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Advances in Artificial Intelligence Learning Technologies

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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 December 2021) | Viewed by 48141

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Special Issue Editors

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; emotion recognition
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Valentina Franzoni

E-Mail 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, Collections and Topics in MDPI journals

Dr. Giulio Biondi

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Guest Editor

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

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 (14 papers)

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Research

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7 pages, 199 KiB

Open AccessArticle

Virtual and Traditional Lecturing Technique Impact on Dental Education

by Cristina Bartok-Nicolae, Gheorghe Raftu, Raluca Briceag, Liliana Sachelarie, Aureliana Caraiane, Mihaela Duta and Dorina Maria Farcas

Appl. Sci. 2022, 12(3), 1678; https://0-doi-org.brum.beds.ac.uk/10.3390/app12031678 - 06 Feb 2022

Cited by 4 | Viewed by 1331

Abstract

This study tries to identify the influence of the new coronavirus pandemic on dental education by assessing dental students’ perception and their didactic performance regarding virtual and traditional lectures. The final academic performances of students from different years of study at the Faculty of Dental Medicine who participated in undergraduate courses through two different lecturing modes (traditional and virtual) were compared. The same students were evaluated in terms of their preference between the two lecturing techniques. There was a statistically significant difference in the mean values for final grades of virtual and traditional technique in favor of the latter one. In pandemic conditions, because of safety reasons, virtual lecturing was the most preferred technique. For dental faculty, this process of transitioning from traditional to virtual is a continuous process, which was suddenly imposed, but which at this moment offers multiple opportunities from a didactic point of view. Analyzing the grade, the virtual lecturing techniques demonstrated superior didactic performance. Although students preferred the virtual lecturing technique more than the traditional one, better-designed research is required to verify the long-term effect of the two lecturing techniques on students’ formation and deepening of knowledge. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence Learning Technologies)

17 pages, 817 KiB

Open AccessArticle

Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function

by Nora Alturayeif and Hamzah Luqman

Appl. Sci. 2021, 11(22), 10694; https://0-doi-org.brum.beds.ac.uk/10.3390/app112210694 - 12 Nov 2021

Cited by 15 | Viewed by 3116

Abstract

The outbreak of coronavirus disease (COVID-19) has affected almost all of the countries of the world, and has had significant social and psychological effects on the population. Nowadays, social media platforms are being used for emotional self-expression towards current events, including the COVID-19 pandemic. The study of people’s emotions in social media is vital to understand the effect of this pandemic on mental health, in order to protect societies. This work aims to investigate to what extent deep learning models can assist in understanding society’s attitude in social media toward COVID-19 pandemic. We employ two transformer-based models for fine-grained sentiment detection of Arabic tweets, considering that more than one emotion can co-exist in the same tweet. We also show how the textual representation of emojis can boost the performance of sentiment analysis. In addition, we propose a dynamically weighted loss function (DWLF) to handle the issue of imbalanced datasets. The proposed approach has been evaluated on two datasets and the attained results demonstrate that the proposed BERT-based models with emojis replacement and DWLF technique can improve the sentiment detection of multi-dialect Arabic tweets with an F1-Micro score of 0.72. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence Learning Technologies)

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14 pages, 2973 KiB

Open AccessArticle

The Moderating Effect of Debriefing on Learning Outcomes of IVR-Based Instruction: An Experimental Research

by Qinna Feng, Heng Luo, Wenhao Li, Ying Chen and Jiakai Zhang

Appl. Sci. 2021, 11(21), 10426; https://0-doi-org.brum.beds.ac.uk/10.3390/app112110426 - 05 Nov 2021

Cited by 3 | Viewed by 2246

Abstract

With its ability to afford immersive and interactive learning experiences, virtual reality has been widely used to support experiential learning, of which the learning effectiveness is promoted by the instructional component of debriefing. The current literature on debriefing mainly focuses on the traditional learning contexts while little is known on its effectiveness in immersive virtual reality (IVR) learning environments. Based on the theories of experiential learning and debriefing, this study designed a debriefing strategy based on simulated learning experience and investigated its effectiveness on knowledge and behavioral learning in an IVR learning program, using a randomized controlled trial with 77 elementary students from Hubei province in China. The study results support the efficacy of IVR on improving knowledge acquisition and behavioral performance, and reveal a significant moderating effect of debriefing on the effectiveness of IVR learning environments. The study confirms the critical role of debriefing in IVR-based instruction and provides theoretical and practical implications for the design and implementation of effective IVR learning environments. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence Learning Technologies)

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16 pages, 1907 KiB

Open AccessArticle

What Are the Drivers of Citations?: Application in Tourism and Hospitality Journals

by Eunhye Park and Woohyuk Kim

Appl. Sci. 2021, 11(19), 9288; https://0-doi-org.brum.beds.ac.uk/10.3390/app11199288 - 06 Oct 2021

Cited by 2 | Viewed by 1635

Abstract

In line with the qualitative and quantitative growth of academic papers, it is critical to understand the factors driving citations in scholarly articles. This study discovered the up-to-date academic structure in the tourism and hospitality literature and tested the comprehensive sets of factors driving citation counts using articles published in first-tier hospitality and tourism journals found on the Web of Science. To further test the effects of research topic structure on citation counts, unsupervised topic modeling was conducted with 9910 tourism and hospitality papers published in 12 journals over 10 years. Articles specific to online media and the sharing economy have received numerous citations and that recently published papers with particular research topics (e.g., rural tourism and eco-tourism) were frequently cited. This study makes a major contribution to hospitality and tourism literature by testing the effects of topic structure and topic originality discovered by text mining on citation counts. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence Learning Technologies)

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23 pages, 537 KiB

Open AccessArticle

A Case Study on User Evaluation of Scientific Publication Summarization by Japanese Students

by Shintaro Yamamoto, Ryota Suzuki, Tsukasa Fukusato, Hirokatsu Kataoka and Shigeo Morishima

Appl. Sci. 2021, 11(14), 6287; https://0-doi-org.brum.beds.ac.uk/10.3390/app11146287 - 07 Jul 2021

Cited by 1 | Viewed by 2080

Abstract

Summaries of scientific publications enable readers to gain an overview of a large number of studies, but users’ preferences have not yet been explored. In this paper, we conduct two user studies (i.e., short- and long-term studies) where Japanese university students read summaries of English research articles that were either manually written or automatically generated using text summarization and/or machine translation. In the short-term experiment, subjects compared and evaluated the two types of summaries of the same article. We analyze the characteristics in the generated summaries that readers regard as important, such as content richness and simplicity. The experimental results show that subjects are mainly judged based on four criteria, including content richness, simplicity, fluency, and format. In the long-term experiment, subjects read 50 summaries and answered whether they would like to read the original papers after reading the summaries. We discuss the characteristics in the summaries that readers tend to use to determine whether to read the papers, such as topic, methods, and results. The comments from subjects indicate that specific components of scientific publications, including research topics and methods, are important to judge whether to read or not. Our study provides insights to enhance the effectiveness of automatic summarization of scientific publications. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence Learning Technologies)

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21 pages, 835 KiB

Open AccessArticle

Jupyter Notebooks in Undergraduate Mobile Robotics Courses: Educational Tool and Case Study

by Jose-Raul Ruiz-Sarmiento, Samuel-Felipe Baltanas and Javier Gonzalez-Jimenez

Appl. Sci. 2021, 11(3), 917; https://0-doi-org.brum.beds.ac.uk/10.3390/app11030917 - 20 Jan 2021

Cited by 7 | Viewed by 3314

Abstract

Jupyter notebooks are recently emerging as a valuable pedagogical resource in academy, being adopted in educational institutions worldwide. This is mainly due to their ability to combine the expressiveness of traditional explanations from textbooks, with the interaction capabilities of software applications, which provides numerous benefits for both students and lecturers of different fields. One of the areas that could benefit from their adoption is such of mobile robotics, whose recent popularity has resulted in an increasing demand of trained practitioners with a solid theoretical and practical background. Therefore, there is a need of high quality learning materials adapted to modern tools and methodologies. With that in mind, this work explores how the introduction of Jupyter notebooks in undergraduate mobile robotic courses contributes to improve both teaching and learning experiences. For that, we first present a series of (publicly available) educational notebooks encompassing a variety of topics relevant for robotics, with a particular emphasis in the study of mobile robots and commonly used sensors. Those documents have been built from the ground up to take advantage of the Jupyter Notebook framework, bridging the typical gap between theoretical frame and interactive code. We also present a case study describing the notebooks usage in undergraduate courses at University of Málaga, including a discussion on the promising results and findings obtained. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence Learning Technologies)

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21 pages, 1448 KiB

Open AccessArticle

A Hands-On Laboratory for Intelligent Control Courses

by Hugo Torres-Salinas, Juvenal Rodríguez-Reséndiz, Adyr A. Estévez-Bén, M. A. Cruz Pérez, P. Y. Sevilla-Camacho and Gerardo I. Perez-Soto

Appl. Sci. 2020, 10(24), 9070; https://0-doi-org.brum.beds.ac.uk/10.3390/app10249070 - 18 Dec 2020

Cited by 9 | Viewed by 2295

Abstract

This research focused on developing a methodology that facilitates the learning of control engineering students, specifically developing skills to design a complete control loop using fuzzy logic. The plant for this control loop is a direct current motor, one of the most common actuators used by educational and professional engineers. The research was carried out on a platform developed by a group of students. Although the learning techniques for the design and implementation of controllers are extensive, there has been a delay in teaching techniques that are relatively new compared to conventional control techniques. Then, the hands-on laboratory offers a tool for students to acquire the necessary skills in driver tuning. In addition to the study of complete systems, the ability to work in a team is developed, a fundamental skill in the professional industrial area. A qualitative and quantitative analysis of student learning was carried out, integrating a multidisciplinary project based on modern tools. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence Learning Technologies)

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17 pages, 3350 KiB

Open AccessArticle

Development of Adaptive Formative Assessment System Using Computerized Adaptive Testing and Dynamic Bayesian Networks

by Younyoung Choi and Cayce McClenen

Appl. Sci. 2020, 10(22), 8196; https://0-doi-org.brum.beds.ac.uk/10.3390/app10228196 - 19 Nov 2020

Cited by 25 | Viewed by 4805

Abstract

Online formative assessments in e-learning systems are increasingly of interest in the field of education. While substantial research into the model and item design aspects of formative assessment has been conducted, few software systems embodied with a psychometric model have been proposed to allow us to adaptively implement formative assessments. This study aimed to develop an adaptive formative assessment system, called computerized formative adaptive testing (CAFT) by using artificial intelligence methods based on computerized adaptive testing (CAT) and Bayesian networks as learning analytics. CAFT can adaptively administer personalized formative assessment to a learner by dynamically selecting appropriate items and tests aligned with the learner’s ability. Forty items in an item bank were evaluated by 410 learners, moreover, 1000 learners were recruited for a simulation study and 120 learners were enrolled to evaluate the efficiency, validity, and reliability of CAFT in an application study. The results showed that, through CAFT, learners can adaptively take item s and tests in order to receive personalized diagnostic feedback about their learning progression. Consequently, this study highlights that a learning management system which integrates CAT as an artificially intelligent component is an efficient educational evaluation tool for a remote personalized learning service. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence Learning Technologies)

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13 pages, 661 KiB

Open AccessArticle

Secure Learning Management System Based on User Behavior

by Alin Zamfiroiu, Diana Constantinescu, Mădălina Zurini and Cristian Toma

Appl. Sci. 2020, 10(21), 7730; https://0-doi-org.brum.beds.ac.uk/10.3390/app10217730 - 31 Oct 2020

Cited by 6 | Viewed by 2071

Abstract

The COVID-19 outbreak is an international problem and has affected people and students all over the world. When lockdowns were imposed internationally, learning management systems began to be used more than in the previous period. These systems have been used also for traditional forms of learning and not only for online learning. This pandemic has highlighted the need for online learning systems in the educational environment, but it is very important for these systems to be secure and to verify the authenticity of the students when they access a course or evaluation questions. In this period, everything is moving towards the digital world, with students that are connected from a distance to online systems. All activities in the educational environment will soon be performed digitally on learning management systems, which includes also the evaluation process of the students. In this paper, we propose a secure learning management system that uses the student’s behavior to identify if they are an authentic student or not. This system can support the teacher’s activities in the learning process and verify the authenticity of the students logged on to the system. This paper is aimed at learning management system developers, who can use the proposed algorithms in their developed platforms, and also at teachers, who should understand the importance of the identification of students on these platforms. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence Learning Technologies)

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21 pages, 5194 KiB

Open AccessArticle

A ROS-Based Open Tool for Intelligent Robotics Education

by José M. Cañas, Eduardo Perdices, Lía García-Pérez and Jesús Fernández-Conde

Appl. Sci. 2020, 10(21), 7419; https://0-doi-org.brum.beds.ac.uk/10.3390/app10217419 - 22 Oct 2020

Cited by 36 | Viewed by 5155

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 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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19 pages, 449 KiB

Open AccessArticle

Automatic Classification of Text Complexity

by Valentino Santucci, Filippo Santarelli, Luciana Forti and Stefania Spina

Appl. Sci. 2020, 10(20), 7285; https://0-doi-org.brum.beds.ac.uk/10.3390/app10207285 - 18 Oct 2020

Cited by 26 | Viewed by 4375

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 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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25 pages, 2120 KiB

Open AccessFeature PaperArticle

Artificial Intelligence Visual Metaphors in E-Learning Interfaces for Learning Analytics

by Valentina Franzoni, Alfredo Milani, Paolo Mengoni and Fabrizio Piccinato

Appl. Sci. 2020, 10(20), 7195; https://0-doi-org.brum.beds.ac.uk/10.3390/app10207195 - 15 Oct 2020

Cited by 17 | Viewed by 3736

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, 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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18 pages, 1566 KiB

Open AccessArticle

Development of an Intelligent Tutoring System Using Bayesian Networks and Fuzzy Logic for a Higher Student Academic Performance

by Meltem Eryılmaz and Afaf Adabashi

Appl. Sci. 2020, 10(19), 6638; https://0-doi-org.brum.beds.ac.uk/10.3390/app10196638 - 23 Sep 2020

Cited by 19 | Viewed by 4424

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 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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Review

Jump to: Research

23 pages, 2539 KiB

Open AccessReview

Systematic Literature Review on Machine Learning and Student Performance Prediction: Critical Gaps and Possible Remedies

by Boran Sekeroglu, Rahib Abiyev, Ahmet Ilhan, Murat Arslan and John Bush Idoko

Appl. Sci. 2021, 11(22), 10907; https://0-doi-org.brum.beds.ac.uk/10.3390/app112210907 - 18 Nov 2021

Cited by 33 | Viewed by 5883

Abstract

Improving the quality, developing and implementing systems that can provide advantages to students, and predicting students’ success during the term, at the end of the term, or in the future are some of the primary aims of education. Due to its unique ability to create relationships and obtain accurate results, artificial intelligence and machine learning are tools used in this field to achieve the expected goals. However, the diversity of studies and the differences in their content create confusion and reduce their ability to pioneer future studies. In this study, we performed a systematic literature review of student performance prediction studies in three different databases between 2010 and 2020. The results are presented as percentages by categorizing them as either model, dataset, validation, evaluation, or aims. The common points and differences in the studies are determined, and critical gaps and possible remedies are presented. The results and identified gaps could be eliminated with standardized evaluation and validation strategies. It is determined that student performance prediction studies should be more frequently focused on deep learning models in the future. Finally, the problems that can be solved using a global dataset created by a global education information consortium, as well as its advantages, are presented. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence Learning Technologies)

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