Learning analytics dashboards (LADs) are designed as feedback tools for learners, but until recently, learners rarely have had a say in how LADs are designed and what information they receive through LADs. To overcome this shortcoming, we have developed a customisable LAD for Coursera MOOCs on which learners can set goals and choose indicators to monitor. Following a mixed-methods approach, we analyse 401 learners' indicator selection behaviour in order to understand the decisions they make on the LAD and whether learner goals and self-regulated learning skills influence these decisions. We found that learners overwhelmingly chose indicators about completed activities. Goals are not associated with indicator selection behaviour, while help-seeking skills predict learners' choice of monitoring their engagement in discussions and time management skills predict learners' interest in procrastination indicators. The findings have implications for our understanding of learners' use of LADs and their design.

Active reading strategies - -such as content annotations (through the use of highlighting and note-taking, for example) - -have been shown to yield improvements to a learner's knowledge and understanding of the topic being explored. This has been especially notable in long and complex learning endeavours. With web search engines nowadays used as the primary gateway for learners (or users) to find content that helps them realise their learning goals, they are often poorly equipped with the necessary tools to aid in sense-making, an important aspect of theSearch as Learning (SAL) process. Within theInformation Retrieval (IR) community, research efforts have explored ways to keep track of users' search context by providing a notepad-like interface for the collection of relevant articles, and aid them during the exploratory search process. However, these studies did not explicitly measure the effect that such tools have on knowledge and understanding during a complex, learning-oriented search task. In this paper, we address this research gap by carrying out an InteractiveIR experiment with highlighting and note-taking tools built into the search interface. We conducteda crowdsourced between-subjects study (N=115), where participants were assigned to one of four conditions: (i) control (a standard web search interface); (ii) high (highlighting enabled);(iii) note (note-taking enabled); and (iv) highnote (both highlighting and note-taking enabled). We assess participants' learning with a recall-oriented vocabulary learning task, and a cognitively more taxing essay writing task. We find that(i) active reading tools do not aid in the vocabulary learning task. However,(ii) participants in high covered 34% more subtopics, and participants in note covered 34% more facts in their essays when compared to control. Furthermore, (iii) we observed that incorporating active learning tools significantly changed the search behaviour of participants across a number of measures. This is the first work that sheds light on the effect of active reading tools on the SAL process, with important design implications for learning-oriented search systems.

Prior work in education research has shown that various active reading strategies, notably highlighting and note-taking, benefit learning outcomes. Most of these findings are based on observational studies where learners learn from a single document. In a Search as Learning (SAL) context where learners have to iteratively scan and explore a large number of documents to address their learning objective, the effect of these active reading strategies is largely unexplored. To address this research gap, we carried out a crowd-sourced user study, and explored the effects of different highlighting and note-taking strategies on learning during a complex, learning-oriented search task. Out of five hypotheses derived from the education literature we could confirm three in the SAL context. Our findings have important design implications on aiding learning through search. Learners can benefit from search interfaces equipped with active reading tools—but some learning strategies employing these tools are more effective than others. (This research has been supported by DDS (Delft Data Science) and NWO projects SearchX (639.022.722) and Aspasia (015.013.027).)

Massive Open Online Courses (MOOCs), delivered on platforms such as edX and Coursera, have led to a surge in large-scale learning research. MOOC platforms gather a continuous stream of learner traces, which can amount to several Gigabytes per MOOC, that learning analytics researchers use to conduct exploratory analyses as well as to evaluate deployed interventions. edX has proven to be a popular platform for such experiments, as the data each MOOC generates is easily accessible to the institution running the MOOC. One of the issues researchers face is the preprocessing, cleaning and formatting of those large-scale learner traces. It is a tedious process that requires considerable computational skills. To reduce this burden, a number of tools have been proposed and released with the aim of simplifying this process. Those tools though still have a significant setup cost, are already out-of-date or require already preprocessed data as a starting point. In contrast, in this paper we introduce ELAT, the edX Log file Analysis Tool, which is browser-based (i.e., no setup costs), keeps the data local (i.e., no server is necessary and the privacy-sensitive learner data is not send anywhere) and takes edX data dumps as input. ELAT does not only process the raw data, but also generates semantically meaningful units (learner sessions instead of just click events) that are visualized in various ways (learning paths, forum participation, video watching sequences). We report on two evaluations we conducted: (i) a technological evaluation and a (ii) user study with potential end users of ELAT. ELAT is open-source and available at https://mvallet91.github.io/ELAT/.

Effectively modelling and querying experience items like movies, books, or games in databases is challenging because these items are better described by their resulting user experience or perceived properties than by factual attributes. However, such information is often subjective, disputed, or unclear. Thus, social judgments like comments, reviews, discussions, or ratings have become a ubiquitous component of most Web applications dealing with such items, especially in the e-commerce domain. However, they usually do not play major role in the query process, and are typically just shown to the user. In this paper, we will discuss how to use unstructured user reviews to build a structured semantic representation of database items such that these perceptual attributes are (at least implicitly) represented and usable for navigational queries. Especially, we argue that a central challenge when extracting perceptual attributes from social judgments is respecting the subjectivity of expressed opinions. We claim that no representation consisting of only a single tuple will be sufficient. Instead, such systems should aim at discovering shared perspectives, representing dominant perceptions and opinions, and exploiting those perspectives for query processing.