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Advanced Techniques in the Analysis and Prediction of Students' Behaviour...
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Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts

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 May 2020) | Viewed by 115296

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Juan A. Gómez-Pulido

E-Mail Website
Guest Editor
Department of Technologies of Computers and Communications, University of Extremadura, 10003 Cáceres, Spain
Interests: optimization and computational intelligence; machine learning; reconfigurable computing and FPGAs; wireless communications; bioinformatics
Special Issues, Collections and Topics 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, Collections and Topics in MDPI journals
Prof. Dr. Ricardo Soto

E-Mail Website
Guest Editor
School of Computer Engineering, Pontificia Universidad Católica de Valparaíso, Valparaiso, Chile
Interests: discrete and continuous optimization; metaheuristics; machine learning; artificial intelligence; decision system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Analysing and predicting individuals' behaviour are important topics in academic environments, especially after the increasing development and deployment of software tools for supporting learning stages. The automation of many processes involved in the usual students' activity allows for processing massive volumes of data collected from teaching-enhanced learning (TEL) platforms, leading to useful applications for academic personnel. In this way, monitoring and analysing students' behaviour are key activities required for the improvement of students' learning. Recommendations of activities, dropout prediction, performance and knowledge analysis, and resources optimization, among other students-centred interests, are complex tasks that involve many elements that need to be considered. Therefore, it becomes necessary that these efforts search for support from other fields in the 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 intelligent systems, 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 the analysis and prediction of the students' behaviour in academic contexts.

This Special Issue provides a collection of papers of original advances in the analysis, prediction, and recommendation of applications propelled by artificial intelligence, 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
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

  • Teaching-enhanced learning and teaching
  • Personalized learning
  • Intelligent tutoring Systems
  • Data mining and big data analysis
  • Intelligent systems
  • Machine and deep learning
  • Recommender systems
  • Collaborative filtering
  • Software tools
  • Performance prediction
  • Knowledge analysis
  • Optimization

Published Papers (18 papers)

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Editorial

Jump to: Research, Review

6 pages, 175 KiB  
Editorial
Advanced Techniques in the Analysis and Prediction of Students’ Behaviour in Technology-Enhanced Learning Contexts
by Juan A. Gómez-Pulido, Young Park and Ricardo Soto
Appl. Sci. 2020, 10(18), 6178; https://doi.org/10.3390/app10186178 - 05 Sep 2020
Cited by 3 | Viewed by 1899
Abstract
The development and promotion of teaching-enhanced learning tools in the academic field is leading to the collection of a large amount of data generated from the usual activity of students and teachers. The analysis of these data is an opportunity to improve many [...] Read more.
The development and promotion of teaching-enhanced learning tools in the academic field is leading to the collection of a large amount of data generated from the usual activity of students and teachers. The analysis of these data is an opportunity to improve many aspects of the learning process: recommendations of activities, dropout prediction, performance and knowledge analysis, resources optimization, etc. However, these improvements would not be possible without the application of computer science techniques that have demonstrated a high effectiveness for this purpose: data mining, big data, machine learning, deep learning, collaborative filtering, and recommender systems, among other fields related to intelligent systems. This Special Issue provides 17 papers that show advances in the analysis, prediction, and recommendation of applications propelled by artificial intelligence, big data, and machine learning in the teaching-enhanced learning context. Full article
(This article belongs to the Special Issue Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts)

Research

Jump to: Editorial, Review

34 pages, 1045 KiB  
Article
A Multi-Analytical Approach to Predict the Determinants of Cloud Computing Adoption in Higher Education Institutions
by Yousef A. M. Qasem, Shahla Asadi, Rusli Abdullah, Yusmadi Yah, Rodziah Atan, Mohammed A. Al-Sharafi and Amr Abdullatif Yassin
Appl. Sci. 2020, 10(14), 4905; https://doi.org/10.3390/app10144905 - 17 Jul 2020
Cited by 42 | Viewed by 6413
Abstract
Cloud computing (CC) delivers services for organizations, particularly for higher education institutions (HEIs) anywhere and anytime, based on scalability and pay-per-use approach. Examining the factors influencing the decision-makers’ intention towards adopting CC plays an essential role in HEIs. Therefore, this study aimed to [...] Read more.
Cloud computing (CC) delivers services for organizations, particularly for higher education institutions (HEIs) anywhere and anytime, based on scalability and pay-per-use approach. Examining the factors influencing the decision-makers’ intention towards adopting CC plays an essential role in HEIs. Therefore, this study aimed to understand and predict the key determinants that drive managerial decision-makers’ perspectives for adopting this technology. The data were gathered from 134 institutional managers, involved in the decision making of the institutions. This study applied two analytical approaches, namely variance-based structural equation modeling (i.e., PLS-SEM) and artificial neural network (ANN). First, the PLS-SEM approach has been used for analyzing the proposed model and extracting the significant relationships among the identified factors. The obtained result from PLS-SEM analysis revealed that seven factors were identified as significant in influencing decision-makers’ intention towards adopting CC. Second, the normalized importance among those seven significant predictors was ranked utilizing the ANN. The results of the ANN approach showed that technology readiness is the most important predictor for CC adoption, followed by security and competitive pressure. Finally, this study presented a new and innovative approach for comprehending CC adoption, and the results can be used by decision-makers to develop strategies for adopting CC services in their institutions. Full article
(This article belongs to the Special Issue Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts)
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25 pages, 3688 KiB  
Article
Automated Assessment and Microlearning Units as Predictors of At-Risk Students and Students’ Outcomes in the Introductory Programming Courses
by Jan Skalka and Martin Drlik
Appl. Sci. 2020, 10(13), 4566; https://doi.org/10.3390/app10134566 - 30 Jun 2020
Cited by 23 | Viewed by 3479
Abstract
The number of students who decided to study information technology related study programs is continually increasing. Introductory programming courses represent the most crucial milestone in information technology education and often reflect students’ ability to think abstractly and systematically, solve problems, and design their [...] Read more.
The number of students who decided to study information technology related study programs is continually increasing. Introductory programming courses represent the most crucial milestone in information technology education and often reflect students’ ability to think abstractly and systematically, solve problems, and design their solutions. Even though many students who attend universities have already completed some introductory courses of programming, there is still a large group of students with limited programming skills. This drawback often increases during the first term, and it is often the main reason why students leave study too early. There is a myriad of technologies and tools which can be involved in the programming course to increase students’ chances of mastering programming. The introductory programming courses used in this study has been gradually extended over the four academic years with the automated source code assessment of students’ programming assignments followed by the implementation of a set of suitably designed microlearning units. The final four datasets were analysed to confirm the suitability of automated assessment and microlearning units as predictors of at-risk students and students’ outcomes in the introductory programming courses. The research results proved the significant contribution of automated code assessment in students’ learning outcomes in the elementary topics of learning programming. Simultaneously, it proved a moderate to strong dependence between the students’ activity and achievement in the activities and final students’ outcomes. Full article
(This article belongs to the Special Issue Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts)
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28 pages, 2278 KiB  
Article
An Early Warning System to Detect At-Risk Students in Online Higher Education
by David Bañeres, M. Elena Rodríguez, Ana Elena Guerrero-Roldán and Abdulkadir Karadeniz
Appl. Sci. 2020, 10(13), 4427; https://doi.org/10.3390/app10134427 - 27 Jun 2020
Cited by 51 | Viewed by 7340
Abstract
Artificial intelligence has impacted education in recent years. Datafication of education has allowed developing automated methods to detect patterns in extensive collections of educational data to estimate unknown information and behavior about the students. This research has focused on finding accurate predictive models [...] Read more.
Artificial intelligence has impacted education in recent years. Datafication of education has allowed developing automated methods to detect patterns in extensive collections of educational data to estimate unknown information and behavior about the students. This research has focused on finding accurate predictive models to identify at-risk students. This challenge may reduce the students’ risk of failure or disengage by decreasing the time lag between identification and the real at-risk state. The contribution of this paper is threefold. First, an in-depth analysis of a predictive model to detect at-risk students is performed. This model has been tested using data available in an institutional data mart where curated data from six semesters are available, and a method to obtain the best classifier and training set is proposed. Second, a method to determine a threshold for evaluating the quality of the predictive model is established. Third, an early warning system has been developed and tested in a real educational setting being accurate and useful for its purpose to detect at-risk students in online higher education. The stakeholders (i.e., students and teachers) can analyze the information through different dashboards, and teachers can also send early feedback as an intervention mechanism to mitigate at-risk situations. The system has been evaluated on two undergraduate courses where results shown a high accuracy to correctly detect at-risk students. Full article
(This article belongs to the Special Issue Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts)
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20 pages, 714 KiB  
Article
A Learning Analytics Approach to Identify Students at Risk of Dropout: A Case Study with a Technical Distance Education Course
by Emanuel Marques Queiroga, João Ladislau Lopes, Kristofer Kappel, Marilton Aguiar, Ricardo Matsumura Araújo, Roberto Munoz, Rodolfo Villarroel and Cristian Cechinel
Appl. Sci. 2020, 10(11), 3998; https://doi.org/10.3390/app10113998 - 09 Jun 2020
Cited by 40 | Viewed by 5416
Abstract
Contemporary education is a vast field that is concerned with the performance of education systems. In a formal e-learning context, student dropout is considered one of the main problems and has received much attention from the learning analytics research community, which has reported [...] Read more.
Contemporary education is a vast field that is concerned with the performance of education systems. In a formal e-learning context, student dropout is considered one of the main problems and has received much attention from the learning analytics research community, which has reported several approaches to the development of models for the early prediction of at-risk students. However, maximizing the results obtained by predictions is a considerable challenge. In this work, we developed a solution using only students’ interactions with the virtual learning environment and its derivative features for early predict at-risk students in a Brazilian distance technical high school course that is 103 weeks in duration. To maximize results, we developed an elitist genetic algorithm based on Darwin’s theory of natural selection for hyperparameter tuning. With the application of the proposed technique, we predicted the student at risk with an Area Under the Receiver Operating Characteristic Curve (AUROC) above 0.75 in the initial weeks of a course. The results demonstrate the viability of applying interaction count and derivative features to generate prediction models in contexts where access to demographic data is restricted. The application of a genetic algorithm to the tuning of hyperparameters classifiers can increase their performance in comparison with other techniques. Full article
(This article belongs to the Special Issue Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts)
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20 pages, 2378 KiB  
Article
Predicting Student Performance in Higher Educational Institutions Using Video Learning Analytics and Data Mining Techniques
by Raza Hasan, Sellappan Palaniappan, Salman Mahmood, Ali Abbas, Kamal Uddin Sarker and Mian Usman Sattar
Appl. Sci. 2020, 10(11), 3894; https://doi.org/10.3390/app10113894 - 04 Jun 2020
Cited by 101 | Viewed by 11224
Abstract
Technology and innovation empower higher educational institutions (HEI) to use different types of learning systems—video learning is one such system. Analyzing the footprints left behind from these online interactions is useful for understanding the effectiveness of this kind of learning. Video-based learning with [...] Read more.
Technology and innovation empower higher educational institutions (HEI) to use different types of learning systems—video learning is one such system. Analyzing the footprints left behind from these online interactions is useful for understanding the effectiveness of this kind of learning. Video-based learning with flipped teaching can help improve student’s academic performance. This study was carried out with 772 examples of students registered in e-commerce and e-commerce technologies modules at an HEI. The study aimed to predict student’s overall performance at the end of the semester using video learning analytics and data mining techniques. Data from the student information system, learning management system and mobile applications were analyzed using eight different classification algorithms. Furthermore, data transformation and preprocessing techniques were carried out to reduce the features. Moreover, genetic search and principle component analysis were carried out to further reduce the features. Additionally, the CN2 Rule Inducer and multivariate projection can be used to assist faculty in interpreting the rules to gain insights into student interactions. The results showed that Random Forest accurately predicted successful students at the end of the class with an accuracy of 88.3% with an equal width and information gain ratio. Full article
(This article belongs to the Special Issue Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts)
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15 pages, 1005 KiB  
Article
Predicting Students’ Behavioral Intention to Use Open Source Software: A Combined View of the Technology Acceptance Model and Self-Determination Theory
by F. José Racero, Salvador Bueno and M. Dolores Gallego
Appl. Sci. 2020, 10(8), 2711; https://doi.org/10.3390/app10082711 - 14 Apr 2020
Cited by 45 | Viewed by 6346
Abstract
This study focuses on students’ behavioral intention to use Open Source Software (OSS). The article examines how students, who were trained in OSS, are motivated to continue using it. A conceptual model based on Self-Determination Theory and the Technological Acceptance Model (TAM) was [...] Read more.
This study focuses on students’ behavioral intention to use Open Source Software (OSS). The article examines how students, who were trained in OSS, are motivated to continue using it. A conceptual model based on Self-Determination Theory and the Technological Acceptance Model (TAM) was defined in order to test the behavioral intention to use OSS, comprising six constructs: (1) autonomy, (2) competence, (3) relatedness, (4) perceived ease of use, (5) perceived usefulness and (6) behavioral intention to use. A survey was designed for data collection. The participants were recent secondary school graduates, and all of them had received mandatory OSS training. A total of 352 valid responses were used to test the proposed structural model, which was performed using the Lisrel software. The results clearly confirmed the positive influence of the intrinsic motivations; autonomy and relatedness, to improve perceptions regarding the usefulness and ease of use of OSS, and; therefore, on behavioral intention to use OSS. In addition, the implications and limitations of this study are considered. Full article
(This article belongs to the Special Issue Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts)
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19 pages, 1649 KiB  
Article
Prediction of High Capabilities in the Development of Kindergarten Children
by Yenny Villuendas-Rey, Carmen F. Rey-Benguría, Oscar Camacho-Nieto and Cornelio Yáñez-Márquez
Appl. Sci. 2020, 10(8), 2710; https://doi.org/10.3390/app10082710 - 14 Apr 2020
Cited by 3 | Viewed by 1677
Abstract
Analysis and prediction of children’s behavior in kindergarten is a current need of the Cuban educational system. Despite such an early age, the kindergarten institutions are devoted to facilitate the integral children development. However, the early detection of high capabilities in a child [...] Read more.
Analysis and prediction of children’s behavior in kindergarten is a current need of the Cuban educational system. Despite such an early age, the kindergarten institutions are devoted to facilitate the integral children development. However, the early detection of high capabilities in a child is not always accomplished accurately; due to teachers being mostly focused on the performance of the children that are lagging behind to achieve their age range’s stated goals. In addition, the amount of children with high capabilities is usually low, which makes the prediction an imbalanced data problem. Thus, such children tend to be misguided and overlaid, with a negative impact in their sociological development. The purpose of this research is to propose an efficient algorithm that enhances the prediction in the kindergarten children data. We obtain a useful set of instances and features, thus improving the Nearest Neighbor accuracy according to the Area under the Receiving Operating Characteristic curve measure. The obtained results are of great interest for Cuban educational system, regarding the rapidly and precise prediction of the presence or absence of high capabilities for integral personality development in kindergarten children. Full article
(This article belongs to the Special Issue Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts)
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18 pages, 495 KiB  
Article
Technology-Enhanced Learning for Graduate Students: Exploring the Correlation of Media Richness and Creativity of Computer-Mediated Communication and Face-to-Face Communication
by Shan-Hui Chao, Jinzhang Jiang, Chia-Hsuan Hsu, Yi-Te Chiang, Eric Ng and Wei-Ta Fang
Appl. Sci. 2020, 10(5), 1602; https://doi.org/10.3390/app10051602 - 28 Feb 2020
Cited by 7 | Viewed by 4922
Abstract
The objective of the research was to explore and compare the differences in potential creative thinking that media richness had on learners in creativity training through two different types of communication formats; computer-mediated communication, and face-to-face communication. The results indicated that the computer-mediated [...] Read more.
The objective of the research was to explore and compare the differences in potential creative thinking that media richness had on learners in creativity training through two different types of communication formats; computer-mediated communication, and face-to-face communication. The results indicated that the computer-mediated communication format performed better than the face-to-face in terms of the fluency, flexibility, and originality dimensions of creative thinking. The computer-mediated communication format also had a greater level of media richness perception (i.e., use of multiple cues, language diversity, and personal focus of the medium) than the face-to-face format. In terms of the combined effectiveness of computer-mediated communication, and face-to-face formats, the use of multiple cues, language variety of perception of media richness had direct effects on the fluency of creativity. There was also a positive correlation between the elaboration of creativity and the use of multiple cues, language variety, and personal focus of the medium in the perception of media richness. Furthermore, language variety was correlated with creativity and flexibility. The research findings highlighted the importance of the availability of immediate feedback on media richness, whereas creativity cognition should focus on the breadth and depth of the information, which contributes to enhancing the creativity of individuals or a group of employees. Full article
(This article belongs to the Special Issue Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts)
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17 pages, 1515 KiB  
Article
The Relationship between the Facial Expression of People in University Campus and Host-City Variables
by Hongxu Wei, Richard J. Hauer and Xuquan Zhai
Appl. Sci. 2020, 10(4), 1474; https://doi.org/10.3390/app10041474 - 21 Feb 2020
Cited by 24 | Viewed by 2287
Abstract
Public attitudes towards local university matters for the resource investment to sustainable science and technology. The application of machine learning techniques enables the evaluation of resource investments more precisely even at the national scale. In this study, a total number of 4327 selfies [...] Read more.
Public attitudes towards local university matters for the resource investment to sustainable science and technology. The application of machine learning techniques enables the evaluation of resource investments more precisely even at the national scale. In this study, a total number of 4327 selfies were collected from the social network services (SNS) platform of Sina Micro-Blog for check-in records of 92 211-Project university campuses from 82 cities of 31 Provinces across mainland China. Photos were analyzed by the FireFACETM-V1.0 software to obtain scores of happy and sad facial expressions and a positive response index (PRI) was calculated (happy-sad). One-way analysis of variance indicated that both happy and PRI scores were highest in Shandong University and lowest in Harbin Engineering University. The national distribution of positive expression scores was highest in Changchun, Jinan, and Guangzhou cities. The maximum likelihood estimates from general linear regression indicated that the city-variable of the number of regular institutions of higher learning had the positive contribution to the happy score. The number of internet accesses and area of residential housing contributed to the negative expression scores. Therefore, people tend to show positive expression at campuses in cities with more education infrastructures but fewer residences and internet users. The geospatial analysis of facial expression data can be one approach to supply theoretical evidence for the resource arrangement of sustainable science and technology from universities. Full article
(This article belongs to the Special Issue Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts)
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23 pages, 1139 KiB  
Article
Towards Portability of Models for Predicting Students’ Final Performance in University Courses Starting from Moodle Logs
by Javier López-Zambrano, Juan A. Lara and Cristóbal Romero
Appl. Sci. 2020, 10(1), 354; https://doi.org/10.3390/app10010354 - 03 Jan 2020
Cited by 35 | Viewed by 4132
Abstract
Predicting students’ academic performance is one of the older challenges faced by the educational scientific community. However, most of the research carried out in this area has focused on obtaining the best accuracy models for their specific single courses and only a few [...] Read more.
Predicting students’ academic performance is one of the older challenges faced by the educational scientific community. However, most of the research carried out in this area has focused on obtaining the best accuracy models for their specific single courses and only a few works have tried to discover under which circumstances a prediction model built on a source course can be used in other different but similar courses. Our motivation in this work is to study the portability of models obtained directly from Moodle logs of 24 university courses. The proposed method intends to check if grouping similar courses by the degree or the similar level of usage of activities provided by the Moodle logs, and if the use of numerical or categorical attributes affect in the portability of the prediction models. We have carried out two experiments by executing the well-known classification algorithm over all the datasets of the courses in order to obtain decision tree models and to test their portability to the other courses by comparing the obtained accuracy and loss of accuracy evaluation measures. The results obtained show that it is only feasible to directly transfer predictive models or apply them to different courses with an acceptable accuracy and without losing portability under some circumstances. Full article
(This article belongs to the Special Issue Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts)
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27 pages, 2919 KiB  
Article
Implementing AutoML in Educational Data Mining for Prediction Tasks
by Maria Tsiakmaki, Georgios Kostopoulos, Sotiris Kotsiantis and Omiros Ragos
Appl. Sci. 2020, 10(1), 90; https://doi.org/10.3390/app10010090 - 20 Dec 2019
Cited by 62 | Viewed by 7058
Abstract
Educational Data Mining (EDM) has emerged over the last two decades, concerning with the development and implementation of data mining methods in order to facilitate the analysis of vast amounts of data originating from a wide variety of educational contexts. Predicting students’ progression [...] Read more.
Educational Data Mining (EDM) has emerged over the last two decades, concerning with the development and implementation of data mining methods in order to facilitate the analysis of vast amounts of data originating from a wide variety of educational contexts. Predicting students’ progression and learning outcomes, such as dropout, performance and course grades, is regarded among the most important tasks of the EDM field. Therefore, applying appropriate machine learning algorithms for building accurate predictive models is of outmost importance for both educators and data scientists. Considering the high-dimensional input space and the complexity of machine learning algorithms, the process of building accurate and robust learning models requires advanced data science skills, while is time-consuming and error-prone in most cases. In addition, choosing the proper method for a given problem formulation and configuring the optimal parameters’ values for a specific model is a demanding task, whilst it is often very difficult to understand and explain the produced results. In this context, the main purpose of the present study is to examine the potential use of advanced machine learning strategies on educational settings from the perspective of hyperparameter optimization. More specifically, we investigate the effectiveness of automated Machine Learning (autoML) for the task of predicting students’ learning outcomes based on their participation in online learning platforms. At the same time, we limit the search space to tree-based and rule-based models in order to achieving transparent and interpretable results. To this end, a plethora of experiments were carried out, revealing that autoML tools achieve consistently superior results. Hopefully our work will help nonexpert users (e.g., educators and instructors) in the field of EDM to conduct experiments with appropriate automated parameter configurations, thus achieving highly accurate and comprehensible results. Full article
(This article belongs to the Special Issue Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts)
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23 pages, 724 KiB  
Article
Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems
by Luiz Antonio Buschetto Macarini, Cristian Cechinel, Matheus Francisco Batista Machado, Vinicius Faria Culmant Ramos and Roberto Munoz
Appl. Sci. 2019, 9(24), 5523; https://doi.org/10.3390/app9245523 - 15 Dec 2019
Cited by 45 | Viewed by 4665
Abstract
Algorithms and programming are some of the most challenging topics faced by students during undergraduate programs. Dropout and failure rates in courses involving such topics are usually high, which has raised attention towards the development of strategies to attenuate this situation. Machine learning [...] Read more.
Algorithms and programming are some of the most challenging topics faced by students during undergraduate programs. Dropout and failure rates in courses involving such topics are usually high, which has raised attention towards the development of strategies to attenuate this situation. Machine learning techniques can help in this direction by providing models able to detect at-risk students earlier. Therefore, lecturers, tutors or staff can pedagogically try to mitigate this problem. To early predict at-risk students in introductory programming courses, we present a comparative study aiming to find the best combination of datasets (set of variables) and classification algorithms. The data collected from Moodle was used to generate 13 distinct datasets based on different aspects of student interactions (cognitive presence, social presence and teaching presence) inside the virtual environment. Results show there are no statistically significant difference among models generated from the different datasets and that the counts of interactions together with derived attributes are sufficient for the task. The performances of the models varied for each semester, with the best of them able to detect students at-risk in the first week of the course with AUC ROC from 0.7 to 0.9. Moreover, the use of SMOTE to balance the datasets did not improve the performance of the models. Full article
(This article belongs to the Special Issue Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts)
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17 pages, 2465 KiB  
Article
Short CFD Simulation Activities in the Context of Fluid-Mechanical Learning in a Multidisciplinary Student Body
by Manuel Rodríguez-Martín, Pablo Rodríguez-Gonzálvez, Alberto Sánchez-Patrocinio and Javier Ramón Sánchez
Appl. Sci. 2019, 9(22), 4809; https://doi.org/10.3390/app9224809 - 10 Nov 2019
Cited by 18 | Viewed by 3081
Abstract
Simulation activities are a useful tool to improve competence in industrial engineering bachelors. Specifically, fluid simulation allows students to acquire important skills to strengthen their theoretical knowledge and improve their future professional career. However, these tools usually require long training times and they [...] Read more.
Simulation activities are a useful tool to improve competence in industrial engineering bachelors. Specifically, fluid simulation allows students to acquire important skills to strengthen their theoretical knowledge and improve their future professional career. However, these tools usually require long training times and they are usually not available in the subjects of B.Sc. degrees. In this article, a new methodology based on short lessons is raised and evaluated in the fluid-mechanical subject for students enrolled in three different bachelor degree groups: B.Sc. in Mechanical Engineering, B.Sc. in Electrical Engineering and B.Sc. in Electronic and Automatic Engineering. Statistical results show a good acceptance in terms of usability, learning, motivation, thinking over, satisfaction and scalability. Additionally, a machine-learning based approach was applied to find group peculiarities and differences among them in order to identify the need for further personalization of the learning activity. Full article
(This article belongs to the Special Issue Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts)
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16 pages, 1943 KiB  
Article
How to Extract Meaningful Insights from UGC: A Knowledge-Based Method Applied to Education
by Jose Ramon Saura, Ana Reyes-Menendez and Dag R. Bennett
Appl. Sci. 2019, 9(21), 4603; https://doi.org/10.3390/app9214603 - 29 Oct 2019
Cited by 25 | Viewed by 5461
Abstract
New analysis and visualization techniques are required to glean useful insights from the vast amounts of data generated by new technologies and data sharing platforms. The aim of this article is to lay a foundation for such techniques so that the age of [...] Read more.
New analysis and visualization techniques are required to glean useful insights from the vast amounts of data generated by new technologies and data sharing platforms. The aim of this article is to lay a foundation for such techniques so that the age of big data may also be the age of knowledge, visualization, and understanding. Education is the keystone area used in this study because it is deeply affected by digital platforms as an educational medium and also because it deals mostly with digital natives who use information and communication technology (ICT) for all manner of purposes. Students and teachers are therefore a rich source of user generated content (UGC) on social networks and digital platforms. This article shows how useful knowledge can be extracted and visualized from samples of readily available UGC, in this case the text published in tweets from the social network Twitter. The first stage employs topic-modeling using LDA (latent dirichlet allocation) to identify topics, which are then subjected to sentiment analysis (SA) using machine-learning (developed in Python). The results take on meaning through an application of data mining techniques and a data visualization algorithm for complex networks. The results obtained show insights related to innovative educational trends that practitioners can use to improve strategies and interventions in the education sector in a short-term future. Full article
(This article belongs to the Special Issue Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts)
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16 pages, 3396 KiB  
Article
Predicting Student Grades Based on Their Usage of LMS Moodle Using Petri Nets
by Zoltán Balogh and Michal Kuchárik
Appl. Sci. 2019, 9(20), 4211; https://doi.org/10.3390/app9204211 - 09 Oct 2019
Cited by 19 | Viewed by 4024
Abstract
This paper deals with the possibility of predicting student’s grades based on their usage of Learning Management System (LMS) Moodle. It is important to know what materials would be best suited in LMS as study materials and what materials could be improved or [...] Read more.
This paper deals with the possibility of predicting student’s grades based on their usage of Learning Management System (LMS) Moodle. It is important to know what materials would be best suited in LMS as study materials and what materials could be improved or removed based on the student’s usage of the materials and the final grade. In order to do this, the correlations between access to materials and the final grade were observed. These correlations could also be used to predict the grades of the student. Therefore, a model with Petri nets was created that based on the highest correlation would be able to predict what grade the student would get based on his usage of LMS. Obviously, it would not be possible to predict every result with certainty, however, more precise predictions could be obtained with higher correlations. Full article
(This article belongs to the Special Issue Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts)
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14 pages, 409 KiB  
Article
The Machine Learning-Based Dropout Early Warning System for Improving the Performance of Dropout Prediction
by Sunbok Lee and Jae Young Chung
Appl. Sci. 2019, 9(15), 3093; https://doi.org/10.3390/app9153093 - 31 Jul 2019
Cited by 75 | Viewed by 7513
Abstract
A dropout early warning system enables schools to preemptively identify students who are at risk of dropping out of school, to promptly react to them, and eventually to help potential dropout students to continue their learning for a better future. However, the inherent [...] Read more.
A dropout early warning system enables schools to preemptively identify students who are at risk of dropping out of school, to promptly react to them, and eventually to help potential dropout students to continue their learning for a better future. However, the inherent class imbalance between dropout and non-dropout students could pose difficulty in building accurate predictive modeling for a dropout early warning system. The present study aimed to improve the performance of a dropout early warning system: (a) by addressing the class imbalance issue using the synthetic minority oversampling techniques (SMOTE) and the ensemble methods in machine learning; and (b) by evaluating the trained classifiers with both receiver operating characteristic (ROC) and precision–recall (PR) curves. To that end, we trained random forest, boosted decision tree, random forest with SMOTE, and boosted decision tree with SMOTE using the big data samples of the 165,715 high school students from the National Education Information System (NEIS) in South Korea. According to our ROC and PR curve analysis, boosted decision tree showed the optimal performance. Full article
(This article belongs to the Special Issue Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts)
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Review

Jump to: Editorial, Research

16 pages, 1072 KiB  
Review
Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review
by Juan L. Rastrollo-Guerrero, Juan A. Gómez-Pulido and Arturo Durán-Domínguez
Appl. Sci. 2020, 10(3), 1042; https://doi.org/10.3390/app10031042 - 04 Feb 2020
Cited by 169 | Viewed by 26748
Abstract
Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things. These are benefited by the automation of many [...] Read more.
Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things. These are benefited by the automation of many processes involved in usual students’ activities which handle massive volumes of data collected from software tools for technology-enhanced learning. Thus, analyzing and processing these data carefully can give us useful information about the students’ knowledge and the relationship between them and the academic tasks. This information is the source that feeds promising algorithms and methods able to predict students’ performance. In this study, almost 70 papers were analyzed to show different modern techniques widely applied for predicting students’ performance, together with the objectives they must reach in this field. These techniques and methods, which pertain to the area of Artificial Intelligence, are mainly Machine Learning, Collaborative Filtering, Recommender Systems, and Artificial Neural Networks, among others. Full article
(This article belongs to the Special Issue Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts)
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