Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference

In modern computer science education, massive open online courses (MOOCs) log thousands of hours of data about how students solve coding challenges. Being so rich in data, these platforms have garnered the interest of the machine learning community, with many new algorithms attempting to autonomously provide feedback to help future students learn. But what about those first hundred thousand students? In most educational contexts (i.e. classrooms), assignments do not have enough historical data for supervised learning. In this paper, we introduce a human-in-the-loop "rubric sampling" approach to tackle the "zero shot" feedback challenge. We are able to provide autonomous feedback for the first students working on an introductory programming assignment with accuracy that substantially outperforms data-hungry algorithms and approaches human level fidelity. Rubric sampling requires minimal teacher effort, can associate feedback with specific parts of a student's solution and can articulate a student's misconceptions in the language of the instructor. Deep learning inference enables rubric sampling to further improve as more assignment specific student data is acquired. We demonstrate our results on a novel dataset from Code.org, the world's largest programming education platform.Comment: To appear at AAAI 2019; 9 page

Abandoning presumptive antimalarial treatment for febrile children aged less than five years--a case of running before we can walk?

Current guidelines recommend that all fever episodes in African children be treated presumptively with antimalarial drugs. But declining malarial transmission in parts of sub-Saharan Africa, declining proportions of fevers due to malaria, and the availability of rapid diagnostic tests mean it may be time for this policy to change. This debate examines whether enough evidence exists to support abandoning presumptive treatment and whether African health systems have the capacity to support a shift toward laboratory-confirmed rather than presumptive diagnosis and treatment of malaria in children under five

Alert Hockey: An Endogenous Learning Game

We describe a video game designed with a subtle and implicit learning mechanism that tracks aggressive and negligent play then uses this data to reduce players abilities and their chance of winning against the computer. By converging the goals of game play with learning we argue the experience produced is both endogenous and outcome oriented. Sixty two participants between 12 and 14 years old played the game at least 15 times each. Both aggressive play and negligence measures were reduced during the study (F(2, 40) = 10.589, p = 0.0002). Implicit learning mechanisms like this have potential to provide specific learning outcomes at little expense to the enjoyment of interactive gameplay