The promotion of well-being across the lifespan, and particularly in later life, has motivated the development of personalized systems that support physical activity, highlighting the need for effective user modeling to foster active ageing. However, if the design of these systems does not explicitly involve older adults, their user models may fail to fully reflect experiential, affective and contextual aspects that are critical for acceptance and sustained engagement. This paper presents EMERGE, a multi-stage participatory elicitation methodology that combines exploration of lived experience and co-design through enacted use scenarios involving older adults, emphasizing an inclusive and intergenerational perspective. A virtual reality system illustrating Tai Chi movements was used throughout the elicitation process, leveraging its immersive nature to support iterative requirements refinement and facilitate changes in participants’ perceptions through direct engagement. Beyond the specific user model dimensions elicited, this work provides methodological insights into participatory elicitation strategies that support the integration of embodied and experiential knowledge from older adults in building adaptive systems for active ageing.

Understanding how individual physical characteristics shape movement is key to building personalized, transparent, and trustworthy user models in psychomotor domains. In physically demanding practices, differences in body mass can modulate kinematics and loading, potentially affecting performance quality and risk; however, such information is seldom incorporated into data-driven inertial models. This paper introduces a force-aware explainable user-modelling pipeline for complex lower-body movement analysis using wearable IMU data. We inject anthropometric information directly into feature construction by estimating a simple force proxy via F = ma, thereby scaling acceleration-derived descriptors by each participant’s body mass to better reflect individual loading demands. On top of these representations, we employ an explainable setup and report explanations both at the feature level and aggregated by sensor modality/axis to support interpretation.

This paper explores the dynamics of learning interactions between practitioners (those learning skills for real-world activities, sports trainee), and facilitators (those guiding the learning process, sports coach), with a focus on cognitive engagement in adaptive psychomotor learning contexts. Furthermore, this paper examines how to establish an appropriate environment for replicating tangible activities, such as creating optimal conditions for learning how to move in sport scenarios. In particular, we explore how to personalize psychomotor learning approaches through Learning Management Systems (LMS) where the personalization of the learning of motor skills is driven by the Sensing, Modeling, Design and Delivery (SMDD) process model that is based on Artificial Intelligence (A1) support, and the optimization of the learning workflow is managed by the Learning Analytics' enhanced Reflective Task (LA-ReflecT) platform integrated in Moodle LMS.

Abstract This paper introduces a novel approach for leveraging inertial data to discern expertise levels in motor skill execution, specifically distinguishing between experts and beginners. By implementing inertial data transformation and fusion techniques, we conduct a comprehensive analysis of motor behaviour. Our approach goes beyond conventional assessments, providing nuanced insights into the underlying patterns of movement. Additionally, we explore the potential for utilising this data-driven methodology to aid novice practitioners in enhancing their performance. The findings showcase the efficacy of this approach in accurately identifying proficiency levels and lay the groundwork for personalised interventions to support skill refinement and mastery. This research contributes to the field of motor skill assessment and intervention strategies, with broad implications for sports training, physical rehabilitation, and performance optimisation across various domains.

No abstract available.

The increasing progress in ubiquitous technology makes it easier and cheaper to track students' physical actions unobtrusively, making it possible to consider such data for supporting research, educator interventions, and provision of feedback to students. In this paper, we reflect on the underexplored, yet important area of learning analytics applied to physical/motor learning tasks and to the physicality aspects of `traditional' intellectual tasks that often occur in physical learning spaces. Based on Distributed Cognition theory, the concept of Internet of Things and multimodal learning analytics, this paper introduces a theoretical perspective for bringing learning analytics into physical spaces. We present three prototypes that serve to illustrate the potential of physical analytics for teaching and learning. These studies illustrate advances in proximity, motion and location analytics in collaborative learning, dance education and healthcare training.

The relation between affect-driven feedback and engagement on a given task has been largely investigated. This relation can be used to make personalised instructional decisions and/or modify the affect content within the feedback. However, although it is generally assumed that providing encouraging feedback to students should help them adopt a state of flow, there are instances where those messages might result counterproductive. In this paper, we present a case study with 48 secondary school students using an Intelligent Tutoring System for arithmetical word problem solving. This system, which makes some common assumptions on how to relate affective state with performance, takes into account subjective (user's affective state) and objective information (previous problem performance) to decide the upcoming difficulty levels and the type of affective feedback to be delivered. Surprisingly, results revealed that feedback was more effective when no emotional content was included, and lead to the conclusion that purely instructional and concise help messages are more important than the emotional reinforcement contained therein. This finding shows that this is still an open issue. Different settings present different constraints generating related compounding factors that affect obtained results. This research confirms that new approaches are required to determine when, how and where affect-driven feedback is needed. Affect-driven feedback, engagement and their mutual relation have been largely investigated. Student's interactions combined with their emotional state can be used to make personalised instructional decisions and/or modify the affect content within the feedback, aiming to entice engagement on the task. However, although it is generally assumed that providing encouraging feedback to the students should help them adopt a state of flow, there are instances where those encouraging messages might result counterproductive. In this paper, we analyze these issues in terms of a case study with 48 secondary school students using an Intelligent Tutoring System for arithmetical word problem solving. This system, which makes some common assumptions on how to relate affective state with performance, takes into account subjective (user's affective state) and objective (previous problem performance) information to decide the difficulty level of the next exercise and the type of affective feedback to be delivered. Surprisingly, findings revealed that feedback was more effective when no emotional content was included in the messages, and lead to the conclusion that purely instructional and concise help messages are more important than the emotional reinforcement contained therein. This finding, which coincides with related work, shows that this is still an open issue. Different settings present different constraints and there are related compounding factors that affect obtained results, such as the message's contents and their target, how to measure the effect of the message on engagement through affective variables considering other issues involved, and to what extent engagement can be manipulated solely in terms of affective feedback. The contribution here is that this research confirms that new approaches are needed to determine when, how and where affect-driven feedback is needed. In particular, based on our previous experience in developing educational recommender systems, we suggest the combination of user-centred design methodologies with data mining methods to yield a more effective feedback.

A large number of learning tools offering some sort of personalisation features rely mainly on the analysis of logged interactions between students and particular user interfaces. Much less attention has been given to the analysis of physical aspects so often present in 'traditional' intellectual tasks, although these are both important in the full development of a life-long learner. This paper (1) discusses existing literature focused on supporting learning using proximity and location analytics and sensors, and, based on this, (2) illustrates the feasibility and potential of these analytics for teaching and learning through an study in the context of proximity and location analytics in a team-based health simulation classroom.

Despite many research efforts focused on the development of algebraic reasoning and the resolution of story problems, several investigations have reported that relatively advanced students experience serious difficulties in symbolizing certain meaningful relations by using algebraic equations. In this paper, we describe and justify the Graphical User Interface of an Intelligent Tutoring System that allows learning and practising the procedural aspects involved in translating the information contained in a story problem into a symbolic representation. The application design has been driven by cognitive findings from several previous investigations. First, the process of translating a word problem into an algebraic form has been treated in isolation, and clearly separated from algebraic manipulation. Second, the user interface has been devised to force a systematic approach to problem solving, and also avoid the use of a non-algebraic reasoning. Third, sensor-free affective support has been added by using a machine learning approach that relies on data captured from a series of experimental sessions involving 48 subjects. The evaluation of the resulting application has revealed a positive and significant impact in learning gains.

Most previous work in emotion recognition has fixed the available classes in advance, and attempted to classify samples into one of these classes using a supervised learning approach. In this paper, we present preliminary work on combining supervised and unsupervised learning to discover potential latent classes which were not initially considered. To illustrate the potential of this hybrid approach, we have used a Self-Organizing Map (SOM) to organize a large number of Electroencephalogram (EEG) signals from subjects watching videos, according to their internal structure. Results suggest that a more useful labelling scheme could be produced by analysing the resulting topology in relation to user reported valence levels (i.e., pleasantness) for each signal, refining the original set of target classes.

Teachers need to continuously update their information and communication technologies (ICT) knowledge, but they are usually not trained to deal with the problems arising from their use. In fact, studies in the literature report techno-anxiety (i.e. unpleasant physiological activation and discomfort due to present or future use of ICT) in teachers. Thus, the goal of this action research is to study if teachers’ techno-anxiety can be reduced by increasing their ability to solve technological problems. An inter-subject experiment has been carried out with 46 teachers. High school teachers were chosen because they are digital immigrants, while at the moment of this research their students are digital natives (born around year 2000). Since we could not find any specific training for teachers to increase their resolution skills of technological problems, in order to apply the treatment for our study, we have designed and deployed an online course about ICT problem-solving skills based on the 70/20/10 model for learning and development. Results show the success of the course when it comes to increasing the ICT problem-solving skills and to reducing techno-anxiety.

Abstract Collaborative indicators derived from participants' interactions can be used to support and improve their collaborative behaviour. In this research, we focus on automatically identifying recommendation opportunities in the Collaborative Logical Framework from participants' interactions. Different information sources have been considered: (a) statistical collaborative indicators; (b) social interactions; (c) opinions received by the participants via ratings; and (d) users' affective state and personality. The recommendations have been elicited considering the generality and transferability of the participants' interactions provided by the Collaborative Logical Framework. As a result, three scenarios have been identified that lead us to propose meaningful grouping suggestions and recommendations, which ultimately aimed to ground an informed personalized support to the participants in intensive collaborative frameworks.

Abstract Social online learning environments provide new recommendation opportunities to meet users' needs. However, current educational recommender systems do not usually take advantage of these opportunities. To progress on this issue, we have proposed a knowledge engineering approach based on human–computer interaction (i.e. user‐centred design as defined by the standard ISO 9241‐210:2010) and artificial intelligence techniques (i.e. data mining) that involve educators in the process of eliciting educational oriented recommendations. To date, this approach differs from most recommenders in education in focusing on identifying relevant actions to be recommended on e‐learning services from a user‐centric perspective, thus widening the range of recommendation types. This approach has been used to identify 32 recommendations that consider several types of actions, which focus on promoting active participation of learners and on strengthening the sharing of experiences among peers through the usage of the social services provided by the learning environment. The paper describes where data mining techniques have been applied to complement the user‐centred design methods to produce social oriented recommendations in online learning environments.

The emotional situation of the learner can influence the learning process. For this reason, we are researching how educational recommender systems can take advantage of affective computing to improve the recommendation support in educational scenarios. The paper reports works carried out involving 18 educators and 77 learners to elicit and design emotional feedback to be provided for learners in terms of personalized recommendations. To this end, user centered design methods and data mining techniques are used.

In this paper we propose a series of indicators, which derive from user's interactions with mouse and keyboard. The goal is to evaluate their use in identifying affective states and behavior changes in an e-learning platform by means of non-intrusive and low cost methods. The approach we have followed study user's interactions regardless of the task being performed and its presentation, aiming at finding a solution applicable in any domain. In particular, mouse movements and clicks, as well as keystrokes were recorded during a math problem solving activity where users involved in the experiment had not only to score their degree of valence (i.e., pleasure versus displeasure) and arousal (i.e., high activation versus low activation) of their affective states after each problem by using the Self-Assessment-Manikin scale, but also type a description of their own feelings. By using that affective labeling, we evaluated the information provided by these different indicators processed from the original user's interactions logs. In total, we computed 42 keyboard indicators and 96 mouse indicators.

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.

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.

This workshop presents the current status related to the design, development and evaluation of recommender systems in educational settings. It emphasizes the importance of recommender systems for Technology Enhanced Learning (TEL) to support learners with personalized learning resources and suitable peer learners to improve their learning process. Moreover, it proposes a dataTEL challenge to obtain data sets from TEL applications that can be used to benchmark algorithms specifically for the TEL context.

This research is focused on a semantic recommendations model that affects the life cycle of eLearning and can be used to build a knowledge-based recommender system to provide adaptive capabilities to existing learning management systems with the aim to support users in an inclusive and personalized way.

When building a knowledge-based recommender along the eLearning life cycle, the following issues have to be considered: a) the user interface design of the tools required, b) the process to design/generate the recommendations, c) the process to select the appropriate recommendations, and d) the management of the users' interactions. We are defining a user-centered evaluation approach that copes with those issues and drives the recommender building process in three consecutive steps: 1) elicitation of pedagogically sound recommendations validated by users with a collaborative review, 2) acquisition and validation of the user features to select the appropriate recommendations for the current context, and 3) analysis of the recommendations provided and evaluation of their impact on the user.

Active modeling is required in learning settings to cope with the dynamic evolution of the knowledge, since learners competences evolves over time as they participate in the course activities. Moreover, one of the main issues in a competence based eLearning process is to deliver personalized instructional designs adjusted to both 1) intrinsic characteristics of users (i.e. learning styles) and 2) the desired and achieved competences in the learning process (i.e. specific and generic competences). This delivery includes the adaptation of the content and the activities in a learning scenario based on a dynamic user model that evolves according to user interactions. In this paper, an approach to support Conditional Plans Generation (IMS Learning Designs) in the context of a virtual learning environment is presented. The process is supported by a pervasive usage of standards and specifications (IMS family of specifications) in conjunction with an integral user modeling.

This paper describes our approach to develop Computer Adaptive Testing functionality for different Learning Management Systems following educational standards and specifications (defined by the IMSConsortium). This functionality can be integrated into standard-based personalized learning routes and is being developed as a Service Oriented Architecture(SOA) to support its generality and applicability.

Education and work are inextricably linked to the peoplepsilas lives in our modern society and they are the main agents for a successful social integration, but at the same time they can become barriers for the inclusion of the disabled. The technology can be used to overcome these barriers offering an adaptive layer that can cope with the needs of the people with disabilities. Our current research is focused on building user models and generating dynamic recommendations to provide personalized e-learning and e-mentoring. The research is applied in the context of communities for health and independent living.

In this paper we present research works we are addressing in EU4ALL project (IST-2006-034778) to enable Higher Education (HE) institutions to support and attend the accessibility needs of their students. This approach is based on integrating learning and management of the learning in terms of workflows to support the different types of existing scenarios with a twofold objective. First, involving non-technical staff in their definition. Second, using standard-based learning management systems (LMS). A combination of design and runtime adaptations through IMS Learning Design (IMS-LD) specification is being used, following the aLFanet approach (IST-2001-33288).

The technology is expected to attend the learning needs of the students in a personalized and inclusive way. Our approach relies on combining design and runtime adaptations for all with a pervasive use of standards and specifications. However, there is currently insufficient support provided by the specifications. For this reason, we propose a multi-agent architecture to produce dynamic recommendations that complements limitations of the universal design approach. Evaluations with users justify the need of this support.

Dynamic support in adaptive inclusive educational systems depends on properly managing the adaptation in the eLearning life cycle by combining design and runtime adaptations and making a pervasive usage of standards along the eLearning life cycle. My Ph.D research focuses on recommender systems for lifelong learning inclusive scenarios, which have particular differences in their need for personalized recommendations. The research presented here makes a proposal for addressing some of the existing challenges. It goes beyond issues that are usually considered when building recommender systems and focuses also on closing the cycle. In particular, I propose a graphical representation that will help to compare the recommenders' performance in eLearning scenarios.