interaction in distance learning

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Learning Users' and Personality-Gender Preferences in Close Human-Robot Interaction

Learning Users' and Personality-Gender Preferences in Close Human-Robot Interaction

IV. RESULTS AND DISCUSSION A. Training the robot The values for each parameter, obtained at the end of the training of the robot are shown in Fig. 4, and in Table V, where the values in parenthesis are the values that the robot can perform, and that are the actual values that were used in the second part of the experiment. The model predicts that extroverted female participants will prefer a closer distance than the other groups of participants, also that female participants will prefer gestures with higher amplitude than male participants with the same personality as theirs (extroversion/introversion) and that extroverted male participants will prefer a faster speed for the gestures than the other groups of participants. As our model is based on a weighted mean of the activation values of each parameter in the EM, we applied a weighted t-test using the software R for a statistical analysis on the differences of the predicted parameters. The only significant difference was found in the preferred amplitude between the extrovert female group (0.83 m) and the introvert male group (0.44 m) with t = 2.327, df = 7.905, p = 0.048, and Std.err = 0.167.
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Personalisation of learning in virtual learning environments

Personalisation of learning in virtual learning environments

Tracked Data for Instructors and Researchers Some authors expressed interest for the exploitation of different kinds of interaction footprints by researchers [48, 49]. Others speculated about its benefit for instructors [50]. Among them, Nagi & Suesawaluk [51] recommended tutors to make use of the students data tracked by the Moodle eLearning platform in order to better regulate their courses. With a tool called CourseVis, Mazza & Dimitrova [52] took student tracking data collected by content management systems and generated graphical representations useful to instructors to gain an understanding of what is happening in distance learning classes. This work lead to the production of Gismo, a tool managing the visualization of data tracked in Moodle [53]. In a similar vein and on the same platform, Zhang et al. [54] developed a VLE log analysis tool, called Moodog, to track students’ online learning activities. The goals of Moodog were twofold: first, to provide instructors with insight about how students interact with online course materials, and second, to allow students to easily compare their own progress to others in the class. The latter objective sounded congruent with the approach defined in this article. However the authors eventually postponed its achievement to a subsequent study. Scheuer and Zinn [55] developed an interesting tracking system called the Student Inspector. In their conclusion, they only evoked the possibility to open the tool to students.
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Tridem et interaction à l'oral et à l'écrit dans une formation à distance en langue.

Tridem et interaction à l'oral et à l'écrit dans une formation à distance en langue.

Abstract The question of an optimal link between oral and written production arises in any language classes. Which indices in the speech of the learners show that the learner took advantage of the interaction between writing and oral? We will study this question in the case of a distance language learning program with 2 different technological environments. Language learners are initially gathered in tridems and come each one of three different universities. Each international tridem is in a blog and, for oral production, in the audio-synchronous platform Lyceum. We are interested here to find examples of linking between writing and oral production and to detect situations where a native helps the oral and written expression of a learner. Key-words: tandem, blog, audio-synchronous, link between written and oral production
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The role of semantic distance in learning and generalization of novel names in typically developing and atypically developing children

The role of semantic distance in learning and generalization of novel names in typically developing and atypically developing children

semantically unrelated item (e.g., Christmas ball) and a same-superordinate-but-perceptually- dissimilar category, either close (e.g., banana) or remote (e.g., meat) (see Figure 1). ID and TD children were split in two groups a High and Low Raven score. A 2 (Learning Distance: close or far) x 2 (Generalization Distance: close or far) x 2 (Group: ID or TD children) x Raven score (Low or High) ANOVA was carried out on the taxonomic choices. It revealed that ID children were better than the matched TD children, suggesting functional lexical learning mechanisms. Even ID low-Raven-scores children, surprisingly, obtained better results than high-Raven-scores TD children. ID children, who were significantly older than TD children, could rely on their more developed world knowledge to learn and extend novel names. Close generalization was also significantly better than far generalization. There was no interaction between Group and the other factors. Interestingly, there was an interaction between Raven score and Learning: High-Raven-scores individuals (HR) outperformed Low-Raven-score participants (LR) in the far learning condition whereas they did not differ on the close learning condition. Also, LR participants were better in the close learning than in the far learning case, while the reverse was true for the HR participants. Importantly, this suggests that LR children had more difficulties to conceptually unify dissimilar training stimuli whereas HR children benefited more from “learning distance”. In sum, what these results show is that ID per se was not the crucial factor here, but rather the level of cognitive functioning which interacted with learning distance. We suggest that ID people can extend their vocabulary in familiar conceptual domain. We interpret our results in terms of cognitive constraints associated with comparison activities which might impact LR children in remote conceptual domains. We predict that ID participants should experience more difficulties with less familiar conceptual domains or with more difficult concepts such as relational concepts, which we currently test.
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Modèle de m-learning et conception d'applications mobiles comme outils de support pour l'enseignement à distance en informatique et génie logiciel

Modèle de m-learning et conception d'applications mobiles comme outils de support pour l'enseignement à distance en informatique et génie logiciel

publications, de collaborer dans diérents projets, et de participer à des concours de twittérature (ensemble des textes littéraires publiés dans Twitter) [ 11 ]. Poursuivant ces idées de partage et de collaboration, l'utilisation des systèmes de ré- ponse dans les salles de classe pour créer une interaction entre le professeur et son public est une autre façon d'introduire l'apprentissage mobile dans l'éducation. Ces systèmes permettent aux professeurs d'avoir l'opinion de l'auditoire et d'établir un mécanisme informel d'évaluation et de participation. Manuel Caeiro et al. ont développé un sys- tème de participation de l'auditoire (Audience Response System ARS). Grâce à cet outil, le professeur peut soumettre une série des questions à ses étudiants et recevoir instantanément les réponses. Cette pratique a augmenté le taux de participation des étudiants parce que les réponses sont anonymes et que les étudiants peuvent s'exprimer sans se sentir gênés de donner de mauvaises réponses [ 7 ].
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Exact Distance Colouring in Trees

Exact Distance Colouring in Trees

χ(H 2 , d) grows with d or can be bounded independently of d. As noticed by Kahle (see [3]), it is not known whether χ(H 2 , d) ≥ 5 for some real d > 0. Parlier and Petit [6] recently suggested to study infinite regular trees as a discrete analog of the hyperbolic plane. Note that any graph G can be considered as a metric space (whose elements are the vertices of G and whose metric is the graph distance in G), and in this context χ(G, d) is precisely the minimum number of colors in a vertex coloring of G such that vertices at distance d apart are assigned different colors. Note that χ(G, d) can be equivalently defined as the chromatic number of the exact d-th power of G, that is, the graph with the same vertex-set as G in which two vertices are adjacent if and only if they are at distance exactly d in G. Let T q denote the infinite q-regular tree. Parlier and Petit [6] observed that when d
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Learning at a distance

Learning at a distance

o Web conferencing for synchronous activities (Teams and Adobe) o File management on the cloud (365 and OneDrive) There are many relatively new technological tools available for taking DL courses. Each has its own technical requirements in the way of computer specifications, software, types of accessories used, etc. If you have not received any information about such requirements before the semester begins, check your institutional email inbox, and then contact your program coordinator promptly if necessary.

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Distance-preserving orderings in graphs

Distance-preserving orderings in graphs

of G i , then no edges are added. Otherwise, by Lemma 1, this means that two non-adjacent neighbours x, y ∈ V (G i+1 ) ∩ N (v i ) of v i have no common neighbour in G i+1 . Hence, the algorithm adds edges between every such a pair of vertices. We have used these generators to perform basic experiments on a standard laptop, using the Sagemath open-source mathematical software [30] to implement the algorithms and IBM Ilog CPLEX [23] to solve the ILP formulations. Our first observation is that the ILP formulation is generally able to decide if a graph with up to 50 nodes has a distance-preserving ordering in a few minutes (we also tried Erdős-Rényi and Barabási-Albert random graphs). However, it can hardly be used for larger graphs due to excessive running time. Our second observation is that the Greedy_Pruning heuristic is not effective at all. It is able to find a distance-preserving ordering on very few small graphs (less than 20 nodes) only. The Greedy_Reverse_Pruning heuristic guided by the maximum degree is much more efficient. We have executed it on graphs generated by the INC generator (100 n-node graphs, for each n ∈ {20, 30, · · · , 100}). The heuristic has been able to confirm that more than 96% of these graphs have a distance-preserving ordering. Also, this heuristic appears to be particularly efficient on dense graphs. Precisely, we performed many experiments on Erdős-Rényi random graphs (100 n-node graphs, for each n ∈ {100, · · · , 200} and p ∈ {0.1, 0.2, · · · , 0.5}) and our heuristic returns that more than 99% of them actually have a distance-preserving ordering when the probability is high (p ≥ 0.3). The latter supports a recent conjecture from [27]. Further experimental and theoretical investigations are needed to determine the minimum probability upon which Erdős-Rényi random graphs have a distance- preserving ordering asymptotically almost surely. We let this interesting question as an open problem for future research.
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Perception and human interaction for developmental learning of objects and affordances

Perception and human interaction for developmental learning of objects and affordances

V. CONCLUSIONS In this paper, we presented the architecture we are using in MACSi to design experiments in developmental robotics scenarii, where the robot interacts with caregivers to learn objects and their affordances, and eventually to take decisions autonomously. The main feature of our solution is that it is natively designed for learning experiments, where social guidance is combined and gradually replaced by artificial curiosity and autonomous exploration. We performed several experiments to assess the efficiency of the proposed solution, setting a solid base for future research in affordance recogni- tion. We focused on the perception and interaction modules; for lack of space, we did not introduced how objects, actions and caregivers are formally represented in the architecture. The internal representation and the techniques for active learning of affordances will be object of forthcoming papers. The next step in the evolution of the CA will be to integrate intrinsic motivation in the decision making process [14], to gradually diminish the role of the caregiver and make the robot take its own decisions autonomously, driven by its artificial curiosity.
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Filamentation laser femtoseconde IR : Interaction de deux filaments et Source de rayonnement secondaire longue distance

Filamentation laser femtoseconde IR : Interaction de deux filaments et Source de rayonnement secondaire longue distance

11 Introduction En 1960, le physicien américain Théodore Maiman obtient pour la première fois une émission laser au moyen d’un cristal de rubis. En peu de temps, les lasers trouvent de nombreux débouchés industriels. L’une des premières applications du laser fut réalisée en 1965 et consistait à usiner un trou de 4,7 mm de diamètre et 2 mm de profondeur dans un diamant. Cette opération n’aura pris que 15 minutes, alors qu’elle nécessitait 24 heures par des méthodes classiques. Le laser est aujourd’hui utilisé couramment dans l’industrie, les télécommunications, l’informatique ou encore la médecine. C’est aussi un outil puissant pour l’étude des phénomènes physiques et biologiques. Grâce à de nouvelles technologies, telles que l’invention du CPA (amplification à dérive de fréquence) [Strickland 85] et le développement des cristaux de Titane:Saphir, il est possible générer des impulsions de quelques dizaines de femtosecondes et de les amplifier pour atteindre des puissances crêtes de plusieurs centaines de Térawatts. On a longtemps pensé que les impulsions ultra-courtes n’étaient pas adaptées à la propagation sur de grandes distances dans l’air. Par exemple, dans un régime de propagation linéaire l’intensité crête d’une impulsion de 30 fs avec un faisceau de 5 mm est supposée être réduite d’un facteur après une propagation d’un kilomètre dans l’air, en raison des effets combinés de la diffraction (facteur 100) et de la dispersion de vitesse de groupe (facteur 50). Cependant, les études de propagation d’intenses impulsions infrarouges d’une centaine de femtosecondes ont prouvé le contraire. En 1995, Braun et al., ont découvert que l’intensité de l’impulsion au cours de sa propagation augmentait au lieu de décroitre. L’intensité était telle qu’à une distance de 10 m des micro-brulures ont été faites sur un miroir. Les chercheurs de l’université
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Towards a Capitalization of Processes Analyzing Learning Interaction Traces

Towards a Capitalization of Processes Analyzing Learning Interaction Traces

Analysis Tools. TEL community has at its disposal a variety of cross-field analysis tools, like RapidMiner 3 or R 4 , and specialized solutions. For instance, UnderTracks (UT) takes into consideration data and operators life cycle within analysis [6]. We can also cite Usage Tracking Language (UTL), which calculates and describes indicators by mapping data coming from heterogeneous traces into more generic ones expressed in XML [3]. All these tools can be classified into three categories [6]: data storage, data analysis (like R) and both data storage and analysis (like UT). Our work concerns only the tools designed for analyses. Capitalization. Since analysis tools implement operators that are strongly dependent of data formalism in order to be computed, they are poorly permissive. As a result, some works suggest to work with a more generic data formalism before making any analysis, like Caliper Analytics 5 or UTL. These tools map
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Using IT for Distance Learning : Benefts and Challenges for African Learners

Using IT for Distance Learning : Benefts and Challenges for African Learners

Conceptual aspects Our conceptual framework considers the specificity of the African context in the study of ODL compared to the Western context, which has been largely studied. The idea is to highlight the sociocultural aspects of Africa that play a role in the ODL experience. First, we should keep in mind that ICT use is always culturally anchored, and consequently varies across cultures (Mattelart, 1991). A number of models, such as the theory of reasoned action (Ajzen, 1985; Fishbein & Ajzen, 1975) and the technology acceptance model (Davis, 1989) have attempted to explain variations in ICT adoption (or not) by individuals. Davis’ theory dominates the literature (Venkatesh & Bala, 2008). It posits two variables in ICT adoption: perceived usefulness and perceived ease of use. These determine the user’s intention to use ICT, and ultimately their effective use of ICT. Although this model has been refined with time, it still has some limitations, notably for purposes of our study. First, it is applied mainly to management studies (Kharbeche, 2006), whereas our field is education. Moreover, it is important to note that the above-mentioned ICT adoption models are mainly descriptive. They therefore provide little information about how interventions can effectively foster ICT adoption, even though studies are beginning to address this issue (Venkatesh & Bala, 2008). Finally, ICT adoption models have been developed by Western authors, which raises the question of their validity for African cultures. Thus, beyond the structural and economic factors that clearly hinder ICT adoption in Africa (see Karsenti & Collin, 2010), it is useful to consider the sociocultural factors that may pose obstacles to ICT adoption in Africa. For this purpose, studies generally refer to the work of Hofstede (1980), who identified five main cultural dimensions of countries: power distance, individualism versus collectivism, masculinity versus femininity, uncertainty avoidance, and long-term orientation. These dimensions address general cultural characteristics of populations. However, they do not allow capturing local cultural particularities. For that purpose, Agboton (2006) identified several other dimensions, including knowledge sources, whereby knowledge is attributed more to experience and wisdom passed down from ancestors than to formal education. Oral communication and direct relationships between people, in other words language and communication, are therefore vital in Africa, a continent with many languages but relatively little Web presence. That said, ICT adoption also depends on certain individual aptitudes, such as knowing how to read, write, and understand English.
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Where are MOOCs going? What is the future of distance learning?

Where are MOOCs going? What is the future of distance learning?

Correction Iteration TEST TRY Requirements Affordances GAP Opport- unity.. Personalized Personal[r]

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Exact distance coloring in trees

Exact distance coloring in trees

log d . It was not known that the chromatic number of this graph grows with d. As a simple corollary of our result, we give a negative answer to a problem of Van den Heuvel and Naserasr, asking whether there is a constant C such that for any odd integer d, any planar graph can be colored with at most C colors such that any pair of vertices at distance exactly d have distinct colors. Finally, we study interval coloring of trees (where vertices at distance at least d and at most cd, for some real c > 1, must be assigned distinct colors), giving a sharp upper bound in the case of bounded degree trees.
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Donsker's theorem in {Wasserstein}-1 distance

Donsker's theorem in {Wasserstein}-1 distance

In infinite dimension, a new problem arises which is best explained by going back to the roots of the Stein’s method in dimension 1. Consider that we want to estimate the K-R distance in the standard Central Limit Theorem. Let (X n , n ≥ 1) be a sequence of independent, identically distributed random variables with E [X] = 0 and E X 2 

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Virtual Distance Estimation in a CAVE

Virtual Distance Estimation in a CAVE

Second, virtual architectural models are often viewed by more than one person at a time. For this reason, walk-throughs often take place on a large screen or in a CAVE, as opposed to an HMD. Most past virtual distance estimation research has been conducted using an HMD, so those results may provide a good starting point, but they fail to predict the interplay between virtual and physical distance cues that are present in a CAVE. The studies reported in this paper were conducted in a four-sided CAVE. Because trials were quick, participants were not expected to achieve high levels of presence, meaning that they might use distance cues from both the VE and the physical system. This paper will not address additional distortions that arise as multiple untracked users move away from the center of projection [1].
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Blogs in Learning

Blogs in Learning

Create a context. Like the author facing a blank sheet of paper, a blogger will be perplexed unless given something specific to write about. Have students blog about a current issue, about a specific peice of writing, or some question that comes up in the course. Encourage interaction. Blogging should not be a solo activity. Encourage bloggers to read each other's works and to comment on them. Encouraging students to set up an RSS reader with each other's blogs will make reading and commenting a lot easier. Teachers, also, should subscribe to student blogs and offer comments, again setting an example of the expected practice.
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Dictionary-based Learning in MR Fingerprinting: Statistical Learning versus Deep Learning

Dictionary-based Learning in MR Fingerprinting: Statistical Learning versus Deep Learning

the robustness of DB-DL, e.g. data augmentation as suggested in , we can expect major improvements. For all methods, the error on VSI is large but this comes in part from the measuring sequence: VSI mainly depends on few signal samples. Other acquisition methods could reduce this error. There is a discrepancy between results from toy signals and microvascular signals: for toys signals (3 to 7 parameters), DBL methods provide by far better results than DBM. However, for microvascular signals (2 parameters), DBM can provide similar and even better results. It suggests that DBL methods are sensitive to how fingerprints properly encode information about the parameters of interest. An indicator of the model quality or confidence in estimates could be used to detect these cases.
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Online Language Learning to Perform and Describe Actions for Human-Robot Interaction

Online Language Learning to Perform and Describe Actions for Human-Robot Interaction

The humanoid robot is an iCub [2] who interacts around a instrumented tactile table (ReacTable TM ) on which objects can be manipulated by both human and robot. A sensory system has been developed to extract spatial relations. A speech recognition and text to speech off-the-shelf tool allows spoken communication. In parallel, the robot has a small set of actions (put(object, location), grasp(object), point(object)). These spatial relations, and action definitions form the meanings that are to be linked to sentences in the learned grammatical constructions.
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A Multimodal Dataset for Object Model Learning from Natural Human-Robot Interaction

A Multimodal Dataset for Object Model Learning from Natural Human-Robot Interaction

II. R ELATED WORK There are many works in the literature in which the robot interacts directly with the objects in a scene to learn new models. For example, Collet et al. [3] created a 3D model of the objects in the scene that a robotic hand has to grasp. Kenney et al. [7] proposed to improve object segmentation in cluttered scenarios by manipulating the objects. Addition- ally there are multiple works which use robotic hands to interact with objects in the scene. For example Iravani et al. [6] proposed a system where the robot manipulates the objects presented in front of the camera until the model is learned. Krainin et al. [8] proposed to use a robotic hand to grasp the object and rotate it to obtain different views. Sinapov et al. [16] used the robotic hands to interact with plastic jars and obtain multimodal information to learn the content of the jars. These approaches typically need prior information to be able to grasp the objects. Our approach is complementary to these works and focuses on scenarios that require human interaction, e.g. if the object is out the robot’s reach the affordances are completely unknown or the grasping capabilities are limited.
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