在跟踪数据驱动的分析中,对教师的就业需求进行主题建模和集群
Tiina Kalliomäki-Levanto1, Ilkka Kivimäki2, Pekka Varje2
1Finnish Institute of Occupational Health (Työterveyslaitos), P.O. Box 40, FI-00032, Helsinki, Finland. tiina.kalliomaki-levanto@ttl.fi.
Scientific reports
|October 21, 2023
概括
这项研究引入了一种新的方法,用于分析工作需求,使用来自教育平台的数字追踪数据. 这种方法提供了对教师工作的动态,具体的背景见解,与传统的调查不同.
科学领域:
- 职业健康心理学 职业健康心理学
- 教育技术的教育技术
- 数据科学数据科学数据科学
背景情况:
- 评估心理社会工作环境特征的传统方法,如工作需求,严重依赖调查数据.
- 调查数据提供定期快照,可能无法捕捉不断变化的工作环境的动态性质.
- 需要采用更具活力,特定于环境和数据驱动的方法来了解现代工作条件.
研究的目的:
- 通过使用与工作相关的跟踪数据,提出和验证一种用于分析工作需求的替代方法.
- 调查高等教育教师所经历的工作需求的动态性质.
- 证明跟踪数据对于理解职业福利和确定干预目标的有用性.
主要方法:
- 利用了90周内从Moodle教育在线平台收集的跟踪数据.
- 分析了教师在Moodle.com上执行的目标和行动 (例如,message_sent) 的对.
- 采用数据驱动的方法来确定主题,主题,时间过程和员工集群.
主要成果:
- 通过使用Moodle跟踪数据,成功分析了教师的工作需求,揭示了动态模式.
- 确定了与教师工作相关的关键工作任务,主题和时间过程.
- 根据他们的Moodle活动对员工进行集群,为不同的工作档案提供了洞察力.
- 证明跟踪数据可以产生以行动为导向,特定于环境和动态的信息.
结论:
- 与工作相关的跟踪数据为研究工作需求提供了传统调查方法的强大,动态的替代方案.
- 这种数据驱动的方法为工作的变化性质提供了有价值的实时洞察力,特别是在教育环境中.
- 该方法适用于更广泛的组织环境和职业福利研究,有助于干预计划和潜在的预测建模.
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