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This paper aims to identify self-regulation strategies from students' interactions with the learning management system (LMS). We used learning analytics techniques to identify metacognitive and cognitive strategies in the data. We define three research questions that guide our studies analyzing i) self-assessments of motivation and self regulation strategies using standard methods to draw a baseline, ii) interactions with the LMS to find traces of self regulation in observable indicators, and iii) self regulation behaviours over the course duration. The results show that the observable indicators can better explain self-regulatory behaviour and its influence in performance than preliminary subjective assessments.

Figure 1. Comparison between modulated and unmodulated video. (Left) Frame and details from an unmodulated video. (Right)Same frame and details after modulation. Differences may be seen when compared side by side, but evidence of modification is difficult to see when viewing the modulated version in isolation. ABSTRACTIn augmented reality, it is often necessary to draw the user's attention to particular objects in the real world without distracting her from her task. We explore the effectiveness of directing a user's attention by imperceptibly modifying existing features of a video. We present three user studies of the effects of applying a saliency modulation technique to video; evaluating modulation awareness, attention, and memory. Our results validate the saliency modulation technique as an alternative means to convey information to the user, suggesting attention shifts and influencing recall of selected regions without perceptible changes to visual input.

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Information-seeking tasks with learning or investigative purposes are usually referred to as exploratory search. Exploratory search unfolds as a dynamic process where the user, amidst navigation, trial-and-error and on-the-fly selections, gathers and organizes information (resources). A range of innovative interfaces with increased user control have been developed to support exploratory search process. In this work we present our attempt to increase the power of exploratory search interfaces by using ideas of social search, i.e., leveraging information left by past users of information systems. Social search technologies are highly popular nowadays, especially for improving ranking. However, current approaches to social ranking do not allow users to decide to what extent social information should be taken into account for result ranking. This paper presents an interface that integrates social search functionality into an exploratory search system in a user-controlled way that is consistent with the nature of exploratory search. The interface incorporates control features that allow the user to (i) express information needs by selecting keywords and (ii) to express preferences for incorporating social wisdom based on tag matching and user similarity. The interface promotes search transparency through color-coded stacked bars and rich tooltips. In an online study investigating system accuracy and subjective aspects with a structural model we found that, when users actively interacted with all its control features, the hybrid system outperformed a baseline content-based-only tool and users were more satisfied.