Special lssues
Table of Content

Cognitive Computing and Systems in Education and Research

Submission Deadline: 30 April 2023 (closed)

Guest Editors

Prof. Xinguo Yu, Central China Normal University, China.
Dr. Jun Shen, University of Wollongong, Australia.
Dr. Yuan Sun, National Institute of Informatics, Japan.

Summary

In the recent years, artificial intelligence is evolving from perceptual intelligence to cognitive intelligence. Education and scientific research are two active areas of cognitive intelligence. To promote the research in cognitive computing and systems in education and research, Faculty of Artificial Intelligence in education, Central China Normal University and Hubei Society of Artificial Intelligence in Research and Education jointly established the international annual conference named as International Conference on Intelligent Education and Intelligent Research (IEIR). IEIR 2022 is the first conference, which we have use a long period to prepare for it. Cognitive computing and systems in education and research have a long list of topics, however this special issue mainly promote the following four active research topics: Cognitive Computing in Teaching and Learning; Cognitive Computing in Research; Applications Built on Cognitive Computing for Education or Research; Metaverse Education. All these four topics are very active and fruitful in the past several years. Many research teams that work on these topics will attend IEIR 2022. Hence, we can select a batch of high-quality papers to form a special issue on your journal.


Topics of this special issue include but are not limited to the following:

*  Cognitive Computing in Teaching and Learning   

* Cognitive Computing in Research  

* Applications Built on Cognitive Computing in Education and Research

* Metaverse Education


Keywords

Cognitive Computing, Humanoid Reasoning, Research Modelling, AI Aided Research, Humanoid Education Systems

Published Papers


  • Open Access

    ARTICLE

    Automated Video Generation of Moving Digits from Text Using Deep Deconvolutional Generative Adversarial Network

    Anwar Ullah, Xinguo Yu, Muhammad Numan
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2359-2383, 2023, DOI:10.32604/cmc.2023.041219
    (This article belongs to the Special Issue: Cognitive Computing and Systems in Education and Research)
    Abstract Generating realistic and synthetic video from text is a highly challenging task due to the multitude of issues involved, including digit deformation, noise interference between frames, blurred output, and the need for temporal coherence across frames. In this paper, we propose a novel approach for generating coherent videos of moving digits from textual input using a Deep Deconvolutional Generative Adversarial Network (DD-GAN). The DD-GAN comprises a Deep Deconvolutional Neural Network (DDNN) as a Generator (G) and a modified Deep Convolutional Neural Network (DCNN) as a Discriminator (D) to ensure temporal coherence between adjacent frames. The… More >

  • Open Access

    ARTICLE

    Exercise Recommendation with Preferences and Expectations Based on Ability Computation

    Mengjuan Li, Lei Niu
    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 263-284, 2023, DOI:10.32604/cmc.2023.041193
    (This article belongs to the Special Issue: Cognitive Computing and Systems in Education and Research)
    Abstract In the era of artificial intelligence, cognitive computing, based on cognitive science; and supported by machine learning and big data, brings personalization into every corner of our social life. Recommendation systems are essential applications of cognitive computing in educational scenarios. They help learners personalize their learning better by computing student and exercise characteristics using data generated from relevant learning progress. The paper introduces a Learning and Forgetting Convolutional Knowledge Tracking Exercise Recommendation model (LFCKT-ER). First, the model computes studentsʼ ability to understand each knowledge concept, and the learning progress of each knowledge concept, and the… More >

  • Open Access

    ARTICLE

    Solving Algebraic Problems with Geometry Diagrams Using Syntax-Semantics Diagram Understanding

    Litian Huang, Xinguo Yu, Lei Niu, Zihan Feng
    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 517-539, 2023, DOI:10.32604/cmc.2023.041206
    (This article belongs to the Special Issue: Cognitive Computing and Systems in Education and Research)
    Abstract Solving Algebraic Problems with Geometry Diagrams (APGDs) poses a significant challenge in artificial intelligence due to the complex and diverse geometric relations among geometric objects. Problems typically involve both textual descriptions and geometry diagrams, requiring a joint understanding of these modalities. Although considerable progress has been made in solving math word problems, research on solving APGDs still cannot discover implicit geometry knowledge for solving APGDs, which limits their ability to effectively solve problems. In this study, a systematic and modular three-phase scheme is proposed to design an algorithm for solving APGDs that involve textual and… More >

  • Open Access

    ARTICLE

    Solving Arithmetic Word Problems of Entailing Deep Implicit Relations by Qualia Syntax-Semantic Model

    Hao Meng, Xinguo Yu, Bin He, Litian Huang, Liang Xue, Zongyou Qiu
    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 541-555, 2023, DOI:10.32604/cmc.2023.041508
    (This article belongs to the Special Issue: Cognitive Computing and Systems in Education and Research)
    Abstract Solving arithmetic word problems that entail deep implicit relations is still a challenging problem. However, significant progress has been made in solving Arithmetic Word Problems (AWP) over the past six decades. This paper proposes to discover deep implicit relations by qualia inference to solve Arithmetic Word Problems entailing Deep Implicit Relations (DIR-AWP), such as entailing commonsense or subject-domain knowledge involved in the problem-solving process. This paper proposes to take three steps to solve DIR-AWPs, in which the first three steps are used to conduct the qualia inference process. The first step uses the prepared set… More >

  • Open Access

    ARTICLE

    An Enhanced Automatic Arabic Essay Scoring System Based on Machine Learning Algorithms

    Nourmeen Lotfy, Abdulaziz Shehab, Mohammed Elhoseny, Ahmed Abu-Elfetouh
    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1227-1249, 2023, DOI:10.32604/cmc.2023.039185
    (This article belongs to the Special Issue: Cognitive Computing and Systems in Education and Research)
    Abstract Despite the extensive effort to improve intelligent educational tools for smart learning environments, automatic Arabic essay scoring remains a big research challenge. The nature of the writing style of the Arabic language makes the problem even more complicated. This study designs, implements, and evaluates an automatic Arabic essay scoring system. The proposed system starts with pre-processing the student answer and model answer dataset using data cleaning and natural language processing tasks. Then, it comprises two main components: the grading engine and the adaptive fusion engine. The grading engine employs string-based and corpus-based similarity algorithms separately.… More >

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