Qualitative coding of brief videos that teach about the h-index

Dataset of qualitative coding of 31 Youtube videos on the h-index. The study aimed to characterize educational videos about the h-index to understand available resources and provide recommendations for future educational initiatives. Data.csv: contains the metadata and qualitative coding for 31 videos. ReadMe.csv: contains the codebook including a description of variables. Abstract. The authors analyzed videos on the h-index posted to YouTube. Videos were identified by searching YouTube and were screened by two authors. To code the videos the authors created a coding sheet, which assessed content and presentation style with a focus on the videos’ educational quality based on Cognitive Load Theory. Two authors coded each video independently with discrepancies resolved by group consensus. Thirty-one videos met inclusion criteria. Twenty-one videos (68%) were screencasts and seven used a “talking head” approach. Twenty-six videos defined the h-index (83%) and provided examples of how to calculate and find it. The importance of the h-index in high-stakes decisions was raised in 14 (45%) videos. Sixteen videos (52%) described caveats about using the h-index, with potential disadvantages to early researchers the most prevalent (n=7; 23%). All videos incorporated various educational approaches with potential impact on viewer cognitive load. Most videos (n=21; 68%) displayed amateurish production quality. The videos featured content with potential to enhance viewers’ metrics literacies such that many defined the h-index and described its calculation, providing viewers with skills to recognize and interpret the metric. However, less than half described the h-index as an author quality indicator, which has been contested, and caveats about h-index use were inconsistently presented, suggesting room for improvement. While most videos integrated practices to facilitate balancing viewers’ cognitive load, few (32%) were of professional production quality. Some videos missed opportunities to adopt particular practices that could benefit learning.本数据集为针对h指数（h-index）相关的31个YouTube视频开展质性编码的数据集。本研究旨在对h指数相关教学视频进行特征刻画，以厘清现有可获取的教学资源，并为未来相关教育活动提供优化建议。Data.csv文件包含31个视频的元数据与质性编码结果；ReadMe.csv文件则包含编码手册（codebook），其中对各变量进行了说明。【摘要】本研究团队对上传至YouTube平台的h指数相关视频展开分析。研究团队通过YouTube站内检索筛选得到目标视频，并由两名研究者独立完成初筛。为完成视频编码工作，研究团队编制了编码表，基于认知负荷理论（Cognitive Load Theory），从内容与呈现风格两个维度对视频的教学质量进行评估。每个视频均由两名研究者独立编码，若出现编码分歧，则通过团队会商达成共识以解决。最终共有31个视频符合纳入标准。其中21个视频（占比68%）为屏幕录制视频（screencasts），另有7个采用‘头像口播（talking head）’形式。26个视频（占比83%）对h指数进行了定义，并提供了该指数的计算与查询方法示例。14个视频（占比45%）提及了h指数在高风险决策中的重要性。16个视频（占比52%）阐述了使用h指数时的注意事项，其中提及早期研究者可能面临的潜在弊端的视频占比最高（共7个，占比23%）。所有视频均采用了多样化的教学方法，这些方法会对观众的认知负荷产生不同影响。多数视频（共21个，占比68%）的制作质量较为粗糙。这些视频的内容具备提升观众指标识读能力的潜力：多数视频对h指数进行了定义并讲解了其计算方法，帮助观众掌握识别与解读该指标的技能。但仅有不到半数的视频将h指数视作作者学术质量的评价指标——这一观点本身尚存争议；且相关使用注意事项的阐述并不统一，说明该类教学视频仍有优化空间。尽管多数视频采用了有助于平衡观众认知负荷的教学策略，但仅有32%的视频具备专业制作水准。部分视频未能采用可助力学习效果提升的特定教学方法。