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Tools for automatic grading programming assignments, also known as Online Judges, have been widely used to support computer science (CS) courses. Nevertheless, few studies have used these tools to acquire and analyse interaction data to better understand the students' performance and behaviours, often due to data availability or inadequate granularity. To address this problem, we propose an Online Judge called CodeBench, which allows for fine-grained data collection of student interactions, at the level of, eg, keystrokes, number of submissions, and grades. We deployed CodeBench for 3 years (2016-18) and collected data from 2058 students from 16 introductory computer science (CS1) courses, on which we have carried out fine-grained learning analytics, towards early detection of effective/ineffective behaviours regarding learning CS concepts. Results extract clear behavioural classes of CS1 students, significantly differentiated both semantically and statistically, enabling us to better explain how student behaviours during programming have influenced learning outcomes. Finally, we also identify behaviours that can guide novice students to improve their learning performance, which can be used for interventions. We believe this work is a step forward towards enhancing Online Judges and helping teachers and students improve their CS1 teaching/learning practices.

Previous research has not always found that boys and girls are treated differently in rural India. However estimates of the effect of gender on parental investments could be biased if girls end up in larger families due to son-biased stopping rules. Using a novel identification strategy that exploits that gender at conception is random, we document that boys receive more childcare time than girls, they are breastfed longer and they get more vitamin supplementation. Compared to other developing countries, boys have an advantage in height and weight relative to girls. Neither greater needs nor anticipated family size explain the results.

Introductory computing courses have a high failure rate worldwide. At the Federal University of Amazonas, this also happens and, since 2016 a group of professors decided to reformulate the course at the institution and some learning analytics initiatives have been adopted. The reformulation included a review of the course program and the use of an online judge. After all these years of research, the group has enough material and data and it is a good moment to summarize what has been done and the achievements so far. In this article, the focus will be the learning analytics in three main areas: student performance prediction, classification of difficulty of programming exercises, and gamification. Also, as a contribution, for the first time in a journal, the whole dataset is available to the community. [ABSTRACT FROM AUTHOR]

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We study the effect of financial resources on decision-making. Low-income US households are randomly assigned to receive an online survey before or after payday. The survey collects measures of cognitive function and administers risk and intertemporal choice tasks. The study design generates variation in cash, checking and savings balances, and expenditures. Before-payday participants behave as if they are more present-biased when making intertemporal choices about monetary rewards but not when making intertemporal choices about nonmonetary real-effort tasks. Nor do we find before-after differences in risk-taking, the quality of decision-making, the performance in cognitive function tasks, or in heuristic judgments. (JEL C83, D14, D81, D91, I32) [ABSTRACT FROM AUTHOR]