Special Issue "Artificial Intelligence Applications for Education"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 1 March 2021.

Special Issue Editors

Dr. Kevin Gary
Website
Guest Editor
School of Computing, Informatics and Decision Systems Engineering, The Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ, USA
Interests: AI in software engineering; agile methods; software engineering education; mHealth
Dr. Ajay Bansal
Website
Guest Editor
School of Computing, Informatics and Decision Systems Engineering, The Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ, USA
Interests: intelligent systems; knowledge representation and reasoning; computational logic; declarative programming

Special Issue Information

Dear Colleagues,

The MDPI journal Information is inviting submissions to a Special Issue on “Artificial Intelligence Applications for Education”.

Artificial intelligence (AI) plays an increasingly important and pervasive role in society, including in the area of education. Recent events have cast a spotlight on technology’s role in education, and AI is leading the way in creating intelligent, impactful, and scalable education solutions. From intelligent tutors to machine learning for learning analytics, AI’s impact is being felt on multiple levels—from the individual learner–teacher relationship to organizational strategies for achieving large-scale outcomes.

This Special Issue seeks novel research reports on the spectrum of AI’s influence on education. The editors welcome submissions on all forms of AI approaches, though with an emphasis on applications of these approaches in real-world settings with fully analyzed research results. Quantitative, qualitative, and mixed methods studies are welcome, as are case studies and experience reports if they describe an impactful application at scale that delivers useful lessons to journal readership.

Topics of Interest include (but are not limited to):

  • Intelligent tutoring systems
  • Applications of learning analytics to learning situations
  • Personalized and adaptive learning systems
  • AI in support of behavior change models for learning
  • Hybrid teacher–agent implementation support for teachers
  • AI impacts on pedagogy
  • AI for learning at scale
  • Intelligent assessment models
  • Natural language processing (NLP) in education
  • Challenges implementing AI in real-world scenarios
  • Modeling learner types using AI
  • Modeling domain expertise using AI
  • Human–AI hybrid systems for learning
  • Modeling learning contexts using AI
  • Informal learning using educational games
  • Domain-specific learning using AI
  • Evaluation of AI-based learning systems

Dr. Kevin Gary
Dr. Ajay Bansal
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • personalized and adaptive learning
  • learning analytics
  • intelligent tutoring systems

Published Papers (1 paper)

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Research

Open AccessArticle
Online At-Risk Student Identification using RNN-GRU Joint Neural Networks
Information 2020, 11(10), 474; https://doi.org/10.3390/info11100474 - 09 Oct 2020
Abstract
Although online learning platforms are gradually becoming commonplace in modern society, learners’ high dropout rates and serious academic performance require more attention within the virtual learning environment (VLE). This study aims to predict students’ performance in a specific course as it is continuously [...] Read more.
Although online learning platforms are gradually becoming commonplace in modern society, learners’ high dropout rates and serious academic performance require more attention within the virtual learning environment (VLE). This study aims to predict students’ performance in a specific course as it is continuously running, using the statistic personal biographical information and sequential behavior data with VLE. To achieve this goal, a novel recurrent neural network (RNN)-gated recurrent unit (GRU) joint neural network is proposed to fit both static and sequential data, where the data completion mechanism is also adopted to fill the missing stream data. To incorporate the sequential relationship of learning data, three kinds of time-series deep neural network algorithms: simple RNN, GRU, and LSTM are first taken into consideration as baseline models. Their performances are compared in identifying at-risk students. Experimental results on Open University Learning Analytics Dataset (OULAD) show that simple methods like GRU and simple RNN have better results than the relatively complex LSTM model. The results also reveal that different models have different peak performance time, which results in the proposed joint model that achieves over 80% prediction accuracy of at-risk students at the end of the semester. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Education)
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