Andrew W Vargo

Motoi Iwata

Mathilde Hutin

Sofiya Kobylyanskaya

Ioana Vasilescu

Olivier Augereau

Ko Watanabe

Shoya Ishimaru

Benjamin Tag

Tilman Dingler

Koichi Kise
email: kise@omu.ac.jp

Laurence Devillers
email: laurence.devillers@lisn.upsaclay.fr

Andreas Dengel

Learning Cyclotron: An Ecosystem of Knowledge Circulation

Keywords: Human learning, Intelligence Augmentation, Knowledge Circulation, Nudge

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

In the coming age of 100-year life, the conventional three-stage model of "learn, work, retire" will no longer be viable, but a multistage model in which learning and working intersect in multiple stages will become a necessity. Under these circumstances, it is necessary to have a mechanism to connect those who have knowledge with those who want it, and to distribute knowledge flexibly and quickly. We call it an ecosystem of knowledge circulation.

Project Learning Cyclotron (LeCycl) aims to build a framework to realize such an ecosystem. This framework is implemented by three functions: Perceive, Master, and Transfer. Perceive is a function to know what knowledge is lacking for the learner, Master is a function to make the lacking knowledge usable, and Transfer is a function to better convey the acquired knowledge to others. In addition, nudge strategies are important to encourage people to participate in the ecosystem.

This paper summarizes the technology currently being developed for LeCycl, which is at the midpoint of the project, and discusses future directions.

PROJECT OVERVIEW

Fig. 1 shows the overall picture of LeCycl. The following 3 phases: Perceive, Master, and Transfer, are repeated in LeCycl to circulate knowledge. First, the learner recognizes areas where his or her own knowledge is lacking at the Perceive phase. Specifically, the system senses the learner's behavior and analyzes the sensing results to suggest weak points. Next, the system compensates for the discovered weaknesses to make the learner use the knowledge at the Master phase. Concretely, it is achieved through the use of knowledge such as exercises. The degree to which the knowledge is secure is determined based on the learner's behavior during the exercises and the results of the exercises. The last phase Transfer confirms the certainty of one's own knowledge by transferring the acquired knowledge to others, and also contributes to the circulation of knowledge. This is achieved by matching acquired knowledge with learners who need that knowledge. In practice, not only knowledge matching but also factors such as compatibility between people are taken into account.

In order to make learners perform the three phases, it is necessary to design incentives and a mechanism (nudge strategy) to motivate learners to do so. In this project, we will study what kind of strategies are suitable for these points as well.

Currently, English as a second language and programming are being studied as fields in which the above framework can be evaluated. These subjects are important for Japanese, German, and French undergraduate and graduate students at different levels. Another important aspect of this project is to examine whether there are differences in the functions needed for knowledge circulation (three phases as well as nudge strategies) in different countries. For the case of text, it is possible to estimate unknown words automatically from the reading behavior through the small window.

In the following sections, we summarize the efforts to date in each country for each field.

LEARNING ENGLISH 3.1 Text-Based Technologies

3.1.1 Perceive. As a way for learning English vocabulary and grammar, we have focused on Multiple Choice Questions (MCQs), and we are developing a method for estimating the confidence in the answers [START_REF] Ishimaru | Confidence-Aware Learning Assistant[END_REF]. This enables us to review the cases in which a correct answer is given without confidence, and to draw attention to the cases in which a wrong answer is given with confidence. We have employed a variety of sensors such as eye trackers, key-loggers, pen tablets, wristband accelerometers, and webcams to estimate confidence (For example, [START_REF] Tanaka | Confidence Estimation via Wrist Movement[END_REF]).

As a different method for learning English vocabulary, we have taken up flashcards and worked on their multimedia version called multimedia vocabulometer. Specifically, as shown in Fig. 2(a)-(d), word learning using not only text but also video, music, and manga has been realized on smartphones [START_REF] Kato | Manga Vocabulometer, A new support system for extensive reading with Japanese manga translated into English[END_REF][START_REF] Yamaguchi | Mobile Vocabulometer: A Context-based Learning Mobile Application to Enhance English Vocabulary Acquisition[END_REF]. We are also developing a method for estimating unknown words by using a small windowtype interface when reading text on a smartphone (Fig. 2(e)) [START_REF] Higashimura | Helping Mobile Learners Know Unknown Words through their Reading Behavior[END_REF].

Master.

In general, repeated practice using exercises makes it possible to master the knowledge. If the difficulty level of an exercise is too easy or too difficult, it is often difficult to continue practicing. Conversely, it is desirable that the level of difficulty matches the level of one's knowledge in order to continue practicing problems. As a fundamental technology for such adaptive question generation, we are working on automatic generation of fill-in-the-blank and four-choice questions.

Automatic generation of a fill-in-the-blank question is a method to automatically select a word that should fill a blank space from a plain sentence without a blank space. We have realized this function by fine-tuning the network to select words that should fill in the blanks, based on a Masked Language Model called RoBERTa.

For automatic generation of 4-choice questions, we realized a method for automatic generation of distractors (wrong choices) based on RoBERTa as well. In general, the difficulty level of 4choice questions varies depending on the wrong choices. Therefore, by sorting the distractors in order of difficulty, and selecting appropriate ones based on the difficulty, it is possible to generate 4-choice questions of various difficulty levels.

3.1.3 Nudging Technologies. We are also working on nudging technologies to promote English learning. Here, we present an example of a technology for facilitating individual learning: habit-attached learning assistance. The method we have developed, which we call habit-attachment learning, is a pseudo-habit-forming approach in which micro-learning is attached to the learner's habitual actions (e.g., browsing the Web or watching YouTube). The content to be learned is automatically selected based on the action the user took immediately before learning. Experimental results show that this system increases learning opportunities compared to simple notification-based learning facilitation.

Speech-Based Technologies

3.2.1 Perceive. When we think about estimating learners' cognitive state with speech-based technologies, reading English documents aloud is a good choice. Reading aloud is a manner of learning English that connects vocabulary, the knowledge of grammar, and the knowledge of pronunciation by reading English sentences aloud. In addition to speech, eye gaze can be recorded and used for the estimation of the performance of the speaker while reading aloud [START_REF] Baha | Eye Got It: a System for Automatic Calculation of the Eye-Voice Span[END_REF]. In particular, we focus on two questions.

First, we estimate unknown words based on speech and eye gaze in reading aloud Japanese manga translated into English, where texts are distributed by a speech balloon on a page. This arrangement makes the balloon-wise analysis possible, where each balloon includes a short sentence. We investigate the unknown word estimation for each episode, page, and balloon based on speech and eye gaze features.

Second, we estimate fluency in English as a second language by coupling acoustic analyses of speech with eye-tracking measures [START_REF] Kobylyanskaya | Speech and Eye Tracking Features for L2 Acquisition: A Multimodal Experiment[END_REF]. To that extent, participants read a text aloud in front of a computer screen equipped with an eye-tracker. Then the recording of their performance is automatically aligned using WebMAUS, the online open-access aligner from the University of Munich [START_REF] Kipp | MAUS Goes Iterative[END_REF][START_REF] Schiel | Automatic Phonetic Transcription of Non-Prompted Speech[END_REF]. In a first step, the error-rates of the recognition software, designed for native speech, will be highly informational. In a second step, the revised alignment will allow us to draw conclusions regarding the relationship between English knowledge and pronunciation.

3.2.2

Master with nudge strategies. The combination of acoustic and eye-tracking measures will allow us to propose a device spotting students' challenges often represented by disfluencies (such as hesitations, pauses, repeating), suggesting uncertainty in their own knowledge or stress levels. An automatic alignment tool will be used to perceive such disfluencies in real time during exercise sessions. In order to extend our learning framework to spontaneous speech, we will use machine learning techniques to also develop an automatic speech recognition (ASR) system. Finally, we hope to develop a model in which the perception of disfluencies by the ASR system triggers the appropriate nudge and help students practice more on the less-mastered points.

LEARNING PROGRAMMING

We are developing a platform for learning programming as shown in Fig. 3. The unique aspect of the proposed platform is that a learner acquires knowledge from experts directly or indirectly. Behaviors of learning and teaching are tracked by sensors and are utilized to personalize learning materials. People usually write program code while searching the Web for syntax and library usage. Some programmers write code by following a document, and others try to find working code by using an error message as a search query. Our hypothesis is that following the browsing behavior of someone slightly above the learner and explaining the coding process to others is effective in learning programming.

Tracking browsing and solving behaviors while programming exercises

As the perceiving and mastering phase, we have focused on the activity of solving programming exercises. In our pilot study, we invited participants to the lab/lecture and asked them to write code on our online system (example: "Define a function c2f that converts Celsius to Fahrenheit in Scheme Language. Note that f = 1.8c + 32"). Submissions were automatically executed and participants were informed of the results and errors. They were allowed to search the Web during the experiment, and the browsing behaviors were stored by our browser extension [START_REF] Makhlouf | A Privacy-Aware Browser Extension to Track User Search Behavior for Programming Course Supplement[END_REF]. As a result, browsing and solving data from 33 participants have revealed that the level of programming domain knowledge can be classified into two classes with an accuracy of 95 % by utilizing behavior-oriented features and Random Forests.

Tracking micro-behaviors and affective states while social interactions

Not only indirect transfers (e.g., sharing documents with other learners) but also direct transfers such as teaching are also in our interests. As a starting point, we developed algorithms to detect micro-behaviors in social interaction, including speaking, nodding, and smiling. The proposed algorithms were evaluated in an inperson (offline) and a remote (online) condition [START_REF] Watanabe | DisCaaS: Micro Behavior Analysis on Discussion by Camera as a Sensor[END_REF], and published as software [START_REF] Chen | Quantitative Evaluation System for Online Meetings Based on Multimodal Microbehavior Analysis[END_REF]. As an extension of the work, we are also investigating how to estimate the affective states of teachers/learners (e.g., the level of engagement and comprehension).

NUDGING STRATEGIES FOR PERCEPTION, MASTER AND KNOWLEDGE TRANSFER

Nudges are a concept established by Thaler and Sunstein [START_REF] Thaler | Nudge: Improving decisions about health, wealth, and happiness[END_REF]. A digital nudge, as any kind of nudge, is an almost imperceptible incentive in the design of a digital system to drive behavior that is supposed to improve personal or collective well-being. Sludges are mostly considered as a particular kind of nudges, which add friction to the decision-making process. At present, digital nudging mechanisms in education are beginning to use familiar online technologies and different strategies such as peer-effect, habits, repetition, etc. From the sensors' signals (vision, speech, eye-tracking), cognitive/affective states including also attention, interest, concentration, mental workload, and self-confidence will be extracted. The estimated states can be utilized to select how to nudge learners.

A large literature describes the use of nudging strategies for education [START_REF] Trier | Nudging in education[END_REF]. In [START_REF] Ali Mehenni | Nudges with conversational agents and social robots: a first experiment with children at a primary school[END_REF], we presented an experimental protocol during which children from 5 to 10 years interact with a dialog system capable of nudging, i.e. to influence through indirect suggestions which can affect behavior and decision making. We reported on an automatic user states' detection experiment based on paralinguistic and linguistic cues [START_REF] Ali Mehenni | Children as Candidates to Verbal Nudging in a Human-robot Experiment[END_REF] for driving an automatic nudging system that adapts to the user. First results in a Wizard-of-Oz experimental protocol highlighted that the conversational agent and the robot are more influential in nudging children than a human interlocutor.

In the LeCycl Project, nudging strategies can be used for all the steps of the learning phase: perception, master and transfer learning. During the perceiving and mastering phase, nudging mechanisms such as habit-attached learning assistance or bio-feedback enhancement devices can be used to solve exercises. During the Knowledge transfer, we can extract the level of engagement of the learners and give feedback to the teachers in the platform in order to help them modify their strategies. All these strategies will be tested in our virtual environments in our 3 labs in Japan, France and Germany, for instance by measuring whether such nudging information (popup window with informational nudges, assistance or other kind of alerts in the design) actually increases learners' motivation (that could be measured by the duration and regularity of learning sessions on the platform but also by micro-behaviors and affective states automatically detected) and subsequent performance (that could be measured by tests).

All digital nudges using AI mechanisms as statistical inferences from users' behavior, are raising new ethical concerns [START_REF] Devillers | AI & Human Values Inequalities, Biases, Fairness, Nudge, and Feedback Loops[END_REF] in particular with children. In fact, these nudges augmented by AI reinforce the ability to achieve the designer goals using cognitive biases, emotional impulses and other human behavioral mechanisms both intentionally (with the informed consent of the user) or unintentionally (caused by automatic inferences of the system). Ethical issues, including in the learning environment, will also be highlighted. For instance, poor experimental designs could result in discrimination, as some nudges may have better effects on some populations than on others.

CONCLUSION

We have presented the objective and the current status of our trilateral project "Learning Cyclotron (LeCycl). " The goal of LeCycl is to build a knowledge ecosystem that is supported by AI technologies to boost circulating knowledge among people. In the project, we focus on two modalities (text and speech) in two fields, learning English as a second language and programming to develop the cyclotron consisting of the three functions perceiving, mastering, and transferring. The digital nudging strategies are one of the key technologies in all three functions, for encouraging learners to perform the functions.

Although we have already obtained some results, many are still ongoing. Till the end of the project, which is in 1.5 years, we would like to complete our missions and show the details of results at a next workshop and/or as papers.

Figure 1 :

 1 Figure 1: LeCycl Overview.

Figure 2 :

 2 Figure 2: Multimedia Vocabulometer. Text (a), video (b) and music (c) can be used to find unknown words for the learners. The multimedia flashcard (d) can be used for better review.For the case of text, it is possible to estimate unknown words automatically from the reading behavior through the small window.

Figure 3 :

 3 Figure 3: An overview of the proposed platform to teach programming to each other.

ACKNOWLEDGMENTS

This trilateral work has been supported by a funding agency of each country: JST in Japan (Grant No. JPMJCR20G3), DFG in Germany (Project No. 442581111) and ANR (Grant No. ANR-20-IADJ-0007) in France.


Wijdane Kaiss
email: wijdane.kaiss@univ-ubs.fr

Khalifa Mansouri

Franck Poirier

Towards a Chatbot-based Learning Object Recommendation: A Comparative Experiment

Keywords: E-learning, Learning Object Recommendation, Experimental Design, Adaptive Learning, Chatbot

An adaptive learning system aims to provide learning that is adapted to a learner's current status, different from the traditional classroom experience.

A key element of an adaptive learning system is the recommendation system, which provides the most suitable resources based on learner profiles. Recommending the most appropriate learning resources to learners has always been a challenge in the field of e-learning. Thus, learners may have difficulties in choosing the appropriate material when faced with a large volume of recommended material during their learning process. This challenge led us to implement a chatbot to help learners improve their learning experience and knowledge. New solutions use artificial intelligence (AI) techniques such as machine learning (ML) and natural language processing (NLP). The use of our chatbot integrated in Moodle, named LearningPartnerBot, provides learners a personalized recommendation of learning objects according to two strategies, one based on their knowledge level (KL) and the other based on their learning style (LS). The objective of this article is to compare the learning outcomes obtained after the realization of the two experiments based on these two approaches centered mainly on the use of the LearningPartnerBot. Consequently, the approach of recommending learning objects based on the knowledge level gave promising results by guaranteeing a more adapted learning to the learners.

Introduction

In recent years, educational technology has increased at a rapid rate. Once learning experiences are personalized, e-learning content is getting richer and more diversified [START_REF] El-Sabagh | The Relationship between Learning-Styles and Learning Motivation of Students at Umm Al-Qura University[END_REF], [START_REF] Yang | Optimizing classroom environment to support technology enhanced learning[END_REF]. E-learning provides constructive learning outcomes as it allows learners to actively participate in learning at any time and place [START_REF] Chen | Engaging online learners: The impact of Web-based learning technology on college student engagement[END_REF], [START_REF] Lee | Exploring factors, and indicators for measuring students' sustainable engagement in e-learning[END_REF]. Recently, adaptive e-learning has become a commonly implemented approach by higher education institutions. Adaptive e-learning is a learning process in which content is adapted based on student profiles [START_REF] Afini Normadhi | Identification of personal traits in adaptive learning environment: Systematic literature review[END_REF], [START_REF] Kolekar | Prediction of Learner's Profile Based on Learning Styles in Adaptive E-learning System[END_REF].

An adaptive learning system is a recommendation strategy that sequentially makes decisions about what to learn based on currently available information. According to [START_REF] Maravanyika | An adaptive recommender-system based framework for personalised teaching and learning on e-learning platforms[END_REF], a recommender system in an e-learning environment has the potential to help learners discover relevant learning actions that match an individual learner's profile, and thus, provide learners with personalized content "at the right time, in the right context, and in the right way."

By providing personalized content, adaptive e-learning environments enhance the quality of e-learning. Adaptive e-learning dynamically changes the level of instruction based on students' knowledge levels and learning styles and personalizes instruction to increase or accelerate a student's success.

However, in e-learning environments, learners may have difficulty in choosing appropriate materials when faced with a large volume of materials during their learning process [START_REF] Udupi | Big data integration for transition from e-learning to smart learning framework[END_REF]. Although, the teacher is not able to provide personalized recommendations in real time, either due to availability or due to the number of learners, the more number of learners and resources, the more difficult and complicated it becomes. This is the reason for building our chatbot, named LearningPartnerBot that we integrated into Moodle, to have an adaptive learning capable of handling complicated queries and providing relevant learning objects to learners in need. It has been shown in [START_REF] Pérez | Rediscovering the use of chatbots in education: A systematic literature review[END_REF] that a chatbot can assist learning with the same benefits as those obtained from a "real" interview. In addition to making the learning process helpful, the conversational chatbot could make students more likely to improve their level because they do not feel judged.

We conducted our experimentation on the C programming techniques module and 71 learners used the LearningPartnerBot. At the beginning, we programmed our chatbot to recommend learning objects according to the learners' knowledge level (approach 1: chatbot-based LO recommendation based KL). After the learners used it and obtained their learning outcomes, we reprogrammed it to provide learning object recommendations based on learning style (approach 2: chatbot-based LO recommendation based LS), in order to determine which approach helps to improve the learning experience. This paper presents the comparison between these two approaches, answering our research question: "Which approach has an impact on learners' learning outcomes?"

The rest of this article is organized as follows: In Section 2, discusses previous research related to our topic. In section 3 describes the methodology (the participants of our experimentation, the experimental setup, and the procedure). The experimental results are discussed in section 4, and section 5 concludes this comparative experiment.

2

Theoretical Background

Adaptive E-Learning Environments

Adaptive systems are defined as systems that modify their output and behavior based on different characteristics of the user, such as preferences, skills, and personality. Adaptive systems can be described as "the technological component of joint humanmachine systems that can modify their behavior to meet the changing needs of their users, often without explicit user instructions" [START_REF] Feigh | Toward a characterization of adaptive systems: A framework for researchers and system designers[END_REF].

In the field of human-computer interaction (HCI), adaptability involves adjusting a system, graphical user interface, or content to meet the needs of a user [START_REF] Brusilovsky | Adaptive hypermedia[END_REF]- [START_REF] Klašnja-Milićević | Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions[END_REF]. Learning strategies can be matched and adapted to learners' profiles (their learning styles, knowledge level). The term personalization is also relevant to adaptability; personalizing means designing an object following the needs of a specific user. Adaptive technologies can be applied to a wide variety of different domains such as ehealth, e-commerce, and e-learning. For instance, AEADS provides adverts based on a user's preferences and behavior in the e-commerce field [START_REF] Qaffas | Lightweight Adaptive E-Advertising Model[END_REF]. The CHAIN approach helps users accomplish tasks by incorporating adaptive help and assistance within a user interface [START_REF] Akiki | CHAIN: Developing model-driven contextual help for adaptive user interfaces[END_REF]. Adaptivity is an essential component of modern e-learning systems [START_REF] Rodrigues | Tracking e-learning through published papers: A systematic review[END_REF]. It facilitates student learning by recommending appropriate learning strategies, providing relevant learning materials, and guiding navigation through the material [START_REF] Brusilovsky | Methods and techniques of adaptive hypermedia[END_REF]. Adaptive and personalized learning based on knowledge level, preferences, and learning style is still an important educational consideration [START_REF] Xie | Trends and development in technologyenhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017[END_REF].

Student modeling represents a more focused stream that involves the storage, representation, and maintenance of student attributes such as motivation, knowledge level, and learning style [START_REF] Afini Normadhi | Identification of personal traits in adaptive learning environment: Systematic literature review[END_REF], [START_REF] Chrysafiadi | Student modeling approaches: A literature review for the last decade[END_REF].

The authors in [START_REF] Simko | Lightweight domain modeling for adaptive web-based educational system[END_REF], proposed an approach for automatic domain modeling in a way that allows the system to take into account different modes of adaptation that provide personalized and adaptive learning material.

Many attempts have been made to build and evaluate adaptive e-learning systems (AESs). However, there is a dearth of studies that consider detailed, carefully designed and controlled experimental evaluations that assess learning effectiveness [START_REF] Afini Normadhi | Identification of personal traits in adaptive learning environment: Systematic literature review[END_REF], [START_REF] Rodrigues | Tracking e-learning through published papers: A systematic review[END_REF], [START_REF] Xie | Trends and development in technologyenhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017[END_REF], [START_REF] Akbulut | Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011[END_REF]- [START_REF] Truong | Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities[END_REF] Many models of learning style exist, the Felder-Silverman learning style model is commonly used, especially in online learning research [START_REF] Akbulut | Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011[END_REF], [START_REF] Alshammari | Adaptivity in E-learning systems[END_REF]. The dimensions of the model are comprehensively detailed, and each dimension is associated with one or more learning strategies [START_REF] Felder | Learning and Teaching Styles in Engineering Education[END_REF]. The model includes four dimensions, including Processing, Reception, Understanding, and Perception. The Index of Learning Style (ILS) tool that can be used to identify learning styles is also based on this model [START_REF] Felder | Applications, Reliability and Validity of the Index of Learning Styles[END_REF], which consists of 44 questions that have been shown to be effective in identifying the learning style of each learner.

According to the Felder-Silverman model, the Processing dimension (active-reflective) details the technique students use to process information. Active students learn by interacting with and manipulating something and by communicating with their peers. Reflective students think deeply about something before they act.

The Reception dimension (visual-verbal) is concerned with the presentation of information. For example, visual students' learning can be enhanced by using pictures, videos, graphs, and diagrams. Verbal students can be supported by offering oral information and written details. In the article [START_REF] Kollöffel | Exploring the relation between visualizer-verbalizer cognitive styles and performance with visual or verbal learning material[END_REF], the authors examined the effect of learning with this dimension in mind.

The Understanding dimension (sequential-global) addresses the desired structure of information. Sequential students understand learning material better if it is presented in a linear and logical manner, with each step of the learning process described in detail.

Global students learn best when they are provided with an overview of the information before being given details.

The Perception dimension (sensory-intuitive) concerns the types of information preferred. Concrete learning resources are more likely to benefit sensory students, while abstract resources help intuitive students develop their understanding of the concept being studied. Examples of concrete information include facts, examples, simulations, and interactive lessons. Examples of abstract information include mathematical notations, definitions, and theories.

Recommendation Through Chatbot in E-Learning

According to [START_REF] Ho | Developing a Chatbot for College Student Programme Advisement[END_REF], academic advising in China is an essential support service for students in higher education, but it is not always accessible to learners due to the lack of sensitive information, availability of the advisor, or other financial constraints. To overcome these problems, the authors proposed an interactive chatbot called "EASElective", dedicated to serve many learners at the same time and at any time of the day and to complement existing academic advising. The implemented chatbot provides the same expected advising service for all students.

Other research has confirmed that chatbots can be virtual companions for users intended to resolve availability issues, provide support and customer assistance [START_REF] Wu | Advantages and constraints of a hybrid model k-12 e-learning assistant chatbot[END_REF], [START_REF] Yang | Opportunities and Challenges in Using AI Chatbots in Higher Education[END_REF] powered by artificial intelligence.

In this field of education, chatbot as a learning tool for teaching is still in its infancy [START_REF] Gonda | Evaluation of Developing Educational Chatbots Based on the Seven Principles for Good Teaching[END_REF]. However, to our knowledge, there is no chatbot that has been used in the recommendation of educational resources on the Moodle platform. Recommending appropriate educational resources has become a current challenge for educators and researchers to develop new ideas to help learners improve their learning process. This is the reason for building our chatbot that we named LearningPartnerBot, and integrated it into the Moodle platform. The chatbot we have implemented is dedicated to recommending learning objects to learners according to two approaches, one based on their knowledge level (KL) and the other based on their learning style (LS). This is the objective of this article, which is to compare the learning outcomes obtained after the realization of the experiments based on these two approaches.

The following section describes the methodology such as the participants of our experiment, the experimental setup, and the research experimental design.

Methodology

Experimental Setup

The e-learning platform we used for our experiment is Moodle. It is a flexible and secure platform that can be adapted and extended for a variety of possibilities to create personalized learning environments. It can even run on mobile devices. We implemented the LearningPartnerBot chatbot and integrated it into Moodle, which aims to provide recommendations of learning objects adapted to learners' profiles. The tool we used to implement it is Google DialogFlow, which allows users to develop human-computer interaction technologies capable of handling natural language understanding (NLU).

Research participants

We programmed our chatbot on the C programming techniques module to provide the recommendation of learning objects. We have chosen the Ecole Normale Supérieure de l'Enseignement Technique de Mohammedia (ENSET), an engineering school in Morocco to conduct our experiment. The sample included 71 students (52 males and 19 females), who participated in this study. The participants were first-year engineering students in the Software Engineering and Distributed Computing Systems (GLSID) and Big Data and Cloud Computing Engineering (BDCC) fields, and ranged in age from 20 to 21. Participants were allowed to use our LearningPartnerBot chatbot on any device of their choice.

Procedure

As shown in Fig. 1, our experiment is divided into two. The first one is the approach of recommending learning objects according to the knowledge level (KL) of the learners, and the second is the one based on their learning style, in order to compare the results obtained from each approach. Participants will pass a pre-test in C programming to determine their knowledge level and identify the concepts they have not mastered and on which the LearningPart-nerBot chatbot will rely to provide personalized learning object recommendations based on their level. The use of our chatbot will be for two weeks for the first approach (the one based on KL).

After this first experience, participants will have to answer the ILS questionnaire [START_REF] Soloman | Index of Learning Styles Questionnaire[END_REF] to determine their preferred learning style. Thus, we reprogram our chatbot to be based on the learners' learning style characteristics in order to provide them learning object recommendations. After two weeks of participants learning, using our chatbot, they will pass a formative evaluation as a second post-test.

The learning results from these two post-tests will be compared to determine in which approach the learners have progressed the most.

4

Results and discussion

Recommendations based on the learners' knowledge level

As presented in the previous section, at the beginning, we programmed our chatbot to provide personalized recommendations of learning objects based on the learners' knowledge level. In their initial login into Moodle, they passed a pre-test in C programming using QuizCbot [START_REF] Kaiss | Pre-Evaluation with a Personalized Feedback Conversational Agent Integrated in Moodle[END_REF] to determine their knowledge level in all C concepts. Fig. 2 shows the results obtained in this pre-test. Out of 71 learners, 72% of learners (51 learners) are at the low beginner level, 25% of learners (18 learners) are at the medium beginner level, and 3% of learners (2 learners) are at the high beginner level. Following the pre-test, LearningPartnerBot provided learning objects to the learners according to the level they belong to (on the concepts they did not master). During the two weeks of the first experiment, that of the approach chatbot-based Learning Object recommendation-based Knowledge Level, the learners were given to use the learning resources on the different concepts of C programming techniques that were added in Moodle, such as exercises, lessons, quizzes.

After these two weeks of learning with support of our chatbot (using the recommendations it provides to them), they were given a formative evaluation considered as a post-test. The result obtained is shown in Fig. 3. It shows that there is an improvement in the results, the low beginner level became inexistent and the medium beginner level decreased from 25% (18 learners) in the pretest to 10% (7 learners) in the post-test. For the high beginner level, there was an improvement from 3% (2 learners) to 51% (36 learners). For the intermediate level, there is an increase from 0% (no learners) to 39% (28 learners).

Recommendations based on the learning style

Then, in the third week, we moved on to the second experiment. To determine the preferred learning style of the learners, we used the ILS questionnaire because it is the most widely used in educational systems and in technology-enhanced learning and some researchers even argue that it is the most appropriate learning style model for use in adaptive learning systems such as [START_REF] Carver | Enhancing student learning through hypermedia courseware and incorporation of student learning styles[END_REF], [START_REF] García | Evaluating Bayesian networks' precision for detecting students' learning styles[END_REF], as well as being easy to implement [START_REF] Lu | A Personalized e-Learning Material Recommender System[END_REF], [START_REF] Hwang | A Learning Style Perspective to Investigate the Necessity of Developing Adaptive Learning Systems[END_REF].

After the participants answered this questionnaire, we found that the learning style to which most of the learners belong is the "visual learning style", followed by the "verbal learning style", those of the Reception dimension (explanations of different dimensions and learning styles are presented in section 2.1). The learning objects are provided in different formats and media to suit the learning styles of each learner. These can be text documents (e.g., pdfs), presentations (e.g., Powerpoint slides), videos, etc. For example, a visual learner will prefer to watch a video rather than read a pdf document, while a verbal learner will choose the opposite.

Our chatbot relies on these questionnaire results to provide recommendations of learning objects according to the preferred learning style for each learner. The pre-test for this second approach (chatbot-based LO recommendation-based LS) is the result of the last formative evaluation done which is the result of the post-test (Fig. 3) of the first approach (chatbot-based LO recommendation-based KL).

After using the chatbot for two weeks, the learners are submitted to another post-test to evaluate their learning result. The results are shown in Fig. 4. Out of 71 learners, there are no more learners classified at the medium beginner level, so the number of learners who were classified at both levels (medium beginner, high beginner) in the pre-test became only classified at the high level in the post-test, which is why I renamed the high beginner class to beginner class. The beginner level decreased from 61% in the pre-test (10% medium beginner and 51% high beginner) to 45% (32 learners) in the post-test. For the intermediate level, there was an improvement from 39% (28 learners) to 49% (35 learners). For the advanced level, there is an increase from 0% (no learners) to 6% (4 learners).

Fig. 5 shows a comparison of the learners who improved in learning outcomes after using our chatbot in the two approaches, 72% of the learners (28 learners) improved in the first approach, while just 28% of the learners (15 learners) improved in the second approach, which indicates that the chatbot-based LO recommendation based KL approach improved the learners' learning outcomes compared to the learning style-based approach. We asked learners after each post-test the following questions: "Q1: Were the recommendations provided by our LearningPartnerBot chatbot useful in your learning process?"; "Q 2: How would you rate the overall experience with LearningPart-nerBot?", to find out their satisfaction towards the recommendations and their satisfaction towards the chatbot for each of the two approaches. Table 1 shows the distribution of responses to each question. Not at all interesting 0% 2% 0% 0%

It is shown that learners were satisfied with recommendation in the first case (chatbot-based LO recommendation-based KL) more than in the second case (chatbot-based LO recommendation-based LS). Thus, based on these results, we conclude that our LearningPartnerBot chatbot was perceived as interesting and helpful in providing learners personalized recommendations of learning objects.

Conclusion

In this paper, we compared the learning outcomes obtained after the realization of two experiments based on two approaches, one concerns the use of the chatbot-based learning object recommendation according to the knowledge level (KL) of the learners, and the other concerns the use of the chatbot-based learning object recommendation according to their learning style (LS). The experimentation was conducted on 71 learners, on the C programming techniques module, and the results obtained show that the one based on the knowledge level (KL) improved the learning outcomes of the learners compared to the one based on the learning style (LS). Based on the results obtained from questions asked to the learners after each post-test, which were conducted to assess their satisfaction towards the recommendations and the chatbot, we concluded that our LearningPartnerBot chatbot is perceived as interesting and useful and shows a positive attitude of the learners. Future work will focus on personalizing learning path recommendations by combining these two approaches together (KL&LS), also based on the use of the chatbot integrated in Moodle, in order to improve the learning experience of learners.

Fig. 1 .

 1 Fig. 1. Research Experimental design.

Fig. 2 .

 2 Fig. 2. Results of the pre-test.

Fig. 3 .

 3 Fig. 3. Results of the post-test (approach 1).

Fig. 4 .

 4 Fig. 4. Results of the post-test (approach 2).

Fig. 5 .

 5 Fig. 5. Comparison of learners who progressed in learning outcomes between KL and LS.

Table 1 .

 1 Evaluation questions.

		Recommendation satisfaction	Chatbot satisfaction
		Approach 1	Approach 2	Approach 1	Approach 2
		(based on KL)	(based on LS)	(based on KL)	(based on LS)
	Very interesting	92%	73%	94%	91%
	Interesting	8%	25%	6%	9%


Hiba Gharbaoui
email: hiba.gharbaoui-etu@etu.univh2c.makhmansouri@hotmail.com

Khalifa Mansouri

Franck Poirier
email: franck.poirier@univ-ubs.fr

Towards a social learning analysis using Learning Management System and Learning Experience to predict learners' success

Keywords: E-learning, social learning, learning analysis, Learning Management System, Learning Experience Platform

Online learning is an alternative to the traditional (face-to-face) educational system. Contrary to traditional teaching (classroom), distance learning is characterized by the lack of physical contact between instructors and students and also between students within a classroom. Unfortunately, some learners may fail or even stop learning online quite quickly. In addition, learners find it more difficult to learn, not because there is not enough content, but because there is too much and they cannot find what is useful and up-to-date. This study aims to prove the importance of using social and adaptive learning to overcome the problems that learners face in online learning platforms, we propose a model to determine the effects of using the two types of platforms in online learning, namely Learning Management System (LMS) and Learning Experience Platform (LXP) for two groups of learners in Moroccan higher education applying social learning analysis (SLA). Specifically, in this proposal we attempt to analyze data from the LMS and LXP platforms to build knowledge models about students and determine their learning styles based on their interactions on these platforms, improve their learning experience and predict the conditions that favor their progress and success in order to recreate the conditions for quality social learning and help learners find possible paths to success in the online learning platforms in Morocco.

Introduction

The social learning hypothesis, proposed by Albert Bandura, accentuates the significance of noticing, displaying and imitating the ways of behaving, perspectives, and profound reactions of others. Social learning theory examines the interaction of environmental and cognitive factors in influencing human learning and behavior [START_REF]Albert Bandura's Social Learning Theory | Simply Psychology[END_REF].

In order to better understand the role of learners' social presence, this study tries to find out how these interactions work this will give us a lot of information to adapt our teaching to the way our students learn. The approach aims to enhance the effectiveness of student participation, for this we will use Learning Management System (LMS) and Learning Experience System (LXP) which are two types of platforms in online learning [START_REF] Díaz-Lázaro | Social Learning Analytics in Higher Education. An experience at the Primary Education stage[END_REF], and specific social learning analytics methods to analyze the data from both platforms such as the connections and interactions between students in order to predict the conditions that enable their progress and find the most appropriate type of learning for each. In this paper, Section 2 presents a theoretical background to understand the concepts of social presence in online learning and also presents the methods of social learning analysis as well as an explanation of LMS and LXP online learning platforms. Section 3 to discuss the research that was being done in this area. Our proposed system is to analyze data from the LMS and LXP platforms using social learning analytics in order to predict the conditions favoring learner success in e-learning platforms in Morocco this will be discussed in Section 4. The last section presents a conclusion and identifies our perspectives and the challenges that need to be addressed in the future.

2

Theoretical background

Social presence in online learning (social networks / social networks analytics)

Social networks allow people to quickly exchange information and knowledge and have been promoted with various applications for interpersonal interaction, communication and sharing [START_REF] Chen | Mining learner profile utilizing association rule for web-based learning diagnosis[END_REF] [4] Furthermore, learning is a social and collective outcome that is achieved through conversations, knowledge dissemination and social networks [START_REF] Brown | Organizational Learning and Communities-of-Practice: Toward a Unified View of Working, Learning, and Innovation[END_REF]. According to Christine Greenhow, "Now that we know what skills students are learning on the Internet, we need to encourage and help them to develop them further" [START_REF]Article 4 -Les réseaux sociaux : outils d'apprentissage en devenir ? -Article 1 -Apprenance[END_REF]. Teenagers are not always aware of developing such abilities, which can be useful from both an academic and professional point of view. Going further, we also learn that the reduction of the digital divide between social backgrounds would take place with the use of these community media [START_REF] Pinte | Vers des réseaux sociaux d'apprentissage en éducation[END_REF].

In recent years, Social Network Analysis (SNA) has attracted increasing attention as a methodology in the field of education [START_REF] De Laat | Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for Social Network Analysis[END_REF]. SNA is often applied to analyze interconnected forum user networks to study the structural patterns and underlying relational organization of learning communities in online courses. The participation gap between active and inactive learners has been studied by Poquet and Dawson [START_REF] Oleksandra | Untangling MOOC learner networks[END_REF] [START_REF] Zou | Exploring the relationship between social presence and learners' prestige in MOOC discussion forums using automated content analysis and social network analysis[END_REF]. Thoughtfully, Social Network Analysis (SNA) considers each actor as a node of the whole network, Whereas the various connections between them are conceptualized as lines interfacing the hubs and called edges or connections [START_REF] Hernández-García | Visualizations of Online Course Interactions for Social Network Learning Analytics[END_REF].

Learning Analytics / Social Learning Analytics

Currently, it is possible to retrieve social data from various social sources, such as Twitter, Facebook, Instagram, YouTube and learning platforms like LMSs and LXPs. This data can be the main source of information to do the Learning analytics (LA) and Social Learning analytics (SLA).

Learning analytics (LA) is a discipline that aims to exploit the hidden potential of interaction data generated by the use of online learning environments [START_REF] Ferguson | Learning analytics: drivers, developments and challenges[END_REF]. According to Sclater [START_REF]Learning Analytics Explained | Niall Sclater | Taylor & Francis Group[END_REF], The goal is to analyze the digital traces left by learners in order to better understand them and optimize learning. In fact, learning analytics focuses on the detection, collection, analysis and exploitation of digital traces left by learners in their daily activities, in order to understand and optimize learning and the different environments in which it occurs [START_REF] Mansouri | TaBAT: Design and Experimentation of a Learning Analysis Dashboard for Teachers and Learners[END_REF], [START_REF]Best Coursera Courses & Certificates in 25 categories[END_REF]. On the other hand, Learning Analytics (LA) is a very important discipline of artificial intelligence, due to its ability to exploit learning data, generate information and knowledge to improve learning processes. One of the subclasses of Learning Analytics (LA) is Social Learning Analytics (SLA), The SLA focuses on relationship building and provides an opportunity to identify interventions that can enhance the potential of the network to support stakeholder learning [START_REF] Díaz-Lázaro | Social Learning Analytics in Higher Education. An experience at the Primary Education stage[END_REF]. Social Learning Analytics (SLA) seeks to gain hidden insights from large amounts of data, usually educational in nature. SLA focuses primarily on Social Network Analysis (SNA) and web analysis to uncover patterns of interaction and behavior of educational social actors. Social Learning Analytic (SLA) is a subfield of LA, which focuses on how students acquire knowledge in a social context, often without being controlled, in collaboration with other [START_REF] Ferguson | Social learning analytics: five approaches[END_REF] [12] [START_REF] Aguilar | Social learning analytics for determining learning styles in a smart classroom[END_REF].

Learning Management System / Learning Experience Platform

Supporting learners via web-based platforms is not new. For decades, learning management systems (LMS) such as Blackboard and Moodle have served as the foundation for online learner support. LMSs provide a comprehensive digital learning environment where teaching and learning materials can be delivered, stored, and assessed. Often these systems are also linked to administrative functions such as student registration and student performance tracking [START_REF] Cockrill | From Learning Management Systems to Learning Experience Platforms: Do they keep what they promise? Reflections on a rapidly changing learning environment[END_REF]. However in recent years, third generation platforms called LXP have been developed that claim to provide a more interactive, current and social learning experience, the socalled learning experience platforms recently begun to replace the use of LMSs in higher education. LXPs combine aspects of a social media platform with a virtual learning environment. The term was coined by Josh Bersin, who also compared these new platforms to "Netflix for learning" and the expression is still used today [START_REF]A New World of Corporate Learning Arrives: And It Looks Like TV -JOSH BERSIN[END_REF]. Uploading content is quick and easy for all users, with the integration of a limited number of applications -an LXP functions as a platform rather than a system with different building blocks like an LMS.

Related works

Social networks can enhance peer-to-peer and educator-student interaction, especially when used to search for data, publish articles, engage in discussions and communicate [START_REF] Wu | Using smart mobile devices in social-network-based health education practice: A learning behavior analysis[END_REF]. Other than social networks, there are user-generated content (UGC) platforms in e-learning such as LMSs and LXPs. Content creation is no longer limited to the course instructor, but in an LXP, everyone creates content [START_REF] Lms Vs | LXP: How and Why They're Different[END_REF]. In the LMS, the learning journey is largely linear, with a learner completing one task and then moving on to the next.

An LXP system aims to increase engagement and experience sharing among users, rather than functioning as a repository of information. The recent push in higher education toward online and distance learning, already a trend prior to the pandemic, has accelerated and allowed for a more rapid introduction of LXPs into higher education [START_REF] Cockrill | From Learning Management Systems to Learning Experience Platforms: Do they keep what they promise? Reflections on a rapidly changing learning environment[END_REF]. To analyze student interactions in online learning platforms, researchers have introduced five categories of analysis. The first two categories are intrinsically social (Social network analysis, Discourse analysis), while the other three are potentially "socialized", i.e., usefully applied in social settings (Content analysis, Alignment analysis and Contextual analysis) [START_REF] Ferguson | Social learning analytics: five approaches[END_REF].

In [START_REF] Aguilar | Social learning analytics for determining learning styles in a smart classroom[END_REF], They suggest analyzing external data from the web and social networks to build knowledge models about students and to improve the learning processes that occur in smart classrooms. For this there are several factors and dimensions to consider that could affect the learning experience (LX) to decrease the failure or dropout rate in Online Learning Environments (OLEs). In [START_REF] Safsouf | A New Model of Learner Experience in Online Learning Environments[END_REF], the proposed research model consists of 38 factors categorized along 5 dimensions: learner characteristics, instructor characteristics, system characteristics, course characteristics, and social aspects. This study [START_REF] Safsouf | Smart learning environment, measure online student satisfaction: a case study in the context of higher education in Morocco[END_REF] examined learner satisfaction with e-learning systems in higher education. Results indicate that diversity of assessments, course flexibility, social interactions, system quality, and perceived usefulness have positive effects on learner satisfaction. The results of this research will be useful to both institutions and practitioners of e-learning systems.

Proposed system

Our model (figure 1) focuses on social learning as a solution to our problem in order to predict the success or failure of learners in Moroccan universities (higher education), improve their learning experience and suggest the type of platform suitable for each student. For this we proposed to use two e-learning platforms LMS and LXP, LMS to focus more on the administration of learning than on the experience, whereas LXP to focus on the ability to facilitate a personalized, learner-oriented learning. The chosen LMS is Moodle which is a learning platform designed to provide teachers, administrators and learners with a single robust, secure and integrated system for creating personalized learning environments [START_REF]Moodle -Open-source learning platform[END_REF], and the chosen LXP is Myskillcamp [START_REF]About us | myskillcamp[END_REF] which is an all-in-one learning experience platform. One can create, edit, import and purchase courses directly in Myskillcamp, build and launch learners on personalized learning paths and even check progress and completion rates with their analytics.

We chose these two platforms because they are designed for teaching and learning, easy to use, free, with no licensing costs, their multilingual capabilities ensure that no language barrier hinders online learning, they are flexible and customizable platforms that can serve small classrooms as well as large organizations, robust, secure and private with a large number of resources available.

So we will collect the data from these platforms using the data retrieval technique, after this generated data will be cleaned and preprocessed. There are several tools to facilitate data mining, namely MMT (Moodle Mining Tool) and ADE (Automatic Data Extraction) [START_REF] Ali | Moodle Data Retrieval for Educational Data Mining[END_REF]. Generally speaking, there are three methods of data extraction, the first method of extraction is xAPI. For example, Watershed which has a way to extract data from virtually any LRS "Learning Record Store" which is a database used to store all the information of a learning ecosystem in xAPI format, even if it is not fully compliant. And many LRSs have xAPI functionality that can pass xAPI data to Watershed as an activity provider. An API connection is used for the second approach. Most platforms include APIs that enable access to their data or enable the creation and download of reports. API access involves technical know-how, comprehension of the applicable documentation, as well as the necessary tools and credentials. The final approach uses an Excel or CSV file. You may get a report from most platforms and explore with the data in Excel or another data tool of your choosing [START_REF]How Do I Extract Learning Data from My LMS or LXP?[END_REF].

The data that will be extracted is the content of the links between students, which means the interests that connect them, their interactions in the learning groups and the level of engagement in each group. After cleaning the data we will have interpretable data that will be analyzed using Social Learning Analytics (SLA) where we seek to obtain information hidden in large amounts of educational data. This concept focuses mainly on Social Network Analysis (SNA) and web analysis, to discover interaction and behavior patterns of educational social actors [START_REF] Aguilar | Social learning analytics for determining learning styles in a smart classroom[END_REF].

Fig. 1. Social learning as a solution to predict the success of learners

The analysis will therefore focus on three main axes "the content of the links between students", "the interaction of the learners in the work groups" and the "level of commitment (increase or decrease)" in the two LMS and LXP platforms, we will therefore look for the relationship between these axes and the success rate of the learners. This will allow us to conclude the role of social learning in the success of students in these platforms specifically their interactions and participations in the online discussion forums of the courses and thus track the progress of learners and give them targeted and better feedback, in order to provide them with an improved learning experience that will help them succeed and self-regulate.

Conclusion

The social environment in which an e-learning action takes place can play a major role in a learner's persistence and success. Peer and instructor opinions can influence learners' choices in using a learning system. This study aims to test the hypothesis of the role and importance of the use of social and adaptive learning to overcome the problems that learners face in online learning platforms and its role in their success, we propose a model to determine the effects of the use of the two main online learning platforms "LMS and LXP" in Moroccan higher education, We seek to analyze the interactions and social presence in learning groups as well as the level of engagement of learners, how their activity is evaluated on the basis of social learning analysis (SLA) in order to predict the success and the best types of learning for each learner, thus improving their learning experience. Our future goal is to conduct an experiment where we will apply SLA concepts to analyze data extracted from the LMS and LXP platforms.

From this analysis, we will dedicate the importance of social learning in e-learning environments and suggest the best learning method for each learner in order to predict and guarantee the success of learners in e-learning environments in Moroccan educational systems.


Aline Fugeray-Scarbel

Jérôme Enjalbert

Myriam Tisserand

Myriam Tisserand Mobidiv -Mobilizing

MoBiDiv -MObilizing and Breeding Intra and inter-specific crop DIVersity for a

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

••

  Fig1. An effective decrease in pesticide use with PC : (Wheat) Pure crop; VM : Variety mixture; CM : Crop mixture

Fig

  Fig 2. Progression of mixture use in farms


Guillaume Grégoire

Comment on "Phase Transitions in Systems of Self-Propelled Agents and Related Network Models"

In a recent letter [1], Aldana et al. study order-disorder phase transitions in random network models and show that the nature of these transitions may change with the way noise is implemented in the dynamics. Arguing that these networks are limiting cases of simple models of interacting self-propelled agents of the type of the Vicsek model (VM) [2], they claim that the conclusions reached for the networks may carry over to the transitions to collective motion of the VM-like systems. They suggest in particular that in the case of "angular" noise (i.e. as in the original VM [START_REF] Note | use a slightly different updating rule than[END_REF], or in their Eq. ( 1)) the transition to collective motion is continuous, in contradiction with some of the conclusions of [3]. While we agree with the analysis of the network models, we argue here that it has no bearing on VM-like systems. We show in particular that the transition to collective motion, for angular noise, remains discontinuous for any finite microscopic velocity v and finite density ρ, however large, confirming [3].

In [3], is was shown that the transition in the original VM appears continuous only when the linear system size L is smaller than some crossover size L * (ρ, v) and thus is discontinuous in the thermodynamic limit (see Fig. 2a there). These results were obtained for values of ρ and v of order unity. The same scenario occurs in the large-v or large ρ limit of interest here. Figure 1a here displays results akin to Fig. 1 of [1] except that the VM order parameter curves were obtained in systems such that L > L * (ρ, v), whereas in [1] L ≃ 100 < L * (ρ, v) (see below for our estimates of L * ). Clearly, the transition is discontinuous, as demonstrated by the minimum shown by the Binder cumulant (Fig. 1b) and by the bimodality of the order parameter distribution in the transition region (not shown).

The key difference between the network models and interacting self-propelled agents is indeed that the latter move, inducing a local coupling between order and density, which is well-known to be crucial for understanding collective properties of active particles [4]. In the VM, this coupling gives rise to strong density and order variations on lengthscales of the order of L * . While the network models in [1] capture the long-range interactions due to large velocities, they obviously cannot account for any coupling between density and order. The network models only represent VM-like models of size L L * .

The crossover scale L * is difficult to estimate with high accuracy, but our data indicate that L * increases roughly linearly with v (for ρ = 2, L * = 150 ± 25, 175 ± 25, 250 ± 50, 550 ± 50 for v = 5, 10, 20, 40 respectively). Thus, we expect L * to be finite at any finite v. As a consequence, the transition is always discontinuous in the thermodynamic limit, although its asymptotic behavior is harder to observe as v is taken larger and larger. When v is For the network, the number of nodes is N = 4 × 10 5 and the average connectivity is set to K = 2π (i.e. each node interacts with 6 or 7 neighbors, with suitable probabilities), which corresponds to ρ = 2 in the VM.

taken to infinity first, the transition is continuous for all finite "size", but then the notion of distance in physical space is abolished.

Summary: the transition to collective motion in VMlike systems with angular noise remains discontinuous for large v values. Thus, the networks studied in [1] at best constitute a singular v → ∞ limit of these systems [6].

Hugues Chaté and Francesco Ginelli

CEA -Service de Physique de l'Etat Condensé 91191 Gif-sur-Yvette, France

5 G

 5 FIG. 1: (Color online) Order-disorder transition for the Vicsek model at different v values (solid lines) and the network of fixed vectors of [1] (dashed line), both with angular noise. (a) Time-averaged order parameter ψ vs noise strength η. (b) same as (a) for the Binder cumulant G = 1 -ψ 2 2 / ψ 4 . For the VM, ρ = 2, and v = 5 (circles), v = 10 (squares), v = 20 (triangles) with L = 200, 250 and 300 respectively.For the network, the number of nodes is N = 4 × 10 5 and the average connectivity is set to K = 2π (i.e. each node interacts with 6 or 7 neighbors, with suitable probabilities), which corresponds to ρ = 2 in the VM.