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Santos O.C.,ADeNu Research Group
Algorithms | Year: 2017

Personal tracking technologies allow sensing of the physical activity carried out by people. Data flows collected with these sensors are calling for big data techniques to support data collection, integration and analysis, aimed to provide personalized support when learning motor skills through varied multisensorial feedback. In particular, this paper focuses on vibrotactile feedback as it can take advantage of the haptic sense when supporting the physical interaction to be learnt. Despite each user having different needs, when providing this vibrotactile support, personalization issues are hardly taken into account, but the same response is delivered to each and every user of the system. The challenge here is how to design vibrotactile user interfaces for adaptive learning of motor skills. TORMES methodology is proposed to facilitate the elicitation of this personalized support. The resulting systems are expected to dynamically adapt to each individual user's needs by monitoring, comparing and, when appropriate, correcting in a personalized way how the user should move when practicing a predefined movement, for instance, when performing a sport technique or playing a musical instrument. © 2017 by the authors.

Santos O.C.,aDeNu Research Group | Boticario J.G.,aDeNu Research Group
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

One of the most challenging context features to detect when making recommendations in educational scenarios is the learner’s affective state. Usually, this feature is explicitly gathered from the learner herself through questionnaires or self-reports. In this paper, we analyze if affective recommendations can be produced with a low cost approach using the open source electronics prototyping platform Arduino together with corresponding sensors and actuators. TORMES methodology (which combines user centered design methods and data mining techniques) can support the recommendations elicitation process by identifying new recommendation opportunities in these emerging social ubiquitous networking scenarios. © Springer International Publishing Switzerland 2014.

Santos O.C.,ADeNu Research Group
International Journal of Artificial Intelligence in Education | Year: 2016

This paper argues that the research field of Artificial Intelligence in Education (AIED) can benefit from integrating recent technological advances (e.g.; wearable devices, big data processing, 3D modelling, 3D printing, ambient intelligence) and design methodologies, such as TORMES, when developing systems that address the psychomotor learning domain. In particular, the acquisition of motor skills could benefit from individualized instruction and support just as cognitive skills learning has over the last decades. To this point, procedural learning has been considered since the earliest days of AIED (dating back to the 1980's). However, AIED developments in motor skills learning have lagged significantly behind. As technology has evolved, and supported by the do-it-yourself and quantified-self movements, it is now possible to integrate emerging interactive technologies in order to provide personal awareness and reflection for behavioural change at low cost and with low intrusion. Many activities exist that would benefit from personalizing motor skills learning, such as playing a musical instrument, handwriting, drawing, training for surgery, improving the technique in sports and martial arts, learning sign language, dancing, etc. In this context, my suggestions for AIED research in the coming 25 years focus on addressing challenges regarding 1) modelling the psychomotor interaction, and 2) providing appropriate personalized psychomotor support. © 2016 International Artificial Intelligence in Education Society.

Santos O.C.,ADeNu Research Group | Salmeron-Majadas S.,ADeNu Research Group | Boticario J.G.,ADeNu Research Group
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Emotions detection and their management are key issues to provide personalize support in educational scenarios. Literature suggests that combining several input sources can improve the performance of affect recognition. To gain a better understanding of this issue, we carried out a large scale experiment in our laboratory where about 100 participants performed several mathematical exercises while emotional information was gathered from different input sources, including a written emotional report. As a first step, we have explored emotions detection from traditional methods by combining analysis of user behavior when typing this report with sentiment analysis on the text. Moreover, an expert labeled these reports. All these data were used to feed several machine learning algorithms to infer user's emotions. Preliminary results are not conclusive, but lead some light on how to proceed with the analysis. © 2013 Springer-Verlag Berlin Heidelberg.

Santos O.C.,ADeNu Research Group | Boticario J.G.,ADeNu Research Group
Computers and Education | Year: 2014

There is a need for designing educationally oriented recommendations that deal with educational goals as well as learners' preferences and context in a personalised way. They have to be both based on educators' experience and perceived as adequate by learners. This paper compiles practical guidelines to produce personalised recommendations that are meant to foster active learning in online courses. These guidelines integrate three different methodologies: i) user centred design as defined by ISO 9241-210, ii) the e-learning life cycle of personalised educational systems, and iii) the layered evaluation of adaptation features. To illustrate guidelines actual utility, generality and flexibility, the paper describes their applicability to design educational recommendations in two different contexts, which in total involved 125 educators and 595 learners. These applications show benefits for learners and educators. Following this approach, we are targeting to cope with one of the main challenges in current massive open online courses, which are expected to provide personalised education to an increasing number of students without the continuous involvement of educators in supporting learners during their course interactions. © 2014 Elsevier Ltd. All rights reserved.

Santos O.C.,ADeNu Research Group | Boticario J.G.,ADeNu Research Group | Perez-Marin D.,Rey Juan Carlos University
Science of Computer Programming | Year: 2014

In this paper we address an open key issue during the development of web-based educational systems. In particular, we provide an educational-oriented approach for building personalised e-learning environments that focuses on putting the learners' needs in the centre of the development process. Our approach proposes user centred design methodologies involving interdisciplinary teams of software developers and domain experts. It is illustrated in an adaptive e-learning system, where a MOOC (Massive Open Online Course) was taken by nearly 400 learners. In particular, we report where user centred design methods can be applied along the e-learning life cycle to designing and evaluating personalisation support through recommendations in learning management systems. © 2013 Elsevier B.V.

Pascual-Nieto I.,aDeNu Research Group | Santos O.C.,aDeNu Research Group | Perez-Marin D.,Rey Juan Carlos University | Boticario J.G.,aDeNu Research Group
IJCAI International Joint Conference on Artificial Intelligence | Year: 2011

Willow is a free-text Adaptive Computer Assisted Assessment system, which supports natural language processing and user modeling. In this paper we discuss the benefits coming from extending Willow with recommendations. The approach combines human computer interaction methods to elicit the recommendations with data mining techniques to adjust their definition. Following a scenario-based approach, 12 recommendations were designed and delivered in a large scale evaluation with 377 learners. A statistically significant positive impact was found on indicators dealing with the engagement in the course, the learning effectiveness and efficiency, as well as the knowledge acquisition. We present the overall system functionality, the interaction among the different subsystems involved and some evaluation findings.

Santos O.C.,aDeNu Research Group | Saneiro M.,aDeNu Research Group | Boticario J.G.,aDeNu Research Group | Rodriguez-Sanchez M.C.,Rey Juan Carlos University
New Review of Hypermedia and Multimedia | Year: 2016

This work explores the benefits of supporting learners affectively in a context-aware learning situation. This features a new challenge in related literature in terms of providing affective educational recommendations that take advantage of ambient intelligence and are delivered through actuators available in the environment, thus going beyond previous approaches which provided computer-based recommendation that present some text or tell aloud the learner what to do. To address this open issue, we have applied TORMES elicitation methodology, which has been used to investigate the potential of ambient intelligence for making more interactive recommendations in an emotionally challenging scenario (i.e. preparing for the oral examination of a second language learning course). Arduino open source electronics prototyping platform is used both to sense changes in the learners affective state and to deliver the recommendation in a more interactive way through different complementary sensory communication channels (sight, hearing, touch) to cope with a universal design. An Ambient Intelligence Context-aware Affective Recommender Platform (AICARP) has been built to support the whole experience, which represents a progress in the state of the art. In particular, we have come up with what is most likely the first interactive context-aware affective educational recommendation. The value of this contribution lies in discussing methodological and practical issues involved. © 2015 Taylor & Francis.

This paper summarizes the main contributions of the PhD Dissertation with the same title, which focused on adding personalization support in existing learning management systems by considering some artificial intelligence techniques (i.e. user modelling, web usage mining, machine learning, recommendation strategies and human computer interaction). In particular, adaptive navigation support in terms of recommendations was deployed in two learning systems through a web based recommendation service provided by a semantic educational recommender system (SERS) and making an extensive use of standards. Moreover, a user-centred design methodology (i.e. TORMES) was defined to design and formatively evaluate educational oriented recommendations. © IBERAMIA and the authors.

Motor skill learning is hardly considered in current AIED literature. However, there are many learning tasks that require consolidating motor tasks into memory through repetition towards accurate movements, such as learning to write, to draw, to play a musical instrument, to practice a sport technique, to dance, to use sign language or to train for surgery. The field of Artificial Intelligence (AI) needs new sap to cope with the challenges in the Educational (ED) domain aimed to support psychomotor learning. This new sap can be provided by novel interactive technologies around the Internet of the Things that deal with Quantified-self wearable devices, 3D modelling, Big Data processing, etc. The paper aims to identify opportunities and challenges for AI + ED that can be discussed during the workshop. Some of the issues raised are illustrated within a case study instantiated in the Aikido practice, a defensive martial art that involves learning skilled movements by training both the body and the mind, and which is not only part of extra-curricular activity in many schools, but has also been reported of value for teaching in STEM (Science, Technology, Engineering and Mathematics) education, in particular, some laws of mechanics.

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