用隐马尔可夫模型分析数据分析师学习动态，发现三类成长阶段。
Dynamic Learning and Productivity for Data Analysts: A Bayesian Hidden Markov Model Perspective
- 构建贝叶斯隐马尔可夫模型，追踪分析师在写查询和看同行查询中的状态切换。
- 识别出新手、中级、高级三类学习状态，越往后生产力提升越明显。
- 新手写查询收益最大，看同行查询可能拖累高阶者，适合个性化训练设计。
数据分析师在组织中至关重要，将原始数据转化为驱动决策的洞察。本研究探讨了分析师在协作平台上的学习动态，聚焦于写查询和查看同行查询两类关键学习活动。传统研究常假设性能随积累学习稳步提升，但此类静态模型无法捕捉真实学习的动态性。为此，本文提出一种隐马尔可夫模型（HMM），追踪分析师基于参与活动的状态转移。基于包含2,001名分析师和79,797条查询的行业数据集，研究识别出三种学习状态：新手、中级、高级。生产力随状态提升而增长，体现学习的累积效应。写查询对所有状态均有帮助，新手获益最大；查看同行查询对新手有益，但可能因认知过载或效率低下阻碍高阶分析师。状态间转移不均，从中级到高级尤为困难。本研究深化了对知识工作者动态学习行为的理解，为系统设计、培训优化、个性化学习和有效知识共享提供了实践启示。
原文摘要 · Abstract (English)
Data analysts are essential in organizations, transforming raw data into insights that drive decision-making and strategy. This study explores how analysts' productivity evolves on a collaborative platform, focusing on two key learning activities: writing queries and viewing peer queries. While traditional research often assumes static models, where performance improves steadily with cumulative learning, such models fail to capture the dynamic nature of real-world learning. To address this, we propose a Hidden Markov Model (HMM) that tracks how analysts transition between distinct learning states based on their participation in these activities. Using an industry dataset with 2,001 analysts and 79,797 queries, this study identifies three learning states: novice, intermediate, and advanced. Productivity increases as analysts advance to higher states, reflecting the cumulative benefits of learning. Writing queries benefits analysts across all states, with the largest gains observed for novices. Viewing peer queries supports novices but may hinder analysts in higher states due to cognitive overload or inefficiencies. Transitions between states are also uneven, with progression from intermediate to advanced being particularly challenging. This study advances understanding of into dynamic learning behavior of knowledge worker and offers practical implications for designing systems, optimizing training, enabling personalized learning, and fostering effective knowledge sharing.
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