Special Issue "Advances in Artificial Intelligence Learning Technologies"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editors

Prof. Dr. Alfredo Milani

Guest Editor
Department of Mathematics and Computer Science, University of Perugia, 06123 Perugia, Italy
Interests: artificial intelligence; e-learning; AI for education; emotion recognition; machine learning
Special Issues and Collections in MDPI journals
Prof. Dr. Valentina Franzoni
Website
Guest Editor
Department of Mathematics and Computer Science, University of Perugia, 06123 Perugia, Italy
Interests: artificial intelligence; emotion recognition; learner behaviour modeling; semantic proximity measures; link prediction; deep learning algorithms
Special Issues and Collections in MDPI journals
Dr. Giulio Biondi

Guest Editor
Dipartimento di Matematica e Informatica (DiMaI), University of Florence, Florence, Italy
Interests: artificial intelligence; e-learning; link prediction; complex networks
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Learning technologies are dramatically improving education on an academic level, and its relevance has exploded worldwide during the current COVID-19 pandemic emergency. E-learning platforms represent a ubiquitous reality and widely support the student–lecturer communication channel, to kindergarten level, where kids often use storytelling tools, while learning the basics of computational thinking in an effective, constructivist way. Teaching the core skills in science, technology, engineering and mathematics (STEM) through all the age levels is paramount for the future of the social communities of scientists.

The main aim of this workshop is to bring together advanced expertise in the field of learning technologies focusing contributions on the application of artificial intelligence methodologies to conventional blended learning management systems, and technologies supporting the learning process of analytics, computational thinking, and coding. The scope of the submitted contributions is expected to range from theoretical models and methods to architectures, system implementations, and reports of field experiences.

The future of education lies in the ability to develop learning technologies which integrate seamless artificially intelligent components in the educational process, in order to deliver a personalized learning service remotely.

A large number of conventional knowledge transfer and learning systems already integrate AI components, e.g., for supporting learner profiling and learning analytics, while a great potential for AI technologies is represented by the personalization and automation of the different phases of the learning process. In a scenario which demands education to be quick, effective, and responding to fast-changing topics and social safe remote collaboration, the role of the AI model and technology is crucial.

Topics include, but are not be limited to, models, architectures, systems and field experiences on:

  • Artificial intelligence (AI) and learning technologies;
  • AI for MOOCS;
  • AI and storytelling;
  • Artificial characters and avatars;
  • Augmented reality and 3D/4D REALITY in education, virtual labs;
  • Adaptive or supported teaching or tutoring;
  • Distributed repositories for collaborative teaching;
  • E-learning gamification;
  • Learning management systems;
  • E-learning strategies and approaches for pandemics emergencies;
  • Learning analytics;
  • User behavior models;
  • Tool and models for special educational needs;
  • Knowledge extraction and classification;
  • Human–computer interaction;
  • Automatic learning evaluation;
  • STEM and computational thinking, STEM and coding
  • AI in mobile learning systems;
  • Student performance prediction and automated classification;
  • Automatic tests generation;
  • Tracking devices and sensors for monitoring user emotional feedback;
  • Intelligent automated bots for student or teacher assistance;
  • Deep learning in education;
  • Virtual community for distance classes collaboration;
  • Virtual ecosystems for teacher collaboration and knowledge sharing;
  • AI coding environments in educational systems;
  • AI computational thinking models and support tools;
  • Case studies integrating AI computational thinking…

Prof. Dr. Alfredo Milani
Prof. Dr. Valentina Franzoni
Dr. Giulio Biondi
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 1800 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

  • Artificially intelligent technologies for learning and education
  • Learning management systems
  • Learning analytics
  • Learner behavior models
  • Knowledge models and taxonomy for learning
  • User modeling
  • Adaptive teaching
  • Gamification
  • Artificial characters in education
  • Tool for special educational needs
  • Knowledge extraction
  • Human–computer interaction
  • Augmented reality and virtual reality in education
  • Virtual lab and virtual environments for education
  • Automatic learner evaluation
  • Personalized training…

Published Papers (4 papers)

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Research

Open AccessArticle
A ROS-Based Open Tool for Intelligent Robotics Education
Appl. Sci. 2020, 10(21), 7419; https://doi.org/10.3390/app10217419 - 22 Oct 2020
Abstract
This paper presents an open-access platform for practical learning of intelligent robotics in engineering degrees: Robotics-Academy. It comprises a collection of exercises including recent service robot applications in real life, with different robots such as autonomous cars, drones or vacuum cleaners. It uses [...] Read more.
This paper presents an open-access platform for practical learning of intelligent robotics in engineering degrees: Robotics-Academy. It comprises a collection of exercises including recent service robot applications in real life, with different robots such as autonomous cars, drones or vacuum cleaners. It uses Robot Operating System (ROS) middleware, the de facto standard in robot programming, the 3D Gazebo simulator and the Python programming language. For each exercise, a software template has been developed, performing all the auxiliary tasks such as the graphical interface, connection to the sensors and actuators, timing of the code, etc. This also hosts the student’s code. Using this template, the student just focuses on the robot intelligence (for instance, perception and control algorithms) without wasting time on auxiliary details which have little educational value. The templates are coded as ROS nodes or as Jupyter Notebooks ready to use in the web browser. Reference solutions for illustrative purposes and automatic assessment tools for gamification have also been developed. An introductory course to intelligent robotics has been elaborated and its contents are available and ready to use at Robotics-Academy, including reactive behaviors, path planning, local/global navigation, and self-localization algorithms. Robotics-Academy provides a valuable complement to master classes in blended learning, massive online open courses (MOOCs) and online video courses, devoted to addressing theoretical content. This open educational tool connects that theory with practical robot applications and is suitable to be used in distance education. Robotics-Academy has been successfully used in several subjects on undergraduate and master’s degree engineering courses, in addition to a pre-university pilot course. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Learning Technologies)
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Open AccessArticle
Automatic Classification of Text Complexity
Appl. Sci. 2020, 10(20), 7285; https://doi.org/10.3390/app10207285 - 18 Oct 2020
Abstract
This work introduces an automatic classification system for measuring the complexity level of a given Italian text under a linguistic point-of-view. The task of measuring the complexity of a text is cast to a supervised classification problem by exploiting a dataset of texts [...] Read more.
This work introduces an automatic classification system for measuring the complexity level of a given Italian text under a linguistic point-of-view. The task of measuring the complexity of a text is cast to a supervised classification problem by exploiting a dataset of texts purposely produced by linguistic experts for second language teaching and assessment purposes. The commonly adopted Common European Framework of Reference for Languages (CEFR) levels were used as target classification classes, texts were elaborated by considering a large set of numeric linguistic features, and an experimental comparison among ten widely used machine learning models was conducted. The results show that the proposed approach is able to obtain a good prediction accuracy, while a further analysis was conducted in order to identify the categories of features that influenced the predictions. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Learning Technologies)
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Open AccessFeature PaperArticle
Artificial Intelligence Visual Metaphors in E-Learning Interfaces for Learning Analytics
Appl. Sci. 2020, 10(20), 7195; https://doi.org/10.3390/app10207195 - 15 Oct 2020
Abstract
This work proposes an innovative visual tool for real-time continuous learners analytics. The purpose of the work is to improve the design, functionality, and usability of learning management systems to monitor user activity to allow educators to make informed decisions on e-learning design, [...] Read more.
This work proposes an innovative visual tool for real-time continuous learners analytics. The purpose of the work is to improve the design, functionality, and usability of learning management systems to monitor user activity to allow educators to make informed decisions on e-learning design, usually limited to dashboards graphs, tables, and low-usability user logs. The standard visualisation is currently scarce, and often inadequate to inform educators about the design quality and students engagement on their learning objects. The same low usability can be found in learning analytics tools, which mostly focus on post-course analysis, demanding specific skills to be effectively used, e.g., for statistical analysis and database queries. We propose a tool for student analytics embedded in a Learning Management System, based on the innovative visual metaphor of interface morphing. Artificial intelligence provides in remote learning immediate feedback, crucial in a face-to-face setting, highlighting the students’ engagement in each single learning object. A visual metaphor is the representation of a person, group, learning object, or concept through a visual image that suggests a particular association or point of similarity. The basic idea is that elements of the application interface, e.g., learning objects’ icons and student avatars, can be modified in colour and dimension to reflect key performance indicators of learner’s activities. The goal is to provide high-affordance information on the student engagement and usage of learning objects, where aggregation functions on subsets of users allow a dynamic evaluation of cohorts with different granularity. The proposed visual metaphors (i.e., thermometer bar, dimensional morphing, and tag cloud morphing) have been implemented and experimented within academic-level courses. Experimental results have been evaluated with a comparative analysis of user logs and a subjective usability survey, which show that the tool obtains quantitative, measurable effectiveness and the qualitative appreciation of educators. Among metaphors, the highest success is obtained by Dimensional morphing and Tag cloud transformation. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Learning Technologies)
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Open AccessArticle
Development of an Intelligent Tutoring System Using Bayesian Networks and Fuzzy Logic for a Higher Student Academic Performance
Appl. Sci. 2020, 10(19), 6638; https://doi.org/10.3390/app10196638 - 23 Sep 2020
Abstract
In this experimental study, an intelligent tutoring system called the fuzzy Bayesian intelligent tutoring system (FB-ITS), is developed by using artificial intelligence methods based on fuzzy logic and the Bayesian network technique to adaptively support students in learning environments. The effectiveness of the [...] Read more.
In this experimental study, an intelligent tutoring system called the fuzzy Bayesian intelligent tutoring system (FB-ITS), is developed by using artificial intelligence methods based on fuzzy logic and the Bayesian network technique to adaptively support students in learning environments. The effectiveness of the FB-ITS was evaluated by comparing it with two other versions of an Intelligent Tutoring System (ITS), fuzzy ITS and Bayesian ITS, separately. Moreover, it was evaluated by comparing it with an existing traditional e-learning system. In order to evaluate whether the academic performance of the students in different learning groups differs or not, analysis of covariance (ANCOVA) was used based on the students’ pre-test and post-test scores. The study was conducted with 120 undergraduate university students. Results showed that students who studied using FB-ITS had significantly higher academic performance on average compared to other students who studied with the other systems. Regarding the time taken to perform the post-test, the results indicated that students who used the FB-ITS needed less time on average compared to students who used the traditional e-learning system. From the results, it could be concluded that the new system contributed in terms of the speed of performing the final exam and high academic success. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Learning Technologies)
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