发表机构
University of Toronto（多伦多大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出HELIX框架，在研究生编程课程中嵌入以人为本数据科学内容，通过试点验证其可行性，并发布材料包以支持推广。
AI 中文摘要
随着人工智能和数据驱动系统在实践中的普及，教师亟需将社会影响和伦理内容嵌入计算课程中。为此，我们提出了面向信息科学项目的以人为本信息与可解释计算学习（HELIX）教育框架，该框架围绕三个迭代支柱组织——知识构建、决策制定和赋权——并为教师和学生提供了具体行动。我们在一个研究生入门编程课程中应用了该框架，采用了阅读材料、算法设计活动和基于场景的反思。我们展示了该框架的试点实施，以考察学生（n=22）在数据科学中以人为本视角方面的知识获取、决策过程和自我反思的变化。我们发布了一套匿名化材料包（调查问卷、作业、分析代码）以支持推广。我们讨论了设计张力（工作量、评估、对多样化信息科学学习者的相关性），并提供了在不压倒技术成果的情况下整合以人为本内容的指导方针。研究结果表明，HELIX框架在信息科学情境中具有可行性，未来工作应使用比较调查评估来加强因果推断。
英文摘要
As AI and data-driven systems pervade practice, there is an imperative for instructors to embed societal impact and ethics content into computing courses. In response, we present the Human-Centered Education for Learning in Information and eXplainable Computing (HELIX) framework for information science programs, organized around three iterative pillars - knowledge building, decision-making, and empowerment - with concrete actions for instructors and students. We applied the framework in a graduate, introductory programming course using readings, algorithmic design activities, and scenario-based reflections. We present a pilot implementation of this framework to examine changes in students' (n=22) knowledge acquisition, decision-making processes, and self-reflection regarding human-centered perspectives in data science. We release an anonymized materials kit (survey, assignments, analysis code) to support adoption. We discuss design tensions (workload, assessment, relevance to diverse information science learners) and provide guidelines for integrating human-centered content without overwhelming technical outcomes. Findings suggest that the HELIX Framework is feasible in information science contexts and future work should use comparative survey assessment to strengthen causal inferences.
