Despite the importance of attention in user performance, current methods for attention classification do not allow to discriminate between different attention types. We propose a novel method that combines thermal imaging and eye tracking to unobtrusively classify four types of attention: sustained, alternating, selective, and divided. We collected a data set in which we stimulate these four attention types in a user study (N = 22) using combinations of audio and visual stimuli while measuring users' facial temperature and eye movement. Using a Logistic Regression on features extracted from both sensing technologies, we can classify the four attention types with high AUC scores up to 75.7% for the user independent-condition independent, 87% for the user-independent-condition dependent, and 77.4% for the user-dependent prediction. Our findings not only demonstrate the potential of thermal imaging and eye tracking for unobtrusive classification of different attention types but also pave the way for novel applications for attentive user interfaces and attention-aware computing.
Mind wandering is a drift of attention away from the physical world and towards our thoughts and concerns. Mind wandering affects our cognitive state in ways that can foster creativity but hinder productivity. In the context of learning, mind wandering is primarily associated with lower performance. This study has two goals. First, we investigate the effects of text semantics and music on the frequency and type of mind wandering. Second, using eye-tracking and electrodermal features, we propose a novel technique for automatic, user-independent detection of mind wandering. We find that mind wandering was most frequent in texts for which readers had high expertise and that were combined with sad music. Furthermore, a significant increase in task-related thoughts was observed for texts for which readers had little prior knowledge. A Random Forest classification model yielded an F 1 -Score of 0.78 when using only electrodermal features to detect mind wandering, of 0.80 when using only eye-movement features, and of 0.83 when using both. Our findings pave the way for building applications which automatically detect events of mind wandering during reading.
Head movement is widely used as a uniform type of input for human-computer interaction. However, there are fundamental differences between head movements coupled with gaze in support of our visual system, and head movements performed as gestural expression. Both Head-Gaze and Head Gestures are of utility for interaction but difer in their afordances. To facilitate the treatment of Head-Gaze and Head Gestures as separate types of input, we developed HeadBoost as a novel classifer, achieving high accuracy in classifying gaze-driven versus gestural head movement ( 1 -Score: 0.89). We demonstrate the utility of the classifer with three applications: gestural input while avoiding unintentional input by Head-Gaze; target selection with Head-Gaze while avoiding Midas Touch by head gestures; and switching of cursor control between Head-Gaze for fast positioning and Head Gesture for refnement. The classifcation of Head-Gaze and Head Gesture allows for seamless head-based interaction while avoiding false activation. CCS CONCEPTS• Human-centered computing → Gestural input; Virtual reality;
Task difficulty (TD) reflects students' subjective judgement on the complexity of a task. We examine the TDs data of 236 undergraduate students in a simulation-based Predict-Observe-Explain (POE) environment using three different labels easy, medium and hard. Generally, the students who perceive the tasks to be easy or hard perform poorly at the transfer task than the students who perceive the tasks to be medium or moderately difficult. Sequences of students' TDs are analysed which consist of a set of several judgements, collected once for each task in a POE sequence. The analysis suggests that given a sequence of TDs, difficulty level hard followed by a hard may lead to poorer learning outcomes at the transfer task. By contrast, difficulty level medium followed by a medium may lead to better learning outcomes at the transfer task. In terms of the TD models, we identify student behaviours that can be reflective of their perceived difficulties. Generally, the students who report that the tasks are easy, adopt a trial-and-error behaviour where they spend lesser time and make more attempts on tasks. By comparison, the students who complete the tasks in a longer time by making more attempts are likely to report that the following task is hard. For the students who report medium TDs, mostly these students seem to reflect on tasks where they spend a long time and require fewer attempts for task completions. Additionally, these students provide longer texts for explaining their hypothesis reasoning. Understanding how student behaviours and TDs manifest over time and how they impact students' learning outcomes is useful, especially when designing for real-time educational interventions, where the difficulty of the tasks could be optimised for students. It can also help in designing and sequencing the tasks for the development of effective teaching strategies that can maximise students' learning.
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