Previous research has shown that having learners construct concept maps can bring better learning outcome. However, in video learning scenario, there is not sufficient support for learners to create concept maps from educational videos. Through a preliminary study, we identified two main difficulties video learners face in creating concept maps: navigation difficulty and learning difficulty. To help users to overcome such difficulties, we design scaffolds to assist learners in concept mapping. We present ScaffoMapping, a system aiming for scaffolded concept map creation on educational videos through automatic concept extraction and timestamp generation. Our study, which compares ScaffoMapping with the baseline approach, shows that (1) Learners can create higher quality concept maps with ScaffoMapping. (2) ScaffoMapping enables better learning outcomes in video learning scenario.
While numerous studies have explored using various sensing techniques to measure attention states, moment-to-moment attention fluctuation measurement is unavailable. To bridge this gap, we applied a novel paradigm in psychology, the gradual-onset continuous performance task (gradCPT), to collect the ground truth of attention states. GradCPT allows for the precise labeling of attention fluctuation on an 800 ms time scale. We then developed a new technique for measuring continuous attention fluctuation, based on a machine learning approach that uses the spectral properties of EEG signals as the main features. We demonstrated that, even using a consumer grade EEG device, the detection accuracy of moment-to-moment attention fluctuations was 73.49%. Next, we empirically validated our technique in a video learning scenario and found that our technique match with the classification obtained through thought probes, with an average F1 score of 0.77. Our results suggest the effectiveness of using gradCPT as a ground truth labeling method and the feasibility of using consumer-grade EEG devices for continuous attention fluctuation detection.
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