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Institut National des Sciences Appliquées de Lyon, France

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24 Papers

A Pipeline for the Automatic Evaluation of Dental Surgery Gestures in Preclinical Training from Captured Motions

Topics: Metrics and Performance Measurement; Next Generation Teaching and Learning Environments

Authors: Mohamed Hefied ¹ , Ludovic Hamon ¹ , Sébastien George ¹ , Valériane Loison ² , Fabrice Pirolli ² , Serena Lopez ² and Raphaëlle Crétin-Pirolli ²

Affiliations: ¹ LIUM, Le Mans Université, France , ² CREN, Le Mans Université, France

Digital Object Identifier: 10.5220/0012685000003693

Proceedings of CSEDU 2024, ISBN: 978-989-758-697-2

Quick Abstract

This work in progress proposes an automatic evaluation pipeline for dental surgery gestures based on teacher’s demonstrations and observation needs. This pipeline aims at supporting learning in preclinical situations for the first years of study in the dental school. It uses the Random Forest (RF) algorithm to train a model based on specific descriptors for each gesture component, that are designed to cover the evolution of the observation needs. The inputs are the captured motion parts whose labels are defined by the teachers with their own vocabulary, to represent expected or no-wanted geometrical or kinematic features. The overall evaluation (for example, weighted average of each component) and the component evaluation can be given to students to improve their postures and motor skills. A preliminary test correctly classifies a back correct posture and three main flaws (”Twisted Back and Bent Head”, ”Leaning Back”, ”Leaning Back and Bent Back”) by the RF model, for the posture component. This approach is designed for the adaptation to the expert’s evolving observation needs while minimizing the need for a heavy re-engineering process and enhancing the system acceptance.

Building Suitable Observation Points to Enhance the Learner’s Perception of Information in Virtual Environment for Gesture Learning

Topics: Virtual and Augmented Learning Environments ; Next Generation Teaching and Learning Environments

Authors: Vincent Agueda ^{1

;

2} , Ludovic Hamon ¹ , Sébastien George ¹ and Pierre-Jean Petitprez ²

Affiliations: ¹ LIUM, EA 4023, Le Mans Université, 72085 Le Mans, Cedex 9, France , ² HRV Simulation, 53810 Changé, France

Digital Object Identifier: 10.5220/0012686100003693

Proceedings of CSEDU 2024, ISBN: 978-989-758-697-2

Quick Abstract

This paper presents an architecture to build Virtual Pedagogical Resources (VPR) dedicated to gesture learning. This architecture proposes: (a) to replay any captured gesture from an expert, in a 1:1-scaled Virtual Environment (VE) using a Virtual Reality (VR) headset (b), a full control of the replay process (play, pause, speed control, replay, etc.) and (c), a method to generate observation points from the activity traces of the learners in their observation process. Most of the Virtual Learning Environments (VLE) dedicated to gesture learning, put the learner into a practising process, neglecting the observation and study time of the gesture to learn. In addition, the VLE with dedicated observation functionalities are very specific to the task to learn, or lack of relevant strategies regarding the appropriate viewpoints to recommend. Therefore, this work in progress proposes a method able to make a VLE as a relevant pedagogical resource for observing and studying the gesture outside or during the practical session, with the appropriate point of view. A description of a first experiment is presented, which aims at validating the consistency and the pedagogical relevance of the generated viewpoints.

Empowering Students: A Reflective Learning Analytics Approach to Enhance Academic Performance

Topics: Metrics and Performance Measurement; Active Learning; Mentoring and Tutoring; Feedback and Learning Support

Authors: Dynil Duch ^{1

;

2} , Madeth May ¹ and Sébastien George ¹

Affiliations: ¹ LIUM, Le Mans Université, 72085 Le Mans, Cedex 9, France , ² Institute of Digital Research & Innovation, Cambodia Academy of Digital Technology, Phnom Penh, Cambodia

Digital Object Identifier: 10.5220/0012634600003693

Proceedings of CSEDU 2024, ISBN: 978-989-758-697-2

Quick Abstract

The surge in online education has accentuated the importance of practical Learning Analytics (LA) tools, traditionally designed to support educators. In the meantime, a notable gap exists in empowering students directly through user progress insights and reflective components. This paper presents our research effort in designing a novel approach: a Self-reflective Tool (SRT) with data indicators on student performance designed to actively engage students in their learning journey. Our research explores the landscape of existing LA tools, pinpointing the lack of technological supports for students, and the limitations in empowering students. The methodology involves data extraction, and a comparative analysis of classifiers to predict student performance (SP). Our reflective tool is therefore built, not only to support students in their learning activities, but also to provide them with a more relevant assistance according to their SP. Surveys are made to assess our proposal of SRT. The findings illustrate how students perceive it and how SRT oriented data indicators increase awareness, regulation, and motivation of individual learning patterns. Our qualitative analysis also demonstrates a positive correlation between student engagement with the reflective tool and improvements in academic outcomes. This research contributes to the discourse on LA by emphasizing the importance of reflective tools for students in Metacognition Online Learning Environments (MOLE), providing valuable insights for future developments in student-centric approaches to education.

Peephole Technology for Mobile Collaborative Learning: An In- Classroom Exploratory Study

Topics: Mobile Learning; Tools for Educational Communication and Collaboration; Virtual and Augmented Learning Environments

Authors: Sebastian Simon , Iza Marfisi-Schottman and Sébastien George

Affiliation: LIUM, Le Mans Université, 72085 Le Mans, Cedex 9, France

Digital Object Identifier: 10.5220/0012621700003693

Proceedings of CSEDU 2024, ISBN: 978-989-758-697-2

Quick Abstract

Technology to support Mobile Computer Supported Collaborative Learning (MCSCL) is a compromise between screen space and mobility. While MCSCL usually leverages small portable screens, such as tablets or smartphones, large interactive tabletops have been found to effectively support collaborative learning. In this case study, we strike a compromise by using small portable screens on a large static surface by using dynamic peephole interactions. The proposed technology allows learners to augment static surfaces, such as paper maps, by sliding a tablet or a smartphone on it. An exploratory study was conducted on eight groups of four K12 students. Results point to enhanced cognitive awareness among group members.

Students’ Performance in Learning Management System: An Approach to Key Attributes Identification and Predictive Algorithm Design

Topics: Data Mining; Data Analytics; Predictive Modeling; Deep Learning

Authors: Dynil Duch ^{1

;

2} , Madeth May ¹ and Sébastien George ¹

Affiliations: ¹ LIUM, Le Mans Université, 72085 Le Mans, Cedex 9, France , ² Institute of Digital Research & Innovation, Cambodia Academy of Digital Technology, Phnom Penh, Cambodia

Digital Object Identifier: 10.5220/0012754200003756

Proceedings of DATA 2024, ISBN: 978-989-758-707-8

Quick Abstract

The study we present in this paper explores the use of learning analytics to predict students’ performance in Moodle, an online Learning Management System (LMS). The student performance, in our research context, refers to the measurable outcomes of a student’s academic progress and achievement. Our research effort aims to help teachers spot and solve problems early on to increase student productivity and success rates. To achieve this main goal, our study first conducts a literature review to identify a broad range of attributes for predicting students’ performance. Then, based on the identified attributes, we use an authentic learning situation, lasting a year, involving 160 students from CADT (Cambodia Academy of Digital Technology), to collect and analyze data from student engagement activities in Moodle. The collected data include attendance, interaction logs, submitted quizzes, undertaken tasks, assignments, time spent on courses, and the outcome score. The collected data is then used to train with different classifiers, thus allowing us to determine the Random Forest classifier as the most effective in predicting students’ outcomes. We also propose a predictive algorithm that utilizes the coefficient values from the classifier to make predictions about students’ performance. Finally, to assess the efficiency of our algorithm, we analyze the correlation between previously identified attributes and their impact on the prediction accuracy.

A 3D Descriptive Model for Designing Multimodal Feedbacks in any Virtual Environment for Gesture Learning

Topics: User-centered Design; Application Software

Authors: Djadja Djadja , Ludovic Hamon and Sébastien George

Affiliation: LIUM, Le Mans University, Le Mans, France

Digital Object Identifier: 10.5220/0012081000003538

Proceedings of ICSOFT 2023, ISBN: 978-989-758-665-1

Quick Abstract

This paper addresses the problem of creation and re-usability of pedagogical feedbacks, in Virtual Learning Environments (VLE), adapted to the needs of teachers for gesture learning. One of the main strengths of VLE is their ability to provide multimodal (i.e. visual, haptic, audio, etc.) feedbacks to help the learners in evaluating their skills, the task progress or its good execution. The feedback design strongly depends on the VLE and the pedagogical strategy. In addition, past studies mainly focus on the impact of the feedback modality on the learning situation, without considering others design elements (e.g. triggering rules, features of the motion to learn, etc.). However, most existing gesture-based VLEs are not editable without IT knowledge and therefore, failes in considering the evolution of pedagogical strategies. Consequently, this paper presents the GEstural FEedback EDitor (GEFEED) allowing non-IT teachers to create their multimodal and pedagogical feedbacks into any VLE developed under Unity3D. This editor operationalises a three dimensional descriptive model (i.e. feedback virtual representation, its triggering rules, involved 3D objects) of a pedagogical feedback dedicated to gesture leaning. Five types of feedbacks are proposed (i.e. visual color or text, audio from a file or a text and haptic vibration) and can be associated with four kinds of triggers (i.e. time, contact between objects, static spatial configuration, motion metric). In the context of a dilution task in biology, an experimental study is conducted in which teachers generate their feedbacks according to pre-defined or chosen pedagogical objectives. The results mainly show : (a) the acceptance of GEEFED and the underlying model and (b), the most used types of modalities (i.e. visual color, vibration, audio from text), triggering rules (i.e. motion metric, spatial configuration and contact) and (c), the teacher satisfaction in reaching their pedagogical objectives.

An Analysis Framework for Designing Declarative Knowledge Training Games Using Roguelite Genre

Topic: Game-Based and Simulation-Based Learning

Authors: Bérénice Lemoine , Pierre Laforcade and Sébastien George

Affiliation: LIUM Computer Science Laboratory, Le Mans Université, Laval, France

Digital Object Identifier: 10.5220/0011840200003470

Proceedings of CSEDU 2023, ISBN: 978-989-758-641-5

Quick Abstract

The training of declarative knowledge requires repetition and adaptation to learners’ needs. Learning games for training purposes should then offer a wide range of adapted and varied game situations where facts are questioned. Furthermore, answering these questions may involve many gameplays that keep the learner-players engaged and motivated to practice again and again. This article presents and justifies how the Roguelite game genre is well-adapted to tackle these challenges. It also proposes an analysis framework to support pluridisciplinary teams of teachers and game developers in identifying the key orientations for designing such training games. This framework is composed of questions to consider during the preliminary design of the training game. We have identified and used this proposition in a specific research context about multiplication tables training. The article illustrates the first results obtained which led to our first playable prototype. Finally, we outlined the major drawbacks of our first game design (i.e., first analysis), which brought us to carry out a second analysis through our proposed framework.

Automatic Prediction of 3D Checkpoints for Technical Gesture Learning in Virtual Environments

Topics: e-Learning Applications and Computer Graphics; Human Performance and Usability of Virtual Environments

Authors: Noura Joudieh ¹ , Djadja Jean Delest Djadja ² , Ludovic Hamon ² and Sébastien George ²

Affiliations: ¹ Faculty of Sciences, Lebanese University, Hadat, Lebanon , ² LIUM, Le Mans University, Le Mans, France

Digital Object Identifier: 10.5220/0011618100003417

Proceedings of GRAPP 2023/VISIGRAPP, ISBN: 978-989-758-634-7

Quick Abstract

Nowadays, Virtual Learning Environments (VLE) dedicated to learning gestures are more and more used in sports, surgery, and in every domain where accurate and complex technical skills are required. Indeed, one can learn from the observation and imitation of a recorded task, performed by the teacher, through a 3D virtual avatar. In addition, the student’s performance can be automatically compared to that of the teacher by considering kinematic, dynamic, or geometric properties. The motions of the body parts or the manipulated objects can be considered as a whole, or temporally and spatially decomposed into a set of ordered steps, to make the learning process easier. In this context, CheckPoints (CPs) i.e. simple 3D shapes acting as “visible landmarks”, with which a body part or an object must go through, can help in the definition of those steps. However, manually setting CPs can be a tedious task especially when they are numerous. In this paper, we propose a machine learning-based system that predicts the number and the 3D position of CPs, given some demonstrations of the task to learn in the VLE. The underlying pipeline used two models: (a) the “window model” predicts the temporal parts of the demonstrated motion that may hold a CP and (b) the “position model” predicts the 3D position of the CP for each predicted part from (a). The pipeline is applied to three learning activities: (i) glass manipulation (ii), geometric shapes drawing and (iii), a dilution process in biology. For each activity, the F1-score is equal to or higher than 70% for the “window model”, while the Normalized Root Mean Squared Error (NRMSE) is below 0.07 for the “position model”.

Mapping Task Types and Gameplay Categories in the Context of Declarative Knowledge Training

Topic: Game-Based and Simulation-Based Learning

Authors: Bérénice Lemoine , Pierre Laforcade and Sébastien George

Affiliation: LIUM Computer Science Laboratory, Le Mans Université, Laval, France

Digital Object Identifier: 10.5220/0011840100003470

Proceedings of CSEDU 2023, ISBN: 978-989-758-641-5

Quick Abstract

Learning games for the training of declarative knowledge must offer learners a wide variety of game situations in order to keep them engaged. Designing such situations remains a challenge due to the inherent entanglement of didactical objectives and gaming implementations. This article proposes to tackle the need for mapping different training tasks to different gameplays in order to help the design of relevant gameplay-oriented training situations. We identified an approach during the design of a Roguelite-oriented training game for the multiplication tables. This approach has been intentionally specified towards a genericity purpose by using domain-independent task types and abstract gameplays. This article details the method we followed to identify this approach and presents the resulting mappings when applied to our specific application context.

Design of a Motion-based Evaluation Process in Any Unity 3D Simulation for Human Learning

Topics: Augmented, Mixed and Virtual Environments; Simulation for Education and Training; Human Performance and Usability of Virtual Environments

Authors: Djadja Jean Delest Djadja , Ludovic Hamon and Sébastien George

Affiliation: LIUM - EA 4023, Le Mans Université, 72085 Le Mans, Cedex 9, France

Digital Object Identifier: 10.5220/0008989001370148

Proceedings of GRAPP 2020/VISIGRAPP, ISBN: 978-989-758-402-2

Quick Abstract

This paper discusses the usability of a generic method for the evaluation of the user activity in Virtual Learning Environments (VLE) and its implementation with Unity. In the context of motion-based tasks, the learning process relies on the observation and imitation of the task demonstrated by the teacher. The learner task is compared to the teacher one in terms of: (a) motions shape of the user and the manipulated artefacts and (b), the sequential order of 3D checkpoints that the user must collide with. The integration of the evaluation system into any existing VLE rises challenges regarding the system architecture and the Human Computer Interface to set up the evaluation process. A usability test related to the design of this process is conducted for a pool shooting, a dart throwing and a letter writing simulation. The preliminary results show that: (i) the integration of an existing VLE into the evaluation system is feasible despite issues related to the interaction assets and (ii), all participants are satisfied by their designed evaluation process for pool shooting and dart throwing, they were unable to set up a satisfying evaluation for letter writing due to scale issues.