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Artificial Intelligence Applications for Education
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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: closed (30 June 2022) | Viewed by 29149

Special Issue Editors

Dr. Kevin Gary

E-Mail Website
Guest Editor
School of Computing and Augmented Intelligence, 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

E-Mail Website
Guest Editor
School of Computing and Augmented Intelligence, 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 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. 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 1600 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 (7 papers)

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Research

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25 pages, 1175 KiB  
Article
Early Prediction of At-Risk Students in Secondary Education: A Countrywide K-12 Learning Analytics Initiative in Uruguay
by Emanuel Marques Queiroga, Matheus Francisco Batista Machado, Virgínia Rodés Paragarino, Tiago Thompsen Primo and Cristian Cechinel
Information 2022, 13(9), 401; https://0-doi-org.brum.beds.ac.uk/10.3390/info13090401 - 23 Aug 2022
Cited by 3 | Viewed by 2466
Abstract
This paper describes a nationwide learning analytics initiative in Uruguay focused on the future implementation of governmental policies to mitigate student retention and dropouts in secondary education. For this, data from a total of 258,440 students were used to generate automated models to [...] Read more.
This paper describes a nationwide learning analytics initiative in Uruguay focused on the future implementation of governmental policies to mitigate student retention and dropouts in secondary education. For this, data from a total of 258,440 students were used to generate automated models to predict students at risk of failure or dropping out. Data were collected from primary and secondary education from different sources and for the period between 2015 and 2020. Such data contains demographic information about the students and their trajectories from the first grade of primary school to the second grade of secondary school (e.g., student assessments in different subjects over the years, the amount of absences, participation in social welfare programs, and the zone of the school, among other factors). Predictive models using the random forest algorithm were trained, and their performances were evaluated with F1-Macro and AUROC measures. The models were planned to be applied in different periods of the school year for the regular secondary school and for the technical secondary school ((before the beginning of the school year and after the first evaluation meeting for each grade). A total of eight predictive models were developed considering this temporal approach, and after an analysis of bias considering three protected attributes (gender, school zone, and social welfare program participation), seven of them were approved to be used for prediction. The models achieved outstanding performances according to the literature, with an AUROC higher than 0.90 and F1-Macro higher than 0.88. This paper describes in depth the characteristics of the data gathered, the specifics of data preprocessing, and the methodology followed for model generation and bias analysis, together with the architecture developed for the deployment of the predictive models. Among other findings, the results of the paper corroborate the importance given in the literature of using the previous performances of the students in order to predict their future performances. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Education)
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16 pages, 2235 KiB  
Article
An Effective Student Grouping and Course Recommendation Strategy Based on Big Data in Education
by Yu Guo, Yue Chen, Yuanyan Xie and Xiaojuan Ban
Information 2022, 13(4), 197; https://0-doi-org.brum.beds.ac.uk/10.3390/info13040197 - 14 Apr 2022
Cited by 5 | Viewed by 2172
Abstract
Personalized education aims to provide cooperative and exploratory courses for students by using computer and network technology to construct a more effective cooperative learning mode, thus improving students’ cooperation ability and lifelong learning ability. Based on students’ interests, this paper proposes an effective [...] Read more.
Personalized education aims to provide cooperative and exploratory courses for students by using computer and network technology to construct a more effective cooperative learning mode, thus improving students’ cooperation ability and lifelong learning ability. Based on students’ interests, this paper proposes an effective student grouping strategy and group-oriented course recommendation method, comprehensively considering characteristics of students and courses both from a statistical dimension and a semantic dimension. First, this paper combines term frequency–inverse document frequency and Word2Vec to preferably extract student characteristics. Then, an improved K-means algorithm is used to divide students into different interest-based study groups. Finally, the group-oriented course recommendation method recommends appropriate and quality courses according to the similarity and expert score. Based on real data provided by junior high school students, a series of experiments are conducted to recommend proper social practical courses, which verified the feasibility and effectiveness of the proposed strategy. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Education)
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14 pages, 612 KiB  
Article
Detecting Learning Patterns in Tertiary Education Using K-Means Clustering
by Emmanuel Tuyishimire, Wadzanai Mabuto, Paul Gatabazi and Sylvie Bayisingize
Information 2022, 13(2), 94; https://0-doi-org.brum.beds.ac.uk/10.3390/info13020094 - 17 Feb 2022
Cited by 4 | Viewed by 2753
Abstract
We are in the era where various processes need to be online. However, data from digital learning platforms are still underutilised in higher education, yet, they contain student learning patterns, whose awareness would contribute to educational development. Furthermore, the knowledge of student progress [...] Read more.
We are in the era where various processes need to be online. However, data from digital learning platforms are still underutilised in higher education, yet, they contain student learning patterns, whose awareness would contribute to educational development. Furthermore, the knowledge of student progress would inform educators whether they would mitigate teaching conditions for critically performing students. Less knowledge of performance patterns limits the development of adaptive teaching and learning mechanisms. In this paper, a model for data exploitation to dynamically study students progress is proposed. Variables to determine current students progress are defined and are used to group students into different clusters. A model for dynamic clustering is proposed and related cluster migration is analysed to isolate poorer or higher performing students. K-means clustering is performed on real data consisting of students from a South African tertiary institution. The proposed model for cluster migration analysis is applied and the corresponding learning patterns are revealed. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Education)
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21 pages, 4827 KiB  
Article
Predicting Student Dropout in Self-Paced MOOC Course Using Random Forest Model
by Sheran Dass, Kevin Gary and James Cunningham
Information 2021, 12(11), 476; https://0-doi-org.brum.beds.ac.uk/10.3390/info12110476 - 17 Nov 2021
Cited by 32 | Viewed by 4074
Abstract
A significant problem in Massive Open Online Courses (MOOCs) is the high rate of student dropout in these courses. An effective student dropout prediction model of MOOC courses can identify the factors responsible and provide insight on how to initiate interventions to increase [...] Read more.
A significant problem in Massive Open Online Courses (MOOCs) is the high rate of student dropout in these courses. An effective student dropout prediction model of MOOC courses can identify the factors responsible and provide insight on how to initiate interventions to increase student success in a MOOC. Different features and various approaches are available for the prediction of student dropout in MOOC courses. In this paper, the data derived from a self-paced math course, College Algebra and Problem Solving, offered on the MOOC platform Open edX partnering with Arizona State University (ASU) from 2016 to 2020 is considered. This paper presents a model to predict the dropout of students from a MOOC course given a set of features engineered from student daily learning progress. The Random Forest Model technique in Machine Learning (ML) is used in the prediction and is evaluated using validation metrics including accuracy, precision, recall, F1-score, Area Under the Curve (AUC), and Receiver Operating Characteristic (ROC) curve. The model developed can predict the dropout or continuation of students on any given day in the MOOC course with an accuracy of 87.5%, AUC of 94.5%, precision of 88%, recall of 87.5%, and F1-score of 87.5%, respectively. The contributing features and interactions were explained using Shapely values for the prediction of the model. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Education)
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16 pages, 1179 KiB  
Article
WebPGA: An Educational Technology That Supports Learning by Reviewing Paper-Based Programming Assessments
by Yancy Vance Paredes and I-Han Hsiao
Information 2021, 12(11), 450; https://0-doi-org.brum.beds.ac.uk/10.3390/info12110450 - 29 Oct 2021
Cited by 1 | Viewed by 1787
Abstract
Providing feedback to students is one of the most effective ways to enhance their learning. With the advancement of technology, many tools have been developed to provide personalized feedback. However, these systems are only beneficial when interactions are done on digital platforms. As [...] Read more.
Providing feedback to students is one of the most effective ways to enhance their learning. With the advancement of technology, many tools have been developed to provide personalized feedback. However, these systems are only beneficial when interactions are done on digital platforms. As paper-based assessment is still a dominantly preferred evaluation method, particularly in large blended-instruction classes, the sole use of electronic educational systems presents a gap between how students learn the subject from the physical and digital world. This has motivated the design and the development of a new educational technology that facilitates the digitization, grading, and distribution of paper-based assessments to support blended-instruction classes. With the aid of this technology, different learning analytics can be readily captured. A retrospective analysis was conducted to understand the students’ behaviors in an Object-Oriented Programming and Data Structures class from a public university. Their behavioral differences and the associated learning impacts were analyzed by leveraging their digital footprints. Results showed that students made significant efforts in reviewing their examinations. Notably, the high-achieving and the improving students spent more time reviewing their mistakes and started doing so as soon as the assessment became available. Finally, when students were guided in the reviewing process, they were able to identify items where they had misconceptions. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Education)
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11 pages, 697 KiB  
Article
Online At-Risk Student Identification using RNN-GRU Joint Neural Networks
by Yanbai He, Rui Chen, Xinya Li, Chuanyan Hao, Sijiang Liu, Gangyao Zhang and Bo Jiang
Information 2020, 11(10), 474; https://0-doi-org.brum.beds.ac.uk/10.3390/info11100474 - 09 Oct 2020
Cited by 40 | Viewed by 4352
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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26 pages, 609 KiB  
Review
Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision
by Hussan Munir, Bahtijar Vogel and Andreas Jacobsson
Information 2022, 13(4), 203; https://0-doi-org.brum.beds.ac.uk/10.3390/info13040203 - 17 Apr 2022
Cited by 27 | Viewed by 9713
Abstract
The use of artificial intelligence and machine learning techniques across all disciplines has exploded in the past few years, with the ever-growing size of data and the changing needs of higher education, such as digital education. Similarly, online educational information systems have a [...] Read more.
The use of artificial intelligence and machine learning techniques across all disciplines has exploded in the past few years, with the ever-growing size of data and the changing needs of higher education, such as digital education. Similarly, online educational information systems have a huge amount of data related to students in digital education. This educational data can be used with artificial intelligence and machine learning techniques to improve digital education. This study makes two main contributions. First, the study follows a repeatable and objective process of exploring the literature. Second, the study outlines and explains the literature’s themes related to the use of AI-based algorithms in digital education. The study findings present six themes related to the use of machines in digital education. The synthesized evidence in this study suggests that machine learning and deep learning algorithms are used in several themes of digital learning. These themes include using intelligent tutors, dropout predictions, performance predictions, adaptive and predictive learning and learning styles, analytics and group-based learning, and automation. artificial neural network and support vector machine algorithms appear to be utilized among all the identified themes, followed by random forest, decision tree, naive Bayes, and logistic regression algorithms. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Education)
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