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7 pages, 224 KiB

Open AccessEditorial

Smart Learning

by Francisco José García-Peñalvo, Cristina Casado-Lumbreras, Ricardo Colomo-Palacios and Aman Yadav

Appl. Sci. 2020, 10(19), 6964; https://doi.org/10.3390/app10196964 - 5 Oct 2020

Cited by 10 | Viewed by 3337

Abstract

Artificial intelligence applied to the educational field has a vast potential, especially after the effects worldwide of the COVID-19 pandemic. Online or blended educational modes are needed to respond to the health situation we are living in. The tutorial effort is higher than [...] Read more.

Artificial intelligence applied to the educational field has a vast potential, especially after the effects worldwide of the COVID-19 pandemic. Online or blended educational modes are needed to respond to the health situation we are living in. The tutorial effort is higher than in the traditional face-to-face approach. Thus, educational systems are claiming smarter learning technologies that do not pretend to substitute the faculty but make their teaching activities easy. This Special Issue is oriented to present a collection of papers of original advances in educational applications and services propelled by artificial intelligence, big data, machine learning, and deep learning. Full article

(This article belongs to the Special Issue Smart Learning)

Research

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18 pages, 4390 KiB

Open AccessArticle

Monitoring Students at the University: Design and Application of a Moodle Plugin

by María Consuelo Sáiz-Manzanares, Raúl Marticorena-Sánchez and César Ignacio García-Osorio

Appl. Sci. 2020, 10(10), 3469; https://doi.org/10.3390/app10103469 - 18 May 2020

Cited by 26 | Viewed by 5330

Abstract

Early detection of at-risk students is essential, especially in the university environment. Moreover, personalized learning has been shown to increase motivation and lower student dropout rates. At present, the average dropout rates among students following courses leading to the award of Spanish university [...] Read more.

Early detection of at-risk students is essential, especially in the university environment. Moreover, personalized learning has been shown to increase motivation and lower student dropout rates. At present, the average dropout rates among students following courses leading to the award of Spanish university degrees are around 18% and 42.8% for presential teaching and online courses, respectively. The objectives of this study are: (1) to design and to implement a Modular Object-Oriented Dynamic Learning Environment (Moodle) plugin, “eOrientation”, for the early detection of at-risk students; (2) to test the effectiveness of the “eOrientation” plugin on university students. We worked with 279 third-year students following health sciences degrees. A process for extracting information records was also implemented. In addition, a learning analytics module was developed, through which both supervised and unsupervised Machine Learning techniques can be applied. All these measures facilitated the personalized monitoring of the students and the easier detection of students at academic risk. The use of this tool could be of great importance to teachers and university governing teams, as it can assist the early detection of students at academic risk. Future studies will be aimed at testing the plugin using the Moodle environment on degree courses at other universities. Full article

(This article belongs to the Special Issue Smart Learning)

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18 pages, 4678 KiB

Open AccessArticle

Representing Data Visualization Goals and Tasks through Meta-Modeling to Tailor Information Dashboards

by Andrea Vázquez-Ingelmo, Francisco José García-Peñalvo, Roberto Therón and Miguel Ángel Conde

Appl. Sci. 2020, 10(7), 2306; https://doi.org/10.3390/app10072306 - 27 Mar 2020

Cited by 23 | Viewed by 5198

Abstract

Information dashboards are everywhere. They support knowledge discovery in a huge variety of contexts and domains. Although powerful, these tools can be complex, not only for the end-users but also for developers and designers. Information dashboards encode complex datasets into different visual marks [...] Read more.

Information dashboards are everywhere. They support knowledge discovery in a huge variety of contexts and domains. Although powerful, these tools can be complex, not only for the end-users but also for developers and designers. Information dashboards encode complex datasets into different visual marks to ease knowledge discovery. Choosing a wrong design could compromise the entire dashboard’s effectiveness, selecting the appropriate encoding or configuration for each potential context, user, or data domain is a crucial task. For these reasons, there is a necessity to automatize the recommendation of visualizations and dashboard configurations to deliver tools adapted to their context. Recommendations can be based on different aspects, such as user characteristics, the data domain, or the goals and tasks that will be achieved or carried out through the visualizations. This work presents a dashboard meta-model that abstracts all these factors and the integration of a visualization task taxonomy to account for the different actions that can be performed with information dashboards. This meta-model has been used to design a domain specific language to specify dashboards requirements in a structured way. The ultimate goal is to obtain a dashboard generation pipeline to deliver dashboards adapted to any context, such as the educational context, in which a lot of data are generated, and there are several actors involved (students, teachers, managers, etc.) that would want to reach different insights regarding their learning performance or learning methodologies. Full article

(This article belongs to the Special Issue Smart Learning)

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16 pages, 929 KiB

Open AccessArticle

Computational Characterization of Activities and Learners in a Learning System

by Alberto Real-Fernández, Rafael Molina-Carmona and Faraón Llorens-Largo

Appl. Sci. 2020, 10(7), 2208; https://doi.org/10.3390/app10072208 - 25 Mar 2020

Cited by 7 | Viewed by 2645

Abstract

For a technology-based learning system to be able to personalize its learning process, it must characterize the learners. This can be achieved by storing information about them in a feature vector. The aim of this research is to propose such a system. In [...] Read more.

For a technology-based learning system to be able to personalize its learning process, it must characterize the learners. This can be achieved by storing information about them in a feature vector. The aim of this research is to propose such a system. In our proposal, the students are characterized based on their activity in the system, so learning activities also need to be characterized. The vectors are data structures formed by numerical or categorical variables such as learning style, cognitive level, knowledge type or the history of the learner’s actions in the system. The learner’s feature vector is updated considering the results and the time of the activities performed by the learner. A use case is also presented to illustrate how variables can be used to achieve different effects on the learning of individuals through the use of instructional strategies. The most valuable contribution of this proposal is the fact that students are characterized based on their activity in the system, instead of on self-reporting. Another important contribution is the practical nature of the vectors that will allow them to be computed by an artificial intelligence algorithm. Full article

(This article belongs to the Special Issue Smart Learning)

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14 pages, 2042 KiB

Open AccessArticle

The Effectiveness of Embodied Pedagogical Agents and Their Impact on Students Learning in Virtual Worlds

by Foteini Grivokostopoulou, Konstantinos Kovas and Isidoros Perikos

Appl. Sci. 2020, 10(5), 1739; https://doi.org/10.3390/app10051739 - 3 Mar 2020

Cited by 52 | Viewed by 6539

Abstract

Over the last years, the successful integration of virtual reality in distance education contexts has led to the development of various frameworks related to the virtual learning approaches. 3D virtual worlds are an integral part of the landscape of education and demonstrate novel [...] Read more.

Over the last years, the successful integration of virtual reality in distance education contexts has led to the development of various frameworks related to the virtual learning approaches. 3D virtual worlds are an integral part of the landscape of education and demonstrate novel learning possibilities that can open new directions in education. An important aspect of virtual worlds relates to the intelligent, embodied pedagogical agents that are employed to enhance the interaction with students and improve their overall learning experience. The proper design and integration of embodied pedagogical agents in virtual learning environments are highly desirable. Although virtual agents constitute a vital part of virtual environments, their exact impact needs are yet to be addressed and assessed. The aim of the present study is to thoroughly examine and deeply understand the effect that embodied pedagogical agents have on the learning experience of students as well as on their performance. We examine how students perceive the role of pedagogical agents as learning companions during specific game-based activities and the effect that their assistance has on students’ learning. A concrete experimental study was conducted in AVARES, a 3D virtual world educational environment that teaches the domain of environmental engineering and energy generation. The results of the study point out that embodied pedagogical agents can improve students’ learning experience, enhance their engagement with learning activities and, most of all, improve their knowledge construction and performance. Full article

(This article belongs to the Special Issue Smart Learning)

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24 pages, 3570 KiB

Open AccessArticle

Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning

by Pedro Manuel Moreno-Marcos, Pedro J. Muñoz-Merino, Carlos Alario-Hoyos and Carlos Delgado Kloos

Appl. Sci. 2020, 10(5), 1722; https://doi.org/10.3390/app10051722 - 3 Mar 2020

Cited by 10 | Viewed by 4338

Abstract

In education, several studies have tried to track student persistence (i.e., students’ ability to keep on working on the assigned tasks) using different definitions and self-reported data. However, self-reported metrics may be limited, and currently, online courses allow collecting many low-level events to [...] Read more.

In education, several studies have tried to track student persistence (i.e., students’ ability to keep on working on the assigned tasks) using different definitions and self-reported data. However, self-reported metrics may be limited, and currently, online courses allow collecting many low-level events to analyze student behaviors based on logs and using learning analytics. These analyses can be used to provide personalized and adaptative feedback in Smart Learning Environments. In this line, this work proposes the analysis and measurement of two types of persistence based on students’ interactions in online courses: (1) local persistence (based on the attempts used to solve an exercise when the student answers it incorrectly), and (2) global persistence (based on overall course activity/completion). Results show that there are different students’ profiles based on local persistence, although medium local persistence stands out. Moreover, local persistence is highly affected by course context and it can vary throughout the course. Furthermore, local persistence does not necessarily relate to global persistence or engagement with videos, although it is related to students’ average grade. Finally, predictive analysis shows that local persistence is not a strong predictor of global persistence and performance, although it can add some value to the predictive models. Full article

(This article belongs to the Special Issue Smart Learning)

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14 pages, 4724 KiB

Open AccessArticle

An Intelligent Tutoring System to Facilitate the Learning of Programming through the Usage of Dynamic Graphic Visualizations

by Santiago Schez-Sobrino, Cristian Gmez-Portes, David Vallejo, Carlos Glez-Morcillo and Miguel Á. Redondo

Appl. Sci. 2020, 10(4), 1518; https://doi.org/10.3390/app10041518 - 23 Feb 2020

Cited by 18 | Viewed by 7287

Abstract

The learning of programming is a field of research with relevant studies and publications for more than 25 years. Since its inception, it has been shown that its difficulty lies in the high level of abstraction required to understand certain programming concepts. However, [...] Read more.

The learning of programming is a field of research with relevant studies and publications for more than 25 years. Since its inception, it has been shown that its difficulty lies in the high level of abstraction required to understand certain programming concepts. However, this level can be reduced by using tools and graphic representations that motivate students and facilitate their understanding, associating real-world elements with specific programming concepts. Thus, this paper proposes the use of an intelligent tutoring system (ITS) that helps during the learning of programming by using a notation based on a metaphor of roads and traffic signs represented by 3D graphics in an augmented reality (AR) environment. These graphic visualizations can be generated automatically from the source code of the programs thanks to the modular and scalable design of the system. Students can use them by leveraging the available feedback system, and teachers can also use them in order to explain programming concepts during the classes. This work highlights the flexibility and extensibility of the proposal through its application in different use cases that we have selected as examples to show how the system could be exploited in a multitude of real learning scenarios. Full article

(This article belongs to the Special Issue Smart Learning)

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11 pages, 351 KiB

Open AccessArticle

Academic Success Assessment through Version Control Systems

by Ángel Manuel Guerrero-Higueras, Camino Fernández Llamas, Lidia Sánchez González, Alexis Gutierrez Fernández, Gonzalo Esteban Costales and Miguel Ángel Conde González

Appl. Sci. 2020, 10(4), 1492; https://doi.org/10.3390/app10041492 - 21 Feb 2020

Cited by 17 | Viewed by 3869

Abstract

Version control systems’ usage is a highly demanded skill in information and communication technology professionals. Thus, their usage should be encouraged by educational institutions. This work demonstrates that it is possible to assess if a student can pass a computer science-related subject by [...] Read more.

Version control systems’ usage is a highly demanded skill in information and communication technology professionals. Thus, their usage should be encouraged by educational institutions. This work demonstrates that it is possible to assess if a student can pass a computer science-related subject by monitoring its interaction with a version control system. This paper proposes a methodology that compares the performance of several machine learning models so as to select the appropriate predicting model for the assessment of the students’ achievements. To fit predicting models, three subjects of the Degree in Computer Science at the University of León are considered to obtain the dataset: computer organization, computer programming, and operating systems extension. The common aspect of these subjects is their assignments, which are based on developing one or several programs with programming languages such as C or Java. To monitor the practical assignments and individual performance, a Git repository is employed allowing students to store source code, documentation, and supporting control versions. According to the presented experience, there is a huge correlation between the level of interaction for each student and the achieved grades. Full article

(This article belongs to the Special Issue Smart Learning)

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25 pages, 692 KiB

Open AccessArticle

Students’ Acceptance and Tracking of a New Container-Based Virtual Laboratory

by Llanos Tobarra, Antonio Robles-Gómez, Rafael Pastor, Roberto Hernández, Andrés Duque and Jesús Cano

Appl. Sci. 2020, 10(3), 1091; https://doi.org/10.3390/app10031091 - 6 Feb 2020

Cited by 30 | Viewed by 4924

Abstract

Presently, the ever-increasing use of new technologies helps people to acquire additional skills for developing an applied critical thinking in many contexts of our society. When it comes to education, and more particularly in any Engineering subject, practical learning scenarios are key to [...] Read more.

Presently, the ever-increasing use of new technologies helps people to acquire additional skills for developing an applied critical thinking in many contexts of our society. When it comes to education, and more particularly in any Engineering subject, practical learning scenarios are key to achieve a set of competencies and applied skills. In our particular case, the cybersecurity topic with a distance education methodology is considered and a new remote virtual laboratory based on containers will be presented and evaluated in this work. The laboratory is based on the Linux Docker virtualization technology, which allows us to create consistent realistic scenarios with lower configuration requirements for the students. The laboratory is comparatively evaluated with our previous environment, LoT@UNED, from both the points of view of the students’ acceptance with a set of UTAUT models, and their behavior regarding evaluation items, time distribution, and content resources. All data was obtained from students’ surveys and platform registers. The main conclusion of this work is that the proposed laboratory obtains a very high acceptance from the students, in terms of several different indicators (perceived usefulness, estimated effort, social influence, attitude, ease of access, and intention of use). Neither the use of the virtual platform nor the distance methodology employed affect the intention to use the technology proposed in this work. Full article

(This article belongs to the Special Issue Smart Learning)

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28 pages, 4372 KiB

Open AccessArticle

An Exploratory Analysis of the Implementation and Use of an Intelligent Platform for Learning in Primary Education

by Natalia Lara Nieto-Márquez, Alejandro Baldominos, Alejandro Cardeña Martínez and Miguel Ángel Pérez Nieto

Appl. Sci. 2020, 10(3), 983; https://doi.org/10.3390/app10030983 - 3 Feb 2020

Cited by 7 | Viewed by 4230

Abstract

Smile and Learn is an intelligent platform with more than 4500 educational activities for children aged 3–12. The digital material developed covers all courses of primary education and most of the subjects with the different topic-related worlds with activities in the field of [...] Read more.

Smile and Learn is an intelligent platform with more than 4500 educational activities for children aged 3–12. The digital material developed covers all courses of primary education and most of the subjects with the different topic-related worlds with activities in the field of logics and mathematics, science, linguistics and tales, visual-spatial and cognitive skills, emotional intelligence, arts, and multiplayer games. This kind of material supports active learning and new pedagogical models for teachers to use in their lessons. The purpose of this paper is to explore the usage of the platform in three pilot groups schools from different regions of Spain, outlining future directions in the design of such digital materials. Usage is assessed via descriptive analysis and variance analysis, with data collected from users interacting with the intelligent platform. The results show a high use of STEM (Science, Technology, Engineering and Maths) activities among all the activities that could be chosen. Cross-curricular activities are also used. Continuation in the development of such materials is concluded necessary, focusing integration of different fields, accentuating games over quizzes, and the value of teacher training for improving their use in schools. Full article

(This article belongs to the Special Issue Smart Learning)

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18 pages, 11873 KiB

Open AccessArticle

Bringing Biometric Sensors to the Classroom: A Fingerprint Acquisition Laboratory for Improving Student Motivation and Commitment

by Marija Bogicevic Sretenovic, Ivan Milenkovic, Bojan Jovanovic, Dejan Simic, Miroslav Minovic and Milos Milovanovic

Appl. Sci. 2020, 10(3), 880; https://doi.org/10.3390/app10030880 - 28 Jan 2020

Cited by 9 | Viewed by 4944

Abstract

This paper presents a research study conducted in a specially developed laboratory for biometric engineering education. The laboratory gives students an opportunity to learn more about fingerprint acquisition and analyze the impact of acquisition on other parts of the biometric authentication process. An [...] Read more.

This paper presents a research study conducted in a specially developed laboratory for biometric engineering education. The laboratory gives students an opportunity to learn more about fingerprint acquisition and analyze the impact of acquisition on other parts of the biometric authentication process. An IoT approach was used, as different types of sensors (biometric sensors, thermometer, and humidity sensor) and components (heaters and workstations) were included in setting up a working surface for biometric data acquisition. Working surfaces create a network where data collected from each working station is recorded in a database. In parallel with biometric data acquisition, environmental condition parameters are recorded. Collected data is available to students for later analysis through the use of a specially developed visualization tool. In order to fully utilize the possibilities the laboratory provides, a flipped classroom approach was used. An evaluation study was done as a part of the course of Biometric technology held at the University of Belgrade. Research results show improvements in student learning outcomes and motivation. Full article

(This article belongs to the Special Issue Smart Learning)

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22 pages, 1292 KiB

Open AccessArticle

An Educational Approach for Present and Future of Digital Transformation in Portuguese Organizations

by Carla Santos Pereira, Natércia Durão, David Fonseca, Maria João Ferreira and Fernando Moreira

Appl. Sci. 2020, 10(3), 757; https://doi.org/10.3390/app10030757 - 21 Jan 2020

Cited by 15 | Viewed by 5173

Abstract

The current technological evolution allows us to easily and quickly obtain more information, computing capacity, communication and connectivity, in addition to allowing new forms of collaboration between disperse networks of diversified actors. This new reality not only offers enormous potential for innovation and [...] Read more.

The current technological evolution allows us to easily and quickly obtain more information, computing capacity, communication and connectivity, in addition to allowing new forms of collaboration between disperse networks of diversified actors. This new reality not only offers enormous potential for innovation and enhanced performance for organizations but also, extends beyond the classic boundaries to affect individuals, other organizations and society in general. At the same time, this reality makes the ability of organizations to uphold their competitive advantage more challenging, since the control of the elements of their operating environment decreases drastically as they increasingly control the elements of the same environment. This is how the digital transformation of organizations becomes unavoidable, because otherwise they tend to disappear. In this context, it is necessary to infer our students’ methods for researching, identifying and taking solutions about if organizations in Portugal are already living the aforementioned digital transformation or if they are aware of the need to adapt to this new reality. The main goal of this research formatted as educational approach, is to evaluate and compare the current state of digital adoption in terms of the preparation according to the prevailing technological categories (pillars and innovation accelerators), as well as future priorities of organizations in the implementation of digital transformation in Portuguese organizations. To evaluate such objectives, a Project Based Learning (PBL) approach was used to reinforce the research and decision-making skills of undergraduate business students. Based on the results obtained, it can be concluded that organizations are aware of the need to accommodate the digital transformation not to fail and disappearing. However, it is not possible to conclude which strategy is to be adopted by the organizations, and how such a strategy will affect the organization as a whole, in particular as in respect of its business model. Full article

(This article belongs to the Special Issue Smart Learning)

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22 pages, 3467 KiB

Open AccessArticle

Extraction, Processing and Visualization of Peer Assessment Data in Moodle

by Julián Chaparro-Peláez, Santiago Iglesias-Pradas, Francisco J. Rodríguez-Sedano and Emiliano Acquila-Natale

Appl. Sci. 2020, 10(1), 163; https://doi.org/10.3390/app10010163 - 24 Dec 2019

Cited by 12 | Viewed by 6000

Abstract

Situated in the intersection of two emerging trends, online self- and peer assessment modes and learning analytics, this study explores the current landscape of software applications to support peer assessment activities and their necessary requirements to complete the learning analytics cycle upon the [...] Read more.

Situated in the intersection of two emerging trends, online self- and peer assessment modes and learning analytics, this study explores the current landscape of software applications to support peer assessment activities and their necessary requirements to complete the learning analytics cycle upon the information collected from those applications. More particularly, the study focuses on the specific case of Moodle Workshops, and proposes the design and implementation of an application, the Moodle Workshop Data EXtractor (MWDEX) to overcome the data analysis and visualization shortcomings of the Moodle Workshop module. This research paper details the architecture design, configuration, and use of the application, and proposes an initial validation of the tool based on the current peer assessment practices of a group of learning analytics experts. The results of the small-scale survey suggest that the use of software tools to support peer assessment is not so extended as it would initially seem, but also highlight the potential of MWDEX to take full advantage of Moodle Workshops. Full article

(This article belongs to the Special Issue Smart Learning)

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17 pages, 8738 KiB

Open AccessArticle

A Granularity-Based Intelligent Tutoring System for Zooarchaeology

by Laia Subirats, Leopoldo Pérez, Cristo Hernández, Santiago Fort and Gomez-Monivas Sacha

Appl. Sci. 2019, 9(22), 4960; https://doi.org/10.3390/app9224960 - 18 Nov 2019

Cited by 3 | Viewed by 3328

Abstract

This paper presents a tutoring system which uses three different granularities for helping students to classify animals from bone fragments in zooarchaeology. The 3406 bone remains, which have 64 attributes, were obtained from the excavation of the Middle Palaeolithic site of El Salt [...] Read more.

This paper presents a tutoring system which uses three different granularities for helping students to classify animals from bone fragments in zooarchaeology. The 3406 bone remains, which have 64 attributes, were obtained from the excavation of the Middle Palaeolithic site of El Salt (Alicante, Spain). The coarse granularity performs a five-class prediction, the medium a twelve-class prediction, and the fine a fifteen-class prediction. In the coarse granularity, the results show that the first 10 most relevant attributes for classification are width, bone, thickness, length, bone fragment, anatomical group, long bone circumference, X, Y, and Z. Based on those results, a user-friendly interface of the tutor has been built in order to train archaeology students to classify new remains using the coarse granularity. A pilot has been performed in the 2019 excavation season in Abric del Pastor (Alicante, Spain), where the automatic tutoring system was used by students to classify 51 new remains. The pilot experience demonstrated the usefulness of the tutoring system both for students when facing their first classification activities and also for seniors since the tutoring system gives them valuable clues for helping in difficult classification problems. Full article

(This article belongs to the Special Issue Smart Learning)

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11 pages, 1191 KiB

Open AccessArticle

Comparative Study on Perceived Trust of Topic Modeling Based on Affective Level of Educational Text

by Youngjae Im, Jaehyun Park, Minyeong Kim and Kijung Park

Appl. Sci. 2019, 9(21), 4565; https://doi.org/10.3390/app9214565 - 28 Oct 2019

Cited by 6 | Viewed by 2901

Abstract

Latent dirichlet allocation (LDA) is a representative topic model to extract keywords related to latent topics embedded in a document set. Despite its effectiveness in finding underlying topics in documents, the traditional algorithm of LDA does not have a process to reflect sentimental [...] Read more.

Latent dirichlet allocation (LDA) is a representative topic model to extract keywords related to latent topics embedded in a document set. Despite its effectiveness in finding underlying topics in documents, the traditional algorithm of LDA does not have a process to reflect sentimental meanings in text for topic extraction. Focusing on this issue, this study aims to investigate the usability of both LDA and sentiment analysis (SA) algorithms based on the affective level of text. This study defines the affective level of a given set of paragraphs and attempts to analyze the perceived trust of the methodologies in regards to usability. In our experiments, the text of the college scholastic ability test was selected as the set of evaluation paragraphs, and the affective level of the paragraphs was manipulated into three levels (low, medium, and high) as an independent variable. The LDA algorithm was used to extract the keywords of the paragraph, while SA was used to identify the positive or negative mood of the extracted subject word. In addition, the perceived trust score of the algorithm was evaluated by the subjects, and this study verifies whether there is a difference in the score according to the affective levels of the paragraphs. The results show that paragraphs with low affect lead to the high perceived trust of LDA from the participants. However, the perceived trust of SA does not show a statistically significant difference between the affect levels. The findings from this study indicate that LDA is more effective to find topics in text that mainly contains objective information. Full article

(This article belongs to the Special Issue Smart Learning)

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12 pages, 1408 KiB

Open AccessArticle

Computer Adaptive Testing Using Upper-Confidence Bound Algorithm for Formative Assessment

by Jaroslav Melesko and Vitalij Novickij

Appl. Sci. 2019, 9(20), 4303; https://doi.org/10.3390/app9204303 - 14 Oct 2019

Cited by 7 | Viewed by 3059

Abstract

There is strong support for formative assessment inclusion in learning processes, with the main emphasis on corrective feedback for students. However, traditional testing and Computer Adaptive Testing can be problematic to implement in the classroom. Paper based tests are logistically inconvenient and are [...] Read more.

There is strong support for formative assessment inclusion in learning processes, with the main emphasis on corrective feedback for students. However, traditional testing and Computer Adaptive Testing can be problematic to implement in the classroom. Paper based tests are logistically inconvenient and are hard to personalize, and thus must be longer to accurately assess every student in the classroom. Computer Adaptive Testing can mitigate these problems by making use of Multi-Dimensional Item Response Theory at cost of introducing several new problems, most problematic of which are the greater test creation complexity, because of the necessity of question pool calibration, and the debatable premise that different questions measure one common latent trait. In this paper a new approach of modelling formative assessment as a Multi-Armed bandit problem is proposed and solved using Upper-Confidence Bound algorithm. The method in combination with e-learning paradigm has the potential to mitigate such problems as question item calibration and lengthy tests, while providing accurate formative assessment feedback for students. A number of simulation and empirical data experiments (with 104 students) are carried out to explore and measure the potential of this application with positive results. Full article

(This article belongs to the Special Issue Smart Learning)

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16 pages, 1836 KiB

Open AccessArticle

Hierarchy-Based Competency Structure and Its Application in E-Evaluation

by Simona Ramanauskaitė and Asta Slotkienė

Appl. Sci. 2019, 9(17), 3478; https://doi.org/10.3390/app9173478 - 23 Aug 2019

Cited by 7 | Viewed by 3404

Abstract

The development of information technologies changes the learning process. The amount of publicly available data of e-learning systems allows personalized studies. Therefore, the tutor sometimes is needed for the student’s evaluation and consultation only. To ensure clear evaluation requirements and objective evaluation process, [...] Read more.

The development of information technologies changes the learning process. The amount of publicly available data of e-learning systems allows personalized studies. Therefore, the tutor sometimes is needed for the student’s evaluation and consultation only. To ensure clear evaluation requirements and objective evaluation process, the learning material, as well as the evaluation system, must be discrete and semantically expressed. The list of mastered competencies and skills is more important to the enterprise; therefore, during the last years, the study process has concentrated on competency evaluation too. However, the current practice, when students’ competencies are summarized and expressed as one quantitative metric (score), do not express the list of students’ competencies and their level. To solve the problem, in this paper, we proposed a method for the design of competencies’ tree. The competency tree has to be formatted based on context modeling principles and analysis of Scope-Commonality-Variability. The usage of competency tree for students’ competencies’ evaluation proposes clearly defined and semantically expressed evaluation method for both human and e-learning evaluation process. This paper presents the results of the empirical experiment to adapt the proposed competency tree design and application for competencies’ e-evaluation method, based on flexibility, adaptability, and granularity of learning material. Full article

(This article belongs to the Special Issue Smart Learning)

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Review

Jump to: Editorial, Research

26 pages, 2179 KiB

Open AccessReview

Systematic Literature Review of Predictive Analysis Tools in Higher Education

by Martín Liz-Domínguez, Manuel Caeiro-Rodríguez, Martín Llamas-Nistal and Fernando A. Mikic-Fonte

Appl. Sci. 2019, 9(24), 5569; https://doi.org/10.3390/app9245569 - 17 Dec 2019

Cited by 37 | Viewed by 8645

Abstract

The topic of predictive algorithms is often regarded among the most relevant fields of study within the data analytics discipline. They have applications in multiple contexts, education being an important one of them. Focusing on higher education scenarios, most notably universities, predictive analysis [...] Read more.

The topic of predictive algorithms is often regarded among the most relevant fields of study within the data analytics discipline. They have applications in multiple contexts, education being an important one of them. Focusing on higher education scenarios, most notably universities, predictive analysis techniques are present in studies that estimate academic outcomes using different kinds of student-related data. Furthermore, predictive algorithms are the basis of tools such as early warning systems (EWS): applications able to foresee future risks, such as the likelihood of students failing or dropping out of a course, and alert of such risks so that corrective measures can be taken. The purpose of this literature review is to provide an overview of the current state of research activity regarding predictive analytics in higher education, highlighting the most relevant instances of predictors and EWS that have been used in practice. The PRISMA guidelines for systematic literature reviews were followed in this study. The document search process yielded 1382 results, out of which 26 applications were selected as relevant examples of predictors and EWS, each of them defined by the contexts where they were applied and the data that they used. However, one common shortcoming is that they are usually applied in limited scenarios, such as a single course, evidencing that building a predictive application able to work well under different teaching and learning methodologies is an arduous task. Full article

(This article belongs to the Special Issue Smart Learning)

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24 pages, 2722 KiB

Open AccessReview

Towards Next Generation Teaching, Learning, and Context-Aware Applications for Higher Education: A Review on Blockchain, IoT, Fog and Edge Computing Enabled Smart Campuses and Universities

by Tiago M. Fernández-Caramés and Paula Fraga-Lamas

Appl. Sci. 2019, 9(21), 4479; https://doi.org/10.3390/app9214479 - 23 Oct 2019

Cited by 108 | Viewed by 9764

Abstract

Smart campuses and smart universities make use of IT infrastructure that is similar to the one required by smart cities, which take advantage of Internet of Things (IoT) and cloud computing solutions to monitor and actuate on the multiple systems of a university. [...] Read more.

Smart campuses and smart universities make use of IT infrastructure that is similar to the one required by smart cities, which take advantage of Internet of Things (IoT) and cloud computing solutions to monitor and actuate on the multiple systems of a university. As a consequence, smart campuses and universities need to provide connectivity to IoT nodes and gateways, and deploy architectures that allow for offering not only a good communications range through the latest wireless and wired technologies, but also reduced energy consumption to maximize IoT node battery life. In addition, such architectures have to consider the use of technologies like blockchain, which are able to deliver accountability, transparency, cyber-security and redundancy to the processes and data managed by a university. This article reviews the state of the start on the application of the latest key technologies for the development of smart campuses and universities. After defining the essential characteristics of a smart campus/university, the latest communications architectures and technologies are detailed and the most relevant smart campus deployments are analyzed. Moreover, the use of blockchain in higher education applications is studied. Therefore, this article provides useful guidelines to the university planners, IoT vendors and developers that will be responsible for creating the next generation of smart campuses and universities. Full article

(This article belongs to the Special Issue Smart Learning)

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