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Emerging Artificial Intelligence (AI) Technologies for Learning

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

Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 45584

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Special Issue Editors

Prof. Dr. Alfredo Milani

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Guest Editor

Department of Mathematics and Computer Science, University of Perugia, 06123 Perugia, Italy
Interests: online evolutionary algorithms; metaheuristic for combinatorial optimization; discrete differential evolution; semantic proximity measures; planning agents and complex network dynamics
Special Issues, Collections and Topics in MDPI journals

Dr. Valentino Santucci

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Guest Editor

Department of Humanities and Social Sciences, University for Foreigners of Perugia, 06123 Perugia, Italy
Interests: natural language processing; evolutionary computation; computational optimization
Special Issues, Collections and Topics in MDPI journals

Dr. Fabio Caraffini

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Guest Editor

Department of Computer Science, Computational Foundry, Swansea University, Bay Campus, Fabian Way, Skewen SA1 8EN, UK
Interests: evolutionary computation; swarm intelligence; computational intelligence; differential evolution; memetic computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The future of education lies in the ability to develop learning technologies which integrate seamless Artificial Intelligent components in the educational process, in order to deliver a personalized learning service, dynamically tailored to the learner's characteristics, abilities, and needs (new educational goals, previous skills, re-training, special educational needs, etc.).

This Special Issue’s aim is to collect research contributions in the area of Artificial Intelligence models, technologies, and applications for supporting the learning process. The potential of AI learning technologies is dramatically increasing their role in modern educational systems from the academic level, where learning management systems platforms are ubiquitous and widely support student–lecturer communications, down to kindergarten level, where kids often use tailored systems, such as toy robots or storytelling software tools, in order to learn the basics of computational thinking.

A large number of conventional knowledge transfer and learning systems already integrate AI components, e.g., for supporting learners 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 educational goals and individualized learners needs, the role of AI model and technology is crucial.
The scope of the submitted contributions is expected to range from theoretical models and methods to architectures, system implementations, and reports of field experiences. Contributions from AI researchers and educational experts with field experiences in the general area of AI applied to learning, as well as integration of AI with STEM, computational thinking, and coding are especially welcome.

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

AI in mobile learning systems
AI in distance learning systems
AI in massive online open courses (MOOC)
AI and storytelling tools
AI in gamification
Autonomous e-learning support system
Teacher oriented learning analytics
Student performance prediction and automated classification
Automatic adaptive teaching
Automatic tests generation
Personalized automated teaching and testing
Learner profiling and behavior modeling
Tracking devices and sensors for monitoring user emotional feedback
Intelligent automated student tutoring
Artificial characters for student assistance and supervision
Augmented reality in education
Deep Learning in education
Machine learning in education
3D/4D reality in education
Learning technologies supporting constructivist educational approach
Virtual community for distance classes collaboration
Virtual ecosystems for teacher collaboration and knowledge sharing
Virtual characters for story telling environments
AI and special educational needs
Questions/tests sharing social networks, repositories and analytics
Distributed repositories for teaching material sharing, retrieval and collaborative debugging and ranking
AI Coding environments in educational systems
AI Computational thinking models and support tools
Virtual learning environment for STEM
Virtual experimental labs for STEM
Field experience reports with integrating AI computational thinking, STEM and coding
Syllabi for integrating AI in computational thinking, STEM and coding

Prof. Dr. Alfredo Milani
Dr. Valentino Santucci
Dr. Fabio Caraffini
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 submissions that pass pre-check are 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 2400 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 learning technologies
Learning technologies
Learning management systems
Learning analytics
User modeling
User behavior models
Learner models
Adaptive teaching
Gamification
Artificial characters in education
Tool for special educational needs
Knowledge extraction
Human–computer interaction
Augmented reality tools for education
Virtual environments for education
Virtual labs
Automatic learner evaluation
Personalized training

Published Papers (12 papers)

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Editorial

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2 pages, 178 KiB

Open AccessEditorial

Special Issue “Emerging Artificial Intelligence (AI) Technologies for Learning”

by Alfredo Milani, Valentino Santucci and Fabio Caraffini

Appl. Sci. 2022, 12(17), 8819; https://doi.org/10.3390/app12178819 - 1 Sep 2022

Viewed by 1118

Abstract

The future of education lies in the ability to develop technologies which integrate seamless artificial intelligence (AI) components into the educational process, in order to deliver a personalized service which is dynamically tailored to the learner’s characteristics, abilities, and needs [...] Full article

(This article belongs to the Special Issue Emerging Artificial Intelligence (AI) Technologies for Learning)

Research

Jump to: Editorial

21 pages, 951 KiB

Open AccessArticle

An e-Learning Toolbox Based on Rule-Based Fuzzy Approaches

by Àngela Nebot, Francisco Mugica and Félix Castro

Appl. Sci. 2020, 10(19), 6804; https://doi.org/10.3390/app10196804 - 28 Sep 2020

Cited by 1 | Viewed by 2009

Abstract

In this paper, an e-Learning toolbox based on a set of fuzzy logic data mining techniques is presented. The toolbox is mainly based on the fuzzy inductive reasoning (FIR) methodology and two of its key extensions: (i) the linguistic rules extraction algorithm (LR-FIR), which extracts comprehensible and consistent sets of rules describing students’ learning behavior, and (ii) the causal relevance approach (CR-FIR), which allows to reduce uncertainty during a student’s performance prediction stage, and provides a relative weighting of the features involved in the evaluation process. In addition, the presented toolbox enables, in an incremental way, detecting and grouping students with respect to their learning behavior, with the main goal to timely detect failing students, and properly provide them with suitable and actionable feedback. The proposed toolbox has been applied to two different datasets gathered from two courses at the Latin American Institute for Educational Communication virtual campus. The introductory and didactic planning courses were analyzed using the proposed toolbox. The results obtained by the functionalities offered by the platform allow teachers to make decisions and carry out improvement actions in the current course, i.e., to monitor specific student clusters, to analyze possible changes in the different evaluable activities, or to reduce (to some extent) teacher workload. Full article

(This article belongs to the Special Issue Emerging Artificial Intelligence (AI) Technologies for Learning)

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18 pages, 1606 KiB

Open AccessArticle

Improvement of an Online Education Model with the Integration of Machine Learning and Data Analysis in an LMS

by William Villegas-Ch, Milton Román-Cañizares and Xavier Palacios-Pacheco

Appl. Sci. 2020, 10(15), 5371; https://doi.org/10.3390/app10155371 - 4 Aug 2020

Cited by 84 | Viewed by 11646

Abstract

The events that took place in the year 2020 have shown us that society is still fragile and that it is exposed to events that rapidly change the paradigms that govern it. This has been shown by a pandemic like Coronavirus disease 2019; this global emergency has changed the way people interact, communicate, study, or work. In short, the way in which society carries out all activities has changed. This includes education, which has bet on the use of information and communication technologies to reach students. An example of the aforementioned is the use of learning management systems, which have become ideal environments for resource management and the development of activities. This work proposes the integration of technologies, such as artificial intelligence and data analysis, with learning management systems in order to improve learning. This objective is outlined in a new normality that seeks robust educational models, where certain activities are carried out in an online mode, surrounded by technologies that allow students to have virtual assistants to guide them in their learning. Full article

(This article belongs to the Special Issue Emerging Artificial Intelligence (AI) Technologies for Learning)

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27 pages, 2372 KiB

Open AccessArticle

Interactive Planning of Competency-Driven University Teaching Staff Allocation

by Eryk Szwarc, Jaroslaw Wikarek, Arkadiusz Gola, Grzegorz Bocewicz and Zbigniew Banaszak

Appl. Sci. 2020, 10(14), 4894; https://doi.org/10.3390/app10144894 - 16 Jul 2020

Cited by 10 | Viewed by 2311

Abstract

This paper focuses on a teacher allocation problem that is specifically concerned with assigning available academic lecturers to remaining courses from a given student curriculum. The teachers are linked to tasks according to competencies, competence requirements enforced by the curriculum as well as the number and type of disruptions that hamper the fulfilment of courses. The problem under consideration boils down to searching links between competencies possessed by teachers and competencies required by the curricula that will, firstly, balance student needs and teacher workload and, secondly, ensure an assumed robustness level of the teaching schedule. The implemented interactive method performs iterative solving of analysis and synthesis problems concerned with alternative evaluation/robustness of the competency framework. Its performance is evaluated against a set of real historical data and arbitrarily selected sets of disruptions. The computational results indicate that our method yields better solutions compared to the manual allocation by the university. Full article

(This article belongs to the Special Issue Emerging Artificial Intelligence (AI) Technologies for Learning)

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15 pages, 750 KiB

Open AccessArticle

Predicting Network Behavior Model of E-Learning Partner Program in PLS-SEM

by Hsing-Yu Hou, Yu-Lung Lo and Chin-Feng Lee

Appl. Sci. 2020, 10(13), 4656; https://doi.org/10.3390/app10134656 - 6 Jul 2020

Cited by 4 | Viewed by 2809

Abstract

The Ministry of Education of Taiwan conducted an e-learning partner program to offer life-accompaniment and subject teaching to elementary and secondary students through a network platform with cooperation from university undergraduates. The aim of the e-learning partner program was to improve the motivation and interest of the children after learning at school. However, the outcome of this program stated that the retention rate of the undergraduates was low over three semesters in the case universities. Therefore, the training cost for the program was wasted each semester, and it was necessary to solve the problem and improve the situation. The evaluation of self-efficacy directly affects a person’s motivation for the job. This research examined inner self-efficacy (teaching and counseling) and outer support (administration and equipment) that would contribute to and predict the success and the persistence of the e-learning partner program. There were 94 valid self-evaluation records in the 2019 academic year. ANOVA, post hoc, and partial least squares (PLS) analyses were conducted. The results showed that the year level, experience, and teacher education program background were significantly different in this study. The network behavior model was set up effectively to predict the retention from four scopes. A higher teaching self-efficacy would have better passion and innovation scores than the others. Using the suggestions for improvement, decreasing the gap between undergraduates’ expectations and promoting sustainability in the e-learning partner program can be achieved. Full article

(This article belongs to the Special Issue Emerging Artificial Intelligence (AI) Technologies for Learning)

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20 pages, 395 KiB

Open AccessArticle

Using Data Mining in Educational Administration: A Case Study on Improving School Attendance

by Raymond Moodley, Francisco Chiclana, Jenny Carter and Fabio Caraffini

Appl. Sci. 2020, 10(9), 3116; https://doi.org/10.3390/app10093116 - 29 Apr 2020

Cited by 12 | Viewed by 4724

Abstract

Pupil absenteeism remains a significant problem for schools across the globe with negative impacts on overall pupil performance being well-documented. Whilst all schools continue to emphasize good attendance, some schools still find it difficult to reach the required average attendance, which in the UK is 96%. A novel approach is proposed to help schools improve attendance that leverages the market target model, which is built on association rule mining and probability theory, to target sessions that are most impactful to overall poor attendance. Tests conducted at Willen Primary School, in Milton Keynes, UK, showed that significant improvements can be made to overall attendance, attendance in the target session, and persistent (chronic) absenteeism, through the use of this approach. The paper concludes by discussing school leadership, research implications, and highlights future work which includes the development of a software program that can be rolled-out to other schools. Full article

(This article belongs to the Special Issue Emerging Artificial Intelligence (AI) Technologies for Learning)

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16 pages, 2747 KiB

Open AccessFeature PaperArticle

Encoding, Exchange and Manipulation of Captured Immersive VR Sessions for Learning Environments: the PRISMIN Framework

by Bruno Fanini and Luigi Cinque

Appl. Sci. 2020, 10(6), 2026; https://doi.org/10.3390/app10062026 - 17 Mar 2020

Cited by 7 | Viewed by 2647

Abstract

Capturing immersive VR sessions performed by remote learners using head-mounted displays (HMDs) may provide valuable insights on their interaction patterns, virtual scene saliency and spatial analysis. Large collected records can be exploited as transferable data for learning assessment, detect unexpected interactions or fine-tune immersive VR environments. Within the online learning segment, the exchange of such records among different peers over the network presents several challenges related to data transport and/or its decoding routines. In the presented work, we investigate applications of an image-based encoding model and its implemented architecture to capture users’ interactions performed during VR sessions. We present the PRISMIN framework and how the underneath image-based encoding can be exploited to exchange and manipulate captured VR sessions, comparing it to existing approaches. Qualitative and quantitative results are presented in order to assess the encoding model and the developed open-source framework. Full article

(This article belongs to the Special Issue Emerging Artificial Intelligence (AI) Technologies for Learning)

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12 pages, 1294 KiB

Open AccessArticle

Prosocial Virtual Reality, Empathy, and EEG Measures: A Pilot Study Aimed at Monitoring Emotional Processes in Intergroup Helping Behaviors

by Francesca D’Errico, Giovanna Leone, Maurizio Schmid and Carmen D’Anna

Appl. Sci. 2020, 10(4), 1196; https://doi.org/10.3390/app10041196 - 11 Feb 2020

Cited by 34 | Viewed by 4394

Abstract

During a non-invasive procedure, participants both helped and helped by a confederate with features that create social distance (membership in an ethnic outgroup or another social group). For this purpose, we created a set of virtual scenarios in which the confederate’s ethnicity (white vs. black) and appearance (business man vs. beggar, with casual dress as a control condition) were crossed. The study aimed to explore how the emotional reactions of participants changed according to the confederate’s status signals as well as signals that they belong to the same or a different ethnic group. Participants’ alertness, calmness, and engagement were monitored using electroencephalogram (EEG) during the original virtual reality (VR) video sessions. Participants’ distress and empathy when exposed to helping interactions were self-assessed after the VR video sessions. The results pointed out that, irrespective of whether they helped the confederate or were helped by him/her, white participants showed higher levels of alertness when exposed to helping interactions involving a white beggar or a black businessman, and their emotional calmness and engagement were higher when interacting with a black beggar or a white businessman. The results for self-assessed distress and empathy followed the same tendency, indicating how physiological and self-assessed measures can both contribute to a better understanding of the emotional processes in virtual intergroup helping situations. Based on the presented results, the methodological and practical implications of VR in terms of enhancing self-reflective capacities in intergroup helping processes are discussed. Full article

(This article belongs to the Special Issue Emerging Artificial Intelligence (AI) Technologies for Learning)

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16 pages, 2608 KiB

Open AccessArticle

Data Enhancement for Plant Disease Classification Using Generated Lesions

by Rongcheng Sun, Min Zhang, Kun Yang and Ji Liu

Appl. Sci. 2020, 10(2), 466; https://doi.org/10.3390/app10020466 - 8 Jan 2020

Cited by 18 | Viewed by 3499

Abstract

Deep learning has recently shown promising results in plant lesion recognition. However, a deep learning network requires a large amount of data for training, but because some plant lesion data is difficult to obtain and very similar in structure, we must generate complete plant lesion leaf images to augment the dataset. To solve this problem, this paper proposes a method to generate complete and scarce plant lesion leaf images to improve the recognition accuracy of the classification network. The advantages of our study include: (i) proposing a binary generator network to solve the problem of how a generative adversarial network (GAN) generates a lesion image with a specific shape and (ii) using the edge-smoothing and image pyramid algorithm to solve the problem that occurs when synthesizing a complete lesion leaf image where the synthetic edge pixels are different and the network output size is fixed but the real lesion size is random. Compared with the recognition accuracy of human experts and AlexNet, it was shown that our method can effectively expand the plant lesion dataset and improve the recognition accuracy of a classification network. Full article

(This article belongs to the Special Issue Emerging Artificial Intelligence (AI) Technologies for Learning)

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17 pages, 2061 KiB

Open AccessArticle

Predicting Student Achievement Based on Temporal Learning Behavior in MOOCs

by Shaojie Qu, Kan Li, Bo Wu, Shuhui Zhang and Yongchao Wang

Appl. Sci. 2019, 9(24), 5539; https://doi.org/10.3390/app9245539 - 16 Dec 2019

Cited by 23 | Viewed by 3907

Abstract

With the development of data mining technology, educational data mining (EDM) has gained increasing amounts of attention. Research on massive open online courses (MOOCs) is an important area of EDM. Previous studies found that assignment-related behaviors in MOOCs (such as the completed number of assignments) can affect student achievement. However, these methods cannot fully reflect students’ learning processes and affect the accuracy of prediction. In the present paper, we consider the temporal learning behaviors of students to propose a student achievement prediction method for MOOCs. First, a multi-layer long short-term memory (LSTM) neural network is employed to reflect students’ learning processes. Second, a discriminative sequential pattern (DSP) mining-based pattern adapter is proposed to obtain the behavior patterns of students and enhance the significance of critical information. Third, a framework is constructed with an attention mechanism that includes data pre-processing, pattern adaptation, and the LSTM neural network to predict student achievement. In the experiments, we collect data from a C programming course from the year 2012 and extract assignment-related features. The experimental results reveal that this method achieves an accuracy rate of 91% and a recall of 94%. Full article

(This article belongs to the Special Issue Emerging Artificial Intelligence (AI) Technologies for Learning)

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Graphical abstract

19 pages, 5082 KiB

Open AccessArticle

Independent Random Recurrent Neural Networks for Infrared Spatial Point Targets Classification

by Dongya Wu, Huanzhang Lu, Moufa Hu and Bendong Zhao

Appl. Sci. 2019, 9(21), 4622; https://doi.org/10.3390/app9214622 - 30 Oct 2019

Cited by 5 | Viewed by 2305

Abstract

Exo-atmospheric infrared (IR) point target discrimination is an important research topic of space surveillance systems. It is difficult to describe the characteristic information of the shape and micro-motion states of the targets and to discriminate different targets effectively by the characteristic information. This paper has constructed the infrared signature model of spatial point targets and obtained the infrared radiation intensity sequences dataset of different types of targets. This paper aims to design an algorithm for the classification problem of infrared radiation intensity sequences of spatial point targets. Recurrent neural networks (RNNs) are widely used in time series classification tasks, but face several problems such as gradient vanishing and explosion, etc. In view of shortcomings of RNNs, this paper proposes an independent random recurrent neural network (IRRNN) model, which combines independent structure RNNs with randomly weighted RNNs. Without increasing the training complexity of network learning, our model solves the problem of gradient vanishing and explosion, improves the ability to process long sequences, and enhances the comprehensive classification performance of the algorithm effectively. Experiments show that the IRRNN algorithm performs well in classification tasks and is robust to noise. Full article

(This article belongs to the Special Issue Emerging Artificial Intelligence (AI) Technologies for Learning)

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27 pages, 2433 KiB

Open AccessArticle

A Method of Ontology Integration for Designing Intelligent Problem Solvers

by Nhon V. Do, Hien D. Nguyen and Thanh T. Mai

Appl. Sci. 2019, 9(18), 3793; https://doi.org/10.3390/app9183793 - 10 Sep 2019

Cited by 14 | Viewed by 2974

Abstract

Nowadays, designing knowledge-based systems which involve knowledge from different domains requires deep research of methods and techniques for knowledge integration, and ontology integration has become the foundation for many recent knowledge integration methods. To meet the requirements of real-world applications, methods of ontology integration need to be studied and developed. In this paper, an ontology model used as the knowledge kernel is presented, consisting of concepts, relationships between concepts, and inference rules. Additionally, this kernel is also added to other knowledge, such as knowledge of operators and functions, to form an integrated knowledge-based system. The mechanism of this integration method works upon the integration of the knowledge components in the ontology structure. Besides this, problems and the reasoning method to solve them on the integrated knowledge domain are also studied. Many related problems in the integrated knowledge domain and the reasoning method for solving them are also studied. Such an integrated model can represent the real-world knowledge domain about operators and functions with high accuracy and effectiveness. The ontology model can also be applied to build knowledge bases for intelligent problem solvers (IPS) in many mathematical courses in college, such as linear algebra and graph theory. These IPSs have great potential in helping students perform better in those college courses. Full article

(This article belongs to the Special Issue Emerging Artificial Intelligence (AI) Technologies for Learning)

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