The goal of this workshop is to present an overview of ongoing research on Human–Computer Interaction (HCI) for teaching and learning. The workshop focuses on how emerging interaction paradigms, such as adaptive interfaces, multimodal interaction, and game-based learning, can support richer, more engaging, and more inclusive Technology-Enhanced Learning (TEL) experiences. The discussion will be structured around three central research directions that remain insufficiently explored: facilitating the design and evaluation of TEL systems that promote effective learning and quality of experience through innovative HCI approaches; adapting TEL systems to the complexity of educational environments; and modeling learners and users in TEL systems to account for their diversity, their preferences, and constraints. The ultimate goal of the workshop is to bring together researchers interested in discussing and converging on the future challenges and opportunities of the field.

International audience

International audience

Many studies have investigated how interpersonal differences between users influence their experience in Virtual Reality (VR) and it is now well recognized that user's subjective experiences and responses to the same VR environment can vary widely. In this study, we focus on player traits, which correspond to users' preferences for game mechanics, arguing that players react differently when experiencing VR scenarios. We developed three scenarios in the same VR environment that rely on different game mechanics, and evaluate the influence of the scenarios, the player traits and the time of practice of the VR environment on users' perceived flow. Our results show that 1) the type of scenario has an impact on specific dimensions of flow; 2) the scenarios have different effects on flow depending on the order they are performed, the flow preconditions being stronger when performed at last; 3) almost all dimensions of flow are influenced by the player traits, these influences depending on the scenario, 4) the Aesthetic trait has the most influences in the three scenarios. We finally discuss the findings and limitations of the present study that we believe have strong implications for the design of scenarios in VR experiences.

Gamification has been widely used to increase learners’ motivation and engagement in digital learning environments. Various studies have highlighted the need to tailor gamification according to users’ characteristics. However, little is known about how tailoring gamification affects learners’ engagement when interacting with the environment. In this paper, we analyse learners’ behaviours in a large-scale field study in real-world classroom conditions over a six-week period. We identify three behavioural patterns and show at a global level that two of these patterns are influenced by adaptation. When we look at how learners’ engagement evolves over time, we see more differences in the adapted condition, specifically in the final lessons of the experiment. Globally learners’ engaged behaviours gradually decreased over time but tailoring the game elements to learners seemed to reduce this decrease or make it more stable, depending on the behavioural patterns.

International audience

International audience

Our work aims at helping teachers to monitor learners' engagement during mind mapping activities. The use of mind maps helps students to elaborate cognitive learning strategies like creating and organizing contents. To assess the quality of these mind maps, teachers need tools to understand students' choices and strategies when constructing their mind maps. We have defined in collaboration with teachers a set of indicators based on learners' interaction traces. Those indicators have been implemented and integrated in a dashboard dedicated to teachers. In this paper, we introduce a first version of the dashboard: its design, implementation, and the results of its evaluation conducted during interviews with real teachers.

Emotions have a strong impact on learning processes and regulation strategies. Increasing learners' awareness on their own emotions and group ones, has the potential to significantly improve learning processes. However, the collection of emotional data is challenging, especially in ecological contexts like distance learning and MOOCs. The lack of generic methods and tools for emotional data collection is a crucial issue for the research community. In this paper we present two self-reporting tools we have developed for distance learning. EMORE-L enables to collect emotions from learners through a focused daily questionnaire. CATE supports emotions sharing via emoticons associated to annotations in documents. We conducted two studies in the same Psychology curriculum to compare the type, the number and the co-occurence of emotions reported by learners with each tool. We discuss the differences we observed according to the tool and to the learning situation, so as to bring researchers and practitioners with a list of factors that may influence the emotional data collection process.

Learners' emotional state has proven to be a key factor for successful learning. Visualizing learners' emotions during synchronous on-line learning activities can help tutors in creating and maintaining socio-affective relationships with their learners. However, few dashboards offer emotional information on the learning activity. The current study focuses on synchronous interactions via a videoconferencing tool dedicated to foreign language training. We collected data on learners' emotions in real conditions during ten sessions (five sessions for two learners). We propose to adopt and combine different models of emotions (discrete and dimensional) and to use heterogeneous APIs for measuring learners' emotions from different data sources (audio, video, self-reporting and interaction traces). Based on a thorough data analysis, we propose an approach to combine different cues to infer information on learners' emotional states. We finally present the EMODA dashboard, an affective multimodal and contextual visual analytics dashboard, which allows the tutor to monitor learners' emotions and better understand their evolution during the synchronous learning activity.

Many learning environments are deserted by the learners, even if they are effective in supporting learning. Gamification is a becoming popular way to motivate user participation on web based activities, by adding game elements in their environment, but it still pays few attention to the individual differences among learners’ motivations. This paper presents a gamification system designed to be plugged on various learning environments. This system can be automatically personalised, based on an analysis of the interaction traces. In the first place, we describe the architecture supporting the genericity of the game elements. In the second place, we describe the user model supporting the adaptivity of the game elements.

International audience

Analysing learners' behaviour continuously and under ecological conditions can help designers, trainers and teachers to analyse, design, validate, and also to adapt and personalize the learning game. Metrics methods propose to collect any interactions between a user and the game. While classical metrics methods fall within quantitative approaches, we aim to extract some qualitative information on high-level behaviours. This paper is focused on learners' engaged-behaviours. Thus, to identify and to qualify learners' engagement from their traces of interaction, we combine a theoretical work on engagement and engaged-behaviours, the Self-Determination Theory, the Activity Theory and a trace framework. We implemented this approach on 12 players' interaction data collected during four months. As a result, we identified and qualified four activities that refer to different types of engaged-behaviours. Thus, this user study show the feasibility and the validity of the proposed approach.

In this paper, we propose an architecture of Project-Based Learning Management System (PBLMS). These systems offer learners indicators to support self-regulation in PBL. Indeed, when students are engaged in learning processes with LMS, they are supposed to be autonomous to find information or to organize their activities. Indeed, most of time, few tools are proposed to help them. Our research focuses on the tools which support meta-cognition process to teach learners how to regulate their learning activities during projects. In this paper, we propose a PBLMS architecture that combines together activity traces (which are recorded automatically by the system) and reporting traces (which are reported by learners themselves). This architecture allows learners to build indicators. They can specify the data to take into account and the visualization modes. We present the details of the reporting tool, used to collect reporting traces, which can enhance learners' reflective processes on their own way to learn.

In this article, we present a co-adaptive design approach named TE-Cap (Tutoring Experience Capitalisation) that we applied for the development of an assistance environment for tutors. Since tasks assigned to tutors in educational contexts are not well defined, we are developing an environment which responds to needs which are not precisely identified or not expressed by these users. Our approach rests on three development cycles which aim at gradually clarifying and refining users' needs and expectations which we translate into computing functionalities. Each cycle is composed of three steps that allow the use of prototypes developed with regard, on the one hand, to theories of users' activities and, on the other hand, to the needs that we have identified to be observed. We finally obtain a stable typology of users' needs and expectations upon which we base the development of the computer environment. We illustrate the two first cycles of this iterative and co-adaptive approach during the development of TE-Cap. We then discuss the difficulties raised by this type of approach.

In their daily practice, practitioners belong to local communities of practice (CoPs) within their organisation. This knowledge is rarely capitalised upon because discussions are mainly verbal. Practitioners can also belong to general CoPs online. Within these general CoPs, discussions are rarely linked to the context in which they appeared, since the members are from different companies or institutions. This paper (1) connects these two levels of CoPs by contacting practitioners belonging to CoPs centred on the same general activity but who are geographically distributed and (2) capitalises on the produced knowledge by contextualising, allowing it to be accessible and reusable by all the members. The authors detail the main results of the research: (1) a model of the interconnection of CoPs (ICP) to support knowledge sharing and dissemination; and (2) a specific knowledge management tool for the ICP knowledge base. The authors apply the model and platform to university tutors by: (1) developing a use case, which links the model and the TE-Cap 2 platform and highlights the new possibilities offered by the knowledge management tool; and (2) conducting a descriptive investigation lasting for five months.