Une exploitation intelligente des données qui circulent sur les réseaux pourrait entraîner une amélioration de la qualité d'expérience (QoE) des utilisateurs. Les techniques d'apprentissage automatique offrent des fonctionnalités multiples, ce qui permet d’optimiser l'utilisation des ressources réseau.Dans cette thèse, deux contextes d’application sont étudiés : les réseaux de capteurs sans fil (WSNs) et les réseaux de contenus (CDNs). Dans les WSNs, il s’agit de prédire la qualité des liens sans fil afin d’améliorer la qualité des routes et donc d’augmenter le taux de remise des paquets ce qui améliore la qualité de service offerte à l’utilisateur. Dans les CDNs, il s’agit de prédire la popularité des contenus vidéo afin de mettre en cache les contenus les plus populaires, au plus près des utilisateurs qui les demandent. Ceci contribue à réduire la latence pour satisfaire les requêtes des utilisateurs.Dans ce travail, nous avons orchestré des techniques d’apprentissage issues de deux domaines différents, à savoir les statistiques et le Machine Learning. Chaque technique est représentée par un expert dont les paramètres sont réglés suite à une analyse hors-ligne. Chaque expert est chargé de prédire la prochaine valeur de la métrique. Vu la variété des experts retenus et comme aucun d’entre eux ne domine toujours tous les autres, un deuxième niveau d’expertise est nécessaire pour fournir la meilleure prédiction. Ce deuxième niveau est représenté par un expert particulier, appelé forecaster. Le forecaster est chargé de fournir des prédictions à partir des prédictions fournies par un sous ensemble des meilleurs experts.Plusieurs méthodes d’identification de ce sous ensemble sont étudiées. Elles dépendent de la fonction de perte utilisée pour évaluer les prédictions des experts et du nombre k, représentant les k meilleurs experts. Les tâches d’apprentissage et de prédiction sont effectuées en-ligne sur des data sets réels issus d’un WSN déployé à Stanford et de YouTube pour le CDN. La méthodologie adoptée dans cette thèse s’applique à la prédiction de la prochaine valeur d’une série temporelle.Plus précisément, nous montrons comment dans le contexte WSN, la qualité des liens peut être évaluée par le Link Quality Indicator (LQI) et comment les experts Single Exponential Smoothing (SES) et Average Moving Window (AMW) peuvent prédire la prochaine valeur de LQI. Ces experts réagissent rapidement aux changements des valeurs LQI que ce soit lors d’une brusque baisse de la qualité du lien ou au contraire lors d’une forte augmentation de la qualité. Nous proposons deux forecasters, Exponential Weighted Average (EWA) et Best Expert (BE), et fournissons la combinaison Expert-Forecaster permettant de fournir la meilleure prédiction.Dans le contexte des CDNs, nous évaluons la popularité de chaque contenu vidéo par le nombre journalier de requêtes. Nous utilisons à la fois des experts statistiques (ARMA) et des experts issus du Machine Learning (DES, régression polynômiale). Nous introduisons également des forecasters qui diffèrent par rapport à l’horizon des observations utilisées pour la prédiction, la fonction de perte et le nombre d’experts utilisés. Ces prédictions permettent de décider quels contenus seront placés dans les caches proches des utilisateurs. L’efficacité de la technique de caching basée sur la prédiction de la popularité est évaluée en termes de hit ratio et d’update ratio. Nous mettons en évidence les apports de cette technique de caching par rapport à un algorithme de caching classique, Least Frequently Used (LFU).Cette thèse se termine par des recommandations concernant l’utilisation des techniques d’apprentissage en ligne et hors-ligne pour les réseaux (WSN, CDN). Au niveau des perspectives, nous proposons différentes applications où l’utilisation de ces techniques permettrait d’améliorer la qualité d’expérience des utilisateurs mobiles ou des utilisateurs des réseaux IoT. / An intelligent exploitation of data carried on telecom networks could lead to a very significant improvement in the quality of experience (QoE) for the users. Machine Learning techniques offer multiple operating, which can help optimize the utilization of network resources.In this thesis, two contexts of application of the learning techniques are studied: Wireless Sensor Networks (WSNs) and Content Delivery Networks (CDNs). In WSNs, the question is how to predict the quality of the wireless links in order to improve the quality of the routes and thus increase the packet delivery rate, which enhances the quality of service offered to the user. In CDNs, it is a matter of predicting the popularity of videos in order to cache the most popular ones as close as possible to the users who request them, thereby reducing latency to fulfill user requests.In this work, we have drawn upon learning techniques from two different domains, namely statistics and Machine Learning. Each learning technique is represented by an expert whose parameters are tuned after an off-line analysis. Each expert is responsible for predicting the next metric value (i.e. popularity for videos in CDNs, quality of the wireless link for WSNs). The accuracy of the prediction is evaluated by a loss function, which must be minimized. Given the variety of experts selected, and since none of them always takes precedence over all the others, a second level of expertise is needed to provide the best prediction (the one that is the closest to the real value and thus minimizes a loss function). This second level is represented by a special expert, called a forecaster. The forecaster provides predictions based on values predicted by a subset of the best experts.Several methods are studied to identify this subset of best experts. They are based on the loss functions used to evaluate the experts' predictions and the value k, representing the k best experts. The learning and prediction tasks are performed on-line on real data sets from a real WSN deployed at Stanford, and from YouTube for the CDN. The methodology adopted in this thesis is applied to predicting the next value in a series of values.More precisely, we show how the quality of the links can be evaluated by the Link Quality Indicator (LQI) in the WSN context and how the Single Exponential Smoothing (SES) and Average Moving Window (AMW) experts can predict the next LQI value. These experts react quickly to changes in LQI values, whether it be a sudden drop in the quality of the link or a sharp increase in quality. We propose two forecasters, Exponential Weighted Average (EWA) and Best Expert (BE), as well as the Expert-Forecaster combination to provide better predictions.In the context of CDNs, we evaluate the popularity of each video by the number of requests for this video per day. We use both statistical experts (ARMA) and experts from the Machine Learning domain (e.g. DES, polynomial regression). These experts are evaluated according to different loss functions. We also introduce forecasters that differ in terms of the observation horizon used for prediction, loss function and number of experts selected for predictions. These predictions help decide which videos will be placed in the caches close to the users. The efficiency of the caching technique based on popularity prediction is evaluated in terms of hit rate and update rate. We highlight the contributions of this caching technique compared to a classical caching algorithm, Least Frequently Used (LFU).This thesis ends with recommendations for the use of online and offline learning techniques for networks (WSN, CDN). As perspectives, we propose different applications where the use of these techniques would improve the quality of experience for mobile users (cellular networks) or users of IoT (Internet of Things) networks, based, for instance, on Time Slotted Channel Hopping (TSCH).

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PlantingScience (PS) is a unique web-based learning system designed to develop secondary students' scientific practices and proficiencies as they engage in hands-on classroom investigations while being mentored online by a scientist. Some students' teachers had the opportunity to attend PS professional development (PD). In this dissertation, I developed a process of assessing student learning outcomes associated with their use of this system and evaluated inquiry engagement within this system. First, I developed a valid and reliable instrument (Online Elements of Inquiry Checklist; OEIC) to measure participants' (students, scientists, and teachers) engagement in scientific practices and proficiencies embedded within an inquiry cycle I collaborated with an expert-group to establish the OEIC's construct and content validities. An inter-rater reliability coefficient of 0.92 was established by scientists and a split half analysis was used to determine the instruments' internal consistency (Spearman-Brown coefficient of 0.96). Next, I used the OEIC to evaluate inquiry cycle engagement by the participants who used the PS online platform designed by the Botanical Society of America which facilitated communication between participants. Students provided more evidence of engagement in the earlier phases of an inquiry cycle. Scientists showed a similar trend but emphasized experimental design and procedures. Teachers rarely engaged online. Exemplary students' outcomes followed similar inquiry cycle trends, but with more evidence of engagement with one notable difference. Exemplary students provided evidence for extensive engagement in immersion activities, implicating immersion as a crucial component of successful inquiry cycle engagement. I also compared engagement outcomes of students whose teachers attended the PD experience to the students of teachers who did not attend PD. Differences found between the two groups occurred throughout the inquiry cycle, typically associated with experiences provided during the PD. As a result of this research I have several recommendations about revisions to the PS online platform and use of approaches to assure students development of scientific practices and proficiencies. The recommendations include additional scaffolding of the platform, explicit inquiry cycle instruction, and continued opportunities for teachers to engage in PD experiences provided by PS.

The purpose of this research is to explore students’ on-line interactivity in an Open Distance Learning institution with other students, educators, study materials and Unisa as the sampled prototypical research subject. A mixed-method of research encompassing both explorative and descriptive aspects was used. Data was collected through myUnisa discussion forum, focus group interviews and an on-line questionnaire from second and third year Health Services Management students at the University of South Africa (Unisa). Although the findings indicated that 84.9% of students owned computers, and 100% owned cellular phones, only 3.8% participated in the discussion forum. On-line discussion forum are critical in Open Distance Learning (ODL) because it allows people who cannot physically attend the educational institution to interact with each other. Almost 40% of these sampled students agreed that the discussion forum allowed them to study with their peers. However, only 53 of the 1,379 students registered for both second and third year studies during the first semester participated in the discussion forum. This indicates that very few students benefit from on-line interaction. Most of the students who are enrolled in Health Services Management course are from 21 to above 50 years of age. This age factor can have an impact on computer literacy. Some of them indicated that they struggled with the utilisation of technology. The majority of these students do not utilise the prescribed on-line interactive tools effectively. Students’ need support cognitively, academically, administratively, institutionally and affectively. The findings suggest that although students are aware of the benefits of using online technologies, they do not have the support from the institution to enable them to better their skills in using these technologies. The other challenge that they have raised is that educators also interact minimally on-line. Therefore, they do not receive the necessary feedback they require. The university systems are sometimes offline, which becomes worse during registration and submission of assignments. The recommendations emanating from the study warrants various interventions of multiple stakeholders to resolve the students’ challenges. / Health Studies / D.Litt, et Phil. (Health Studies)

The purpose of this research is to explore students’ on-line interactivity in an Open Distance Learning institution with other students, educators, study materials and Unisa as the sampled prototypical research subject. A mixed-method of research encompassing both explorative and descriptive aspects was used. Data was collected through myUnisa discussion forum, focus group interviews and an on-line questionnaire from second and third year Health Services Management students at the University of South Africa (Unisa). Although the findings indicated that 84.9% of students owned computers, and 100% owned cellular phones, only 3.8% participated in the discussion forum. On-line discussion forum are critical in Open Distance Learning (ODL) because it allows people who cannot physically attend the educational institution to interact with each other. Almost 40% of these sampled students agreed that the discussion forum allowed them to study with their peers. However, only 53 of the 1,379 students registered for both second and third year studies during the first semester participated in the discussion forum. This indicates that very few students benefit from on-line interaction. Most of the students who are enrolled in Health Services Management course are from 21 to above 50 years of age. This age factor can have an impact on computer literacy. Some of them indicated that they struggled with the utilisation of technology. The majority of these students do not utilise the prescribed on-line interactive tools effectively. Students’ need support cognitively, academically, administratively, institutionally and affectively. The findings suggest that although students are aware of the benefits of using online technologies, they do not have the support from the institution to enable them to better their skills in using these technologies. The other challenge that they have raised is that educators also interact minimally on-line. Therefore, they do not receive the necessary feedback they require. The university systems are sometimes offline, which becomes worse during registration and submission of assignments. The recommendations emanating from the study warrants various interventions of multiple stakeholders to resolve the students’ challenges. / Health Studies / D.Litt. et Phil. (Health Studies)