Editorial

4 pages, 198 KiB

Open AccessEditorial

Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education

by Carmen Lacave and Ana Isabel Molina

Mathematics 2023, 11(6), 1344; https://doi.org/10.3390/math11061344 - 09 Mar 2023

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The COVID-19 pandemic highlighted the importance of health and education and also revealed the need for innovative solutions relative to the challenges confronting these disciplines [...] Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

Research

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16 pages, 2224 KiB

Open AccessArticle

Analysis of Dual-Tasking Effect on Gait Variability While Interacting with Mobile Devices

by David Carneros-Prado, Cosmin C. Dobrescu, Iván González, Jesús Fontecha, Esperanza Johnson and Ramón Hervás

Mathematics 2023, 11(1), 202; https://doi.org/10.3390/math11010202 - 30 Dec 2022

Cited by 3 | Viewed by 1412

Abstract

Cognitive deficits are very difficult to diagnose during the initial stages; tests typically consist of a patient performing punctual dual-task activities, which are subjectively analyzed to determine the cognitive decline impact on gait. This work supports novel and objective diagnosis methods by stating [...] Read more.

Cognitive deficits are very difficult to diagnose during the initial stages; tests typically consist of a patient performing punctual dual-task activities, which are subjectively analyzed to determine the cognitive decline impact on gait. This work supports novel and objective diagnosis methods by stating a baseline on how neurotypical aging affects dual tasks while using a smartphone on the move. With this aim, we propose a twofold research question: Which mobile device tasks performed on the move (dual tasking) have characteristic changes in gait parameters, and which are especially characteristic at older ages? An experiment was conducted with 30 healthy participants where they performed 15 activities (1 single task, 2 traditional dual-tasks and 12 mobile-based dual-tasks) while walking about 50 m. Participants wore a wireless motion tracker (15 sensors) that made the concise analysis of gait possible. The results obtained characterized the gait parameters affected by mobile-based dual-tasking and the impact of normal cognitive decline due to aging. The statistical analysis shows that using smartphone-based dual-tasking produces more significant results than traditional dual-tasking. In the study, 3 out of 10 gait parameters were very significantly affected (p < 0.001) when using the traditional dual tasks, while 5 out of 10 parameters were very significantly affected (p < 0.001) in mobile-based dual-tasking. Moreover, the most characteristic tasks and gait parameters were identified through the obtained results. Future work will focus on applying this knowledge to improve the early diagnosis of MCI. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

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22 pages, 853 KiB

Open AccessArticle

Influenza-like Illness Detection from Arabic Facebook Posts Based on Sentiment Analysis and 1D Convolutional Neural Network

by Abdennour Boulesnane, Souham Meshoul and Khaoula Aouissi

Mathematics 2022, 10(21), 4089; https://doi.org/10.3390/math10214089 - 02 Nov 2022

Cited by 6 | Viewed by 2061

Abstract

The recent large outbreak of infectious diseases, such as influenza-like illnesses and COVID-19, has resulted in a flood of health-related posts on the Internet in general and on social media in particular, in a wide range of languages and dialects around the world. [...] Read more.

The recent large outbreak of infectious diseases, such as influenza-like illnesses and COVID-19, has resulted in a flood of health-related posts on the Internet in general and on social media in particular, in a wide range of languages and dialects around the world. The obvious relationship between the number of infectious disease cases and the number of social media posts prompted us to consider how we can leverage such health-related content to detect the emergence of diseases, particularly influenza-like illnesses, and foster disease surveillance systems. We used Algerian Arabic posts as a case study in our research. From data collection to content classification, a complete workflow was implemented. The main contributions of this work are the creation of a large corpus of Arabic Facebook posts based on Algerian dialect and the proposal of a new classification model based on sentiment analysis and one-dimensional convolutional neural networks. The proposed model categorizes Facebook posts based on the users’ feelings. To counteract data imbalance, two techniques have been considered, namely, SMOTE and random oversampling (ROS). Using a 5-fold cross-validation, the proposed model outperformed other baseline and state-of-the-art models such as SVM, LSTM, GRU, and BiLTSM in terms of several performance metrics. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

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21 pages, 676 KiB

Open AccessArticle

An Empirical Analysis of the Impact of Continuous Assessment on the Final Exam Mark

by María Morales, Antonio Salmerón, Ana D. Maldonado, Andrés R. Masegosa and Rafael Rumí

Mathematics 2022, 10(21), 3994; https://doi.org/10.3390/math10213994 - 27 Oct 2022

Cited by 3 | Viewed by 1615

Abstract

Since the Bologna Process was adopted, continuous assessment has been a cornerstone in the curriculum of most of the courses in the different degrees offered by the Spanish Universities. Continuous assessment plays an important role in both students’ and lecturers’ academic lives. In [...] Read more.

Since the Bologna Process was adopted, continuous assessment has been a cornerstone in the curriculum of most of the courses in the different degrees offered by the Spanish Universities. Continuous assessment plays an important role in both students’ and lecturers’ academic lives. In this study, we analyze the effect of the continuous assessment on the performance of the students in their final exams in courses of Statistics at the University of Almería. Specifically, we study if the performance of a student in the continuous assessment determines the score obtained in the final exam of the course in such a way that this score can be predicted in advance using the continuous assessment performance as an explanatory variable. After using and comparing some powerful statistical procedures, such as linear, quantile and logistic regression, artificial neural networks and Bayesian networks, we conclude that, while the fact that a student passes or fails the final exam can be properly predicted, a more detailed forecast about the grade obtained is not possible. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

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20 pages, 741 KiB

Open AccessArticle

Teaching Probabilistic Graphical Models with OpenMarkov

by Francisco Javier Díez, Manuel Arias, Jorge Pérez-Martín and Manuel Luque

Mathematics 2022, 10(19), 3577; https://doi.org/10.3390/math10193577 - 30 Sep 2022

Cited by 2 | Viewed by 1475

Abstract

OpenMarkov is an open-source software tool for probabilistic graphical models. It has been developed especially for medicine, but has also been used to build applications in other fields and for tuition, in more than 30 countries. In this paper we explain how to [...] Read more.

OpenMarkov is an open-source software tool for probabilistic graphical models. It has been developed especially for medicine, but has also been used to build applications in other fields and for tuition, in more than 30 countries. In this paper we explain how to use it as a pedagogical tool to teach the main concepts of Bayesian networks and influence diagrams, such as conditional dependence and independence, d-separation, Markov blankets, explaining away, optimal policies, expected utilities, etc., and some inference algorithms: logic sampling, likelihood weighting, and arc reversal. The facilities for learning Bayesian networks interactively can be used to illustrate step by step the performance of the two basic algorithms: search-and-score and PC. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

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18 pages, 3964 KiB

Open AccessArticle

ECG Signal Features Classification for the Mental Fatigue Recognition

by Eglė Butkevičiūtė, Aleksėjus Michalkovič and Liepa Bikulčienė

Mathematics 2022, 10(18), 3395; https://doi.org/10.3390/math10183395 - 19 Sep 2022

Cited by 8 | Viewed by 2819

Abstract

Mental fatigue is a major public health issue worldwide that is common among both healthy and sick people. In the literature, various modern technologies, together with artificial intelligence techniques, have been proposed. Most techniques consider complex biosignals, such as electroencephalogram, electro-oculogram or classification [...] Read more.

Mental fatigue is a major public health issue worldwide that is common among both healthy and sick people. In the literature, various modern technologies, together with artificial intelligence techniques, have been proposed. Most techniques consider complex biosignals, such as electroencephalogram, electro-oculogram or classification of basic heart rate variability parameters. Additionally, most studies focus on a particular area, such as driving, surgery, etc. In this paper, a novel approach is presented that combines electrocardiogram (ECG) signal feature extraction, principal component analysis (PCA), and classification using machine learning algorithms. With the aim of daily mental fatigue recognition, an experiment was designed wherein ECG signals were recorded twice a day: in the morning, i.e., a state without fatigue, and in the evening, i.e., a fatigued state. PCA analysis results show that ECG signal parameters, such as Q and R wave amplitude values, as well as QT and T intervals, presented with the largest differences between states compared to other ECG signal parameters. Furthermore, the random forest classifier achieved more than 94.5% accuracy. This work demonstrates the feasibility of ECG signal feature extraction for automatic mental fatigue detection. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

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20 pages, 6009 KiB

Open AccessArticle

Machine Learning Prediction of University Student Dropout: Does Preference Play a Key Role?

by Marina Segura, Jorge Mello and Adolfo Hernández

Mathematics 2022, 10(18), 3359; https://doi.org/10.3390/math10183359 - 16 Sep 2022

Cited by 18 | Viewed by 3391

Abstract

University dropout rates are a problem that presents many negative consequences. It is an academic issue and carries an unfavorable economic impact. In recent years, significant efforts have been devoted to the early detection of students likely to drop out. This paper uses [...] Read more.

University dropout rates are a problem that presents many negative consequences. It is an academic issue and carries an unfavorable economic impact. In recent years, significant efforts have been devoted to the early detection of students likely to drop out. This paper uses data corresponding to dropout candidates after their first year in the third largest face-to-face university in Europe, with the goal of predicting likely dropout either at the beginning of the course of study or at the end of the first semester. In this prediction, we considered the five major program areas. Different techniques have been used: first, a Feature Selection Process in order to identify the variables more correlated with dropout; then, some Machine Learning Models (Support Vector Machines, Decision Trees and Artificial Neural Networks) as well as a Logistic Regression. The results show that dropout detection does not work only with enrollment variables, but it improves after the first semester results. Academic performance is always a relevant variable, but there are others, such as the level of preference that the student had over the course that he or she was finally able to study. The success of the techniques depends on the program areas. Machine Learning obtains the best results, but a simple Logistic Regression model can be used as a reasonable baseline. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

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20 pages, 5727 KiB

Open AccessArticle

Discriminative Convolutional Sparse Coding of ECG Signals for Automated Recognition of Cardiac Arrhythmias

by Bing Zhang and Jizhong Liu

Mathematics 2022, 10(16), 2874; https://doi.org/10.3390/math10162874 - 11 Aug 2022

Cited by 2 | Viewed by 1238

Abstract

Electrocardiogram (ECG) is a common and powerful tool for studying heart function and diagnosing several abnormal arrhythmias. In this paper, we present a novel classification model that combines the discriminative convolutional sparse coding (DCSC) framework with the linear support vector machine (LSVM) classification [...] Read more.

Electrocardiogram (ECG) is a common and powerful tool for studying heart function and diagnosing several abnormal arrhythmias. In this paper, we present a novel classification model that combines the discriminative convolutional sparse coding (DCSC) framework with the linear support vector machine (LSVM) classification strategy. In the training phase, most existing convolutional sparse coding frameworks are unsupervised in the sense that label information is ignored in the convolutional filter training stage. In this work, we explicitly incorporate a label consistency constraint called “discriminative sparse-code error” into the objective function to learn discriminative dictionary filters for sparse coding. The learned dictionary filters encourage signals from the same class to have similar sparse codes, and signals from different classes to have dissimilar sparse codes. To reduce the computational complexity, we propose to perform a max-pooling operation on the sparse coefficients. Using LSVM as a classifier, we examine the performance of the proposed classification system on the MIT-BIH arrhythmia database in accordance with the AAMI EC57 standard. The experimental results show that the proposed DCSC + LSVM algorithm can obtain 99.32% classification accuracy for cardiac arrhythmia recognition. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

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27 pages, 707 KiB

Open AccessFeature PaperArticle

Using Value-Based Potentials for Making Approximate Inference on Probabilistic Graphical Models

by Pedro Bonilla-Nadal, Andrés Cano, Manuel Gómez-Olmedo, Serafín Moral and Ofelia Paula Retamero

Mathematics 2022, 10(14), 2542; https://doi.org/10.3390/math10142542 - 21 Jul 2022

Cited by 1 | Viewed by 1429

Abstract

The computerization of many everyday tasks generates vast amounts of data, and this has lead to the development of machine-learning methods which are capable of extracting useful information from the data so that the data can be used in future decision-making processes. For [...] Read more.

The computerization of many everyday tasks generates vast amounts of data, and this has lead to the development of machine-learning methods which are capable of extracting useful information from the data so that the data can be used in future decision-making processes. For a long time now, a number of fields, such as medicine (and all healthcare-related areas) and education, have been particularly interested in obtaining relevant information from this stored data. This interest has resulted in the need to deal with increasingly complex problems which involve many different variables with a high degree of interdependency. This produces models (and in our case probabilistic graphical models) that are difficult to handle and that require very efficient techniques to store and use the information that quantifies the relationships between the problem variables. It has therefore been necessary to develop efficient structures, such as probability trees or value-based potentials, to represent the information. Even so, there are problems that must be treated using approximation since this is the only way that results can be obtained, despite the corresponding loss of information. The aim of this article is to show how the approximation can be performed with value-based potentials. Our experimental work is based on checking the behavior of this approximation technique on several Bayesian networks related to medical problems, and our experiments show that in some cases there are notable savings in memory space with limited information loss. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

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15 pages, 2250 KiB

Open AccessArticle

Predicting High-Risk Students Using Learning Behavior

by Tieyuan Liu, Chang Wang, Liang Chang and Tianlong Gu

Mathematics 2022, 10(14), 2483; https://doi.org/10.3390/math10142483 - 16 Jul 2022

Cited by 6 | Viewed by 1733

Abstract

Over the past few years, the growing popularity of online education has enabled there to be a large amount of students’ learning behavior data stored, which brings great opportunities and challenges to the field of educational data mining. Students’ learning performance can be [...] Read more.

Over the past few years, the growing popularity of online education has enabled there to be a large amount of students’ learning behavior data stored, which brings great opportunities and challenges to the field of educational data mining. Students’ learning performance can be predicted, based on students’ learning behavior data, so as to identify at-risk students who need timely help to complete their studies and improve students’ learning performance and online teaching quality. In order to make full use of these learning behavior data, a new prediction method was designed based on existing research. This method constructs a hybrid deep learning model, which can simultaneously obtain the temporal behavior information and the overall behavior information from the learning behavior data, so that it can more accurately predict the high-risk students. When compared with existing deep learning methods, the experimental results show that the proposed method offers better predicting performance. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

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25 pages, 4314 KiB

Open AccessArticle

Segmenting Retinal Vessels Using a Shallow Segmentation Network to Aid Ophthalmic Analysis

by Muhammad Arsalan, Adnan Haider, Ja Hyung Koo and Kang Ryoung Park

Mathematics 2022, 10(9), 1536; https://doi.org/10.3390/math10091536 - 03 May 2022

Cited by 8 | Viewed by 1758

Abstract

Retinal blood vessels possess a complex structure in the retina and are considered an important biomarker for several retinal diseases. Ophthalmic diseases result in specific changes in the retinal vasculature; for example, diabetic retinopathy causes the retinal vessels to swell, and depending upon [...] Read more.

Retinal blood vessels possess a complex structure in the retina and are considered an important biomarker for several retinal diseases. Ophthalmic diseases result in specific changes in the retinal vasculature; for example, diabetic retinopathy causes the retinal vessels to swell, and depending upon disease severity, fluid or blood can leak. Similarly, hypertensive retinopathy causes a change in the retinal vasculature due to the thinning of these vessels. Central retinal vein occlusion (CRVO) is a phenomenon in which the main vein causes drainage of the blood from the retina and this main vein can close completely or partially with symptoms of blurred vision and similar eye problems. Considering the importance of the retinal vasculature as an ophthalmic disease biomarker, ophthalmologists manually analyze retinal vascular changes. Manual analysis is a tedious task that requires constant observation to detect changes. The deep learning-based methods can ease the problem by learning from the annotations provided by an expert ophthalmologist. However, current deep learning-based methods are relatively inaccurate, computationally expensive, complex, and require image preprocessing for final detection. Moreover, existing methods are unable to provide a better true positive rate (sensitivity), which shows that the model can predict most of the vessel pixels. Therefore, this study presents the so-called vessel segmentation ultra-lite network (VSUL-Net) to accurately extract the retinal vasculature from the background. The proposed VSUL-Net comprises only 0.37 million trainable parameters and uses an original image as input without preprocessing. The VSUL-Net uses a retention block that specifically maintains the larger feature map size and low-level spatial information transfer. This retention block results in better sensitivity of the proposed VSUL-Net without using expensive preprocessing schemes. The proposed method was tested on three publicly available datasets: digital retinal images for vessel extraction (DRIVE), structured analysis of retina (STARE), and children’s heart health study in England database (CHASE-DB1) for retinal vasculature segmentation. The experimental results demonstrated that VSUL-Net provides robust segmentation of retinal vasculature with sensitivity (Sen), specificity (Spe), accuracy (Acc), and area under the curve (AUC) values of 83.80%, 98.21%, 96.95%, and 98.54%, respectively, for DRIVE, 81.73%, 98.35%, 97.17%, and 98.69%, respectively, for CHASE-DB1, and 86.64%, 98.13%, 97.27%, and 99.01%, respectively, for STARE datasets. The proposed method provides an accurate segmentation mask for deep ophthalmic analysis. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

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21 pages, 2165 KiB

Open AccessArticle

A Framework to Assist in Didactic Planning at Undergraduate Level

by Daniel Alfredo Hernández-Carrasco, César Enrique Rose-Gómez, Samuel González-López, Aurelio López-López, Jesús Miguel García-Gorrostieta and Gilberto Borrego

Mathematics 2022, 10(9), 1355; https://doi.org/10.3390/math10091355 - 19 Apr 2022

Cited by 1 | Viewed by 2026

Abstract

In the teaching-learning process under the competency-based educational model, the instructor is a facilitator and seeks to generate a flexible and adaptable environment for student learning. One of the first tasks of the facilitator is the structuring of didactic planning. Didactic planning includes [...] Read more.

In the teaching-learning process under the competency-based educational model, the instructor is a facilitator and seeks to generate a flexible and adaptable environment for student learning. One of the first tasks of the facilitator is the structuring of didactic planning. Didactic planning includes strategies for teaching and learning, evidence gathering, and choice of evaluation instruments. In this paper, we propose a framework based on natural language processing techniques with the support of an ontology grounded in the experience of instructors and university level course plans in the information systems area. We employ Bloom’s taxonomy in the ontology design, producing an ascending structure for didactic planning, which allows the student to learn gradually. The developed framework can analyze the key elements that a didactic plan must contain and identify inter-related areas. Evaluation results with Cohen’s kappa coefficient between expert judgement and our framework show that is possible to assist instructors in structuring their didactic planning. Out of the nine processes analyzed with the framework, an almost perfect kappa level was achieved in five processes, a substantial level in three processes, and a moderate level for one process. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

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16 pages, 2654 KiB

Open AccessArticle

Optimal Experimental Design for Parametric Identification of the Electrical Behaviour of Bioelectrodes and Biological Tissues

by Àngela Sebastià Bargues, José-Luis Polo Sanz and Raúl Martín Martín

Mathematics 2022, 10(5), 837; https://doi.org/10.3390/math10050837 - 06 Mar 2022

Cited by 4 | Viewed by 2151

Abstract

The electrical behaviour of a system, such as an electrode–tissue interface (ETI) or a biological tissue, can be used for its characterization. One way of accomplishing this goal consists of measuring the electrical impedance, that is, the opposition that a system exhibits to [...] Read more.

The electrical behaviour of a system, such as an electrode–tissue interface (ETI) or a biological tissue, can be used for its characterization. One way of accomplishing this goal consists of measuring the electrical impedance, that is, the opposition that a system exhibits to an alternating current flow as a function of frequency. Subsequently, experimental impedance data are fitted to an electrical equivalent circuit (EEC model) whose parameters can be correlated with the electrode processes occurring in the ETI or with the physiological state of a tissue. The EEC used in this paper is a reasonable approach for simple bio-electrodes or cell membranes, assuming ideal capacitances. We use the theory of optimal experimental design to identify the frequencies in which the impedance is measured, as well as the number of measurement repetitions, in such a way that the EEC parameters can be optimally estimated. Specifically, we calculate approximate and exact D-optimal designs by optimizing the determinant of the information matrix by adapting two of the most algorithms that are routinely used nowadays (REX random exchange algorithm and KL exchange algorithm). The D-efficiency of the optimal designs provided by the algorithms was compared with the design commonly used by experimenters and it is shown that the precision of the parameter estimates can be increased. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

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16 pages, 525 KiB

Open AccessArticle

Predictive Ability of Machine-Learning Methods for Vitamin D Deficiency Prediction by Anthropometric Parameters

by Carmen Patino-Alonso, Marta Gómez-Sánchez, Leticia Gómez-Sánchez, Benigna Sánchez Salgado, Emiliano Rodríguez-Sánchez, Luis García-Ortiz and Manuel A. Gómez-Marcos

Mathematics 2022, 10(4), 616; https://doi.org/10.3390/math10040616 - 17 Feb 2022

Cited by 5 | Viewed by 2344

Abstract

Background: Vitamin D deficiency affects the general population and is very common among elderly Europeans. This study compared different supervised learning algorithms in a cohort of Spanish individuals aged 35–75 years to predict which anthropometric parameter was most strongly associated with vitamin D [...] Read more.

Background: Vitamin D deficiency affects the general population and is very common among elderly Europeans. This study compared different supervised learning algorithms in a cohort of Spanish individuals aged 35–75 years to predict which anthropometric parameter was most strongly associated with vitamin D deficiency. Methods: A total of 501 participants were recruited by simple random sampling with replacement (reference population: 43,946). The analyzed anthropometric parameters were waist circumference (WC), body mass index (BMI), waist-to-height ratio (WHtR), body roundness index (BRI), visceral adiposity index (VAI), and the Clinical University of Navarra body adiposity estimator (CUN-BAE) for body fat percentage. Results: All the anthropometric indices were associated, in males, with vitamin D deficiency (p < 0.01 for the entire sample) after controlling for possible confounding factors, except for CUN-BAE, which was the only parameter that showed a correlation in females. Conclusions: The capacity of anthropometric parameters to predict vitamin D deficiency differed according to sex; thus, WC, BMI, WHtR, VAI, and BRI were most useful for prediction in males, while CUN-BAE was more useful in females. The naïve Bayes approach for machine learning showed the best area under the curve with WC, BMI, WHtR, and BRI, while the logistic regression model did so in VAI and CUN-BAE. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

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12 pages, 243 KiB

Open AccessArticle

Quantile Regression Analysis between the After-School Exercise and the Academic Performance of Korean Middle School Students

by Kyulee Shin and Sukkyung You

Mathematics 2022, 10(1), 58; https://doi.org/10.3390/math10010058 - 24 Dec 2021

Cited by 3 | Viewed by 2005

Abstract

This study deepens our understanding of the prediction and structural relationship between a student’s academic performance and his/her regular after-school exercise by estimating models based upon the quantile regression and the instrumental variable quantile regression methods, respectively. Using data on Korean middle school [...] Read more.

This study deepens our understanding of the prediction and structural relationship between a student’s academic performance and his/her regular after-school exercise by estimating models based upon the quantile regression and the instrumental variable quantile regression methods, respectively. Using data on Korean middle school students, we found that negative relationships were dominant for the prediction models, whereas the relationships were reversed for the structural models, affirming the theoretical and experimental hypotheses observed in prior literature. Furthermore, we also found that the low-performing students, in terms of the academic performance, had stronger associations between the two variables than the high-performing students, overall. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

14 pages, 410 KiB

Open AccessArticle

A Method to Automate the Prediction of Student Academic Performance from Early Stages of the Course

by Alicia Nieto-Reyes, Rafael Duque and Giacomo Francisci

Mathematics 2021, 9(21), 2677; https://doi.org/10.3390/math9212677 - 22 Oct 2021

Cited by 6 | Viewed by 1349

Abstract

The objective of this work is to present a methodology that automates the prediction of students’ academic performance at the end of the course using data recorded in the first tasks of the academic year. Analyzing early student records is helpful in predicting [...] Read more.

The objective of this work is to present a methodology that automates the prediction of students’ academic performance at the end of the course using data recorded in the first tasks of the academic year. Analyzing early student records is helpful in predicting their later results; which is useful, for instance, for an early intervention. With this aim, we propose a methodology based on the random Tukey depth and a non-parametric kernel. This methodology allows teachers and evaluators to define the variables that they consider most appropriate to measure those aspects related to the academic performance of students. The methodology is applied to a real case study obtaining a success rate in the predictions of over the 80%. The case study was carried out in the field of Human-computer Interaction.The results indicate that the methodology could be of special interest to develop software systems that process the data generated by computer-supported learning systems and to warn the teacher of the need to adopt intervention mechanisms when low academic performance is predicted. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

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23 pages, 1510 KiB

Open AccessArticle

Automatic Group Organization for Collaborative Learning Applying Genetic Algorithm Techniques and the Big Five Model

by Oscar Revelo Sánchez, César A. Collazos and Miguel A. Redondo

Mathematics 2021, 9(13), 1578; https://doi.org/10.3390/math9131578 - 05 Jul 2021

Cited by 4 | Viewed by 2233

Abstract

In this paper, an approach based on genetic algorithms is proposed to form groups in collaborative learning scenarios, considering the students’ personality traits as a criterion for grouping. This formation is carried out in two stages: In the first, the information of the [...] Read more.

In this paper, an approach based on genetic algorithms is proposed to form groups in collaborative learning scenarios, considering the students’ personality traits as a criterion for grouping. This formation is carried out in two stages: In the first, the information of the students is collected from a psychometric instrument based on the Big Five personality model; whereas, in the second, this information feeds a genetic algorithm that is in charge of performing the grouping iteratively, seeking for an optimal formation. The results presented here correspond to the functional and empirical validation of the approach. It is found that the described methodology is useful to obtain groups with the desired characteristics. The specific objective is to provide a strategy that makes it possible to subsequently assess in the context what type of approach (homogeneous, heterogeneous, or mixed) is the most appropriate to organize the groups. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

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24 pages, 3686 KiB

Open AccessArticle

A Fuzzy Recommendation System for the Automatic Personalization of Physical Rehabilitation Exercises in Stroke Patients

by Cristian Gmez-Portes, José Jesús Castro-Schez, Javier Albusac, Dorothy N. Monekosso and David Vallejo

Mathematics 2021, 9(12), 1427; https://doi.org/10.3390/math9121427 - 19 Jun 2021

Cited by 3 | Viewed by 2082

Abstract

Stroke is among the top 10 leading causes of death and disability around the world. Patients who suffer from this disease usually perform physical exercises at home to improve their condition. These exercises are recommended by therapists based on the patient’s progress level, [...] Read more.

Stroke is among the top 10 leading causes of death and disability around the world. Patients who suffer from this disease usually perform physical exercises at home to improve their condition. These exercises are recommended by therapists based on the patient’s progress level, and may be remotely supervised by them if technology is an option for both. At this point, two major challenges must be faced. The first one is the lack of specialized medical staff to remotely handle the growing number of stroke patients. The second one is the difficulty of dynamically adapt the patient’s therapy plan in real time whilst they rehabilitate at home, since their evolution varies as the rehabilitation process progresses. In this context, we present a fuzzy system that is able to automatically adapt the rehabilitation plan of stroke patients. The use of fuzzy logic greatly facilitates the monitoring and guidance of stroke patients. Moreover, the system is capable of automatically generating modifications of existent exercises whilst considering their particularities at any given time. A preliminary experiment was conducted to show the advantages of the proposal, and the results suggest that the application of fuzzy logic may help make correct decisions based on the patient’s progress level. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

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21 pages, 1622 KiB

Open AccessArticle

Discovery Model Based on Analogies for Teaching Computer Programming

by Javier Alejandro Jiménez Toledo, César A. Collazos and Manuel Ortega

Mathematics 2021, 9(12), 1354; https://doi.org/10.3390/math9121354 - 11 Jun 2021

Cited by 7 | Viewed by 2587

Abstract

Teaching the fundamentals of computer programming in a first course (CS1) is a complex activity for the professor and is also a challenge for them. Nowadays, there are several teaching strategies for dealing with a CS1 at the university, one of which is [...] Read more.

Teaching the fundamentals of computer programming in a first course (CS1) is a complex activity for the professor and is also a challenge for them. Nowadays, there are several teaching strategies for dealing with a CS1 at the university, one of which is the use of analogies to support the abstraction process that a student needs to carry for the appropriation of fundamental concepts. This article presents the results of applying a discovery model that allowed for the extraction of patterns, linguistic analysis, textual analytics, and linked data when using analogies for teaching the fundamental concepts of programming by professors in a CS1 in university programs that train software developers. For that reason, a discovery model based on machine learning and text mining was proposed using natural language processing techniques for semantic vector space modeling, distributional semantics, and the generation of synthetic data. The discovery process was carried out using nine supervised learning methods, three unsupervised learning methods, and one semi-supervised learning method involving linguistic analysis techniques, text analytics, and linked data. The main findings showed that professors include keywords, which are part of the technical computer terminology, in the form of verbs in the statement of the analogy and combine them in quantitative contexts with neutral or positive phrases, where numerical examples, cooking recipes, and games were the most used categories. Finally, a structure is proposed for the construction of analogies to teach programming concepts and this was validated by the professors and students. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

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15 pages, 488 KiB

Open AccessArticle

Effect of Probability Distribution of the Response Variable in Optimal Experimental Design with Applications in Medicine

by Sergio Pozuelo-Campos, Víctor Casero-Alonso and Mariano Amo-Salas

Mathematics 2021, 9(9), 1010; https://doi.org/10.3390/math9091010 - 29 Apr 2021

Cited by 4 | Viewed by 1690

Abstract

In optimal experimental design theory it is usually assumed that the response variable follows a normal distribution with constant variance. However, some works assume other probability distributions based on additional information or practitioner’s prior experience. The main goal of this paper is to [...] Read more.

In optimal experimental design theory it is usually assumed that the response variable follows a normal distribution with constant variance. However, some works assume other probability distributions based on additional information or practitioner’s prior experience. The main goal of this paper is to study the effect, in terms of efficiency, when misspecification in the probability distribution of the response variable occurs. The elemental information matrix, which includes information on the probability distribution of the response variable, provides a generalized Fisher information matrix. This study is performed from a practical perspective, comparing a normal distribution with the Poisson or gamma distribution. First, analytical results are obtained, including results for the linear quadratic model, and these are applied to some real illustrative examples. The nonlinear 4-parameter Hill model is next considered to study the influence of misspecification in a dose-response model. This analysis shows the behavior of the efficiency of the designs obtained in the presence of misspecification, by assuming heteroscedastic normal distributions with respect to the D-optimal designs for the gamma, or Poisson, distribution, as the true one. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

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17 pages, 10507 KiB

Open AccessArticle

Functional Symmetry and Statistical Depth for the Analysis of Movement Patterns in Alzheimer’s Patients

by Alicia Nieto-Reyes, Heather Battey and Giacomo Francisci

Mathematics 2021, 9(8), 820; https://doi.org/10.3390/math9080820 - 09 Apr 2021

Cited by 8 | Viewed by 1700

Abstract

Black-box techniques have been applied with outstanding results to classify, in a supervised manner, the movement patterns of Alzheimer’s patients according to their stage of the disease. However, these techniques do not provide information on the difference of the patterns among the stages. [...] Read more.

Black-box techniques have been applied with outstanding results to classify, in a supervised manner, the movement patterns of Alzheimer’s patients according to their stage of the disease. However, these techniques do not provide information on the difference of the patterns among the stages. We make use of functional data analysis to provide insight on the nature of these differences. In particular, we calculate the center of symmetry of the underlying distribution at each stage and use it to compute the functional depth of the movements of each patient. This results in an ordering of the data to which we apply nonparametric permutation tests to check on the differences in the distribution, median and deviance from the median. We consistently obtain that the movement pattern at each stage is significantly different to that of the prior and posterior stage in terms of the deviance from the median applied to the depth. The approach is validated by simulation. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education)

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