In recent years, mobile crowdsourcing has emerged as a powerful computation paradigm to harness human power to perform spatial tasks such as collecting real-time traffic information and checking product prices in a specific supermarket. A fundamental problem of mobile crowdsourcing is: When both tasks and crowd workers appear in the platforms dynamically, how to assign an appropriate set of tasks to each worker. Most existing studies focus on efficient assignment algorithms based on bipartite graph matching. However, they overlook an important fact that crowd workers might be unreliable. Thus, their task assignment schemes cannot ensure the overall quality. In this article, we investigate the Quality-aware Online Task Assignment (QAOTA) problem in mobile crowdsourcing. We propose a probabilistic model to measure the quality of tasks and a hitchhiking model to characterize workers’ behavior patterns. We model task assignment as a quality maximization problem and derive a polynomial-time online assignment algorithm. Through rigorous analysis, we prove that the proposed algorithm approximates the offline optimal solution with a competitive ratio of 10/7. Finally, we demonstrate the efficiency and effectiveness of our solution through intensive experiments.

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Previous research suggests that self-regulation interventions are effective in improving students' self-regulatory skill and school performance in a wide variety of educational domains. Inspired by social cognitive theory (Schunk & Zimmerman, 1997) and goal setting theory (Locke & Latham, 1990), I designed, implemented, and examined the beneficial impact of a two-part intervention to teacher effective self-regulation (i.e., goal setting and self-reflection) of 62 high school students with special needs (40 males, 22 females) during in-class math instruction. Results indicate that the two-part intervention led to high self-efficacy judgments and to better math performance compared to students with special needs who were randomly assigned into a delayedtreatment control group. Students in the intervention group also perceived the math instruction they received more positively. Results also show that, after participating in the intervention, all participants students with special needs increased their variety of selfregulatory strategies, and attributed their performance to more controllable (e.g., effort, strategy) causes. The gains in self-regulatory strategies and adaptive attributions, while significant in their own right, helped students experience a significant gain in their postintervention math performance as well.

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