dai Student Performance Prediction Model Based on Discriminative Feature Selection Haixia Lu, Jinsong Yuan An Eight-Layer Model for Mathematical Cognition Marios A. Pappas, Athanasios S. Drigas, Fotini Polychroni Design and Implementation of University Art Education Management System Based on JAVA Technology The Design and Application of Flip Classroom Teaching Based on Computer Technology Jia Li, Xiaoxia Zhang, Zijun Hu Feature Extraction and Learning Effect Analysis for MOOCs Users Based on Data Mining Intelligent System for College English Listening and Writing Training Development of an Accounting Skills Simulation Practice System Based on the B/S Architecture Jianmei Liu, Rong Fu Teaching Quality Evaluation and Scheme Prediction Model Based on Improved Decision Tree Algorithm Sujuan Jia, Yajing Pang Blended Learning Innovation Model among College Students Based on Internet Score Prediction Model of MOOCs Learners Based on Neural Network Yuan Zhang, Wenbo Jiang Design and Implementation of the Online Computer-Assisted Instruction System Based on Object-Oriented Analysis Technology Wenbo Zhou, Lei Shi, Jian Chen Matlab-Realized Visual A* Path Planning Teaching Platform Communication Jigsaw: A Teaching Method that Promotes Scholarly Communication A Tablet-Computer-Based Tool to Facilitate Accurate Self-Assessments in Third- and Fourth-Graders Denise Villanyi, Romain Martin, Philipp Sonnleitner, Christina Siry, Antoine Fischbach Short Papers Application of Blockchain Technology in Online Education Han Sun, Xiaoyue Wang, Xinge Wang The Grading Multiple Choice Tests System via Mobile Phone using Image Processing Technique Worawut Yimyam, Mahasak Ketcham Offline Support Model for Low Bandwidth Users to Survive in MOOCs International Journal of Emerging Technologies in Learning.


Constructivism, Self-Directed Learning and Case-Based Reasoners: AWinning Combination

AAAI Conferences

Most intelligent learning support systems, in particular Intelligent Tutoring Systems (ITS), assume that "knowledge" is a predefined entity. This assumption applies not only to the knowledge that humanity possesses in a particular domain (e.g., our knowledge of mathematics) but also the knowledge that particular individuals must have in order to handle given tasks or understand productively a given domain. Thus an ITS typically has a knowledge base consisting, for example, of the concepts and procedures that high school students are supposed to need in order to carry out routine computational tasks or that math majors are supposed to need in order to demonstrate a theorem. The ITS interacts with users in such a way that the "needed" concepts and procedures become operative in them. What this comes down to, then, is a fundamentally mechanical model of knowledge transmission. No matter how open-ended and adaptive a typical ITS may be, what is inside the system is meant to end up inside the heads of the students.


Teachable Agents Learning by Teaching Environments for Science Domains

AAAI Conferences

The crisis in science education and the need for innovative computer-based learning environments has prompted us to develop a multi-agent system, Betty's Brain that implements the learning by teaching paradigm. The design and implementation of the system based on cognitive science and education research in constructivist, inquiry-based learning, involves an intelligent software agent, Betty, that students teach using concept map representations with a visual interface. Betty is intelligent not because she learns on her own, but because she can apply qualitative-reasoning techniques to answer questions that are directly related to what she has been taught. The results of an extensive study in a fifth grade classroom of a Nashville public school has demonstrated impressive results in terms of improved motivation and learning gains. Reflection on the results has prompted us to develop a new version of this system that focuses on formative assessment and the teaching of selfregulated strategies to improve students' learning, and promote better understanding and transfer.


Artificial Intelligence and Risk Communication

AAAI Conferences

The challenges of effective health risk communication are well known. This paper provides pointers to the health communication literature that discuss these problems. Tailoring printed information, visual displays, and interactive multimedia have been proposed in the health communication literature as promising approaches. On-line risk communication applications are increasing on the internet. However, potential effectiveness of applications using conventional computer technology is limited. We propose that use of artificial intelligence, building upon research in Intelligent Tutoring Systems, might be able to overcome these limitations.


Classifying Learner Engagement Through Integration of Multiple Data Sources

AAAI Conferences

Intelligent tutoring systems (ITS) can provide effective instruction, but learners do not always use such systems effectively. In the present study, high school students' action sequences with a mathematics ITS were machineclassified into five finite-state machines indicating guessing strategies, appropriate help use, and independent problem solving; over 90% of problem events were categorized. Students were grouped via cluster analyses based on self reports of motivation. Motivation grouping predicted ITS strategic approach better than prior math achievement (as rated by classroom teachers). Learners who reported being disengaged in math were most likely to exhibit appropriate help use while working with the ITS, relative to average and high motivation learners. The results indicate that learners can readily report their motivation state and that these data predict how learners interact with the ITS.