ProxyGuard: Direct Reliability Inference for Randomized Data Release Mechanisms with Shared Targets
ProxyGuard通过预设风险和封闭目标集控制错误，评估共享目标机制的可靠性，提高研究中随机数据发布机制的有效性和可靠性。
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Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education
In LLM-augmented education, students often exhibit passive learning behaviors and overreliance on large language models, hindering deep conceptual understanding and metacognitive development. Method: This paper proposes the “Student-as-Teacher” paradigm, wherein students actively design programming problems embedded with *knowledge breakpoints*—intentional gaps in prerequisite knowledge—and guide LLMs to solve them iteratively. We implement Socrates, a lightweight interactive system supporting breakpoint annotation, progressive prompting, and feedback-driven refinement loops. Contribution/Results: By reversing the LLM’s role—from tutor to tutee—the paradigm transforms problem authoring into a high-order learning activity that fosters active engagement and self-regulated learning. A controlled study in undergraduate computer science courses demonstrated statistically significant improvements: +12.3% in final exam scores and enhanced conceptual mastery, validating its efficacy in promoting active learning, reducing tool dependency, and strengthening metacognitive awareness.
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ProxyGuard: Direct Reliability Inference for Randomized Data Release Mechanisms with Shared Targets
ProxyGuard通过预设风险和封闭目标集控制错误，评估共享目标机制的可靠性，提高研究中随机数据发布机制的有效性和可靠性。
0 citationsRead paper
Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education
In LLM-augmented education, students often exhibit passive learning behaviors and overreliance on large language models, hindering deep conceptual understanding and metacognitive development. Method: This paper proposes the “Student-as-Teacher” paradigm, wherein students actively design programming problems embedded with *knowledge breakpoints*—intentional gaps in prerequisite knowledge—and guide LLMs to solve them iteratively. We implement Socrates, a lightweight interactive system supporting breakpoint annotation, progressive prompting, and feedback-driven refinement loops. Contribution/Results: By reversing the LLM’s role—from tutor to tutee—the paradigm transforms problem authoring into a high-order learning activity that fosters active engagement and self-regulated learning. A controlled study in undergraduate computer science courses demonstrated statistically significant improvements: +12.3% in final exam scores and enhanced conceptual mastery, validating its efficacy in promoting active learning, reducing tool dependency, and strengthening metacognitive awareness.
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