Equitable and inclusive publishing practices for engineering education research have received increased attention in recent years. JEE editorials and guest editorials have raised awareness about multiple challenges, including the problematic Whiteness and maleness of much research and the need to make diversity the default condition (Pawley, 2017), racially biased citation patterns (Holly, 2020), and other general aspects of publishing ethics (Loui, 2016). There are also ongoing discussions in an engineering education journal editors' group about how to increase the inclusivity of our collective publishing practices. For instance, topics such as inclusive pronouns, positionality statements, and how to better involve scholars of color without overburdening them have been discussed. However, inclusive visualization practices have not yet received the same critical attention. Importantly, visualizations can play a number of key roles in manuscripts, such as synthesizing frameworks or literature (Eppler, 2006), showing relationships between core variables (Tufte, 1997), providing illustrative examples of focal phenomena (e.g., see Schimpf et al., 2020), or enabling comparisons of intervention outcomes (Gleicher et al., 2011). Thus, their influence has a wide reach. Just as other aspects of publishing can serve as mechanisms for either exclusion or inclusion, so too can our choices when designing visualizations.

In this guest editorial, we highlight the heretofore unexamined topic of visualization to add to those ongoing efforts to increase the inclusivity of engineering education research publishing practices. The three inclusivity dimensions we discuss are (1) communicating to an interdisciplinary audience, (2) representation equity within visualizations, and (3) readers' physical dis/abilities and differences. In discussing these dimensions and how their associated design decisions can affect the inclusivity of engineering education research, we aim to raise awareness, provide reflective prompts for designing and reviewing visualizations, and ultimately decrease the unintentional use of exclusionary practices. These dimensions are not a definitive list but are intended to encourage a wider discussion within the community about inclusive visualization practices.

Our first dimension of inclusivity involves communicating to an interdisciplinary audience. Engineering education is an interdisciplinary field that brings together scholars from engineering disciplines, education disciplines, and social science fields among others. While some types of complex visualizations (e.g., multivariate box plots or three-dimensional bar graphs) may be standard or common in some of these fields, there are others that very rarely use any visualizations at all. Therefore, not all of the interdisciplinary contributors to engineering education research are equally familiar with all visualization approaches. As such, we need to ensure that visualizations are discernable to the full community so that they do not become inadvertent gatekeepers. For example, to read the boxplot in Figure 1, a reader would need to understand the meaning behind the length of the box, the horizontal line and the glyph within the box, the lines extending below and above the box, the dots beyond the lines, and so forth. If a reader is not familiar with these conventions, he or she is likely to be confused by the figure.

Authors can use several strategies to increase the likelihood that their visualizations will be understood by readers from any discipline. First, visualizations in manuscripts should be accompanied by thorough in-text explanations and descriptive captions of the graphic. These explanations or captions should describe central variables, concepts, or categories depicted and provide guidance on how to read the visualization. Authors should likewise clearly and thoroughly label key components of the graphic (Tufte, 2001). Second, while it may be tempting to display more data by incorporating additional variables, categories, or dimensions into a single graphic to allow visualization-savvy readers to dig deeper, authors should not design overly complex visualizations that incorporate information peripheral to the point(s) being conveyed. Information visualization research indicates that individuals' visual working memory is relatively limited (Luck & Vogel, 2013), and thus, more complex visualizations will require considerably more processing for readers. In short, displaying information beyond the scope of the core research story may unnecessarily burden readers, particularly those unfamiliar with such visualizations.

A second dimension of inclusivity concerns representation equity within visualizations themselves. Visualizations can perpetuate systemic social inequities (D'Ignazio & Bhargava, 2020; Dörk et al., 2013; Kennedy et al., 2016; Schwabish & Feng, 2020). Two major mechanisms by which visualizations can perpetuate systemic inequities are (1) reinforcing the position, status, and power of dominant social groups and (2) obscuring realities or lived experiences that should have been captured by the visualization. An example of the first mechanism is a visualization that depicts multiple groups and places White students or White men as the first group, implicitly presenting them as the default category to which all others must be compared. A simple redress is alphabetically listing groups (Schwabish & Feng, 2020) to avoid equating the graphical position of dominant groups with their structural position of power and privilege in engineering. An example of the second mechanism is a bar plot depicting only posttest learning scores by demographic group rather than depicting pre-to-post change scores. As shown in Figure 2a,b, White students may have the highest posttest scores but also have the highest pretest scores, such that their relative change was similar to or lower than other groups. Hence, showing only posttest scores would give the appearance of White students' overperformance. By always showing pre-to-post changes (change scores), the graphic preserves the meaningful learning all groups exhibited. Schwabish and Feng (2020) also identify a variety of other ways in which visualizations can challenge or reinforce systemic inequities, including using language and colors with racial equity awareness, considering what groups are missing, questioning default visualization approaches, and exhibiting empathy for visualized populations.

A third dimension of inclusivity concerns readers' physical dis/abilities and differences. Several ways in which dis/abilities affect readers' interactions with visualizations have been documented (Lee et al., 2020; Lundgard et al., 2019). For example, color vision deficiency (CVD) or color blindness can affect readers' ability to interpret visualizations that rely on color to convey information (Relvas, 2018). In addition, low-vision readers may have difficulties understanding visualizations if color contrast, color saturation, and graphic or font sizes are inadequate (Knaflic, 2018). An example of how green CVD affects visualizations is shown in Figure 3a,b, which displays graduate enrollment in five engineering majors. Figure 3b shows how it can be difficult to distinguish the biomedical and civil plot segments as well as the electrical and industrial segments for individuals with green CVD. This effect would be amplified if readers' CVD impacted their ability to see contrast between shades of brown and blue in Figure 3b. Moreover, if the body of the article referred to specific colors in the visualization, for example, “as can be seen in the red segment …,” this would not be inclusive of readers with CVD.

Knaflic (2018) provides suggestions on how to address some common challenges for both CVD and other vision impairments. For instance, direct labeling of lines or bars rather than relying on color key alone and running a color blindness simulation test (see Colblindor, 2018) prior to submission can help CVD readers. Using white space between colors and checking color contrasts online prior to submission can help low-vision readers. Publishing companies have an important role to play here and should be more proactive about finding new ways to ensure accessibility to their articles. For example, they should be conscious of factors such as color saturation that affects contrast when making production decisions. They should also explore ways to provide textual descriptions of visualizations that can be read by screen readers or other options for alternative text. While not all journals offer alt-text at the present time due to technology challenges (including the publisher of JEE, Wiley), striving for more inclusive solutions should be a goal. At a minimum, detailed textual descriptions of figures could be offered as online supplemental material. For best practices when using alt-text where possible, see Gustafsdottir (2021) and Sollinger (2014). It is worth noting that the visualization community is also addressing accessibility and inclusivity for other challenges, such as blindness (Lee et al., 2020; Lundgard et al., 2019), but those considerations are largely outside the scope of the decision-making of individual engineering education authors and are not limited to visualizations, which are the focus of this editorial.

By critically questioning visualization design decisions, authors, reviewers, and editors can work toward greater inclusivity and equity in engineering education research. To close, we offer a series of questions to help with these decisions. These prompts can help authors reflect on their past choices and, more importantly, can be used prospectively in the design of new visualizations. Below, we provide several broad prompts to consider and a set of prompts for each of the inclusivity dimensions covered above:

General questions

Interdisciplinary audience questions

Equitable visualization questions

Differently abled readers’ questions

近年来，工程教育研究的公平和包容性出版实践受到了越来越多的关注。JEE社论和嘉宾社论提高了人们对多种挑战的认识，包括许多研究存在问题的白度和男性性，以及使多样性成为默认条件的必要性（Pawley，  2017年），种族偏见的引用模式（Holly，  2020年）以及其他一般方面的出版道德（Loui，  2016）。工程教育期刊编辑小组中也正在进行有关如何提高集体出版实践的包容性的讨论。例如，已经讨论了诸如包容性代词，位置陈述以及如何更好地让有色人种参与而又不加重负担等主题。但是，包容性可视化实践尚未得到同样的关注。重要的是，可视化可以在手稿中扮演许多关键角色，例如合成框架或文献（Eppler，  2006），显示核心变量之间的关系（Tufte，  1997），提供焦点现象的说明性示例（例如，参见Schimpf等人。 ，  2020），或比较干预效果（Gleicher等，  2011）。因此，他们的影响范围很广。正如发布的其他方面可以用作排除或包含的机制一样，设计可视化时我们的选择也可以。

在这篇客座社论中，我们重点介绍了可视化迄今为止从未审查过的主题，以增加那些为增加工程学教育研究出版实践的包容性而进行的持续努力。我们讨论的三个包容性维度是（1）与跨学科的受众交流，（2）可视化中的表示公平性，以及（3）读者的身体残疾/能力和差异。在讨论这些维度及其相关的设计决策如何影响工程教育研究的包容性时，我们旨在提高认识，为设计和审查可视化提供反思性提示，并最终减少无意使用排他性做法的情况。

我们的包容性的第一个维度涉及与跨学科的受众进行交流。工程教育是一个跨学科的领域，汇集了来自工程学科，教育学科和社会科学领域的学者。虽然某些类型的复杂可视化（例如，多变量箱形图或三维条形图）在这些领域中的某些领域可能是标准的或常见的，但有些领域却很少使用任何可视化。因此，并非所有工程教育研究的跨学科贡献者都同样熟悉所有可视化方法。因此，我们需要确保整个社区都可以看到可视化内容，以免它们成为疏忽大意的守门人。例如，要阅读图1中的箱线图，读者将需要了解框的长度，框内的水平线和字形，在框的下方和上方延伸的线，线之外的点等的含义。如果读者不熟悉这些约定，那么他或她可能会对数字感到困惑。

作者可以使用多种策略来提高任何学科的读者理解其可视化效果的可能性。首先，稿件中的可视化应伴有详尽的文字说明和图形说明。这些解释或标题应描述所描述的中心变量，概念或类别，并提供有关如何阅读可视化效果的指导。作者也应清楚，彻底地标记图形的关键组成部分（Tufte，  2001年）。）。其次，虽然可能倾向于通过将其他变量，类别或维度合并到单个图形中来显示更多数据，以使精通可视化的读者更深入地研究，但作者不应该设计过于复杂的可视化，而将复杂的可视化并入该点附近）正在传达。信息可视化研究表明，个人的视觉工作记忆是相对有限的（Luck＆Vogel，  2013年），因此，更复杂的可视化将需要更多的读者处理工作。简而言之，显示超出核心研究故事范围的信息可能会给读者带来不必要的负担，尤其是那些不熟悉这种可视化效果的读者。

包容性的第二个方面涉及可视化本身内的表示公平性。可视化可以使系统的社会不平等长期存在（D'Ignazio＆Bhargava，  2020 ;Dörk等，  2013 ; Kennedy等，  2016 ; Schwabish＆Feng，  2020）。可视化可以使系统性不平等永久化的两个主要机制是（1）增强主要社会群体的地位，地位和权力，以及（2）使可视化应该捕捉到的现实或生活经历变得模糊。第一种机制的示例是一个可视化，该可视化描绘了多个组并将白人学生或白人作为第一组，隐式地将它们呈现为必须与所有其他人进行比较的默认类别。一个简单的补救方法是按字母顺序列出组（Schwabish和Feng，  2020年），以避免将优势组的图形位置与其在工程中的权力和特权的结构位置等同起来。第二种机制的示例是仅描绘条形图人口统计学测试后学习得分，而不是描绘出事后变化得分。如图2a，b所示，白人学生的测验后成绩可能最高，但测验前成绩也最高，因此他们的相对变化与其他群体相似或更低。因此，仅显示测验分数将显示白人学生的过分表现。通过始终显示前后变化（变化分数），该图形保留了所有组展示的有意义的学习信息。Schwabish and Feng（2020年）还确定了可视化可以挑战或加强系统性不平等的多种其他方式，包括使用具有种族平等意识的语言和颜色，考虑缺少哪些群体，质疑默认可视化方法以及对可视化人群表现出同情心。

包容性的第三个维度涉及读者的身体残疾/能力和差异。已经记录了残障/能力影响读者与可视化交互的几种方式（Lee等，  2020； Lundgard等，  2019）。例如，色觉不足（CVD）或色盲会影响读者解释依赖颜色来传达信息的可视化效果的能力（Relvas，  2018）。此外，如果色彩对比度，色彩饱和度以及图形或字体大小不足，低视力阅读器可能难以理解可视化效果（Knaflic，  2018年））。图3a，b显示了绿色CVD如何影响可视化效果的示例，其中显示了五个工程专业的研究生入学情况。图3b显示了如何区分具有绿色CVD的个人的生物医学和民用图段以及电气和工业图段。如果读者的CVD影响了他们看到图3b中棕色和蓝色阴影之间的对比度的能力，则这种影响将被放大。此外，如果文章的正文在可视化中引用了特定的颜色，例如“在红色部分中可以看到……”，这将不包括使用CVD的读者。

Knaflic（2018）提供了有关如何应对CVD和其他视力障碍的一些常见挑战的建议。例如，直接标记线条或条形，而不是仅依靠颜色键并运行色盲模拟测试（请参阅Colblindor，  2018年））提交之前可以帮助CVD读者。在提交之前使用颜色之间的空白并在线检查颜色对比可以帮助弱视读者。出版公司在这里起着重要的作用，应该更加积极主动地寻找新的方法来确保其文章的可访问性。例如，他们应该意识到在做出生产决策时会影响对比度的诸如色彩饱和度之类的因素。他们还应该探索提供可视化内容的文本描述的方法，这些内容可以由屏幕阅读器或其他替代文本选项读取。尽管目前由于技术挑战，并非所有期刊都提供替代文本（包括JEE的出版商），Wiley），争取更具包容性的解决方案应该是一个目标。至少，可以将图形的详细文字说明作为在线补充材料提供。有关在可能的情况下使用替代文本的最佳实践，请参阅Gustafsdottir（2021）和Sollinger（2014）。值得注意的是，可视化社区也正在解决盲目性等其他挑战的可访问性和包容性（Lee et al。，  2020 ; Lundgard et al。，  2019），但这些考虑因素在决策范围之外本教程的重点是可视化的单个工程教育作者，而不仅限于可视化。

通过批判性地质疑可视化设计决策，作者，审阅者和编辑者可以在工程教育研究中争取更大的包容性和公平性。最后，我们提出了一系列问题来帮助您做出这些决定。这些提示可以帮助作者反思他们过去的选择，更重要的是，可以在新的可视化设计中前瞻性地使用它们。下面，我们提供了几个要考虑的广泛提示，以及针对以上涵盖的每个包容性维度的一组提示：

一般的问题

跨学科的观众问题

公平的可视化问题

不同能力的读者的问题