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.

翻译：数据分析师在组织中扮演着关键角色，他们将原始数据转化为驱动决策与战略制定的洞察。本研究探讨了分析师在协作平台上的生产力如何演化，重点关注两项核心学习活动：编写查询与查看同行查询。传统研究通常假设静态模型，即绩效随累积学习稳步提升，但此类模型难以捕捉现实学习中动态变化的本质。为此，我们提出一种隐马尔可夫模型（HMM），用以追踪分析师基于参与这些活动而在不同学习状态间的转移规律。通过使用包含2,001名分析师与79,797条查询的行业数据集，本研究识别出三种学习状态：新手、中级与高级。随着分析师向更高状态进阶，生产力随之提升，体现了学习的累积效益。编写查询对所有状态的分析师均有益处，其中新手获益最为显著。查看同行查询对新手具有支持作用，但可能因认知超载或效率问题而对更高级状态的分析师产生阻碍。状态间的转移亦不均衡，从中级向高级的进阶尤为困难。本研究深化了对知识工作者动态学习行为的理解，并为系统设计、培训优化、个性化学习支持及促进有效知识共享提供了实践启示。