Special Issue "Data Analytics and Machine Learning in Education"

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

Deadline for manuscript submissions: 15 January 2022.

Special Issue Editors

Prof. Dr. Juan A. Gómez-Pulido
E-Mail Website
Guest Editor
Department of Technologies of Computers and Communications, Universidad de Extremadura, Cáceres, Spain
Interests: optimization and computational intelligence; machine learning; reconfigurable computing and FPGAs; wireless communications; bioinformatics
Special Issues and Collections in MDPI journals
Prof. Dr. Young Park
E-Mail Website
Guest Editor
Department of Computer Science and Information Systems, Bradley University, Peoria, IL 61625, USA
Interests: personalized recommender systems and prediction systems; personalized and advanced Web search; formal concept analysis and its applications to software engineering; Web search and data mining; software reuse; semantics-based program analysis
Special Issues and Collections in MDPI journals
Prof. Dr. Ricardo Soto
E-Mail Website
Guest Editor
School of Computer Engineering, Pontificia Universidad Católica de Valparaíso, Valparaiso, Chile
Interests: constraint programming; compilers and languages design; global optimization
Special Issues and Collections in MDPI journals
Prof. Dr. José M. Lanza-Gutiérrez
E-Mail Website
Guest Editor
Department of Computer Sciences, University of Alcala, Alcala de Henares, Spain
Interests: machine learning; data science; optimization; edge computing; cognitive systems

Special Issue Information

Dear Colleagues,

The generalization of the use of advanced technological tools in the field of educational is leading to the generation of big data related to academic activities which involve students and teachers. For example, the inclusion of virtual campuses as a regular educational management tool encourages the virtualization of teaching, the online management of grades, the monitoring of student progress, the recording of all kinds of educational variables, etc. In this way, technology-enhanced learning (TEL) platforms allow one to generate and store data that stand out, not only for their huge amount and heterogeneity, but above all, for their link to a time dimension that allows one to analyze and predict student behaviour in its dynamic context, among other purposes.

There are many interesting research lines that deserve to be explored in the education area, such as analyzing and predicting students' behaviour, developing advanced tools for supporting learning stages, recommending activities, predicting dropout, optimizing resources, etc. For these purposes, there are advanced methods from computational science that have demonstrated a high effectiveness when handling data and processes that are strongly interconnected. Data mining, big data, machine learning, deep learning, collaborative filtering, and recommender systems, among other fields related to artificial intelligence, allow for the development of advanced techniques that provide a significant potential for the above purposes, leading to new applications and more effective approaches in academic analysis and prediction.

This Special Issue provides a collection of papers of original advances in the analysis, prediction, and recommendation of applications propelled by artificial intelligence, data science, data analytics, big data, and machine learning, especially in the TEL context. Papers about these topics are welcomed.

Prof. Dr. Juan A. Gómez-Pulido
Prof. Dr. Young Park
Prof. Dr. Ricardo Soto
Prof. Dr. José M. Lanza-Gutiérrez
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 2000 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

  • Technology-enhanced learning and teaching
  • Personalized learning
  • Intelligent tutoring Systems
  • Data science and analytics
  • Data mining and big data analysis
  • Intelligent systems
  • Machine and deep learning
  • Recommender systems
  • Collaborative filtering
  • Deep learning-based recommendations
  • Review-based recommendations
  • Performance prediction
  • Knowledge analysis
  • Optimization

Published Papers (8 papers)

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Research

Article
Toward a Better Understanding of Academic Programs Educational Objectives: A Data Analytics-Based Approach
Appl. Sci. 2021, 11(20), 9623; https://doi.org/10.3390/app11209623 - 15 Oct 2021
Viewed by 32
Abstract
In outcome-based academic programs, Program Education Objects (PEOs) are the key pillars on which program components are built. They are articulated linguistically as broad statements of graduates’ professional and career accomplishments within a few years of graduation. Moreover, PEOs are mapped into a [...] Read more.
In outcome-based academic programs, Program Education Objects (PEOs) are the key pillars on which program components are built. They are articulated linguistically as broad statements of graduates’ professional and career accomplishments within a few years of graduation. Moreover, PEOs are mapped into a set of skills and attributes known as Program Learning Outcomes (PLOs). It goes without saying that a profound understanding of the PEOs is a key factor in the success of an academic program. For this sake, this paper proposes a data analytics-based approach to examine the correlations among PEOs. More specifically, it applies a data similarity-based approach to analyze the correlations among the PEOs of engineering programs. To this end, a dataset of PEOs–PLOs mapping of a set of engineering programs has been extracted from their self-study reports. The collected dataset has undergone preprocessing steps to transform it into a suitable representation. This involves data cleaning, data annotation using a developed set of PEOs labels, and removal of data instances with multiple PEO labels. Each PEO is then represented as a vector space model whose dimensions are the PLOs, and their values are the relative frequencies of PLOs computed from all data instances of that PEO. After that, three data similarity measures, namely Euclidean distance, cosine measure, and Manhattan distance, are applied to measure the similarity between PEOs vector space models. The resultant similarity matrices are then analyzed at the level of a specific measure, an agreement between measures, and average similarity across all measures. The analysis results contribute to a better understanding of the PEOs correlations and provide very useful actionable insights for empowering decision making toward systemization and optimization of academic programs processes. Full article
(This article belongs to the Special Issue Data Analytics and Machine Learning in Education)
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Article
Online Blended Learning in Small Private Online Course
Appl. Sci. 2021, 11(15), 7100; https://doi.org/10.3390/app11157100 - 31 Jul 2021
Viewed by 470
Abstract
In this work, we studied the online blended learning model of computer network experimentation, focusing mainly on the problem of traditional network experiments being limited by location and time, and explore the applicability of the small private online course (SPOC) advanced teaching concepts [...] Read more.
In this work, we studied the online blended learning model of computer network experimentation, focusing mainly on the problem of traditional network experiments being limited by location and time, and explore the applicability of the small private online course (SPOC) advanced teaching concepts to computer network online experiment teaching. Based on the structure of a combination of virtual and real, real and not virtual, an online network experiment platform and management system has been designed and constructed, enabling students to carry out remote online computer network hardware experiments anytime and anywhere, without being restricted by time, space, or content. Using the online network experiment platform, we can organize the experimental modules and knowledge points via the SPOC course concept, by developing online network experimental content, modularizing and fragmenting of the experiments, creating the pre-experimental explanation and experiment preview videos, and evaluating the assignments via peer grading to analyze students’ learning behavior. By exploring online network experimental teaching methods and management models, offering experimental guidance in an interactive manner, and highlighting the openness and sharing characteristics of online experimental teaching platforms, we can improve the utilization rate for teaching resources, and provide ideas for applied scientific research methods. Full article
(This article belongs to the Special Issue Data Analytics and Machine Learning in Education)
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Article
Quantifying the Impact of Student Enrollment Patterns on Academic Success Using a Hidden Markov Model
Appl. Sci. 2021, 11(14), 6453; https://doi.org/10.3390/app11146453 - 13 Jul 2021
Viewed by 512
Abstract
Simplified classifications have often led to college students being labeled as full-time or part-time students. However, student enrollment patterns can be much more complicated at many universities, as it is common for students to switch between full-time and part-time enrollment each semester based [...] Read more.
Simplified classifications have often led to college students being labeled as full-time or part-time students. However, student enrollment patterns can be much more complicated at many universities, as it is common for students to switch between full-time and part-time enrollment each semester based on finances, scheduling, or family needs. While previous studies have identified part-time enrollment as a risk factor to students’ academic success, limited research has examined the impact of enrollment patterns or strategies on academic performance. Unlike traditional methods that use a single-period model to classify students into full-time and part-time categories, in this study, we apply an advanced multi-period dynamic approach using a Hidden Markov Model to distinguish and cluster students’ enrollment strategies into three categories: full-time, part-time, and mixed. We then investigate and compare the academic performance outcomes of each group based on their enrollment strategies while taking into account student type (i.e., first-time-in-college students and transfer students). Analysis of undergraduate student records data collected at the University of Central Florida from 2008 to 2017 shows that the academic performance of first-time-in-college students who apply a mixed enrollment strategy is closer to that of full-time students, as compared to part-time students. Moreover, during their part-time semesters, mixed-enrollment students significantly outperform part-time students. Similarly, analysis of transfer students shows that a mixed-enrollment strategy is correlated with similar graduation rates as the full-time enrollment strategy and more than double the graduation rate associated with part-time enrollment. This finding suggests that part-time students can achieve better overall outcomes by increased engagement through occasional full-time enrollments. Full article
(This article belongs to the Special Issue Data Analytics and Machine Learning in Education)
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Article
Towards Predicting Student’s Dropout in University Courses Using Different Machine Learning Techniques
Appl. Sci. 2021, 11(7), 3130; https://doi.org/10.3390/app11073130 - 01 Apr 2021
Cited by 2 | Viewed by 699
Abstract
Early and precisely predicting the students’ dropout based on available educational data belongs to the widespread research topic of the learning analytics research field. Despite the amount of already realized research, the progress is not significant and persists on all educational data levels. [...] Read more.
Early and precisely predicting the students’ dropout based on available educational data belongs to the widespread research topic of the learning analytics research field. Despite the amount of already realized research, the progress is not significant and persists on all educational data levels. Even though various features have already been researched, there is still an open question, which features can be considered appropriate for different machine learning classifiers applied to the typical scarce set of educational data at the e-learning course level. Therefore, the main goal of the research is to emphasize the importance of the data understanding, data gathering phase, stress the limitations of the available datasets of educational data, compare the performance of several machine learning classifiers, and show that also a limited set of features, which are available for teachers in the e-learning course, can predict student’s dropout with sufficient accuracy if the performance metrics are thoroughly considered. The data collected from four academic years were analyzed. The features selected in this study proved to be applicable in predicting course completers and non-completers. The prediction accuracy varied between 77 and 93% on unseen data from the next academic year. In addition to the frequently used performance metrics, the comparison of machine learning classifiers homogeneity was analyzed to overcome the impact of the limited size of the dataset on obtained high values of performance metrics. The results showed that several machine learning algorithms could be successfully applied to a scarce dataset of educational data. Simultaneously, classification performance metrics should be thoroughly considered before deciding to deploy the best performance classification model to predict potential dropout cases and design beneficial intervention mechanisms. Full article
(This article belongs to the Special Issue Data Analytics and Machine Learning in Education)
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Article
Table Organization Optimization in Schools for Preserving the Social Distance during the COVID-19 Pandemic
Appl. Sci. 2020, 10(23), 8392; https://doi.org/10.3390/app10238392 - 25 Nov 2020
Cited by 1 | Viewed by 1001
Abstract
The COVID-19 pandemic has supposed a challenge for education. The school closures during the initial coronavirus outbreak for reducing the infections have promoted negative effects on children, such as the interruption of their normal social relationships or their necessary physical activity. Thus, most [...] Read more.
The COVID-19 pandemic has supposed a challenge for education. The school closures during the initial coronavirus outbreak for reducing the infections have promoted negative effects on children, such as the interruption of their normal social relationships or their necessary physical activity. Thus, most of the countries worldwide have considered as a priority the reopening of schools but imposing some rules for keeping safe places for the school lessons such as social distancing, wearing facemasks, hydroalcoholic gels or reducing the capacity in the indoor rooms. In Spain, the government has fixed a minimum distance of 1.5 m among the students’ desks for preserving the social distancing and schools have followed orthogonal and triangular mesh patterns for achieving valid table dispositions that meet the requirements. However, these patterns may not attain the best results for maximizing the distances among the tables. Therefore, in this paper, we introduce for the first time in the authors’ best knowledge a Genetic Algorithm (GA) for optimizing the disposition of the tables at schools during the coronavirus pandemic. We apply this GA in two real-application scenarios in which we find table dispositions that increase the distances among the tables by 19.33% and 10%, respectively, with regards to regular government patterns in these classrooms, thus fulfilling the main objectives of the paper. Full article
(This article belongs to the Special Issue Data Analytics and Machine Learning in Education)
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Article
Analysis and Prediction of Engineering Student Behavior and Their Relation to Academic Performance Using Data Analytics Techniques
Appl. Sci. 2020, 10(20), 7114; https://doi.org/10.3390/app10207114 - 13 Oct 2020
Cited by 1 | Viewed by 793
Abstract
This study focuses on identifying personality traits in computer science students and determining whether they are related to academic performance. In addition, the importance of the personality traits based on motivation scale and depression, anxiety, and stress scales were measured. A sample of [...] Read more.
This study focuses on identifying personality traits in computer science students and determining whether they are related to academic performance. In addition, the importance of the personality traits based on motivation scale and depression, anxiety, and stress scales were measured. A sample of 188 students from the Computer Engineering Schools of the Pontifical Catholic University of Valparaíso was used. Through econometric two-stage least squares and paired sample correlation analysis, the results obtained indicate that there is a relation between academic performance and the personality traits measured by educational motivation scale and the ranking of university entrance and gender. In addition, these results led to characterization of students based on their personality traits and provided elements that may enhance the development of an effective personality that allows students to successfully face their environment, playing an important role in the educational process. Full article
(This article belongs to the Special Issue Data Analytics and Machine Learning in Education)
Article
Data Analysis as a Tool for the Application of Adaptive Learning in a University Environment
Appl. Sci. 2020, 10(20), 7016; https://doi.org/10.3390/app10207016 - 09 Oct 2020
Cited by 8 | Viewed by 874
Abstract
Currently, data are a very valuable resource for organizations. Through analysis, it is possible to profile people or obtain knowledge about an event or environment and make decisions that help improve their quality of life. This concept takes on greater value in the [...] Read more.
Currently, data are a very valuable resource for organizations. Through analysis, it is possible to profile people or obtain knowledge about an event or environment and make decisions that help improve their quality of life. This concept takes on greater value in the current pandemic, due to coronavirus disease 2019 (COVID-19), that affects society. This emergency has changed the way people live. As a result, the majority of activities are carried out using the internet, virtually or online. Education is not far behind and has seen the web as the most successful option to continue with its activities. The use of any computer application generates a large volume of data that can be analyzed by a big data architecture in order to obtain knowledge from its students and use it to improve educational processes. The big data, when included as a tool for adaptive learning, allow the analysis of a large volume of data to offer an educational model based on personalized education. In this work, the analysis of educational data through a big data architecture is proposed to generate learning based on meeting the needs of students. Full article
(This article belongs to the Special Issue Data Analytics and Machine Learning in Education)
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Article
Analysis of University Students’ Behavior Based on a Fusion K-Means Clustering Algorithm
Appl. Sci. 2020, 10(18), 6566; https://doi.org/10.3390/app10186566 - 20 Sep 2020
Cited by 4 | Viewed by 873
Abstract
With the development of big data technology, creating the ‘Digital Campus’ is a hot issue. For an increasing amount of data, traditional data mining algorithms are not suitable. The clustering algorithm is becoming more and more important in the field of data mining, [...] Read more.
With the development of big data technology, creating the ‘Digital Campus’ is a hot issue. For an increasing amount of data, traditional data mining algorithms are not suitable. The clustering algorithm is becoming more and more important in the field of data mining, but the traditional clustering algorithm does not take the clustering efficiency and clustering effect into consideration. In this paper, the algorithm based on K-Means and clustering by fast search and find of density peaks (K-CFSFDP) is proposed, which improves on the distance and density of data points. This method is used to cluster students from four universities. The experiment shows that K-CFSFDP algorithm has better clustering results and running efficiency than the traditional K-Means clustering algorithm, and it performs well in large scale campus data. Additionally, the results of the cluster analysis show that the students of different categories in four universities had different performances in living habits and learning performance, so the university can learn about the students’ behavior of different categories and provide corresponding personalized services, which have certain practical significance. Full article
(This article belongs to the Special Issue Data Analytics and Machine Learning in Education)
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