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.

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.

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).