Introduction to Smart Learning Analytics: Foundations and Developments in Video-Based Learning

Smart learning has become a new term to describe technological and social developments (e.g., Big and Open Data, Internet of Things, RFID, and NFC) enable effective, efficient, engaging and personalized learning. Collecting and combining learning analytics coming from different channels can clearly provide valuable information in designing and developing smart learning. Although, the potential of learning analytics to enable smart learning is very promising area, it remains non-investigated and even ill-defined concept. The paper defines the subset of learning analytics that focuses on supporting the features and the processes of smart learning, under the term Smart Learning Analytics. This is followed by a brief discussion on the prospects and drawbacks of Smart Learning Analytics and their recent foundations and developments in the area of Video-Based Learning. Drawing from our experience with the recent international workshops in Smart Environments and Analytics in Video-Based Learning, we present the state-of-the-art developments as well as the four selected contributions. The paper further draws attention to the great potential and need for research in the area of Smart Learning Analytics

Dynamic functional principal components

In this paper, we address the problem of dimension reduction for sequentially observed functional data (X_k : k ∈ Z). Such functional time series arise frequently, e.g., when a continuous time process is segmented into some smaller natural units, such as days. Then each Xk represents one intraday curve. We argue that functional principal component analysis (FPCA), though a key technique in the field and a benchmark for any competitor, does not provide an adequate dimension reduction in a time series setting. FPCA is a static procedure which ignores valuable information in the serial dependence of the functional data. Therefore, inspired by Brillinger’s theory of dynamic principal components, we propose a dynamic version of FPCA which is based on a frequency domain approach. By means of a simulation study and an empirical illustration, we show the considerable improvement our method entails when compared to the usual (static) procedure. While the main part of the article outlines the ideas and the implementation of dynamic FPCA for functional Xk, we provide in the appendices a rigorous theory for general Hilbertian data

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

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

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

How employment constrains participation in MOOCs?

Massive Open Online Courses (MOOCs) changed the way continuous education is perceived. Employees willing to progress their careers can take high quality courses. Students can develop skills outside curriculum. Studies show that most of the MOOC users are pursuing or have received a university degree. Therefore it is beneficial to consider motives and constraints of this class of participants while designing a course. In this study we focus on time constraints experienced by full-time and part-time employees and students. Surprisingly, activities of students and employees are very similar regarding timing. We found that part-time employees spend more time on forum and are more active during the day. Employees are more active in the evening hours from Monday till Thursday. Based on our findings we suggest course design insights for practitioners

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