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To defend against collaborative cheating in code writing questions, instructors of courses with online, asynchronous exams can use the strategy of question variants. These question variants are manually written questions to be selected at random during exam time to assess the same learning goal. In order to create these variants, currently the instructors have to rely on intuition to accomplish the competing goals of ensuring that variants are different enough to defend against collaborative cheating, and yet similar enough where students are assessed fairly. In this paper, we propose data-driven investigation into these variants. We apply our data-driven investigation into a dataset of three midterm exams from a large introductory programming course. Our results show that (1) observable inequalities of student performance exist between variants and (2) these differences are not just limited to score. Our results also show that the information gathered from our data-driven investigation can be used to provide recommendations for improving design of future variants. 

Network quality-of-service (QoS) does not always translate to user quality-of-experience (QoE). Consequently, knowledge of user QoE is desirable in several scenarios that have traditionally operated on QoS information. Examples include traffic management by ISPs and resource allocation by the operating system. But today these systems lack ways to measure user QoE. To help address this problem, we propose offline generation of per-app models mapping app-independent QoS metrics to app-specific QoE metrics. This enables any entity that can observe an app's network traffic-including ISPs and access points-to infer the app's QoE. We describe how to generate such models for many diverse apps with significantly different QoE metrics. We generate models for common user interactions of 60 popular apps. We then demonstrate the utility of these models by implementing a QoE-aware traffic management framework and evaluate it on a WiFi access point. Our approach successfully improves QoE metrics that reflect user-perceived performance. First, we demonstrate that prioritizing traffic for latency-sensitive apps can improve responsiveness and video frame rate, by 46% and 115%, respectively. Second, we show that a novel QoE-aware bandwidth allocation scheme for bandwidth-intensive apps can improve average video bitrate for multiple users by up to 23%.