Semi-automatic annotation of MOOC forum posts

Massive online open courses’ (MOOCs’) students who use discussion forums have higher chances of finishing the course. However, little research has been conducted for understanding the underlying fac- tors. One of the reasons which hinders the analysis is the amount of manual work required for annotating posts. In this paper we use ma- chine learning techniques to extrapolate small set of annotations to the whole forum. These annotations not only allow MOOC producers to sum- marize the state of the forum, but they also allow researchers to deeper understand the role of the forum in the learning process

Translating Head Motion into Attention - Towards Processing of Student’s Body-Language

Evidence has shown that student's attention is a crucial factor for engagement and learning gain. Although it can be accurately assessed ad-hoc by an experienced teacher, continuous contact with all students in a large class is difficult to maintain and requires training for novice practitioners. We continue our previous work on investigating unobtrusive measures of body-language in order to predict student's attention during the class, and provide teachers with a support system to help them to "scale-up" to a large class. Our work here is focused on head-motion, by which we aim to mimic large-scale gaze tracking. By using new computer vision techniques we are able to extract head poses of all students in the video-stream from the class. After defining several measures about head motion, we checked their significance and attempted to demonstrate their value by fitting a mixture model and training support vector machines (SVM) classifiers. We show that drops in attention are reflected in a decreased intensity of head movement. We were also able to reach 65.72% correct classifications of student attention on a 3-point scale

Augmenting Collaborative MOOC Video Viewing with Synchronized Textbook

We designed BOOC, an application that synchronizes textbook content with MOOC (Massive Open Online Courses) videos. The application leverages a tablet display split into two views to present lecture videos and textbook content simultaneously. The display of the book serves as peripheral contextual help for video viewing activities. A five-week user study with 6 groups of MOOC students in a blended on-campus course was conducted. Our study in this paper reports how textbooks are used in authentic MOOC study groups and further explores the effects of the book-mapping feature of the BOOC player in enhancing the collaborative MOOC learning experiences

Semi-Markov model for simulating MOOC students

Large-scale experiments are often expensive and time consuming. Although Massive Online Open Courses (MOOCs) provide a solid and consistent framework for learning analytics, MOOC practitioners are still reluctant to risk resources in experiments. In this study, we suggest a methodology for simulating MOOC students, which allow estimation of distributions, before implementing a large-scale experiment. To this end, we employ generative models to draw independent samples of artificial students in Monte Carlo simulations. We use Semi-Markov Chains for modeling student's activities and Expectation-Maximization algorithm for fitting the model. From the fitted model, we generate simulated students whose processes of weekly activities are similar to these of the real students

MOOC Video Interaction Patterns: What Do They Tell Us?

For MOOC learners, lecture video viewing is the central learning activity. This paper reports a large-scale analysis of in-video interactions. We categorize the video behaviors into patterns by employ- ing a clustering methodology, based on the available types of interactions, namely, pausing, forward and backward seeking and speed changing. We focus on how learners view MOOC videos with these interaction patterns, especially on exploring the relationship between video interaction and perceived video difficulty, video revisiting behaviors and student performance. Our findings provide insights for improving the MOOC learning experiences

How Do In-video Interactions Reflect Perceived Video Difficulty?

Lecture videos are the major components in MOOCs. It is common for MOOC analytics researchers to model video behaviors in order to identify at-risk students. Much of the work emphasized prediction. However, we have little empirical understanding about these video interactions, especially at the click-level. For example, what kind of video interactions may indicate a student has experienced difficulty? To what extent can video interactions tell us about perceived video difficulty? In this paper, we present a video interaction analysis to provide empirical evidence about this issue. We find out that speed decreases, frequent and long pauses, infrequent seeks with high amount of skipping and re-watching indicate higher level of video difficulty. MOOC practitioners and instructors may use the insights to provide students with proper support to enhance the learning experience

How to quantify student's regularity?

Studies carried out in classroom-based learning context, have consistently shown a positive relation between students' conscientiousness and their academic success. We hypothesize that time management and regularity are main constructing blocks of students' conscientiousness in the context of online education. In online education, despite intuitive arguments supporting on-demand courses as more flexible delivery of knowledge, completion rate is higher in the courses with rigid temporal constraints and structure. In this study, we further investigate how students' regularity affects their learning outcome in MOOCs. We propose several measures to quantify students regularity. We validate accuracy of these measures as predictors of students' engagement and success in the course

Inference for stationary functional time series: dimension reduction and regression

Les progrès continus dans les techniques du stockage et de la collection des données permettent d'observer et d'enregistrer des processus d’une façon presque continue. Des exemples incluent des données climatiques, des valeurs de transactions financières, des modèles des niveaux de pollution, etc. Pour analyser ces processus, nous avons besoin des outils statistiques appropriés. Une technique très connue est l'analyse de données fonctionnelles (ADF).L'objectif principal de ce projet de doctorat est d'analyser la dépendance temporelle de l’ADF. Cette dépendance se produit, par exemple, si les données sont constituées à partir d'un processus en temps continu qui a été découpé en segments, les jours par exemple. Nous sommes alors dans le cadre des séries temporelles fonctionnelles.La première partie de la thèse concerne la régression linéaire fonctionnelle, une extension de la régression multivariée. Nous avons découvert une méthode, basé sur les données, pour choisir la dimension de l’estimateur. Contrairement aux résultats existants, cette méthode n’exige pas d'assomptions invérifiables. Dans la deuxième partie, on analyse les modèles linéaires fonctionnels dynamiques (MLFD), afin d'étendre les modèles linéaires, déjà reconnu, dans un cadre de la dépendance temporelle. Nous obtenons des estimateurs et des tests statistiques par des méthodes d’analyse harmonique. Nous nous inspirons par des idées de Brillinger qui a étudié ces models dans un contexte d’espaces vectoriels.Doctorat en Sciencesinfo:eu-repo/semantics/nonPublishe

A note on estimation in Hilbertian linear models

info:eu-repo/semantics/nonPublishe