This page has only limited features, please log in for full access.

Dr. Francisco J. Gallego-Durán
University of Alicante (Spain)

Basic Info


Research Keywords & Expertise

0 Education
0 Game AI
0 Game Architecture
0 Games
0 Gamification

Fingerprints

Learning
Games
Education
Gamification
Machine Learning
Programming
Serious Games
Gamification of learning
Games and gamification

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

Researcher on Serious Games, Gamification, Game AI and Game Design for Learning. Developer of Computer Games, Serious Games and Software. Research background in AI, Machine Learning, Genetic Algorithms and Neural Networks. Interested on improving teaching, learning, knowledge and abilities adquisition, and developing innovative methods and technologies for this adventure.

Following
Followers
Co Authors
Profile ImagePatricia Compañ-Rosique Smart Learning Group, Cáted...
Profile ImageR. Satorre-Cuerda Smart Learning Group, Cáted...
Profile ImageRafael Molina-Carmona Universidad de Alicante
Profile ImageFaraón Llorens-Largo Universidad de Alicante
Following: 5 users
View all

Feed

Journal article
Published: 17 March 2021 in Sustainability
Reads 0
Downloads 0

The design and development of Serious Games is a complex task, including a considerable risk of failure. Many attempts end up in non-fun, non-engaging games that fail to meet the purpose of improving education. Many different proposals have been published in the form of design frameworks, with the aim of helping practitioners succeed. Although these frameworks define and explain relevant concepts and guidelines, there is lack of focus in iterative methodologies. These methodologies have proven valuable in other areas on engineering and are also used by commercial game designers. This work proposes the introduction of iterative design for Serious Games and presents an early stage methodology, along with an example of the core mechanic of a game and a prototype for learning the concept of slope of a line.

ACS Style

Sergio Viudes-Carbonell; Francisco Gallego-Durán; Faraón Llorens-Largo; Rafael Molina-Carmona. Towards an Iterative Design for Serious Games. Sustainability 2021, 13, 3290 .

AMA Style

Sergio Viudes-Carbonell, Francisco Gallego-Durán, Faraón Llorens-Largo, Rafael Molina-Carmona. Towards an Iterative Design for Serious Games. Sustainability. 2021; 13 (6):3290.

Chicago/Turabian Style

Sergio Viudes-Carbonell; Francisco Gallego-Durán; Faraón Llorens-Largo; Rafael Molina-Carmona. 2021. "Towards an Iterative Design for Serious Games." Sustainability 13, no. 6: 3290.

Long paper
Published: 15 November 2020 in Universal Access in the Information Society
Reads 0
Downloads 0

Learning to program is becoming a universally desired ability. Discovering better ways to teach programming and improving existing ones is essential to increase its accessibility. At present, most teaching approaches focus on high-level languages and constructs to ease understanding. However, understanding problems seem to persist making the learning process slow and painful. Moreover, mental models developed by students present gaps and misunderstandings that limit their maximum achievable abilities. This paper presents a new approach to teach students bottom-up, starting from machine code and assembler programming. This approach has been tested on first-year university students for two consecutive years. Experimental groups attended a 16 h course the week before their first term at the university. Then, their performance was comparatively measured against the control group through their marks on the introductory Programming 1 subject. Several potential confounding factors were also considered. Results suggested that such a small intervention could have positive, though limited, influence in their programming abilities. The experimental setup is detailed, and all data gathered are included for reproducibility.

ACS Style

Francisco J. Gallego-Durán; Rosana Satorre-Cuerda; Patricia Compañ-Rosique; Carlos J. Villagrá-Arnedo; Rafael Molina-Carmona; Faraón Llorens-Largo. A low-level approach to improve programming learning. Universal Access in the Information Society 2020, 20, 479 -493.

AMA Style

Francisco J. Gallego-Durán, Rosana Satorre-Cuerda, Patricia Compañ-Rosique, Carlos J. Villagrá-Arnedo, Rafael Molina-Carmona, Faraón Llorens-Largo. A low-level approach to improve programming learning. Universal Access in the Information Society. 2020; 20 (3):479-493.

Chicago/Turabian Style

Francisco J. Gallego-Durán; Rosana Satorre-Cuerda; Patricia Compañ-Rosique; Carlos J. Villagrá-Arnedo; Rafael Molina-Carmona; Faraón Llorens-Largo. 2020. "A low-level approach to improve programming learning." Universal Access in the Information Society 20, no. 3: 479-493.

Conference paper
Published: 21 October 2020 in Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality
Reads 0
Downloads 0

On previous years teaching Logic and Algebra many student conceptual issues were identified by analysing their solution attempts to exercises. Present work proposes a new design of exercises and student workflow to target these issues. Classical algebraic exercises integrate many concepts. Most issues identified were related to low-level concepts. Moreover, students proved unable to identify these issues and solve them by practicing. They tended to get frustrated not knowing the causes of their failures. This design proposal starts with minimal exercises requiring a single step to be solved. Classical exercises are decomposed into these single steps. Simplicity of these exercises helps generating many instances automatically. Design focus is placed on previously identified issues. Designed exercises are composed in a pyramidal model of knowledge. To motivate students to carry out many of the proposed exercises, Gamification techniques are used. Designed exercises are automated using Moodle questionnaires. These questionnaires are contextualized as adventure activities in a role play story line, including Quests, Dungeons, Weapons and Bosses. Rules are designed according to this context. Detailed design is included for replication.

ACS Style

Francisco J. Gallego-Durán; Carlos J. Villagrá Arnedo; Rafael Molina-Carmona; Faraón Llorens-Largo; Spain Francisco J. Gallego-Durán Universidad de Alicante; Spain Carlos J. Villagrá Arnedo Universidad de Alicante; Spain Rafael Molina-Carmona Universidad de Alicante; Spain Faraón Llorens-Largo Universidad de Alicante. Smartly Learning through step decomposition, automation and Gamification. Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality 2020, 1 .

AMA Style

Francisco J. Gallego-Durán, Carlos J. Villagrá Arnedo, Rafael Molina-Carmona, Faraón Llorens-Largo, Spain Francisco J. Gallego-Durán Universidad de Alicante, Spain Carlos J. Villagrá Arnedo Universidad de Alicante, Spain Rafael Molina-Carmona Universidad de Alicante, Spain Faraón Llorens-Largo Universidad de Alicante. Smartly Learning through step decomposition, automation and Gamification. Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality. 2020; ():1.

Chicago/Turabian Style

Francisco J. Gallego-Durán; Carlos J. Villagrá Arnedo; Rafael Molina-Carmona; Faraón Llorens-Largo; Spain Francisco J. Gallego-Durán Universidad de Alicante; Spain Carlos J. Villagrá Arnedo Universidad de Alicante; Spain Rafael Molina-Carmona Universidad de Alicante; Spain Faraón Llorens-Largo Universidad de Alicante. 2020. "Smartly Learning through step decomposition, automation and Gamification." Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality , no. : 1.

Journal article
Published: 05 November 2019 in Informatics
Reads 0
Downloads 0

Many researchers consider Gamification as a powerful way to improve education. Many studies show improvements with respect to traditional methodologies. Several educational strategies have also been combined with Gamification with interesting results. Interest is growing and evidence suggest Gamification has a promising future. However, there is a barrier preventing many researchers from properly understanding Gamification principles. Gamification focuses of engaging trainees in learning with same intensity that games engage players on playing. But only some very well designed games achieve this level of engagement. Designing truly entertaining games is a difficult task with a great artistic component. Although some studies have tried to clarify how Game Design produces fun, there is no scientific consensus. Well established knowledge on Game Design resides in sets of rules of thumb and good practices, based on empirical experience. Game industry professionals acquire this experience through practice. Most educators and researchers often overlook the need for such experience to successfully design Gamification. And so, many research papers focus on single game-elements like points, present non-gaming activities like questionnaires, design non-engaging activities or fail to comprehend the underlying principles on why their designs do not yield expected results. This work presents a rubric for educators and researchers to start working in Gamification without previous experience in Game Design. This rubric decomposes the continuous space of Game Design into a set of ten discrete characteristics. It is aimed at diminishing the entry barrier and helping to acquire initial experience with Game Design fundamentals. The main proposed uses are twofold: to analyse existing games or gamified activities gaining a better understanding of their strengths and weaknesses and to help in the design or improvement of activities. Focus is on Game Design characteristics rather than game elements, similarly to professional game designers. The goal is to help gaining experience towards designing successful Gamification environments. Presented rubric is based on our previous design experience, compared and contrasted with literature, and empirically tested with some example games and gamified activities.

ACS Style

Francisco J. Gallego-Durán; Carlos J. Villagrá-Arnedo; Rosana Satorre-Cuerda; Patricia Compañ-Rosique; Rafael Molina-Carmona; Faraón Llorens-Largo. A Guide for Game-Design-Based Gamification. Informatics 2019, 6, 49 .

AMA Style

Francisco J. Gallego-Durán, Carlos J. Villagrá-Arnedo, Rosana Satorre-Cuerda, Patricia Compañ-Rosique, Rafael Molina-Carmona, Faraón Llorens-Largo. A Guide for Game-Design-Based Gamification. Informatics. 2019; 6 (4):49.

Chicago/Turabian Style

Francisco J. Gallego-Durán; Carlos J. Villagrá-Arnedo; Rosana Satorre-Cuerda; Patricia Compañ-Rosique; Rafael Molina-Carmona; Faraón Llorens-Largo. 2019. "A Guide for Game-Design-Based Gamification." Informatics 6, no. 4: 49.

Conference paper
Published: 24 October 2018 in Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality
Reads 0
Downloads 0
ACS Style

Francisco J. Gallego-Durán; Carlos-José Villagrá-Arnedo. Enchanted Talk. Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality 2018, 668 -673.

AMA Style

Francisco J. Gallego-Durán, Carlos-José Villagrá-Arnedo. Enchanted Talk. Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality. 2018; ():668-673.

Chicago/Turabian Style

Francisco J. Gallego-Durán; Carlos-José Villagrá-Arnedo. 2018. "Enchanted Talk." Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality , no. : 668-673.

Conference paper
Published: 30 May 2018 in Transactions on Petri Nets and Other Models of Concurrency XV
Reads 0
Downloads 0

There are subjects in which teaching and learning is hard by experience. Some subjects in physics, maths or computing seem to be difficult by nature. Teachers test many ways to help student learn these subjects. In Computer Programming the approach seems to be using higher-level languages, concepts and abstractions. It seems reasonable that languages similar to human language can ease the task of computer programming. Similar ideas are explored in other subjects. However, this seems contradictory with the way we construct knowledge: lower-level concepts support the development of higher-level ones. Is it possible to master higher-level concepts without previously mastering lower-level ones? Present work questions two underlying ideas that are basis of nowadays teaching: that lower-level languages like machine code and assembler are difficult by nature, and that lower-level concepts can be skipped by better teaching higher-level ones. Two experiments and one activity are presented. Evidence gathered contradicts both ideas and suggests that low-level concepts might be much more relevant than thought for computer programming.

ACS Style

Francisco J. Gallego-Durán; Carlos-José Villagrá-Arnedo; Rosana Satorre Cuerda; Patricia Compañ; Faraón Llorens-Largo. Effects of Low-Level Development on Learning to Program. Transactions on Petri Nets and Other Models of Concurrency XV 2018, 431 -445.

AMA Style

Francisco J. Gallego-Durán, Carlos-José Villagrá-Arnedo, Rosana Satorre Cuerda, Patricia Compañ, Faraón Llorens-Largo. Effects of Low-Level Development on Learning to Program. Transactions on Petri Nets and Other Models of Concurrency XV. 2018; ():431-445.

Chicago/Turabian Style

Francisco J. Gallego-Durán; Carlos-José Villagrá-Arnedo; Rosana Satorre Cuerda; Patricia Compañ; Faraón Llorens-Largo. 2018. "Effects of Low-Level Development on Learning to Program." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 431-445.

Long paper
Published: 13 July 2017 in Universal Access in the Information Society
Reads 0
Downloads 0

An effective adaptive learning system would theoretically maintain learners in a permanent state of flow. In this state, learners are completely focused on activities. To attain this state, the difficulty of learning activities must match learners’ skills. To perform this matching, it is essential to define, measure and deeply analyze difficulty. However, very few previous works deal with difficulty in depth. Most commonly, difficulty is defined as a one-dimensional value. This permits ordering activities, but limits the possibilities of deep analysis of activities and learners’ performance. This work proposes a new definition of difficulty and a way to measure it. The proposed definition depends on learners’ progress on activities over time. This expands the concept of difficulty over a two-dimensional space, also making it drawable. The difficulty graphs provide a rich interpretation with insights into the learning process. A practical case is presented: the PLMan learning system. This system is formed by a web application and a game to teach computational logic. The proposed definition is applied in this context. Measures are taken and analyzed using difficulty graphs. Some examples of these analyses are shown to illustrate the benefits of this proposal. Singularities and interesting spots are easily identified in graphs, providing insights in the activities. This new information lets experts adapt the learning system by improving activity classification and assignment. This first step lays solid foundations for automation, making the PLMan learning system fully adaptive.

ACS Style

Francisco J. Gallego-Durán; Rafael Molina-Carmona; Faraón Llorens-Largo. Measuring the difficulty of activities for adaptive learning. Universal Access in the Information Society 2017, 17, 335 -348.

AMA Style

Francisco J. Gallego-Durán, Rafael Molina-Carmona, Faraón Llorens-Largo. Measuring the difficulty of activities for adaptive learning. Universal Access in the Information Society. 2017; 17 (2):335-348.

Chicago/Turabian Style

Francisco J. Gallego-Durán; Rafael Molina-Carmona; Faraón Llorens-Largo. 2017. "Measuring the difficulty of activities for adaptive learning." Universal Access in the Information Society 17, no. 2: 335-348.

Journal article
Published: 01 July 2017 in Computers in Human Behavior
Reads 0
Downloads 0

Early prediction systems of student performance can be very useful to guide student learning. For a prediction model to be really useful as an effective aid for learning, it must provide tools to adequately interpret progress, to detect trends and behaviour patterns and to identify the causes of learning problems. White-box and black-box techniques have been described in literature to implement prediction models. White-box techniques require a priori models to explore, which make them easy to interpret but difficult to be generalized and unable to detect unexpected relationships between data. Black-box techniques are easier to generalize and suitable to discover unsuspected relationships but they are cryptic and difficult to be interpreted for most teachers. In this paper a black-box technique is proposed to take advantage of the power and versatility of these methods, while making some decisions about the input data and design of the classifier that provide a rich output data set. A set of graphical tools is also proposed to exploit the output information and provide a meaningful guide to teachers and students. From our experience, a set of tips about how to design a prediction system and the representation of the output information is also provided. Black-box classifiers are proposed to predict student performance.Black-box classifiers are powerful and generalizable but difficult to interpret.Some tips about their design are proposed to improve their expressiveness.Some graphical tools are proposed to exploit the expressiveness and help students.

ACS Style

Carlos-José Villagrá-Arnedo; Francisco J. Gallego-Durán; Faraón Llorens-Largo; Patricia Compañ; Rosana Satorre Cuerda; Rafael Molina-Carmona. Improving the expressiveness of black-box models for predicting student performance. Computers in Human Behavior 2017, 72, 621 -631.

AMA Style

Carlos-José Villagrá-Arnedo, Francisco J. Gallego-Durán, Faraón Llorens-Largo, Patricia Compañ, Rosana Satorre Cuerda, Rafael Molina-Carmona. Improving the expressiveness of black-box models for predicting student performance. Computers in Human Behavior. 2017; 72 ():621-631.

Chicago/Turabian Style

Carlos-José Villagrá-Arnedo; Francisco J. Gallego-Durán; Faraón Llorens-Largo; Patricia Compañ; Rosana Satorre Cuerda; Rafael Molina-Carmona. 2017. "Improving the expressiveness of black-box models for predicting student performance." Computers in Human Behavior 72, no. : 621-631.

Conference paper
Published: 08 June 2017 in DNA Computing
Reads 0
Downloads 0
ACS Style

Vicente A. Quesada Mora; Francisco J. Gallego-Durán; Rafael Molina-Carmona; Faraón Llorens-Largo. Subliminal Learning. What Do Games Teach Us? DNA Computing 2017, 487 -501.

AMA Style

Vicente A. Quesada Mora, Francisco J. Gallego-Durán, Rafael Molina-Carmona, Faraón Llorens-Largo. Subliminal Learning. What Do Games Teach Us? DNA Computing. 2017; ():487-501.

Chicago/Turabian Style

Vicente A. Quesada Mora; Francisco J. Gallego-Durán; Rafael Molina-Carmona; Faraón Llorens-Largo. 2017. "Subliminal Learning. What Do Games Teach Us?" DNA Computing , no. : 487-501.

Chapter
Published: 01 January 2017 in Digital Technology Advancements in Knowledge Management
Reads 0
Downloads 0

Technological ecosystems are a widespread solution to address the challenges of the information technologies in organizations. It is important to have tools to correctly and quickly evaluate them. The Technological Ecosystem Map (TEmap) is a tool to intuitively interpret complex information maintaining both a global and a detailed vision of the technologies. It is a polygonal and structured representation of the main elements of the ecosystem. Each element is evaluated according to its maturity level, indicating how it contributes to fulfil the organization objectives. Each maturity level is represented by a colour, so that the TEmap takes the form of a heat map. The particular case of the University of Alicante is chosen to illustrate its construction. The TEmap is a simple but powerful way to identify the strengths and weaknesses of a technological ecosystem and the possible actions to improve the solution to the strategic questions of the organization.

ACS Style

Rafael Molina-Carmona; Patricia Compañ-Rosique; Rosana Satorre-Cuerda; Carlos-José Villagrá-Arnedo; Francisco J. Gallego-Durán; Faraón Llorens-Largo. Technological Ecosystem Maps for IT Governance. Digital Technology Advancements in Knowledge Management 2017, 50 -80.

AMA Style

Rafael Molina-Carmona, Patricia Compañ-Rosique, Rosana Satorre-Cuerda, Carlos-José Villagrá-Arnedo, Francisco J. Gallego-Durán, Faraón Llorens-Largo. Technological Ecosystem Maps for IT Governance. Digital Technology Advancements in Knowledge Management. 2017; ():50-80.

Chicago/Turabian Style

Rafael Molina-Carmona; Patricia Compañ-Rosique; Rosana Satorre-Cuerda; Carlos-José Villagrá-Arnedo; Francisco J. Gallego-Durán; Faraón Llorens-Largo. 2017. "Technological Ecosystem Maps for IT Governance." Digital Technology Advancements in Knowledge Management , no. : 50-80.

Journal article
Published: 20 October 2016 in IEEE Revista Iberoamericana de Tecnologias del Aprendizaje
Reads 0
Downloads 0

Although several definitions of gamification can be found in the literature, they all have in common certain aspects: the application of strategies, models, dynamics, mechanics and elements of the games in other contexts than games, and the objective of producing a playful experience that fosters motivation, involvement and fun. In this paper, our approach gamifying the learning process of a subject is presented. Our experience throughout time in using games and gamification in learning have led us to propose, lately, a personalized, automated and gamified learning system. As a result of this experience and after several years of continuous feedback from our students, we have learned several lessons on how to approach the task of gamification. These lessons are summarized in the following concepts: fun, motivation, autonomy, progressiveness, feedback, error tolerance, experimentation, creativity and adaptation to the specific case. The final aim is sharing our experience and opening a debate about what key elements the gamification lies in.

ACS Style

Faraon Llorens-Largo; Francisco J. Gallego-Durán; Carlos-José Villagrá-Arnedo; Patricia Compañ; Rosana Satorre Cuerda; Rafael Molina-Carmona. Gamification of the Learning Process: Lessons Learned. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje 2016, 11, 227 -234.

AMA Style

Faraon Llorens-Largo, Francisco J. Gallego-Durán, Carlos-José Villagrá-Arnedo, Patricia Compañ, Rosana Satorre Cuerda, Rafael Molina-Carmona. Gamification of the Learning Process: Lessons Learned. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje. 2016; 11 (4):227-234.

Chicago/Turabian Style

Faraon Llorens-Largo; Francisco J. Gallego-Durán; Carlos-José Villagrá-Arnedo; Patricia Compañ; Rosana Satorre Cuerda; Rafael Molina-Carmona. 2016. "Gamification of the Learning Process: Lessons Learned." IEEE Revista Iberoamericana de Tecnologias del Aprendizaje 11, no. 4: 227-234.

Journal article
Published: 31 July 2016 in International Journal of Design & Nature and Ecodynamics
Reads 0
Downloads 0
ACS Style

Carlos-José Villagrá-Arnedo; Francisco J. Gallego-Duraìn; Patricia Compañ-Rosique; Faraoìn Llorens-Largo; Rafael Molina-Carmona; Alex Rabasa; Carlos A. Brebbia. Predicting academic performance from behavioural and learning data. International Journal of Design & Nature and Ecodynamics 2016, 11, 239 -249.

AMA Style

Carlos-José Villagrá-Arnedo, Francisco J. Gallego-Duraìn, Patricia Compañ-Rosique, Faraoìn Llorens-Largo, Rafael Molina-Carmona, Alex Rabasa, Carlos A. Brebbia. Predicting academic performance from behavioural and learning data. International Journal of Design & Nature and Ecodynamics. 2016; 11 (3):239-249.

Chicago/Turabian Style

Carlos-José Villagrá-Arnedo; Francisco J. Gallego-Duraìn; Patricia Compañ-Rosique; Faraoìn Llorens-Largo; Rafael Molina-Carmona; Alex Rabasa; Carlos A. Brebbia. 2016. "Predicting academic performance from behavioural and learning data." International Journal of Design & Nature and Ecodynamics 11, no. 3: 239-249.

Conference paper
Published: 21 June 2016 in Transactions on Petri Nets and Other Models of Concurrency XV
Reads 0
Downloads 0
ACS Style

Carlos-José Villagrá-Arnedo; Francisco J. Gallego-Durán; Rafael Molina-Carmona; Faraón Llorens-Largo. PLMan: Towards a Gamified Learning System. Transactions on Petri Nets and Other Models of Concurrency XV 2016, 82 -93.

AMA Style

Carlos-José Villagrá-Arnedo, Francisco J. Gallego-Durán, Rafael Molina-Carmona, Faraón Llorens-Largo. PLMan: Towards a Gamified Learning System. Transactions on Petri Nets and Other Models of Concurrency XV. 2016; ():82-93.

Chicago/Turabian Style

Carlos-José Villagrá-Arnedo; Francisco J. Gallego-Durán; Rafael Molina-Carmona; Faraón Llorens-Largo. 2016. "PLMan: Towards a Gamified Learning System." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 82-93.

Conference paper
Published: 21 June 2016 in Recent Advances in Parallel Virtual Machine and Message Passing Interface
Reads 0
Downloads 0
ACS Style

Francisco J. Gallego-Durán; Rafael Molina-Carmona; Faraón Llorens-Largo. An Approach to Measuring the Difficulty of Learning Activities. Recent Advances in Parallel Virtual Machine and Message Passing Interface 2016, 417 -428.

AMA Style

Francisco J. Gallego-Durán, Rafael Molina-Carmona, Faraón Llorens-Largo. An Approach to Measuring the Difficulty of Learning Activities. Recent Advances in Parallel Virtual Machine and Message Passing Interface. 2016; ():417-428.

Chicago/Turabian Style

Francisco J. Gallego-Durán; Rafael Molina-Carmona; Faraón Llorens-Largo. 2016. "An Approach to Measuring the Difficulty of Learning Activities." Recent Advances in Parallel Virtual Machine and Message Passing Interface , no. : 417-428.

Book chapter
Published: 01 January 2016 in Formative Assessment, Learning Data Analytics and Gamification
Reads 0
Downloads 0
ACS Style

F. Llorens-Largo; Carlos-José Villagrá-Arnedo; Francisco J. Gallego-Durán; R. Satorre-Cuerda; P. Compañ-Rosique; R. Molina-Carmona. LudifyME. Formative Assessment, Learning Data Analytics and Gamification 2016, 245 -269.

AMA Style

F. Llorens-Largo, Carlos-José Villagrá-Arnedo, Francisco J. Gallego-Durán, R. Satorre-Cuerda, P. Compañ-Rosique, R. Molina-Carmona. LudifyME. Formative Assessment, Learning Data Analytics and Gamification. 2016; ():245-269.

Chicago/Turabian Style

F. Llorens-Largo; Carlos-José Villagrá-Arnedo; Francisco J. Gallego-Durán; R. Satorre-Cuerda; P. Compañ-Rosique; R. Molina-Carmona. 2016. "LudifyME." Formative Assessment, Learning Data Analytics and Gamification , no. : 245-269.

Conference paper
Published: 26 November 2015 in Transactions on Petri Nets and Other Models of Concurrency XV
Reads 0
Downloads 0

A prediction system to early detect learning problems is presented. The starting point is a gamified learning system from which a massive set of usage and learning data is collected. They are analyzed using Machine Learning techniques and a prediction of each student’s performance is obtained. The information is weekly presented as a progression chart, with valuable information about students’ progression. The system has a high degree of automation, is progressive, uses learning outcomes as well as usage data, allows the evaluation and prediction of the acquired skills, and contributes to a truly formative assessment.

ACS Style

Carlos-José Villagrá-Arnedo; Francisco J. Gallego-Durán; Rafael Molina-Carmona; Faraón Llorens-Largo. Boosting the Learning Process with Progressive Performance Prediction. Transactions on Petri Nets and Other Models of Concurrency XV 2015, 638 -641.

AMA Style

Carlos-José Villagrá-Arnedo, Francisco J. Gallego-Durán, Rafael Molina-Carmona, Faraón Llorens-Largo. Boosting the Learning Process with Progressive Performance Prediction. Transactions on Petri Nets and Other Models of Concurrency XV. 2015; ():638-641.

Chicago/Turabian Style

Carlos-José Villagrá-Arnedo; Francisco J. Gallego-Durán; Rafael Molina-Carmona; Faraón Llorens-Largo. 2015. "Boosting the Learning Process with Progressive Performance Prediction." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 638-641.

Conference paper
Published: 14 November 2015 in Transactions on Petri Nets and Other Models of Concurrency XV
Reads 0
Downloads 0

Learning practical abilities through exercises is a key aspect of any educational environment. To optimize learning, exercise difficulty should match abilities of the learner so that the exercises are neither so easy to bore learners nor so difficult to discourage them. The process of assigning a level of difficulty to an exercise is traditionally manual, so it is subject to teachers’ bias. Our hypothesis is about the possibility of establishing a relation between human and machine learning. In other words, we wonder if exercises that are difficult to be solved by a person are also difficult to be solved by the computer, and vice versa. To try to bring some light to this problem we have used a game for learning Computational Logic, to build neuroevolutionary algorithms to estimate exercise difficulty at the moment of exercise creation, without previous user data. The method is based on measuring the computational cost that neuroevolutionary algorithms take to find a solution and establishing similarities with previously gathered information from learners. Results show that there is a high degree of similarity between learner difficulty to solve different exercises and neuroevolutionary algorithms performance, suggesting that the approach is valid.

ACS Style

Francisco J. Gallego-Durán; Carlos-José Villagrá-Arnedo; Rafael Molina-Carmona; Faraón Llorens Largo. Applying Neuroevolution to Estimate the Difficulty of Learning Activities. Transactions on Petri Nets and Other Models of Concurrency XV 2015, 82 -91.

AMA Style

Francisco J. Gallego-Durán, Carlos-José Villagrá-Arnedo, Rafael Molina-Carmona, Faraón Llorens Largo. Applying Neuroevolution to Estimate the Difficulty of Learning Activities. Transactions on Petri Nets and Other Models of Concurrency XV. 2015; ():82-91.

Chicago/Turabian Style

Francisco J. Gallego-Durán; Carlos-José Villagrá-Arnedo; Rafael Molina-Carmona; Faraón Llorens Largo. 2015. "Applying Neuroevolution to Estimate the Difficulty of Learning Activities." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 82-91.

Conference paper
Published: 01 January 2013 in Computer Vision
Reads 0
Downloads 0

Neuroevolution has come a long way over the last decade. Lots of interesting and successful new methods and algorithms have been presented, with great improvements that make the field become very promising. Concretely, HyperNEAT has shown a great potential for evolving large scale neural networks, by discovering geometric regularities, thus being suitable for evolving complex controllers. However, once training phase has finished, evolved neural networks stay fixed and learning/adaptation does not happen anymore. A few methods have been proposed to address this concern, mainly using Hebbian plasticity and/or Compositional Pattern Producing Networks (CPPNs) like in Adaptive HyperNEAT. This methods have been tested in simple environments to isolate the effectiveness of adaptation from the Neuroevolution. In spite of this being quite convenient, more research is needed to better understand online adaptation in more complex environments. This paper shows a new proposal for online weight adaptation in neuroevolved artificial neural networks, and presents the results of several experiments carried out in a race simulation environment.

ACS Style

Francisco José Gallego-Durán; Rafael Molina-Carmona; Faraón Llorens-Largo. Experiments on Neuroevolution and Online Weight Adaptation in Complex Environments. Computer Vision 2013, 8109, 131 -138.

AMA Style

Francisco José Gallego-Durán, Rafael Molina-Carmona, Faraón Llorens-Largo. Experiments on Neuroevolution and Online Weight Adaptation in Complex Environments. Computer Vision. 2013; 8109 ():131-138.

Chicago/Turabian Style

Francisco José Gallego-Durán; Rafael Molina-Carmona; Faraón Llorens-Largo. 2013. "Experiments on Neuroevolution and Online Weight Adaptation in Complex Environments." Computer Vision 8109, no. : 131-138.

Book chapter
Published: 21 October 2008 in Advances in Intelligent and Soft Computing
Reads 0
Downloads 0

The prey-predator pursuit problem is referenced many times in literature. It is a generic multi-agent problem whose solutions could by applied to many particular instances. Solutions proposed usually apply non-supervised learning algorithms to train prey and predators. Most of these solutions criticize the greedy algorithm originally proposed by Korf. However, we believe that the improvement obtained by these new proposals does not pay off with relation to their complexity. The method used by Korf is a natural way to surround a prey without explicit communication between predators. The knowledge one predator has about others is limited just to what it can see. In Korf’s model, agents are able to see the complete world at once. In this paper we propose to start from Korf’s ideas and extend them to improve his model. First, we propose a simple extension of Korf’s fitness function and we consider the problems related to a partial view of the world. Second, we propose a communication protocol to partially overcome them. The final results suggest that more work needs to be done, and we propose a way to follow-on

ACS Style

Juan Reverte; Francisco Gallego; Faraón Llorens; Francisco J. Gallego-Durán. Extending Korf’s Ideas on the Pursuit Problem. Advances in Intelligent and Soft Computing 2008, 245 -249.

AMA Style

Juan Reverte, Francisco Gallego, Faraón Llorens, Francisco J. Gallego-Durán. Extending Korf’s Ideas on the Pursuit Problem. Advances in Intelligent and Soft Computing. 2008; ():245-249.

Chicago/Turabian Style

Juan Reverte; Francisco Gallego; Faraón Llorens; Francisco J. Gallego-Durán. 2008. "Extending Korf’s Ideas on the Pursuit Problem." Advances in Intelligent and Soft Computing , no. : 245-249.

Conference paper
Published: 14 October 2008 in Computer Vision
Reads 0
Downloads 0
ACS Style

Juan Reverte; Francisco J. Gallego-Durán; Rosana Satorre; Faraón Llorens. Mixing Greedy and Evolutive Approaches to Improve Pursuit Strategies. Computer Vision 2008, 203 -212.

AMA Style

Juan Reverte, Francisco J. Gallego-Durán, Rosana Satorre, Faraón Llorens. Mixing Greedy and Evolutive Approaches to Improve Pursuit Strategies. Computer Vision. 2008; ():203-212.

Chicago/Turabian Style

Juan Reverte; Francisco J. Gallego-Durán; Rosana Satorre; Faraón Llorens. 2008. "Mixing Greedy and Evolutive Approaches to Improve Pursuit Strategies." Computer Vision , no. : 203-212.