Massive Open Online Courses (MOOCs) have become an attractive opportunity for people around the world to gain knowledge and skills. Despite the initial enthusiasm of the first wave of MOOCs and the subsequent research efforts, MOOCs today suffer from retention issues: many MOOC learners start but do not finish. A main culprit is the lack of oversight and directions: learners need to be skilled in self-regulated learning to monitor themselves and their progress, keep their focus and plan their learning. Many learners lack such skills and as a consequence do not succeed in their chosen MOOC. Many of today's MOOCs are centered around video lectures, which provide ample opportunities for learners to become distracted and lose their attention without realizing it. If we were able to detect learners' loss of attention in real-time, we would be able to intervene and ideally return learners' attention to the video. This is the scenario we investigate: we designed a privacy-aware system (IntelliEye) that makes use of learners' Webcam feeds to determine---in real-time---when they no longer pay attention to the lecture videos. IntelliEye makes learners aware of their attention loss via visual and auditory cues. We deployed IntelliEye in a MOOC across a period of 74 days and explore to what extent MOOC learners accept it as part of their learning and to what extent it influences learners' behaviour. IntelliEye is open-sourced at https://github.com/Yue-ZHAO/IntelliEye.@en

Massive Open Online Courses (MOOCs) play an ever more central role in open education. However, in contrast to traditional classroom settings, many aspects of learners' behaviour in MOOCs are not well researched. In this work, we focus on modelling learner behaviour in the context of continuous assessments with completion certificates, the most common assessment setup in MOOCs today. Here, learners can obtain a completion certificate once they obtain a required minimal score (typically somewhere between 50-70%) in tests distributed throughout the duration of a MOOC. In this setting, the course material or tests provided after "passing" do not contribute to earning the certificate (which is ungraded), thus potentially affecting learners' behaviour. Therefore, we explore how ``passing'' impacts MOOC learners: do learners alter their behaviour after this point? And if so how? While in traditional classroom-based learning the role of assessment and its influence on learning behaviour has been well-established, we are among the first to provide answers to these questions in the context of MOOCs.@en

Massive Open Online Courses (MOOCs) play an ever more central role in open education. However, in contrast to traditional classroom settings, many aspects of learners' behaviour in MOOCs are not well researched. In this work, we focus on modelling learner behaviour in the context of continuous assessments with completion certificates, the most common assessment setup in MOOCs today. Here, learners can obtain a completion certificate once they obtain a required minimal score (typically somewhere between 50-70%) in tests distributed throughout the duration of a MOOC. In this setting, the course material or tests provided after "passing" do not contribute to earning the certificate (which is ungraded), thus potentially affecting learners' behaviour. Therefore, we explore how ``passing'' impacts MOOC learners: do learners alter their behaviour after this point? And if so how? While in traditional classroom-based learning the role of assessment and its influence on learning behaviour has been well-established, we are among the first to provide answers to these questions in the context of MOOCs.@en

Mind-wandering or loss of focus is a frequently occurring experience for many learners and negatively impacts learning outcomes. While in a classroom setting, a skilled teacher may be able to react to students’ loss of focus, in Massive Open Online Courses (MOOCs) no such intervention is possible (yet). Previous studies suggest a strong relationship between learners’ mind-wandering and their gaze, making it possible to detect mind-wandering in real-time using eye-tracking devices. Existing research in this area though has made use of specialized (and expensive) hardware, and thus cannot be employed in MOOC scenarios due to the inability to scale beyond lab settings. In order to make a step towards scalable mind-wandering detection among online learners, we propose the use of ubiquitously available consumer grade webcams. In a controlled study, we compare the accuracy of mind-wandering detection from gaze data recorded through a standard webcam and recorded through a specialized and high-quality eye tracker. Our results suggest that a large-scale application of webcam-based mind-wandering detection in MOOCs is indeed possible.@en

The creation time of documents is an important kind of information in temporal information retrieval, especially for document clustering, timeline construction and search engine improvements. Considering the manner in which content on the Web is created, updated & deleted, the common assumption that each document has only one creation time is not suitable for Web documents. In this paper, we investigate to what extent this assumption is wrong. We introduce two methods to timestamp individual parts (sub-documents) of Web documents and analyze in detail the creation & update dynamics of three classes of Web documents.@en

Understanding temporal intents behind users' queries is essential to meet users' time-related information needs. In order to classify queries according to their temporal intent (e.g. Past or Future), we explore the usage of time-series data derived from Wikipedia page views as a feature source. While existing works leverage either proprietary search engine query logs or highly processed and aggregated data (such as Google Trends) for this purpose, we investigate the utility of a freely available data source for this purpose. Our experiments on the NTCIR-12 Temporalia-2 dataset show, that Wikipedia pageview-based time-series data can significantly improve the disambiguation of temporal intents for specific types of queries, in particular those without temporal expressions present in the query string.@en

Mobile devices enable users to access information ubiquitously, including in the online learning scenario. This though requires users to multitask and divide their attention between several tasks at once whilst "on-the-go" (e.g. watching a video, walking down the street and keeping track of the traffic at the same time). In order to accommodate learners in this situation, most of today's Massive Open Online Course (MOOC) platforms provide mobile access to their content. Prior works have conducted lab studies to investigate the impact the learning condition (in particular stationary vs. onthe-go) has on mobile MOOC learners. User studies beyond the lab setting though are scarce. We here describe a study in a more realistic setup where 36 participants each participated in two mini-MOOCs while in a stationary and real-life mobile learning situation. We find participants' learning gains slightly lowered in the on-thego condition (-7%).We also find that on average participants spend 10% more time on video-watching and 23% less time on questionanswering in the learning on-the-go compared to the stationary condition.@en

Knowledge about a (Web) document's creation time has been shown to be an important factor in various temporal information retrieval settings. Commonly, it is assumed that such documents were created at a single point in time. While this assumption may hold for news articles and similar document types, it is a clear oversimplification for general Web documents. In this paper, we investigate to what extent (i) this simplifying assumption is violated for a corpus of Web documents, and, (ii) it is possible to accurately estimate the creation time of individual Web documents' components (so-called sub-documents).@en

The use of mobile technology has become a part of our daily lives and enabled us to perform tasks that once were possible only on stationary computers on-the-go anywhere and at any time. This shift has also affected the way we learn. The use of mobile devices on-the-go requires users to multitask and divide attention between several activi- ties. In the context of learning, it may lead to high cognitive load and potential disruptions. While many MOOC platforms have provided the possibility for learning on mobile devices, the learning situation and its effect on learners’ while using mobile devices on-the-go has not been stud- ied in full. Contrary to the majority of available mobile learning studies which are conducted in lab conditions, we focus on real-life situations commonly experienced by learners while learning on-the-go. We study the differences in MOOC learners’ performance and interactions in two different learning situations with mobile devices: stationary learning and learning on-the-go on the edX platform.@en

As one of the most prominent examples of technology-enhanced learning, massive open online courses (MOOCs) have attracted extensive attention of learners, educators, and researchers since 2012. However, a low completion rate is a ubiquitous and severe problem in MOOCs, which means that only a small portion of learners got scores higher than or equal to the course requirements in MOOCs. Learner engagement is commonly presumed to be highly related to the completion rates of MOOCs...@en

A main weakness of the open online learning movement is retention: a small minority of learners (on average 5-10%, in extreme cases <1%) that start a so-called Massive Open Online Course (MOOC) complete it successfully. There are many reasons why learners are unsuccessful, among the most important ones is the lack of self-regulation: learners are often not able to self-regulate their learning behavior. Designing tools that provide learners with a greater awareness of their learning is vital to the future success of MOOC environments. Detecting learners' loss of focus during learning is particularly important, as this can allow us to intervene and return the learners' attention to the learning materials. One technological affordance to detect such loss of focus are webcams---ubiquitous pieces of hardware available in almost all laptops today. In recent years, researchers have begun to exploit eye tracking and gaze data generated from webcams as part of complex machine learning solutions to detect inattention or loss of focus. Those approaches however tend to have a high detection lag, can be inaccurate, and are complex to design and maintain. In contrast, in this paper, we explore the possibility of a simple alternative---the presence or absence of a face---to detect a loss of focus in the online learning setting. To this end, we evaluate the performance of three consumer and professional eye/face-tracking frameworks using a benchmark suite we designed specifically for this purpose: it contains a set of common xMOOC user activities and behaviours. The results of our study show that even this basic approach poses a significant challenge to current hardware and software-based tracking solutions.@en