Eligibility criteria.

All publications, dated between January 2002 and April 2025, inclusive, discussing the arts in the context of data literacy were eligible for inclusion. January 2002 was chosen as the baseline as we could not find any references to data literacy prior to that. All methodologies, including qualitative, quantitative or mixed methods, and all journal articles, conference papers, reviews, books and book chapters, theses and dissertations, and grey literature were eligible for inclusion in the review. All population groups were included. Only studies in the English language were included as the researchers’ first language is English.

Search strategy.

Our search strategy was iteratively developed by our interdisciplinary team, with expertise in the arts (HP and FG) and data science (AH), in order to identify a body of literature appropriate to our research question. To test the effectiveness of our proposed search strategy, preliminary searches were carried out in Scopus and Web of Science Core Collection in January 2023. These test searches, incorporating minor adaptations following the recommendations of our protocol reviewers, informed our final search strategy.

For the purposes of this scoping review, we systematically searched six databases: Academic Search Complete, Dissertations & Theses A&I (ProQuest), ERIC (ProQuest), Scopus, Sociological Abstracts (ProQuest), Web of Science Core Collection, and the Google Scholar search engine, using the search string below:

(“data literacy” OR “statistical literacy”) AND (arts OR poem OR poetry OR “creative writing” OR novel* OR story* OR stories OR photography OR film OR video OR drawing OR collage OR painting OR graffiti OR textile* OR mosaic* OR masks OR artefact OR artifact OR sculpture OR singing OR song* OR music* OR danc* OR drama OR theatre OR theater OR puppetry OR “live art” OR “body art” OR “performance art” OR STEAM OR ArtScience OR SciArt).

The original search was conducted in July 2023. To capture new publications and to provide a comprehensive overview of recent studies, an updated search was conducted in late April 2025 using our original search strategy. In all searches, minor adaptations were made for varying field headings and search formats in specific databases. In Google Scholar, the first ten pages of results were screened. It was evident from scanning additional pages that there were no relevant results after the first ten pages (see S2 File for full search strategy). Google Scholar was utilized as a back-up search and predominantly yielded duplicate results. The final part of our search strategy involved searching the reference lists of included studies for any potential additional relevant publications.

Expanding understandings of what counts as the arts and data literacy.

Most of the publications within this review named artistic practices which correlate with the working definitions presented in the introduction section above [21,22]. A wide spectrum of artistic practices were referenced, with visual arts and storytelling as the most dominant across the publications. Additional creative practices included designing comics [58,61,70], creating memes [71] and writing poetry [52]. Several artistic practices were coupled with the term “data” to propose an artistic practice with a specific relationship to data, for example data stories [33] data murals [40], data sculptures [9], data theatre [8], data drawing [56], data art [38,47,67,76,77], data comics [61,70] and data memes [72]. These raise interesting questions regarding the value of the art in its own right versus its value in the context of data and whether “data art” should be considered its own category of artistic practice [56].

Related to this is the prevalent use of terms such as “data visualization” (e.g., [13,38,39,47,50,61,65,66,73,77]) and “data storytelling” (e.g., [13,36,47,49,54,72]). Data visualization draws on graphics as a means of communicating data [39]. These encompass a wide spectrum of graphics from simple charts, graphs and tables to more complex infographics and animations. These may be hand drawn as well as digital, as evidenced in this review. Many utilize characteristics common to visual arts practices such as attention to shape, line, form, space and colour. Similarly, data storytelling employs literary techniques which may include narrative, plot and character development. These may be fictionally or factually based. Distinguishing between data visualizations and data stories which may or may not be considered “art” challenges traditional understandings of what constitutes an artistic practice.

Several studies employed an expansive, multi-arts approach (e.g., [2,8,32,36,37,40,41,43–45,58–60,62,68,71,76,79]), a further reminder that the boundaries between artistic practices are often fluid and dynamic. Matuk et al. [57] also noted that “the arts” are not a generic practice and that different artistic media may support different data practices more or less effectively. Reflecting on their own work, they observed that, “narrative-based media (comics and creative writing) supported students’ introspection on their personal experiences relative to broader data trends. Movement-based art (dance) supported embodied sense making of the shape and meaning of data. Meanwhile, photography highlighted how data should be interpreted within the limits of operationalization and sampling decisions, and how images can reveal contextual details that tend to be hidden in statistics” (p.1174). Forster et al. [45] noted that artistic choice is also key: “Giving students the choice to work on what they want is important” (p. 52).

In any study on the role of the arts in data literacy, data practices themselves may invite a re-evaluation of what is understood as an artistic practice. Conversely, studies may also push the boundaries of what is understood as data literacy or data practices. Formal definitions of data literacy were provided in twenty-one publications. Bhargava and D’Ignazio’s [2] definition, as referenced in the introduction above, i.e., “the ability to read, work with, analyze and argue with data” (p. 1) was utilized by Bhargava and D’Ignazio across three additional papers [40,43,44] and in three other studies authored by Amato et al. [34], Bowler et al. [78] and Vacca et al. [70]. In another study, Bhargava et al. [8] referred to this definition but expanded it to define data literacy as, “engaging components related to acquiring data, processing and analyzing data, representing data, and storytelling with data for some purpose” (p.93). Nguyen & Parameswaran [60] referenced Bhargava & D’Ignazio’s [2] definition but also included a description from the Oceans of Data Institute which defined data literacy as including the ability to “identify, collect, evaluate, analyze, interpret, present, and protect data” (p.150). They added a further definition of critical data literacies which, “entail the ability to read and reason with data, critique the implications of data use, and communicate to external audiences in ways that contextualize data in personal and social settings” (p.149). Vacca et al. [71] drew on definitions from both D’Ignazio & Bhargava [43] and Wolff et al. [3]. In their 2019 paper, Wolff et al. [74] referenced their 2016 definition [3] which was also referenced by Li et al. [53]. Akshay & Minces [32] noted that “[b]eing data literate requires gaining a broad set of skills to comprehend, manipulate and utilize data to solve real world problems” (p.1). Markham [55] proposed that “[d]ata literacy is a type of awareness and curiosity that leads to developing competencies needed to grapple with the complex impacts of digital transformation on individual and cultural wellbeing” (p. 229). Stornaiuolo [67] pointed to the need for more critical perspectives in defining data literacy: “More critical perspectives on data literacy are needed to challenge approaches that position data as objective and neutral measures of the social world rather than highlighting their situated, ideological, and racialized nature” (p.82). Payne et al. [64] listed data literacy as one of the project design goals which included the provision of “an accessible interface to empower learners to understand, use, and manipulate their own data for the purposes of making art” (p. 4). Galbraith et al. [47] drew on a number of authors for their definition: “Data literacy describes how students interact with data through analysis, interpretation, evaluation (Shreiner, 2020), and examination of the source(s) and processes used to collect the data (Calabrese Barton et al., 2021; Gould, 2017)” (p. 8). McDowell & Turk [54] drew on Getz & Brodsky [80] to define data literacy as: “a subset of information literacy that teaches students to access, interpret, critically assess and “ethically use data”” (p. 324). In addition, they referred to Ridsdale et al. [81], Gummer and Mandinach [82], and Bhargava et al. [83], saying that their study “draws on all of these definitions of data literacy as a fundamental and evolving 21^st century information skill” (p. 324). Ortega et al. [61] drew on Gray et al. [4] and Wolff et al. [3] for their definition: “This term [data literacy] has different meanings in different contexts, but broadly it is about the knowledge and skills a person must have to successfully manage and navigate (personal) data ecosystems of which she is part” (section 1). Woods et al. [76] drew on Matuk et al. [58] and Stornaiuolo [67] to define data literacy as: “Data literacy […] includes skills related to gathering, constructing meaning from, and telling stories with data” (p. 443). Understanding data literacy as a creative process itself expands an understanding of what counts as art in this context.

Population groups.

The amount of detailed information provided regarding population groups varied across studies. Thirty-four studies provided some details concerning the number of participants involved. These range from three in a single setting [61] to 221 [53] in multiple settings. Amato et al. [33], for example, focused on the analysis of two student experiences from a group of 23 participants. Li et al. [53] included data from 221 undergraduate students across four universities. Ten studies provided information on the specific age range of the participants [32,35,40,43,61,65,68,74,77,78]. In the majority of studies, age ranges were implied according to class ‘grade’ listings, primarily those in elementary, middle or high school settings. Eight studies provided details of gender [13,32,37,56,61,64,65,77] and nine studies provided information on ethnicity [33,34,37,38,57,59,67,71,77]. Akshay & Minces [32] noted that, of the 36 students between 14 and 15 years of age, 27 agreed to participate in a data literacy test. This included 18 girls and 9 boys. Amato et al. [33] described the students as follows: “Participants were 23 eighth graders from a private Catholic middle school located in a large urban area with a predominantly Latinx and Black or African American population (85%). About 70% of students are on a free or reduced price lunch program” (p.1494). In this case, the high percentage of students on free or reduced price lunches was used as a socioeconomic indicator. A similar indicator was used by DesPortes et al. [11] in introducing the setting for the study as a public charter school in the Midwestern USA, “[t]he middle school student population is 65% White, 19% Hispanic, and 6% Black—46% of the student population qualifies for free or reduced lunch” (p. 306). Bertling et al. [38] also used similar indicators in their school population descriptions: “White (School A: 60%; School B: 78%, Black (School A: 23%; School B: 6%, and Hispanic/Lantinx (School A: 14%; School B: 12%, with less than 5% of students identifying as “other”)” (p. 7). Students who qualify for free or reduced rate lunch, School A: 32% and School B: 11% (p.7).

Bowler et al. [78] provided gender data as follows: “Approximately 95 teens [aged 14 to 17] participated in 27 after-school, drop-in workshops […] Of the 95 participants, 52 identified as female and 37 as male, with six leaving the open-ended question about gender blank (‘I identify my gender as…’). The workshops were held in a teen services area in an urban, mid-sized city in the North-Eastern United States” (p. 9). Kahn & Jiang [13] described a study with 17 middle and high school participants, noting that six were male and 11 female who “self-identified as 13 African-American; 3 White; 1 Asian” (p. 130). Sanei et al. [65] described participants as “five teens (2 male, 3 female, ages 14-17, identifying as White, Asian, Latinx, and African American) with little to no coding background, from different high schools across two states in Southeastern USA” (p. 163). Zhao et al. [77] described participants as: 27 student data artists, aged 13−17, with six interview participants described as 4 females and two males: 1 Asian and 5 white. Matuk et al. [57] described the presentation of a symposium with eight poster presentations exploring storytelling in data literacy education with examples from K-12 to university settings, including “over 200 students in non-AP statistics classes from seven high schools with high proportions of Blacks and Latinos” (p. 1783). Vacca et al. [71] was one of the few studies to report on population profiles related to disabilities, noting the participation of “[f]ifty-six middle school students and two teachers across two schools …In one school, the student population was: 33% White, 32% Black, 16% Hispanic, and 16% Asian. 46% of the student population qualifies for free or reduced lunch, 18% of students have disabilities, and 4% are English Language Learners. The other school was a private Catholic middle school located in a large urban area with a predominantly Latinx and Black or African American population (85%)” (p. 214). Population ages were more likely to be provided for younger participants (elementary, middle and high school) and less likely for undergraduate, postgraduate or adult participants. An exception to this is Ortega et al. [61] who described adult participants in data comic creation as [three] women in their 30s. In addition, 34 participants, 17 women and 17 men, completed an online survey evaluating the community-based data comics project and were described as ranging in age from 21 to 59. Population ethnicity was reported primarily among studies which took place in schools in the USA.

Research settings.

The majority of the studies were carried out by scholars based in the USA [2,8,9,11,13,33,34,36–39,43–45,47–49,51–54,56,58–60,64–68,70,71,73,77,78]. Where these studies included an educational or community-based intervention, these also took place in the USA. In other cases, USA based scholars carried out research in different countries. Akshay & Minces [32], for example, undertook a pilot study of a data literacy workshop in India while Bhargava et al. [40] worked with an independent consultant and a specialist in public policies and governmental management from Brazil in a data mural project based at a school in Brazil. Blackburn [41] is based at a German university but the publication reflected on experiences of eLearning using visual arts and fictional storytelling in the University of Queensland, Australia. In one other study, the first author is based in the UK but the study took place in the USA in collaboration with US based scholars [76]. Other studies took place and/or originated in Sweden [35], the UK [46,74], Denmark [55], Austria [62,63], Belgium [72], The Netherlands [61] and Finland [69,79].

Most of these studies provided descriptions and/or evaluations of the use of the arts in data literacy programmes based in school settings. These included elementary/primary school settings (e.g., [35,36,50,74]), middle schools (e.g., [11,13,33,34,38,39,47,49,56]), high schools (e.g., [37,45,49,57,67,74]), as well as projects involving undergraduate and postgraduate college or university programmes (e.g., [2,8,9,43,44,51–54]). A small number of studies took place in the community. Amato et al.’s studies [33,34], for example, were based in a school but used community data from “photo walks” in the community. Bowler et al. [78] reported on after-school, drop-in workshops in a public library setting. Similarly, Kahn & Jiang [13,49] reported on summer workshops for middle and high school youth in a public library setting. Zhao et al. [77] reported on an after-school data-art inquiry programme combining educational and community settings. Markham [55] reported on a public exhibition called MoRM [Museum of Random Memory] which was exhibited in several countries including Denmark, Italy, Spain, Canada and the UK. Some of the studies included professional development programmes and workshops. For example, Fotopoulou [46] included two training workshops with the goal of building capacity within civil society organisations. Tylosky et al. [69] described arts-based and data literacy workshops which were aimed at academics, artists, researchers and other professionals. Two studies reported on online summer programmes [64,65] with Payne et al. [64] noting the role of the COVID 19 pandemic in the decision to offer this summer camp programme online. Nguyen & Parameswaran [60] investigated critical data literacies and social media, focusing on the analysis of 410 TikTok videos. In another online community-based setting, Ortega et al. [61] reported on a two-phase data-comic project. In phase one, three women created the data-comics and, in phase two, members of the public participated in the evaluation of the perceived effectiveness of and engagement with the comics via an online survey.

Increasing accessibility of data.

Several studies [46,48,57,58,61,66,70] identified the role of the arts in increasing the accessibility of data literacy programmes, thus supporting different individuals and groups to learn about and engage with data in creative ways. Fotopoulou [46], for example, noted the need for data literacy programmes to move beyond an exclusive focus on the development of technical skills and using creative approaches such as storytelling “to make data accessible and to equip them with the resources necessary for addressing the critical and ethical questions that relate to datafication” (p. 1642). Giamellaro et al. [48] argued that storytelling can situate important facts and ideas in an accessible narrative which helps to make them more relatable (p.408). Storytelling and other art forms can support personal connections to data and shape its meaning in ways that enhance “sensemaking, identity building and empathy” [58 p.1780]. Spence et al. [66] noted that current barriers to the development of important skills such as data visualization are compounded when these are limited to disciplinary areas such as mathematics or viewed as something which can only be practised by a narrowly defined cohort of “experts”. Ortega et al. [61] noted the importance of enabling the women in their study who had no prior experience of data visualizations to create data comics based on their lived experience, contributing to a creative realm that they argue is typically dominated by data experts. Bertling et al. [38] argued that an arts-based approach to data visualization exceeds the graphical approaches used in STEM fields by finding ways “to produce captivating, memorable, action-inducing stories and experiences” (p. 6). Art can support widening access: “Art is a natural medium for increasing data knowledge and practising data visualization because it is accessible. Currently the skills for creating data-driven storytelling reside with professionals, but data-knowledge skills can be more widely shared through the medium of art” [66]. Similarly, Vacca et al. [70] noted that a narrow focus on statistics or mathematics fails to recognize the importance of students’ personal experiences and misses the opportunity to invite students to understand and interpret data through meaningful reflection of their own lived experiences. In an innovative project which invited students to develop “data memes”, Vacca et al. [71] proposed that creative engagement enhances accessibility by humanising data. This can also reveal important personal or social implications which might otherwise remain hidden in the context of more passive data consumption [58].

Enhancing engagement.

A number of studies [8,32,40,44,46,49–51,55,56,69,70,72] proposed a direct correlation between making data more accessible and increased levels of engagement. Akshay & Minces [32] noted that a key aspect of this involves increased “engagement with real world data” (p. 1). Real world data counters the sometimes abstract way in which data is presented or analysed in more mathematically-led approaches to data literacy, inviting students to consider why the information matters in wider personal and social contexts. In discussing an innovative project which involved the creation of “data murals” in a school and community context in Brazil, Bhargava et al. [40] noted that “technology-centric interventions to building data literacy have missed a crucial opportunity to look outside of the digital world for approaches and inspirations” (p.198). This project built on “a rich tradition of using the arts and the lived experiences of participants as an invitation for civic engagement” (p.198). Not only did the project support data literacy, it engaged the wider community in the project in a way which was creative, playful and fun. D’Ignazio & Bhargava [43] noted that many approaches to data focus on the need to create materials quickly in a fast moving medium, often at the expense of pedagogical engagement. The authors described the development of a number of online tools which support participation. These included tools to analyze and create song lyrics, tell stories through the creation of sketches, and use real world media such as crayons and paper to enhance engagement with online information. D’Ignazio & Bhargava [44] argued further that these approaches are of particular importance for teaching data literacy to “non-technical, adult newcomers such as humanities scholars, journalists, educators, artists and non-profit staff” (p.2) and require an alternative pedagogical approach which they call “creative data literacy”(p.1). Lesser et al. [51] described a project which utilized interactive educational songs to teach introductory statistics, noting that, “A well-written jingle or song with rhymed lyrics can help content make a more memorable impression on students than that same content delivered in prose form” (p.238) and that the use of songs improves not only recall but motivation and engagement. Markham [55] presented a public arts-based interactive museum experience designed to support data literacy, drawing on the experiences of the designers in, “activism, art, computer science, museum curation, architecture, filmmaking, printmaking, university administration, law, photography, and computational art” (p.227) to deepen curiosity, reflexivity and engagement. Tylosky et al. [69] also explored the barriers to data engagement experienced by the general public, arguing that the arts support non-verbal communication as well as experiences of fun, creativity, and empathy: “Combining arts-based approaches with data exploration and sensemaking – especially participatory sensemaking– has good potential for data inclusivity … arts-based methods may also support empathy building through data” (p. 338). In their 2024 study, Kahn & Jiang [49] argued that tension exists between data and students’ personal experiences but they suggested that this leads to learning opportunities and can “open spaces for rich reflection, engagement and learning” (p. 1131).

Developing critical thinking.

A third area explored by a number of publications was the ability of the arts to develop critical thinking in the context of engaging with data. Amato et al. [33] highlighted the ethical issues of representing communities, noting the limits of both data-driven and artistic representations. The development of approaches which combine both (referred to as “arts-integrated data literacy”) attempts to support critical engagement by integrating the strengths of both approaches: “While a visual artwork, such as a photograph, can tell a story about people and places, it can also be used to overgeneralize. On the other hand, quantitative data can be challenging to collect and may over represent a community’s problems (e.g., crime rates). In this study researchers and teachers co-designed an 8th-grade, arts-integrated data literacy unit to engage students critically with community data through photography” (p.1493). In their 2023 analysis of student photo-essays, Amato et al. [34] proposed that “arts-integrated approaches to data literacy have the potential to increase engagement in data reasoning and support students in making data-based arguments” (p. 537). Arastoopour Irgens et al. [36] explored the integration of storytelling and music into an elementary classroom, proposing that early intervention, combining both arts and data literacies is key to successful integrated learning. Building on practices (such as music) in which students are invested in is critical: “using authentic datasets contextualized to what students care about is an engaging way to have them think critically about data” (p. 263). In a study based on “data theatre”, Bhargava et al. [8] argued that such methods help students harness multiple intelligences (such as kinaesthetic and somatic intelligence) to support conceptual and experiential understanding. Such activities offer “an alternate entryway to building a critical data literacy” (p.105).

Lee et al. [50] noted that artistic processes and tools may also support a broader interpretation of data than that available through the exclusive use of digital analytical tools: “using digital data analysis tools provides powerful statistics and visualizations, but can limit students’ opportunities to explore more artistic visualization methods that emphasize trajectories of experience, outliers, and storytelling” p.666). In this vein, Kahn & Jiang [49] noted the importance of investigating the gaps that middle and high school students perceived between [in this study] their family migration stories and data trends as spaces of “epistemic data agency” meaning “students’ capacities to shape knowledge-building of family history data tool use and data reasoning” (p. 1131). They argued for the need to develop critical thinking to challenge the available broader data, to recognize what is missing in relation to the students’ and their families’ lived experiences, and to fill those gaps using personally generated data to tell stories of family migration. Lesser [52] also noted the importance of recognizing this intertwined relationship of data and personal life, arguing that poetry can be used to humanize statistics “where the ideas or language of statistics explores an event in one’s personal life” (p. 146). Woods et al. [76] also argued this point, describing it as claiming “the right to look” (p. 450), meaning that “students rejected data that did not speak to their lived experience” (p. 450). They further argued that their study “reveals that arts-infused pedagogies provide one (not necessarily guaranteed) avenue for students to reclaim this right to look” (p. 456). McDowell & Turk [54] concluded that “storytelling is a powerful way to reframe data literacy that has the potential to address current challenges, in data literacy pedagogy and in teaching data literacy” (p. 339).

Matuk et al. [56] argued for the emergence of a distinct form of art called “data art” which engages learners and consumers more deeply, tapping into the affective and cognitive dimensions of data and supporting more informed, critical reflection and interpretation. Woods et al. [76] argued that creating “data-art helps students to find their voice, ask personally relevant questions about data, and see themselves within data sets and data collection processes” (p. 448). Zhao et al. [77] noted that data-art inquiry programmes effectively merge art and science and “can be a notable example of STEAM education” (17^th page, np). Van Den Bosch et al. [72] probed more deeply into the characteristics of visual and narrative data (in the form of video content, for example), noting the value of character, plot, narrative, sound and image in enhancing empathy, personal connection, trust, and more critical engagement with content. The authors also noted the relative lack of in-depth exploration of content design and context in data literacy pedagogy as a missed opportunity in supporting a better understanding of how artistic processes can support critical engagement and challenge the growing spread of misinformation.

Supporting data justice.

While most of the discussion on the role of the arts focused on its potential as a creative medium, several publications [8,11,35,47,53,54,60,74,84] also pointed to its ability to enhance specific uses of data, particularly around themes of data “justice”. Bhargava et al. [8] proposed creative data literacy as an alternate approach “that builds on processes rooted in questions of justice and equity, de-centering technology and inviting sets of learners who are not engaged through current approaches” (p.105). This builds on rich traditions, particularly in the Global South, of harnessing the arts as a form of civic engagement [40]. DesPortes et al. [11] proposed that expanding data literacy to support concepts such as citizenship and empowerment can help address the social inequities such as the growing gap between the data rich and data poor. They argued further that embodied art forms such as dance create more holistic opportunities for whole-person, experiential engagement. McDowell & Turk [54] argued that data storytelling empowers students to speak up about their concerns, and that “[d]ata storytelling teaches data literacy with agency, placing interpretive power where it belongs: in the hands of storytelling citizens and future information professionals who will use data to advocate for greater justice and inclusion” (p. 326). Li et al. [53] acknowledged data as a currency of power, arguing that transdisciplinary approaches to data, embracing the full spectrum of STEAM disciplines, are necessary to engage users from diverse backgrounds. Focusing on the issue of climate and environmental action Nguyen & Parameswaran [60] asked critical questions about the data practices of online content creators. Looking specifically at TikTok videos, the authors highlighted the need for further knowledge around the use of audio, visual and narrative techniques in the communication of information and misinformation around climate action. Through the exploration of dance [64] and music/ sound-based [32] data literacy projects, authors argued for the empowering impact of creativity, innovative and artistic expression. Wolff et al. [74] proposed that these forms of empowerment can remove barriers to participation and empower understandings and experiences of citizenship. Otani’s reported interactive artworks, a micro-puppet show [62] and a playground installation [63], in the DADA-TATA project were designed with the aim of promoting children’s rights in the digital environment while, at the same time, promoting data literacy for young audiences. The key argument is that the collection of children’s data in the digital realm is a matter of grave concern.

Evaluating the impact of the arts.

The majority of the published studies relate to short-term research projects. While methods of evaluation were primarily qualitative, a number of studies also used quantitative tools such as surveys and questionnaires. Eight studies used pre- and post-surveys/questionnaires in conjunction with ethnographic methods, to evaluate educational interventions aimed at enhancing data literacy [32,38,41,43,56–58,67]. In the context of developing STEAM based curricula, Bertling et al. [38] utilized pre- and –post questionnaires and drawing exercises combined with interviews, observations, and field notes to assess “students’ perceived ability to read, create, and use data visualizations and conceptions and valuing of art, STEAM, and data visualization before and after the curriculum” (p. 7). Matuk et al. [57] utilized pre- and post-surveys to capture interaction between presenters and audience members at a symposium poster exhibition but they specify that “the goal of the symposium was to spark questions about using storytelling in data literacy rather than to evaluate it” (p. 1781). Blackburn [41] used a pre- and post-questionnaire to evaluate “the pedagogical effectiveness of employing a set of pictorial icons with a story-based teaching method in improving outcomes for students” (p. 464). Lesser et al. [51] used a survey incorporating a Likert scale and open-ended questions to evaluate two pilot studies involving interactive songs as a means to develop data literacy. Van den Bosch et al. [72] also used a questionnaire incorporating a Likert scale to evaluate interactions with a video-based storytelling application. Li et al. [53] used a field experiment, classroom evaluation and a questionnaire to evaluate the OCEL.AI (Open Collaborative Experiential Learning. AI) which uses storytelling as a means to enhance critical thinking and data science education. Spence et al. [66] used a combination of interviews, task analysis, pilot development, and a user survey to evaluate an online prototype tool for enhancing data literacy. They employed a usability test to quantify the usability of a prototype for an online educational tool in what is described as “user experience case study” (p. 610). Bowler et al. [42] combined feedback forms from 27 data literacy workshops and observations to evaluate public library programmes supporting data literacy. Sanei et al. [65] combined ethnography and surveys to evaluate a “design-based research project” (p. 162) utilising storytelling. In their co-designed educational project utilising “photo-walks”, Amato et al. [33] focused on the analysis of two students’ experiences in combination with post-implementation interviews with teachers as a means of evaluation. Their 2023 publication [34] focused on the evaluation of 20 student photo-essays. A number of studies were more descriptive, than evaluative, providing an introduction to a design concept or prototype (e.g., [35,55,62,63]), while others focused on arts and data-integrated curriculum development and the provision of relevant educational supports and resources (e.g., [47,52,73]). Bhargava & D’Ignazio [9] shared reflections on the authors’ hands-on experience of using data sculptures in multiple learning settings over a period of 10 years, while other studies utilized thematic analysis extrapolated from interview data (e.g., [11,58,59]) noting that evaluations had been carried out as part of their studies but as this was not the focus of these papers, details were not further elucidated. Wolff et al. [79] had planned to include attitude assessments but were unable to do so when forced to move online. Instead, they used deductive thematic analysis based on empathy constructs. Kahn & Jiang [49] used comparative analysis of middle and high school student cases from a design-based study merging family migration histories and large-scale data in a summer workshop. Ortega et al. [61] combined reflexive thematic analysis with analysis of survey data to evaluate the effectiveness of, and engagement with, their data-comics project.