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Dr. David Bañeres
Universitat Oberta de Catalunya

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0 E-Learning
0 Plagiarism Detection
0 Technology Enhanced Learning
0 e-assessment
0 artificial intelligence for education

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e-assessment
E-Learning
artificial intelligence for education
Plagiarism Detection

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Journal article
Published: 22 June 2021 in Applied Sciences
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Learning analytics is quickly evolving. Old fashioned dashboards with descriptive information and trends about what happened in the past are slightly substituted by new dashboards with forecasting information and predicting relevant outcomes about learning. Artificial intelligence is aiding this revolution. The accessibility to computational resources has increased, and specific tools and packages for integrating artificial intelligence techniques leverage such new analytical tools. However, it is crucial to develop trustworthy systems, especially in education where skepticism about their application is due to the risk of teachers’ replacement. However, artificial intelligence systems should be seen as companions to empower teachers during the teaching and learning process. During the past years, the Universitat Oberta de Catalunya has advanced developing a data mart where all data about learners and campus utilization are stored for research purposes. The extensive collection of these educational data has been used to build a trustworthy early warning system whose infrastructure is introduced in this paper. The infrastructure supports such a trustworthy system built with artificial intelligence procedures to detect at-risk learners early on in order to help them to pass the course. To assess the system’s trustworthiness, we carried out an evaluation on the basis of the seven requirements of the European Assessment List for trustworthy artificial intelligence (ALTAI) guidelines that recognize an artificial intelligence system as a trustworthy one. Results show that it is feasible to build a trustworthy system wherein all seven ALTAI requirements are considered at once from the very beginning during the design phase.

ACS Style

David Baneres; Ana Guerrero-Roldán; M. Rodríguez-González; Abdulkadir Karadeniz. A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System. Applied Sciences 2021, 11, 5781 .

AMA Style

David Baneres, Ana Guerrero-Roldán, M. Rodríguez-González, Abdulkadir Karadeniz. A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System. Applied Sciences. 2021; 11 (13):5781.

Chicago/Turabian Style

David Baneres; Ana Guerrero-Roldán; M. Rodríguez-González; Abdulkadir Karadeniz. 2021. "A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System." Applied Sciences 11, no. 13: 5781.

Review
Published: 15 January 2021 in Sustainability
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Artificial intelligence (AI) has penetrated every layer of our lives, and education is not immune to the effects of AI. In this regard, this study examines AI studies in education in half a century (1970–2020) through a systematic review approach and benefits from social network analysis and text-mining approaches. Accordingly, the research identifies three research clusters (1) artificial intelligence, (2) pedagogical, and (3) technological issues, and suggests five broad research themes which are (1) adaptive learning and personalization of education through AI-based practices, (2) deep learning and machine Learning algorithms for online learning processes, (3) Educational human-AI interaction, (4) educational use of AI-generated data, and (5) AI in higher education. The study also highlights that ethics in AI studies is an ignored research area.

ACS Style

Aras Bozkurt; Abdulkadir Karadeniz; David Baneres; Ana Guerrero-Roldán; M. Rodríguez. Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century. Sustainability 2021, 13, 800 .

AMA Style

Aras Bozkurt, Abdulkadir Karadeniz, David Baneres, Ana Guerrero-Roldán, M. Rodríguez. Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century. Sustainability. 2021; 13 (2):800.

Chicago/Turabian Style

Aras Bozkurt; Abdulkadir Karadeniz; David Baneres; Ana Guerrero-Roldán; M. Rodríguez. 2021. "Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century." Sustainability 13, no. 2: 800.

Journal article
Published: 12 January 2021 in International Journal of Educational Technology in Higher Education
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Trust-based e-assessment systems are increasingly important in the digital age for both academic institutions and students, including students with special educational needs and disabilities (SEND). Recent literature indicates a growing number of studies about e-authentication and authorship verification for quality assurance with more flexible modes of assessment. Yet understanding the acceptability of e-authentication systems among SEND students is underexplored. This study examines SEND students’ views about the use of e-authentication systems, including perceived advantages and disadvantages of new technology-enhanced assessment. This study aims to shed light on this area by examining the attitudes of 267 SEND students who used, or were aware of, an authentication system known as adaptive trust-based e-assessment system for learning (TeSLA). The results suggest a broadly positive acceptability of these e-authentication technologies by SEND students. In the view of these students, the key advantages are the ability of proving the originality of their work, and trust-based e-assessment results; the key disadvantages are the possibility that the technology might not work or present wrong outputs in terms of cheating.

ACS Style

Merja Laamanen; Tarja Ladonlahti; Sanna Uotinen; Alexandra Okada; David Bañeres; Serpil Koçdar. Acceptability of the e-authentication in higher education studies: views of students with special educational needs and disabilities. International Journal of Educational Technology in Higher Education 2021, 18, 1 -17.

AMA Style

Merja Laamanen, Tarja Ladonlahti, Sanna Uotinen, Alexandra Okada, David Bañeres, Serpil Koçdar. Acceptability of the e-authentication in higher education studies: views of students with special educational needs and disabilities. International Journal of Educational Technology in Higher Education. 2021; 18 (1):1-17.

Chicago/Turabian Style

Merja Laamanen; Tarja Ladonlahti; Sanna Uotinen; Alexandra Okada; David Bañeres; Serpil Koçdar. 2021. "Acceptability of the e-authentication in higher education studies: views of students with special educational needs and disabilities." International Journal of Educational Technology in Higher Education 18, no. 1: 1-17.

Conference paper
Published: 10 July 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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In order to attain a good user experience in e-assessment systems, learners should be aware of how they are progressing in the courses and they should feel motivated and engaged. The goal of this paper is to propose an e-assessment system, that aims to increase the self-awareness of learners about the courses’ progress that they are taking, and also to improve the learner’s motivation and engagement. While designing the system, two major design challenges have been identified and addressed. The design challenges include, informing the learners about their progress and making the learners feel motivated to work harder and enhance their learning of the course contents and learning activities. The proposed system informs the learners about their progress and aims to keep them motivated by providing them with minimum grade predictions for the next learning activity they would perform, during the semester of an online course. The learners are also informed of their risk of failing the course throughout the semester. Also, to keep the learners motivated and engaged personalized suggestions are provided by the teachers to enhance the learning of the course. The user experience evaluation of LIS highlights that it has helped the learners to enhance their learning experiences.

ACS Style

Sidra Iftikhar; Ana-Elena Guerrero-Roldán; Enric Mor; David Bañeres. User Experience Evaluation of an e-Assessment System. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 77 -91.

AMA Style

Sidra Iftikhar, Ana-Elena Guerrero-Roldán, Enric Mor, David Bañeres. User Experience Evaluation of an e-Assessment System. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():77-91.

Chicago/Turabian Style

Sidra Iftikhar; Ana-Elena Guerrero-Roldán; Enric Mor; David Bañeres. 2020. "User Experience Evaluation of an e-Assessment System." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 77-91.

Journal article
Published: 27 June 2020 in Applied Sciences
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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.

ACS Style

David Bañeres; M. Elena Rodríguez; Ana Elena Guerrero-Roldán; Abdulkadir Karadeniz. An Early Warning System to Detect At-risk Students in Online Higher Education. Applied Sciences 2020, 10, 4427 .

AMA Style

David Bañeres, M. Elena Rodríguez, Ana Elena Guerrero-Roldán, Abdulkadir Karadeniz. An Early Warning System to Detect At-risk Students in Online Higher Education. Applied Sciences. 2020; 10 (13):4427.

Chicago/Turabian Style

David Bañeres; M. Elena Rodríguez; Ana Elena Guerrero-Roldán; Abdulkadir Karadeniz. 2020. "An Early Warning System to Detect At-risk Students in Online Higher Education." Applied Sciences 10, no. 13: 4427.

Conference paper
Published: 01 March 2020 in INTED2020 Proceedings
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ACS Style

David Bañeres; Abdulkadir Karadeniz; Ana Elena Guerrero-Roldan; M. Elena Rodriguez. DATA LEGIBILITY AND MEANINGFUL VISUALIZATION THROUGH A LEARNING INTELLIGENT SYSTEM DASHBOARD. INTED2020 Proceedings 2020, 5991 -5995.

AMA Style

David Bañeres, Abdulkadir Karadeniz, Ana Elena Guerrero-Roldan, M. Elena Rodriguez. DATA LEGIBILITY AND MEANINGFUL VISUALIZATION THROUGH A LEARNING INTELLIGENT SYSTEM DASHBOARD. INTED2020 Proceedings. 2020; ():5991-5995.

Chicago/Turabian Style

David Bañeres; Abdulkadir Karadeniz; Ana Elena Guerrero-Roldan; M. Elena Rodriguez. 2020. "DATA LEGIBILITY AND MEANINGFUL VISUALIZATION THROUGH A LEARNING INTELLIGENT SYSTEM DASHBOARD." INTED2020 Proceedings , no. : 5991-5995.

Journal article
Published: 23 April 2019 in IEEE Transactions on Learning Technologies
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Identifying at-risk students as soon as possible is a challenge in educational institutions. Decreasing the time lag between identification and real at-risk state, it may significantly reduce the risk of failure or disengage. In small courses, their identification is relatively easy, but it is impractical on larger ones. Current Learning Management Systems store a large amount of data that could help to generate predictive models to early identify those students in online and blended learning. The contribution of this paper is twofold: First, a new adaptive predictive model is presented based only on students' grades specifically trained for each course. A deep analysis has been performed in the whole institution to evaluate its performance accuracy. Second, an early warning system has been developed focusing on dashboards visualization for stakeholders (i.e., students and teachers) and an early feedback prediction system to intervene in the case of at-risk identification. The early warning system has been evaluated in two case studies on first-year undergraduate courses in Computer Science. We show the accuracy of the correct identification of at-risk students, the students' appraisal and the most common factors which lead to at-risk level.

ACS Style

David Baneres; M. Elena Rodriguez-Gonzalez; Montse Serra. An Early Feedback Prediction System for Learners At-Risk Within a First-Year Higher Education Course. IEEE Transactions on Learning Technologies 2019, 12, 249 -263.

AMA Style

David Baneres, M. Elena Rodriguez-Gonzalez, Montse Serra. An Early Feedback Prediction System for Learners At-Risk Within a First-Year Higher Education Course. IEEE Transactions on Learning Technologies. 2019; 12 (2):249-263.

Chicago/Turabian Style

David Baneres; M. Elena Rodriguez-Gonzalez; Montse Serra. 2019. "An Early Feedback Prediction System for Learners At-Risk Within a First-Year Higher Education Course." IEEE Transactions on Learning Technologies 12, no. 2: 249-263.

Conference paper
Published: 29 November 2018 in Transactions on Petri Nets and Other Models of Concurrency XV
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Nowadays, the amount of open data sources is increasing exponentially from both the public and private sectors. These data are commonly available from different end-point services that can be queried following the standards of the technology used to create the service. Despite the great potential of open data, and the valuable information that its usage can report to improve the data itself, currently most of the data providers are unaware of how their data is used by end-users. This paper focuses on the design of a Model-Driven Analytical tool for Open Data APIs. Our tool is able to visualize how end-users interact with open data sources regarding two types of metrics: (1) performance metrics, focused on general usage parameters like response time; and (2) semantic metrics, focused to analyze contextualized data. The tool is described and a case study is presented based on a model manually composed from two Open Data APIs. The monitoring of the open data consumption reports highly valuable information to data owners, guaranteeing the return-on-investment.

ACS Style

Elena Planas; David Baneres. Model-Driven Analytics for Open Data APIs. Transactions on Petri Nets and Other Models of Concurrency XV 2018, 176 -182.

AMA Style

Elena Planas, David Baneres. Model-Driven Analytics for Open Data APIs. Transactions on Petri Nets and Other Models of Concurrency XV. 2018; ():176-182.

Chicago/Turabian Style

Elena Planas; David Baneres. 2018. "Model-Driven Analytics for Open Data APIs." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 176-182.

Conference paper
Published: 26 August 2018 in Advances on P2P, Parallel, Grid, Cloud and Internet Computing
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Nowadays, online learning has become a promising solution to personalize and increase flexibility in the learning-teaching process. However, e-assessment is still questioned in terms of authorship and identity checking. Some virtual learning environments are introducing technological solutions, such as plagiarism detection tools, to increase the security when submitting assessment activities. However, this is a partial solution. When the activities are performed on third-party tools, as it is the case of intelligent tutoring systems, the identity and authorship checking can fail. This paper introduces a modular plagiarism detection tool that combines different input data sources in order to verify the authorship. A case study is presented to show the potential of the tool.

ACS Style

David Bañeres; Ingrid Noguera; M. Elena Rodríguez; Ana Guerrero-Roldán. Using an Intelligent Tutoring System with Plagiarism Detection to Enhance e-Assessment. Advances on P2P, Parallel, Grid, Cloud and Internet Computing 2018, 363 -372.

AMA Style

David Bañeres, Ingrid Noguera, M. Elena Rodríguez, Ana Guerrero-Roldán. Using an Intelligent Tutoring System with Plagiarism Detection to Enhance e-Assessment. Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 2018; ():363-372.

Chicago/Turabian Style

David Bañeres; Ingrid Noguera; M. Elena Rodríguez; Ana Guerrero-Roldán. 2018. "Using an Intelligent Tutoring System with Plagiarism Detection to Enhance e-Assessment." Advances on P2P, Parallel, Grid, Cloud and Internet Computing , no. : 363-372.

Conference paper
Published: 18 August 2018 in Programmieren für Ingenieure und Naturwissenschaftler
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For online and blended education institutions, there is a severe handicap when they need to justify how the authentication and authorship of their students are guaranteed during the whole instructional process. Different approaches have been proposed in the past but most of them only depend on specific technological solutions. These solutions in order to be successfully accepted in educational settings have to be transparently integrated with the educational process according to pedagogical criteria. This paper analyses the results of the first pilot based on the TeSLA trustworthy system for a blended and a fully online institutions focused on engineering academic programs.

ACS Style

M. Elena Rodríguez; David Baneres; Malinka Ivanova; Mariana Durcheva. Case Study Analysis on Blended and Online Institutions by Using a Trustworthy System. Programmieren für Ingenieure und Naturwissenschaftler 2018, 40 -53.

AMA Style

M. Elena Rodríguez, David Baneres, Malinka Ivanova, Mariana Durcheva. Case Study Analysis on Blended and Online Institutions by Using a Trustworthy System. Programmieren für Ingenieure und Naturwissenschaftler. 2018; ():40-53.

Chicago/Turabian Style

M. Elena Rodríguez; David Baneres; Malinka Ivanova; Mariana Durcheva. 2018. "Case Study Analysis on Blended and Online Institutions by Using a Trustworthy System." Programmieren für Ingenieure und Naturwissenschaftler , no. : 40-53.

Conference paper
Published: 01 July 2018 in EDULEARN18 Proceedings
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ACS Style

Ingrid Noguera; Anna-Elena Guerrero-Roldán; Esther Huertas; Roger Roca; David Bañeres. ENHANCING THE QUALITY OF ONLINE ASSESSMENT WITH THE SUPPORT OF AN E-AUTHENTICATION SYSTEM. EDULEARN18 Proceedings 2018, 1893 -1903.

AMA Style

Ingrid Noguera, Anna-Elena Guerrero-Roldán, Esther Huertas, Roger Roca, David Bañeres. ENHANCING THE QUALITY OF ONLINE ASSESSMENT WITH THE SUPPORT OF AN E-AUTHENTICATION SYSTEM. EDULEARN18 Proceedings. 2018; ():1893-1903.

Chicago/Turabian Style

Ingrid Noguera; Anna-Elena Guerrero-Roldán; Esther Huertas; Roger Roca; David Bañeres. 2018. "ENHANCING THE QUALITY OF ONLINE ASSESSMENT WITH THE SUPPORT OF AN E-AUTHENTICATION SYSTEM." EDULEARN18 Proceedings , no. : 1893-1903.

Conference paper
Published: 01 April 2018 in 2018 17th International Conference on Information Technology Based Higher Education and Training (ITHET)
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EAssessment uses technology to support online evaluation of students' knowledge and skills. However, challenging problems must be addressed such as trustworthiness among students and teachers in blended and online settings. The TeSLA system proposes an innovative solution to guarantee correct authentication of students and to prove the authorship of their assessment tasks. Technologically, the system is based on the integration of five instruments: face recognition, voice recognition, keystroke dynamics, forensic analysis, and plagiarism. The paper aims to analyze and compare the results achieved after the second pilot performed in an online and a blended university revealing the realization of trust-driven solutions for eAssessment.

ACS Style

Malinka Ivanova; Mariana Durcheva; David Baneres; M. Elena Rodriguez. eAssessment by using a Trustworthy System in Blended and Online Institutions. 2018 17th International Conference on Information Technology Based Higher Education and Training (ITHET) 2018, 1 -7.

AMA Style

Malinka Ivanova, Mariana Durcheva, David Baneres, M. Elena Rodriguez. eAssessment by using a Trustworthy System in Blended and Online Institutions. 2018 17th International Conference on Information Technology Based Higher Education and Training (ITHET). 2018; ():1-7.

Chicago/Turabian Style

Malinka Ivanova; Mariana Durcheva; David Baneres; M. Elena Rodriguez. 2018. "eAssessment by using a Trustworthy System in Blended and Online Institutions." 2018 17th International Conference on Information Technology Based Higher Education and Training (ITHET) , no. : 1-7.

Chapter
Published: 10 February 2018 in Advances on P2P, Parallel, Grid, Cloud and Internet Computing
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Teachers are always in need of new tools to support the learning process. Learning analytics has emerged as a solution to provide feedback about the learning progress of students. This solution does not only provide meaningful information to instructors to analyze and improve the learning process, but also to managers and other stakeholders of the learning processes. In this chapter, we extend the vision of learning analytics to predictive analytics. Currently, we are ready to see further in the future and predict the behavior of students based on their actions, and this idea opens a broad potential for educational settings. This chapter discusses challenges, benefits and weaknesses of a predictive system for education. Additionally, the design of a generic predictive system is proposed and experimental results in a real scenario are shown to validate its potential.

ACS Style

David Baneres; Montse Serra. Predictive Analytics: Another Vision of the Learning Process. Advances on P2P, Parallel, Grid, Cloud and Internet Computing 2018, 1 -25.

AMA Style

David Baneres, Montse Serra. Predictive Analytics: Another Vision of the Learning Process. Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 2018; ():1-25.

Chicago/Turabian Style

David Baneres; Montse Serra. 2018. "Predictive Analytics: Another Vision of the Learning Process." Advances on P2P, Parallel, Grid, Cloud and Internet Computing , no. : 1-25.

Conference paper
Published: 03 November 2017 in Advances on P2P, Parallel, Grid, Cloud and Internet Computing
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Learners require a certain effort to acquire a specific skill or competence. The invested effort can be affected by many factors as previous knowledge, abilities or time available for learning. The evaluation of the effort has been mainly related to cognitive science or instructional psychology due to the relation between effort and mental work. This paper focuses on how the effort can be estimated on e-learning systems. This information can enhance instructional process since teachers can analyze the total time learners invest on acquiring knowledge and they can adjust better the complexity of the course. The paper contributes on principles to design an effort-based system. Finally, a particular application to an intelligent tutoring system is performed.

ACS Style

David Bañeres. Principles for an Effort-Aware System. Advances on P2P, Parallel, Grid, Cloud and Internet Computing 2017, 576 -585.

AMA Style

David Bañeres. Principles for an Effort-Aware System. Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 2017; ():576-585.

Chicago/Turabian Style

David Bañeres. 2017. "Principles for an Effort-Aware System." Advances on P2P, Parallel, Grid, Cloud and Internet Computing , no. : 576-585.

Conference paper
Published: 05 July 2017 in Advances in Intelligent Systems and Computing
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Predictive models to evaluate student’s performance have been widely used in the past. These models have been basically used as a statistical tool to predict whether students will pass a course based on previous background variables such as prior-learning or academic records. These models have a large potential to give support to teachers and learners during the learning process in real time. This paper focuses on the design foundations of a predictive core system. This core system is the essential component to build in the future a predictive support framework. Additionally, experimental results are shown to validate the quality of the designed system.

ACS Style

David Bañeres; Montse Serra. On the Design of a System to Predict Student’s Success. Advances in Intelligent Systems and Computing 2017, 611, 274 -286.

AMA Style

David Bañeres, Montse Serra. On the Design of a System to Predict Student’s Success. Advances in Intelligent Systems and Computing. 2017; 611 ():274-286.

Chicago/Turabian Style

David Bañeres; Montse Serra. 2017. "On the Design of a System to Predict Student’s Success." Advances in Intelligent Systems and Computing 611, no. : 274-286.

Journal article
Published: 27 June 2017 in International Journal of Emerging Technologies in Learning (iJET)
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Is my professional knowledge outdated? Do I have the skills needed for the new challenges of the society? What knowledge do I lack to qualify for a job I like? What universities can I address to get knowledge that improves my employment expectations? These are relevant questions that all employees have done in any moment of their life. In addition, when there are high rates of unemployment and job offers that keep unfilled, the answers to these questions are even more relevant. Answering such questions open new opportunities for employed and unemployed people, by allowing them to design a formative plan according to their skills and expectations. It also provides evidences to employers about the skills and knowledge of the society, making them more aware of the skills of their potential future employees. The companies also will have more knowledge to design the professional career of their employees according to the company needs and the knowledge and skills of their employees. This paper proposes a system that helps people by showing which knowledge and skills a person misses for a given job position and what university courses the person can take to acquire the required skills and knowledge. The system has been implemented as a recommender system that helps users in planning their life-long learning. The paper shows the architecture of the proposed system, a case study to explain how it works, a survey to validate its usefulness and usability and some conclusions after its first experimentation.

ACS Style

David Baneres; Jordi Conesa. A Life-long Learning Recommender System to Promote Employability. International Journal of Emerging Technologies in Learning (iJET) 2017, 12, 77 .

AMA Style

David Baneres, Jordi Conesa. A Life-long Learning Recommender System to Promote Employability. International Journal of Emerging Technologies in Learning (iJET). 2017; 12 (6):77.

Chicago/Turabian Style

David Baneres; Jordi Conesa. 2017. "A Life-long Learning Recommender System to Promote Employability." International Journal of Emerging Technologies in Learning (iJET) 12, no. 6: 77.

Journal article
Published: 27 June 2017 in International Journal of Emerging Technologies in Learning (iJET)
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Nowadays, instructors apply a large variety of learning methodologies to help learners to achieve the learning outcomes and to assess the knowledge acquired across the course. Formative and summative assessment models are mainly applied in multiple combinations independently of the learning environment (on-site, online or blended). When we move to an adaptive learning, the adaption tends to be in the learning process (learning path, activities, educational resources) mainly related to formative models but little adaption can be found related to summative models and very restrictive. In the latter case, grade formulas depending on performed assessment activities are typically defined to provide a personalized learning process. In this paper, we introduce the basis of an innovative personalized summative model based on learner's preferences and effort. Although this model conceptually may allow passing a course without evaluating all learning outcomes, it is not far from conventional summative models where a certain grade is required to pass the course and the learner may not have acquired all the knowledge taught in the course. The paper introduces the model and it also analyses an opinion survey on instructors and learners.

ACS Style

David Baneres. A Personalized Summative Model based on Learner?s Effort. International Journal of Emerging Technologies in Learning (iJET) 2017, 12, 4 .

AMA Style

David Baneres. A Personalized Summative Model based on Learner?s Effort. International Journal of Emerging Technologies in Learning (iJET). 2017; 12 (6):4.

Chicago/Turabian Style

David Baneres. 2017. "A Personalized Summative Model based on Learner?s Effort." International Journal of Emerging Technologies in Learning (iJET) 12, no. 6: 4.

E technical paper
Published: 18 April 2017 in International Journal of Web Information Systems
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Purpose The purpose of this paper is to present an innovative web-based eLearning platform called ICT-FLAG that provides e-assessment tools with general-purpose formative assessment services featuring learning analytics and gamification. Design/methodology/approach The paper reports on the technical development of the platform driven by the Reference Model for Open Distributed Processing software methodology, which guides the platform construction, including the analysis and design steps. Findings The ICT-FLAG platform is technically tested by integrating it into a real e-assessment tool. Results are positive in terms of functional and non-functional aspects as well as user’s satisfaction on usability, emotional state, thus validating the platform as a valuable educational tool. Research limitations/implications Because of the chosen technical paper as article type, validation of the impact of the ICT-FLAG platform in the learning process is not provided. Ongoing research with this platform is to measure the learning outcomes of its use in a real context of eLearning. Practical implications The paper shows implications of the main technical issues and challenges encountered during the integration of the ICT-FLAG platform with external eLearning tools, involving relevant aspects of interoperability, security, modularity, scalability, portability and so on. Originality/value This platform can fill the gap of many e-assessment systems, which currently do not have built-in analytical and gamification tools for learning, thus providing them with the experience to improve the quality of education and learning.

ACS Style

David Gañán; Santi Caballé; Robert Clarisó; Jordi Conesa; David Bañeres. ICT-FLAG: a web-based e-assessment platform featuring learning analytics and gamification. International Journal of Web Information Systems 2017, 13, 25 -54.

AMA Style

David Gañán, Santi Caballé, Robert Clarisó, Jordi Conesa, David Bañeres. ICT-FLAG: a web-based e-assessment platform featuring learning analytics and gamification. International Journal of Web Information Systems. 2017; 13 (1):25-54.

Chicago/Turabian Style

David Gañán; Santi Caballé; Robert Clarisó; Jordi Conesa; David Bañeres. 2017. "ICT-FLAG: a web-based e-assessment platform featuring learning analytics and gamification." International Journal of Web Information Systems 13, no. 1: 25-54.

Journal article
Published: 27 October 2016 in International Journal of Emerging Technologies in Learning (iJET)
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Nowadays, universities (on-site and online) have a large competition in order to attract more students. In this panorama, learning analytics can be a very useful tool since it allows instructors (and university managers) to get a more thorough view of their context, to better understand the environment, and to identify potential improvements. In order to perform analytics efficiently, it is necessary to have as much information as possible about the instructional context. The paper proposes a novel approach to gather information from different aspects within courses. In particular, the approach applies natural language processing (NLP) techniques to analyze the course’s materials and discover what concepts are taught, their relevancy in the course and their alignment with the learning outcomes of the course. The contribution of the paper is a semi-automatic system that allows obtaining a better understanding of courses. A validation experiment on a master of the Open University of Catalonia is presented in order to show the quality of the results. The system can be used to analyze the suitability of course’s materials and to enrich and contextualize other analytical processes.

ACS Style

Isabel Guitart; Jordi Conesa; David Baneres; Joaquim Moré; Jordi Duran; David Gañan. Extraction of Relevant Terms and Learning Outcomes from Online Courses. International Journal of Emerging Technologies in Learning (iJET) 2016, 11, 22 -30.

AMA Style

Isabel Guitart, Jordi Conesa, David Baneres, Joaquim Moré, Jordi Duran, David Gañan. Extraction of Relevant Terms and Learning Outcomes from Online Courses. International Journal of Emerging Technologies in Learning (iJET). 2016; 11 (10):22-30.

Chicago/Turabian Style

Isabel Guitart; Jordi Conesa; David Baneres; Joaquim Moré; Jordi Duran; David Gañan. 2016. "Extraction of Relevant Terms and Learning Outcomes from Online Courses." International Journal of Emerging Technologies in Learning (iJET) 11, no. 10: 22-30.

Conference paper
Published: 27 October 2016 in 2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS)
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Is my professional knowledge outdated? Do I have the skills needed for the new challenges of the society? What knowledge do I lack to qualify for a job I like? What universities can I address to get knowledge that improves my employment expectations? These are relevant questions that an employee has done in any moment of their life. In addition, when there are high rates of unemployment and job offers that keep deserted, the answers to these questions are even more relevant, since they open new opportunities for unemployed people. This paper proposes a system that helps people by showing what are the required knowledge and skills a person misses for a given job position and what courses the person can take in order to acquire such skills and knowledge. The system has been implemented as a recommender system that helps users in planning their life-long learning. The paper shows the architecture of the proposed system, a case study to explain how it works and some conclusions after its first experimentation.

ACS Style

David Baneres; Jordi Conesa. [email protected] -- A Recommender System to Address Life-Long Learning and Promote Employability. 2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS) 2016, 351 -356.

AMA Style

David Baneres, Jordi Conesa. [email protected] -- A Recommender System to Address Life-Long Learning and Promote Employability. 2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS). 2016; ():351-356.

Chicago/Turabian Style

David Baneres; Jordi Conesa. 2016. "[email protected] -- A Recommender System to Address Life-Long Learning and Promote Employability." 2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS) , no. : 351-356.