Online feedback is frequently implemented during second/foreign language (SL/FL) writing tasks and assessments. This meta-analysis investigates the effectiveness of online feedback in SL/FL writing. After careful screening and the application of inclusion and exclusion criteria, this study synthesizes the results of 17 primary studies reporting on students' English SL/FL writing quality after online feedback. The studies involved 1568 students, and the results indicate a Hedges' g effect size of 0.753 for the effectiveness of written feedback in general. Online feedback from teachers/instructors produces a larger effect size (g = 2.248) than online peer feedback (g = 0.777) and online automated feedback (g = 0.696). It was also found that educational levels and task genre mitigate the impact of online feedback on writing quality. Overall, the findings contribute to a better understanding of the impact of online feedback on ESL/EFL writing and provide insights into online ESL/EFL writing instruction.

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The chance-constrained knapsack problem is a variant of the classical knapsack problem where each item has a weight distribution instead of a deterministic weight. The objective is to maximize the total profit of the selected items under the condition that the weight of the selected items only exceeds the given weight bound with a small probability of α. In this paper, consider problem-specific single-objective and multi-objective approaches for the problem. We examine the use of heavy-tail mutations and introduce a problem-specific crossover operator to deal with the chance-constrained knapsack problem. Empirical results for single-objective evolutionary algorithms show the effectiveness of our operators compared to the use of classical operators. Moreover, we introduce a new effective multi-objective model for the chance-constrained knapsack problem. We use this model in combination with the problem-specific crossover operator in multiobjective evolutionary algorithms to solve the problem. Our experimental results show that this leads to significant performance improvements when using the approach in evolutionary multi-objective algorithms such as GSEMO and NSGA-II.

A graduate PHI course was developed and delivered. Additionally, implementation of a subject guide increased the library's profile.

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