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ABSTRACT Lantern bugs are amongst the largest of the jumping hemipteran bugs, with body lengths reaching 44 mm and masses reaching 0.7 g. They are up to 600 times heavier than smaller hemipterans that jump powerfully using catapult mechanisms to store energy. Does a similar mechanism also propel jumping in these much larger insects? The jumping performance of two species of lantern bugs (Hemiptera, Auchenorrhyncha, family Fulgoridae) from India and Malaysia was therefore analysed from high-speed videos. The kinematics showed that jumps were propelled by rapid and synchronous movements of both hind legs, with their trochantera moving first. The hind legs were 20–40% longer than the front legs, which was attributable to longer tibiae. It took 5–6 ms to accelerate to take-off velocities reaching 4.65 m s−1 in the best jumps by female Kalidasa lanata. During these jumps, adults experienced an acceleration of 77 g, required an energy expenditure of 4800 μJ and a power output of 900 mW, and exerted a force of 400 mN. The required power output of the thoracic jumping muscles was 21,000 W kg−1, 40 times greater than the maximum active contractile limit of muscle. Such a jumping performance therefore required a power amplification mechanism with energy storage in advance of the movement, as in their smaller relatives. These large lantern bugs are near isometrically scaled-up versions of their smaller relatives, still achieve comparable, if not higher, take-off velocities, and outperform other large jumping insects such as grasshoppers. 

Knowledge tracing refers to a family of methods that estimate each student’s knowledge component/skill mastery level from their past responses to questions. One key limitation of most existing knowledge tracing methods is that they can only estimate an overall knowledge level of a student per knowledge component/skill since they analyze only the (usually binary-valued) correctness of student responses. Therefore, it is hard to use them to diagnose specific student errors. In this paper, we extend existing knowledge tracing methods beyond correctness prediction to the task of predicting the exact option students select in multiple choice questions. We quantitatively evaluate the performance of our option tracing methods on two large-scale student response datasets. We also qualitatively evaluate their ability in identifying common student errors in the form of clusters of incorrect options across different questions that correspond to the same error.