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Data Visualization Literacy Skills of Information Science Students

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Bibliographic Details
Title: Data Visualization Literacy Skills of Information Science Students
Language: English
Authors: Monica Rogers, SaBrina Jeffcoat
Source: Journal of Education for Library and Information Science. 2024 65(4):410-425.
Availability: Association for Library and Information Science Education. Available from: University of Toronto Press. 5201 Dufferin Street, Toronto, ON, M3H 5T8 Canada. Tel: 416-667–7929; Fax: 416-667–7832; e-mail: journals@utpress.utoronto.ca; e-mail: office@alise.org; Web site: https://www.utpjournals.press/loi/jelis
Peer Reviewed: Y
Page Count: 16
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Visual Aids, Information Science Education, Multiple Literacies, Data, Accreditation (Institutions), Graduate Students, Library Schools, Skill Development
DOI: 10.3138/jelis-2023-0024
ISSN: 0748-5786
2328-2967
Abstract: Data visualization literacy is "the ability and skill to read and interpret visually represented data in and to extract information from data visualizations" and is an emerging literacy type. Even though support exists for data literacy and data visualization use within the academic professions, limited research assessing data visualization literacy skills has been published. This study surveys participants recruited from the 56 institutions with ALA-accredited information programs using Visualization Literacy Assessment Tool (VLAT) content directly from the original test instrument. The results of this study indicate that information science students may possess data visualization literacy skills but have gaps in relation to specific types of data visualizations.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1444739
Database: ERIC
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  Value: <anid>AN0180361925;lii01oct.24;2024Oct22.04:50;v2.2.500</anid> <title id="AN0180361925-1">Data Visualization Literacy Skills of Information Science Students </title> <p>Data visualization literacy is "the ability and skill to read and interpret visually represented data in and to extract information from data visualizations" and is an emerging literacy type. Even though support exists for data literacy and data visualization use within the academic professions, limited research assessing data visualization literacy skills has been published. This study surveys participants recruited from the 56 institutions with ALA-accredited information programs using Visualization Literacy Assessment Tool (VLAT) content directly from the original test instrument. The results of this study indicate that information science students may possess data visualization literacy skills but have gaps in relation to specific types of data visualizations.</p> <p>Keywords: data comprehension; data literacy; data visualization; information science students; VLAT</p> <hd id="AN0180361925-2">Key points:</hd> <p></p> <ulist> <item> LIS educators acknowledge that technology is an essential component of the information landscape. Consequently, they must emphasize the significance of comprehending and embracing new technologies for aspiring professionals.</item> <p></p> <item> In technology courses, LIS educators can address obstacles by employing strategies such as creating a supportive learning environment, encouraging risk taking and experimentation, and implementing collaborative techniques such as pair programming.</item> <p></p> <item> To promote inclusive teaching and learning, LIS educators should adopt a design justice framework. This approach aims to distribute benefits and burdens equitably across diverse groups.</item> </ulist> <p>Data visualization is the representation of information in the form of a chart, diagram, or picture. Data visualization literacy is "the ability and skill to read and interpret visually represented data in and to extract information from data visualizations" ([<reflink idref="bib24" id="ref1">24</reflink>], p. 552). Data visualization literacy, or simply visualization literacy, is an emerging literacy type. Within the library and information science fields, data visualization literacy is growing in relevance ([<reflink idref="bib11" id="ref2">11</reflink>]; [<reflink idref="bib17" id="ref3">17</reflink>]; [<reflink idref="bib35" id="ref4">35</reflink>] as information professionals are increasingly charged with assisting the public in making sense of data and information ([<reflink idref="bib14" id="ref5">14</reflink>]). Academic literature on developing data literacy competencies supports librarians who are leading data visualization literacy and data visualization creation instruction ([<reflink idref="bib11" id="ref6">11</reflink>]; [<reflink idref="bib35" id="ref7">35</reflink>]). In fact, [<reflink idref="bib34" id="ref8">34</reflink>] posits that "understanding and using data visualization is now a core skill that should be incorporated into information literacy goals by librarians and educators" (p. 12).</p> <p>Despite the mounting support for data literacy and data visualization use within the academic professions, limited research assessing data visualization literacy skills has been published. As [<reflink idref="bib27" id="ref9">27</reflink>] noted, "few studies have assessed the skills of science students and practitioners in both reading and interpreting graphs" (p. 89). An IEEE VIS visualization and visual analytics 2014 workshop, "Towards an Open Visualization Literacy Testing Platform" ([<reflink idref="bib37" id="ref10">37</reflink>]), amplified this issue by identifying possible metrics to measure visualization literacy. The workshop raised research questions about visualization literacy tests and reported that researchers lacked a widely usable, validated, and reliable instrument for measuring visualization literacy of users. This measurement gap was further evidenced in a 2017 paper on the creation of the Visualization Literacy Assessment Tool (VLAT). [<reflink idref="bib24" id="ref11">24</reflink>] state, "we believe that we provide one of the earliest validated and reliable instruments measuring visualization literacy of users" (p. 559). Due to the infancy of the VLAT, it has yet to be widely used in research. As a consequence, the VLAT is the assessment tool used in this study. Test items, data visualization types, and task types will be discussed further below.</p> <p>There are very few studies addressing the data visualization literacy skills of information science students. This study describes the level of data visualization literacy skills (per scores on the VLAT) possessed by information science students currently enrolled in an ALA-accredited degree program at a participating Association for Library and Information Science Education (ALISE) institution.</p> <hd id="AN0180361925-3">Statement of the problem</hd> <p>Information science students, soon to be information professionals, may lack the data visualization literacy skills necessary to interpret data visualizations. This concern mirrors information literacy research on the disparity between what students think they know about their information literacy skills and what they actually know ([<reflink idref="bib18" id="ref12">18</reflink>]; [<reflink idref="bib21" id="ref13">21</reflink>]; [<reflink idref="bib26" id="ref14">26</reflink>]; [<reflink idref="bib28" id="ref15">28</reflink>]).</p> <p>As stated above, data visualization literacy is an emerging and increasingly important competency within the information science profession. Therefore, an understanding of the level of data visualization literacy skills currently possessed by information science students is necessary to determine if students perform as experts or at the novice end of the spectrum. If a deficiency in data visualization literacy skills is identified, the findings could be relevant for information science degree program curriculum planning and would allow curriculum planners and instructors to make changes and incorporate more data visualization literacy content into information science coursework.</p> <p>One reason for investigating perceived skills versus actual skill levels was articulated by [<reflink idref="bib20" id="ref16">20</reflink>]: "one of the greatest challenges for information professionals may be in developing outreach efforts that can get the attention of individuals who think they are performing well, but who are not" (p. 160). If courses on data visualization or data visualization literacy are optional, it might be difficult to enroll or engage students who would benefit from data visualization literacy instruction. Instead of seeking out these courses, students would be operating with the perception that they are performing well and that there is little need for further coursework in this domain. Other potential advantages of our research include a heightened focus on increasing literacy about the specific data visualizations of which our findings indicate students are not aware. For example, if findings indicate that questions based on bar charts or pie charts are answered correctly most of the time, but questions based on histograms or scatterplots are not, then planners and instructors can design instruction that increases exposure and experience with these data visualization types for students.</p> <hd id="AN0180361925-4">Data visualization literacy: Evolution and definitions</hd> <p>Expanding from information literacy, there has been a call for data information literacy training for students within the context of preparing them to engage in an "e-research" environment, as researchers increasingly need to integrate data management, curation, and other data techniques into workflows. In a study conducted by [<reflink idref="bib10" id="ref17">10</reflink>], student participants were asked to rank subject importance on a 5-point Likert scale. Data visualization training was ranked at 5.0, demonstrating its importance within research training (p. 19). The authors propose 12 major core competencies for data information literacy, with data visualization being one of them. While not serving as a precise definition, data visualization as a competency of data literacy is listed as follows: "Proficiently use basic visualization tools of discipline. Avoids misleading or ambiguous representations when presenting data. Understands the advantages of different types of visualization, for example, maps, graphs, animations, or videos, when displaying data" (p. 25).</p> <p>Furthermore, the identified data information literacy training areas were then applied through the [<reflink idref="bib1" id="ref18">1</reflink>] information literacy competencies to create outcomes for a data information literacy program. Within the framework of the ACRL's information competencies, [<reflink idref="bib10" id="ref19">10</reflink>] describe data visualization under the context of Standard Two ("access needed information efficiently and effectively") and Standard Four ("use information to accomplish a specific purpose") (pp. 22–23). Still, these standards are insufficient and incomplete. For example, Standard Two is concerned primarily with accessing information but fails to acknowledge the need to extract/export, convert, merge, or perform other data tasks before feeding the data set into a tool for analysis or visualization.</p> <p>Furthering the argument that data visualization literacy should be treated as a new and separate literacy from information literacy, [<reflink idref="bib34" id="ref20">34</reflink>] argues that of the five ACRL Standards (as they existed in 2014), Standards One, Two, and Five are only tangentially related to data visualization literacy, but those competencies related to evaluation and use (Standards Three and Four) are the most relevant. Additionally, Womack contends that the [<reflink idref="bib1" id="ref21">1</reflink>] Visual Literacy Competency Standards are focused on the use of visual imagery and media outside of the data context, and a passing mention within the Visual Literacy Standard Four—"determines the accuracy and reliability of graphical representations of data (e.g., charts, graphs, data models)—is only tangentially related to data visualization literacy, as it applies only to evaluation and not to the use or interpretation of data. As Womack summarizes, "it is natural to consider data visualization as another type of literacy" (p. 12). The [<reflink idref="bib2" id="ref22">2</reflink>] Framework for Information Literacy is frequently cited in information literacy assessment literature as the standard definition for information literacy in pre- and post-tests used in LIS competency studies ([<reflink idref="bib21" id="ref23">21</reflink>], [<reflink idref="bib22" id="ref24">22</reflink>], [<reflink idref="bib23" id="ref25">23</reflink>]; [<reflink idref="bib28" id="ref26">28</reflink>]). However, data visualization literacy is not specifically addressed in the 2015 ACRL Framework, nor is it mentioned in the Framework for Visual Literacy in Higher Education ([<reflink idref="bib3" id="ref27">3</reflink>]). While these more modern standards supersede previous frameworks mentioned above, such as the 2011 ACRL standards Womack is addressing, Womack's critique that data visualization literacy should be treated as a new and separate literacy is still supported, since data visualization is not directly mentioned or addressed in either of the more recent frameworks.</p> <p>Data visualization literacy was also identified as needing elements of other literacies, including both mathematical literacy (also called numeracy) and visual literacy ([<reflink idref="bib7" id="ref28">7</reflink>]), as data visualizations include numbers and data and also rely on visual interpretation and related concepts.</p> <p>Data visualization literacy definitions have also evolved in step with the evolution of information literacy definitions. For example, one paper "loosely defined" visualization literacy as "the ability to use well established data visualizations (e.g., line graphs) to handle information in an effective, efficient and confident manner" ([<reflink idref="bib8" id="ref29">8</reflink>], p. 1963) but later as "the ability to confidently use a given data visualization to translate questions specified in the data domain into visual queries in the visual domain, as well as interpreting visual patterns in the visual domain as properties in the data domain" (p. 1964). Another commonly accepted definition is "the ability to make meaning from and interpret patterns, trends, and correlations in visual representations of data" ([<reflink idref="bib6" id="ref30">6</reflink>] p. 3). For the purposes of this study, we will use the following working definition of data visualization literacy: "the ability and skill to read and interpret visually represented data in and to extract information from data visualizations" ([<reflink idref="bib24" id="ref31">24</reflink>], p. 552).</p> <hd id="AN0180361925-5">Data needs and competencies within the information science field</hd> <p>Numerous studies have underlined the emerging need for data competencies, data services, and data visualization within the information science field. Since 2015, the number of research publications on data science topics in information science journals has been steadily increasing ([<reflink idref="bib33" id="ref32">33</reflink>]). As early as 2011, data information literacy skills and data visualization were identified as a priority for students ([<reflink idref="bib10" id="ref33">10</reflink>]). In 2014, an article on the research librarian "of the future" discussed needed skills and knowledge in data science, as well as training on data carpentry and data visualization ([<reflink idref="bib16" id="ref34">16</reflink>]). [<reflink idref="bib19" id="ref35">19</reflink>] reported that it is advantageous for libraries to use data visualization instead of raw data and provided a framework for thinking about meaningful data visualization use in libraries. Major benefits include facilitating the recognition of patterns and relationships, communicating the message in a compelling and interesting way, and allowing complex data to be easily understood. The research contained an overview of six common data visualization types—scatter plots, line charts, bar charts (including sub-bar charts like stacked bar, multiple bar, and vertical bar) histograms, pie charts, and infographics—and a use-case for each visualization type.</p> <p>Broadly, the information science field has grown to encompass a larger set of data science competencies as the roles of data, data literacy, and data visualization have grown. [<reflink idref="bib12" id="ref36">12</reflink>] gives an overview of the impact that data, big data, and information visualization have had on libraries. Chen also discusses data policies and research needs and identifies where libraries can "bridge the gap" and offer data services, such as cleaning data before analysis. Chen's research speaks to the larger body of data science competencies now needed within the profession. In addition, [<reflink idref="bib29" id="ref37">29</reflink>] discuss the role of data librarians, the challenges they face, and the significance of data in the LIS field, including in data literacy, data support services (management, curation, governance, etc.), internal library use (improving services, acquisitions, etc.), and the data fluency skills needed to meet the technical, social, and ethical demands of information professionals' work.</p> <p>A text-mining study seeking to identify core competencies in the information science field by analyzing job ads used a codebook that included a broad category for technological knowledge and skills ([<reflink idref="bib36" id="ref38">36</reflink>]). Themes in this category that were identified from the text mining were general technology knowledge, database, and programming. Furthermore, [<reflink idref="bib31" id="ref39">31</reflink>] sought to understand and identify the technical skills and competencies required specifically of data librarianship. The skills and competencies identified fall into several broad categories: interpersonal skills and behavioral characteristics; technical skills (data management and curation, data visualization and geospatial representation, understanding metadata standards, knowledge of information technology [i.e., programming] languages such as Python, SQL, XML, etc.), and advanced data reference services. Included in advanced data reference services was knowledge of the "research environment" such as organizational policies on funding, research measures, and so on. The technical skills outlined in the paper speak to data science competencies, while the other categories are aligned with soft skills outside of data science. [<reflink idref="bib25" id="ref40">25</reflink>] also identified skills and competencies necessary for data librarians, including three major areas: data management and curation, data visualization, and advanced data reference services. In a study that sought to define data librarianship, [<reflink idref="bib17" id="ref41">17</reflink>] identified 47 different skills and competencies across nine categories. Major categories focused on the more technical aspects of data work, including data management skills, evaluation and assessment skills, teaching skills, and technology skills. Data visualization was ranked the highest within the technology section, above other skills such as programming (R, Python), statistical software (SPSS, SAS, etc.), and GIS software and data. It is clear that data visualization literacy is situated as part of larger data science competencies within the information science profession.</p> <p>Beyond the literature on data literacy and data visualization competencies for end users, there is also advocacy for librarians to lead this instruction. The 2015 Research, Data, Access and Preservation Summit hosted a panel discussion on the role librarians should play in teaching data information literacy competencies and included venues for informationists at university libraries to deliver instruction on data collection, data analysis, and data visualization ([<reflink idref="bib11" id="ref42">11</reflink>]). A lesson plan by [<reflink idref="bib5" id="ref43">5</reflink>] details how librarians can "emphasize critical data literacy while teaching students to identify, locate, download, clean, interpret, and visualize data" (p. 148). They identified five overarching learning outcomes, with the fifth one being to "produce data visualization with explanatory context to advance an argument" (p. 148). If information science professionals are expected to teach data visualization concepts and techniques in the future, the importance of assessing information science students' data visualization literacy skills is further underlined.</p> <hd id="AN0180361925-6">VLAT Tool</hd> <p>In 2017, the VLAT was systematically developed as the only validated and reliable assessment tool to date to measure data visualization literacy ([<reflink idref="bib24" id="ref44">24</reflink>]). The development of the assessment instrument included a test blueprint, item generation, content validity evaluation, subsequent item analysis, and selection and reliability evaluation. The blueprint and item generation were based broadly on what data visualization types appear most frequently. This included examining the data visualization types most often used in K–12 materials, cross-referenced with those visualizations that can be produced by data visualization software, and cross-referenced further with those visualization types used most in three major newspapers: <emph>The New York Times</emph>, <emph>The Guardian</emph>, and <emph>The Washington Post</emph>. The VLAT consists of 53 test items with associated tasks (34 four-option multiple choice, three three-option multiple-choice items, and 16 true-false items) across the 12 different data visualizations. Table 1 lists the visualizations as well as the types and distribution of questions. Each of the 53 test items had an index score calculated based on difficulty and were then categorized as "easy," "moderate," or "hard," resulting in 17 easy items, 19 moderate items, and 17 hard items. The reliability of the VLAT was estimated based on the reliability coefficient omega, showing that the VLAT had "acceptably good reliability" ([<reflink idref="bib24" id="ref45">24</reflink>], p. 557) and that scores were consistent and not overly influenced by random errors.</p> <p>Table 1: VLAT test items ([<reflink idref="bib24" id="ref46">24</reflink>], p. 558)</p> <p> <ephtml> <table><colgroup span="1"><col align="left" span="1" /><col align="left" span="1" /><col align="center" span="1" /><col align="left" span="1" /><col align="left" span="1" /><col align="left" span="1" /></colgroup><thead><tr><th align="left" rowspan="1" colspan="1">Visualization</th><th align="center" rowspan="1" colspan="1">Visualization Type</th><th align="center" rowspan="1" colspan="1">Item Number</th><th align="left" rowspan="1" colspan="1">Question Type</th><th align="left" rowspan="1" colspan="1">Task</th><th align="left" rowspan="1" colspan="1">Difficulty</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1"><inline-graphic href="jelis-2023-0024-u01.jpg" /></td><td align="center" rowspan="1" colspan="1">Line Chart</td><td align="center" rowspan="1" colspan="1">1234*5</td><td align="left" rowspan="1" colspan="1">Multiple Choice(*Item 4 is a three-option multiple-choice; all others were four-option)</td><td align="left" rowspan="1" colspan="1">Retrieve ValueFind ExtremumDetermine RangeFind Correlation/TrendsMake Comparisons</td><td align="left" rowspan="1" colspan="1">EasyEasyModerateEasyModerate</td></tr><tr><td align="left" rowspan="1" colspan="1"><inline-graphic href="jelis-2023-0024-u02.jpg" /></td><td align="center" rowspan="1" colspan="1">Bar Chart</td><td align="center" rowspan="1" colspan="1">6789</td><td align="left" rowspan="1" colspan="1">Multiple Choice</td><td align="left" rowspan="1" colspan="1">Retrieve ValueFind ExtremumDetermine RangeMake Comparisons</td><td align="left" rowspan="1" colspan="1">EasyEasyModerateHard</td></tr><tr><td align="left" rowspan="1" colspan="1"><inline-graphic href="jelis-2023-0024-u03.jpg" /></td><td align="center" rowspan="1" colspan="1">Stacked Bar Chart</td><td align="center" rowspan="1" colspan="1">101112</td><td align="left" rowspan="1" colspan="1">Multiple Choice</td><td align="left" rowspan="1" colspan="1">Retrieve Value (Absolute value)Retrieve Value (Relative Value)Find Extremum</td><td align="left" rowspan="1" colspan="1">HardHardModerate</td></tr><tr><td align="left" rowspan="1" colspan="1" /><td align="center" rowspan="1" colspan="1" /><td align="center" rowspan="1" colspan="1">1314</td><td align="left" rowspan="1" colspan="1">True/False</td><td align="left" rowspan="1" colspan="1">Make Comparisons (Absolute value)Make Comparisons (Relative Value)</td><td align="left" rowspan="1" colspan="1">ModerateHard</td></tr><tr><td align="left" rowspan="1" colspan="1"><inline-graphic href="jelis-2023-0024-u04.jpg" /></td><td align="center" rowspan="1" colspan="1">100% Stacked Bar Chart</td><td align="center" rowspan="1" colspan="1">1516</td><td align="left" rowspan="1" colspan="1">Multiple Choice</td><td align="left" rowspan="1" colspan="1">Retrieve Value (Relative Value)Find Extremum (Relative Value)</td><td align="left" rowspan="1" colspan="1">HardEasy</td></tr><tr><td align="left" rowspan="1" colspan="1" /><td align="center" rowspan="1" colspan="1" /><td align="center" rowspan="1" colspan="1">17</td><td align="left" rowspan="1" colspan="1">True/False</td><td align="left" rowspan="1" colspan="1">Make Comparisons (Relative Value)</td><td align="left" rowspan="1" colspan="1">Moderate</td></tr><tr><td align="left" rowspan="1" colspan="1"><inline-graphic href="jelis-2023-0024-u05.jpg" /></td><td align="center" rowspan="1" colspan="1">Pie Chart</td><td align="center" rowspan="1" colspan="1">1819</td><td align="left" rowspan="1" colspan="1">Multiple Choice</td><td align="left" rowspan="1" colspan="1">Retrieve Value (Relative Value)Find Extremum (Relative Value)</td><td align="left" rowspan="1" colspan="1">ModerateEasy</td></tr><tr><td align="left" rowspan="1" colspan="1" /><td align="center" rowspan="1" colspan="1" /><td align="center" rowspan="1" colspan="1">20</td><td align="left" rowspan="1" colspan="1">True/False</td><td align="left" rowspan="1" colspan="1">Make Comparisons (Relative Value)</td><td align="left" rowspan="1" colspan="1">Easy</td></tr><tr><td align="left" rowspan="1" colspan="1"><inline-graphic href="jelis-2023-0024-u06.jpg" /></td><td align="center" rowspan="1" colspan="1">Histogram</td><td align="center" rowspan="1" colspan="1">2122</td><td align="left" rowspan="1" colspan="1">Multiple Choice</td><td align="left" rowspan="1" colspan="1">Retrieve Value (Derived Value)Find Extremum (Derived Value)</td><td align="left" rowspan="1" colspan="1">ModerateEasy</td></tr><tr><td align="left" rowspan="1" colspan="1" /><td align="center" rowspan="1" colspan="1" /><td align="center" rowspan="1" colspan="1">23</td><td align="left" rowspan="1" colspan="1">True/False</td><td align="left" rowspan="1" colspan="1">Make Comparisons (Derived Value)</td><td align="left" rowspan="1" colspan="1">Easy</td></tr><tr><td align="left" rowspan="1" colspan="1"><inline-graphic href="jelis-2023-0024-u07.jpg" /></td><td align="center" rowspan="1" colspan="1">Scatterplot</td><td align="center" rowspan="1" colspan="1">24252627</td><td align="left" rowspan="1" colspan="1">Multiple Choice</td><td align="left" rowspan="1" colspan="1">Retrieve ValueFind ExtremumDetermine RangeFind Anomalies</td><td align="left" rowspan="1" colspan="1">ModerateModerateModerateHard</td></tr><tr><td align="left" rowspan="1" colspan="1" /><td align="center" rowspan="1" colspan="1" /><td align="center" rowspan="1" colspan="1">282930</td><td align="left" rowspan="1" colspan="1">True/False</td><td align="left" rowspan="1" colspan="1">Find ClustersFind Correlations/TrendsMake Comparisons</td><td align="left" rowspan="1" colspan="1">EasyModerateModerate</td></tr><tr><td align="left" rowspan="1" colspan="1"><inline-graphic href="jelis-2023-0024-u08.jpg" /></td><td align="center" rowspan="1" colspan="1">Area Chart</td><td align="center" rowspan="1" colspan="1">31323334*</td><td align="left" rowspan="1" colspan="1">Multiple Choice(*Item 34 is a three-option multiple-choice; all others were four-option)</td><td align="left" rowspan="1" colspan="1">Retrieve ValueFind ExtremumDetermine RangeFind Correlations/Trends</td><td align="left" rowspan="1" colspan="1">ModerateHardHardEasy</td></tr><tr><td align="left" rowspan="1" colspan="1"><inline-graphic href="jelis-2023-0024-u09.jpg" /></td><td align="center" rowspan="1" colspan="1">Stacked Area Chart</td><td align="center" rowspan="1" colspan="1">35363738*</td><td align="left" rowspan="1" colspan="1">Multiple Choice(*Item 38 is a three-option multiple-choice; all others were four-option)</td><td align="left" rowspan="1" colspan="1">Retrieve Value (Absolute value)Retrieve Value (Relative value)Find ExtremumFind Correlations/Trends</td><td align="left" rowspan="1" colspan="1">HardHardEasyEasy</td></tr><tr><td align="left" rowspan="1" colspan="1" /><td align="center" rowspan="1" colspan="1" /><td align="center" rowspan="1" colspan="1">3940</td><td align="left" rowspan="1" colspan="1">True/False</td><td align="left" rowspan="1" colspan="1">Make Comparisons (Absolute value)Make Comparisons (Relative Value)</td><td align="left" rowspan="1" colspan="1">HardHard</td></tr><tr><td align="left" rowspan="1" colspan="1"><inline-graphic href="jelis-2023-0024-u10.jpg" /></td><td align="center" rowspan="1" colspan="1">Bubble Chart</td><td align="center" rowspan="1" colspan="1">41424344</td><td align="left" rowspan="1" colspan="1">Multiple Choice</td><td align="left" rowspan="1" colspan="1">Retrieve ValueFind ExtremumDetermine RangeFind Anomalies</td><td align="left" rowspan="1" colspan="1">HardModerateHardModerate</td></tr><tr><td align="left" rowspan="1" colspan="1" /><td align="center" rowspan="1" colspan="1" /><td align="center" rowspan="1" colspan="1">454647</td><td align="left" rowspan="1" colspan="1">True/False</td><td align="left" rowspan="1" colspan="1">Find ClustersFind Correlations/TrendsMake Comparisons</td><td align="left" rowspan="1" colspan="1">ModerateHardModerate</td></tr><tr><td align="left" rowspan="1" colspan="1"><inline-graphic href="jelis-2023-0024-u11.jpg" /></td><td align="center" rowspan="1" colspan="1">Choropleth Map</td><td align="center" rowspan="1" colspan="1">4849</td><td align="left" rowspan="1" colspan="1">Multiple Choice</td><td align="left" rowspan="1" colspan="1">Retrieve Value (Approximate Value)Find Extremum (Approximate Value)</td><td align="left" rowspan="1" colspan="1">HardEasy</td></tr><tr><td align="left" rowspan="1" colspan="1" /><td align="center" rowspan="1" colspan="1" /><td align="center" rowspan="1" colspan="1">50</td><td align="left" rowspan="1" colspan="1">True/False</td><td align="left" rowspan="1" colspan="1">Make Comparisons (Approximate Value)</td><td align="left" rowspan="1" colspan="1">Easy</td></tr><tr><td align="left" rowspan="1" colspan="1"><inline-graphic href="jelis-2023-0024-u12.jpg" /></td><td align="center" rowspan="1" colspan="1">Treemap</td><td align="center" rowspan="1" colspan="1">51</td><td align="left" rowspan="1" colspan="1">Multiple Choice</td><td align="left" rowspan="1" colspan="1">Find Extremum (Relative Value)</td><td align="left" rowspan="1" colspan="1">Moderate</td></tr><tr><td align="left" rowspan="1" colspan="1" /><td align="center" rowspan="1" colspan="1" /><td align="center" rowspan="1" colspan="1">5253</td><td align="left" rowspan="1" colspan="1">True/False</td><td align="left" rowspan="1" colspan="1">Make Comparisons (Relative Value)Identify the Hierarchical Structure</td><td align="left" rowspan="1" colspan="1">HardEasy</td></tr></tbody></table> </ephtml> </p> <p>1 Source: [<reflink idref="bib24" id="ref47">24</reflink>]</p> <hd id="AN0180361925-7">Research methods and design</hd> <p></p> <hd id="AN0180361925-8">Population</hd> <p>Study participants were recruited from the 56 institutions with ALA-accredited information programs who are also member institutions of and included in the [<reflink idref="bib4" id="ref48">4</reflink>] statistical report. Study participants were invited to participate via an email invitation, with a link to the online survey.</p> <p>In addition to showing "major evidence for the validity of the test in terms of the test content," the VLAT is also the premier validated and reliable instrument to measure data visualization literacy of users ([<reflink idref="bib24" id="ref49">24</reflink>], p. 559).</p> <hd id="AN0180361925-9">Data collection</hd> <p>The method of data collection was an electronic survey. Following an IRB-approved study design, a survey invitation was emailed to listed contacts for each of the ALISE participating schools. A second email invitation was sent 10 business days later to all schools that had not had any participation. This second invitation was sent to a secondary contact identified for each school, with special emphasis on identifying an individual person to contact instead of using the generic listed contact for the department or program itself. In both emails, contacts were asked to please distribute the recruitment letter that included the survey link to potential study participants. In some cases these invitations were sent from the chair of the program, but in others it was sent by the program coordinator or posted in the student newsletter or to the student listserv. Email invitations were sent out to contacts at 56 institutions, with a total number of 466 respondents.</p> <hd id="AN0180361925-10">Data analysis</hd> <p>Once data collection was completed, the electronic survey was closed to further entries. The data were exported and then analyzed using Microsoft Excel with the data analysis add-in and PowerQuery, and SPSS (version 23). Incomplete surveys were excluded from the main findings and analysis but were analyzed as a separate data set.</p> <hd id="AN0180361925-11">Limitations</hd> <p>This study has some inherent limitations. The first limitation is that the study sample comprises self-selected participants from ALA-accredited ALISE-participating institutions and may not be representative of the data visualization skills and abilities of those students who chose not to participate. Second, the VLAT creators did not provide any validated and reliable example or alternative questions and/or visualizations within the VLAT materials. It was assumed that participants had not completed the VLAT previously and would not be familiar with the content, but due to the lack of alternative or example visualizations or questions, there was no way to introduce the VLAT to participants without either supplying actual VLAT questions and visualizations or using untested visualizations and invented questions.</p> <hd id="AN0180361925-12">Findings</hd> <p>All results discussed include only the participants who completed the survey, unless noted otherwise. There were a total of 466 respondents, but 119 (25%) did not complete the survey, leaving 347 completed surveys. In order for a participant to be considered to have completed the survey, they had to have a response recorded for the last self-estimate question post-test. Participants who did not complete the study were included for limited data analysis separately from those who did complete the survey.</p> <p>A total of 56 institutions were invited, and 34 institutions were represented in the completed responses. All invited institutions, including the total number of enrolled ALA-accredited Master's degree students identified in the ALISE data who could have potentially been invited to participate, the actual participation counts per institution, and the percentage of study participants per institution can be referenced in [<reflink idref="bib30" id="ref50">30</reflink>]. Additionally, study participants were similar in terms of demographics to the sample population based on the ALISE (2021) data, and full demographic characteristics (age range, gender, race) of participants who completed the study can be found in Rogers. Secondary demographics questions related to type of degree program, degree program completion, highest degree held, professional experience, and results can also be found in that source. There were no findings or relationship between these characteristics and scoring trends worth noting.</p> <hd id="AN0180361925-13">Research Question: What is the level of data visualization literacy possessed by participants...</hd> <p>Table 2 below shows the scoring breakdowns by absolute quartile. The most interesting finding is that everyone scored at least 25% of questions correctly. Participants demonstrated a more than "novice" skill level. The VLAT is a 53-item test, so the lowest quartile would have answered 13 questions or fewer correctly. Everyone who completed the test answered a minimum of 20 items correctly. While no one answered all 53 items correctly, the overwhelming majority of participants (73%) scored in the highest quartile, answering 40 or more questions correctly. Figure 1 shows the distribution of scores, Table 2 shows the percentage of participants by quartile/number of questions answered correctly, and Table 3 contains descriptive statistics for VLAT scores.</p> <p>Table 2: VLAT scores by quartile</p> <p> <ephtml> <table><colgroup span="1"><col align="left" span="1" /><col align="char" char="." span="1" /><col align="char" char="." span="1" /></colgroup><thead><tr><th align="left" rowspan="1" colspan="1">Scoring quartile</th><th align="center" rowspan="1" colspan="1">Count</th><th align="center" rowspan="1" colspan="1">%</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">Quartile 2 (More than 13 but less than 27)</td><td align="center" rowspan="1" colspan="1">11</td><td align="center" rowspan="1" colspan="1">3%</td></tr><tr><td align="left" rowspan="1" colspan="1">Quartile 3 (More than 27 but less than 40)</td><td align="center" rowspan="1" colspan="1">81</td><td align="center" rowspan="1" colspan="1">23%</td></tr><tr><td align="left" rowspan="1" colspan="1">Quartile 4 (40 and above)</td><td align="center" rowspan="1" colspan="1">255</td><td align="center" rowspan="1" colspan="1">73%</td></tr><tr><td align="left" rowspan="1" colspan="1">Total</td><td align="center" rowspan="1" colspan="1">347</td><td align="center" rowspan="1" colspan="1">100%</td></tr></tbody></table> </ephtml> </p> <p>Graph: Figure 1: VLAT scores</p> <p>Table 3: Descriptive statistics for VLAT scores</p> <p> <ephtml> <table><colgroup span="1"><col align="left" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="center" span="1" /></colgroup><thead><tr><th align="left" rowspan="1" colspan="1">Field</th><th align="center" rowspan="1" colspan="1">Minimum</th><th align="center" rowspan="1" colspan="1">Maximum</th><th align="center" rowspan="1" colspan="1">Mean</th><th align="center" rowspan="1" colspan="1">Standard Deviation</th><th align="center" rowspan="1" colspan="1">Variance</th><th align="center" rowspan="1" colspan="1">Count</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">Score</td><td align="center" rowspan="1" colspan="1">20</td><td align="center" rowspan="1" colspan="1">52</td><td align="center" rowspan="1" colspan="1">42.42</td><td align="center" rowspan="1" colspan="1">6.23</td><td align="center" rowspan="1" colspan="1">38.76</td><td align="center" rowspan="1" colspan="1">347</td></tr></tbody></table> </ephtml> </p> <p>Tables 4a–d presents the breakdown of frequency of correctly and incorrectly answered questions by visualization type, question type, task, and difficulty. Each data visualization type pertains to multiple questions, and tasks are associated with multiple questions. For reference, Tables 4a–d includes the <emph>N</emph> (i.e., total questions answered) for each data visualization type, question type, task, and difficulty. Notably, over 70% of the time participants correctly answered questions for all visualization types, except for stacked bar charts and stacked area charts, which were correctly answered only 67% and 55% of the time, respectively. Similarly, over 75% of answers pertaining to all task types were correct except for Find Anomalies (only 67% correct) and Retrieve Value (70% correct). Ninety-eight percent of answers on easy items and 86% on moderate items were correct, but there was a notable drop for hard items, with these being answered correctly only 56% of the time.</p> <p>Table 4a: Breakdown of correct answers by visualization type, question type, task and difficulty</p> <p> <ephtml> <table><colgroup span="1"><col align="left" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="center" span="1" /></colgroup><thead><tr><th align="left" rowspan="1" colspan="1">Visualization</th><th align="center" rowspan="1" colspan="1">N</th><th align="center" rowspan="1" colspan="1">% correct</th><th align="center" rowspan="1" colspan="1">% incorrect</th><th align="left" rowspan="1" colspan="1">Visualization</th><th align="center" rowspan="1" colspan="1">N</th><th align="center" rowspan="1" colspan="1">% correct</th><th align="center" rowspan="1" colspan="1">% incorrect</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">Line chart</td><td align="center" rowspan="1" colspan="1">1735</td><td align="center" rowspan="1" colspan="1">94%</td><td align="center" rowspan="1" colspan="1">6%</td><td align="left" rowspan="1" colspan="1">Scatterplot</td><td align="center" rowspan="1" colspan="1">2429</td><td align="center" rowspan="1" colspan="1">85%</td><td align="center" rowspan="1" colspan="1">15%</td></tr><tr><td align="left" rowspan="1" colspan="1">Bar Chart</td><td align="center" rowspan="1" colspan="1">1388</td><td align="center" rowspan="1" colspan="1">88%</td><td align="center" rowspan="1" colspan="1">12%</td><td align="left" rowspan="1" colspan="1">Area Chart</td><td align="center" rowspan="1" colspan="1">1388</td><td align="center" rowspan="1" colspan="1">83%</td><td align="center" rowspan="1" colspan="1">17%</td></tr><tr><td align="left" rowspan="1" colspan="1">Stacked Bar Chart</td><td align="center" rowspan="1" colspan="1">1735</td><td align="center" rowspan="1" colspan="1">67%</td><td align="center" rowspan="1" colspan="1">33%</td><td align="left" rowspan="1" colspan="1">Stacked Area Chart</td><td align="center" rowspan="1" colspan="1">2082</td><td align="center" rowspan="1" colspan="1">55%</td><td align="center" rowspan="1" colspan="1">45%</td></tr><tr><td align="left" rowspan="1" colspan="1">100% Stacked Bar Chart</td><td align="center" rowspan="1" colspan="1">1041</td><td align="center" rowspan="1" colspan="1">85%</td><td align="center" rowspan="1" colspan="1">15%</td><td align="left" rowspan="1" colspan="1">Bubble Chart</td><td align="center" rowspan="1" colspan="1">2429</td><td align="center" rowspan="1" colspan="1">74%</td><td align="center" rowspan="1" colspan="1">26%</td></tr><tr><td align="left" rowspan="1" colspan="1">Pie Chart</td><td align="center" rowspan="1" colspan="1">1041</td><td align="center" rowspan="1" colspan="1">93%</td><td align="center" rowspan="1" colspan="1">7%</td><td align="left" rowspan="1" colspan="1">Choropleth Map</td><td align="center" rowspan="1" colspan="1">1041</td><td align="center" rowspan="1" colspan="1">76%</td><td align="center" rowspan="1" colspan="1">24%</td></tr><tr><td align="left" rowspan="1" colspan="1">Histogram</td><td align="center" rowspan="1" colspan="1">1041</td><td align="center" rowspan="1" colspan="1">97%</td><td align="center" rowspan="1" colspan="1">3%</td><td align="left" rowspan="1" colspan="1">Treemap</td><td align="center" rowspan="1" colspan="1">1041</td><td align="center" rowspan="1" colspan="1">86%</td><td align="center" rowspan="1" colspan="1">14%</td></tr></tbody></table> </ephtml> </p> <p>Table 4b: Breakdown of correct answers by task</p> <p> <ephtml> <table><colgroup span="1"><col align="left" span="1" /><col align="char" char="." span="1" /><col align="char" char="." span="1" /><col align="char" char="." span="1" /></colgroup><thead><tr><th align="left" rowspan="1" colspan="1">Task</th><th align="center" rowspan="1" colspan="1">N</th><th align="center" rowspan="1" colspan="1">% correct</th><th align="center" rowspan="1" colspan="1">% incorrect</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">Determine range</td><td align="center" rowspan="1" colspan="1">1735</td><td align="center" rowspan="1" colspan="1">86%</td><td align="center" rowspan="1" colspan="1">14%</td></tr><tr><td align="left" rowspan="1" colspan="1">Find Anomalies</td><td align="center" rowspan="1" colspan="1">694</td><td align="center" rowspan="1" colspan="1">67%</td><td align="center" rowspan="1" colspan="1">33%</td></tr><tr><td align="left" rowspan="1" colspan="1">Find Clusters</td><td align="center" rowspan="1" colspan="1">694</td><td align="center" rowspan="1" colspan="1">90%</td><td align="center" rowspan="1" colspan="1">10%</td></tr><tr><td align="left" rowspan="1" colspan="1">Find Correlation/Trends</td><td align="center" rowspan="1" colspan="1">1735</td><td align="center" rowspan="1" colspan="1">80%</td><td align="center" rowspan="1" colspan="1">20%</td></tr><tr><td align="left" rowspan="1" colspan="1">Find Extremum</td><td align="center" rowspan="1" colspan="1">4164</td><td align="center" rowspan="1" colspan="1">91%</td><td align="center" rowspan="1" colspan="1">9%</td></tr><tr><td align="left" rowspan="1" colspan="1">Make comparisons</td><td align="center" rowspan="1" colspan="1">4511</td><td align="center" rowspan="1" colspan="1">77%</td><td align="center" rowspan="1" colspan="1">23%</td></tr><tr><td align="left" rowspan="1" colspan="1">Retrieve value</td><td align="center" rowspan="1" colspan="1">4511</td><td align="center" rowspan="1" colspan="1">70%</td><td align="center" rowspan="1" colspan="1">30%</td></tr><tr><td align="left" rowspan="1" colspan="1">Identify the Hierarchical Structure</td><td align="center" rowspan="1" colspan="1">347</td><td align="center" rowspan="1" colspan="1">95%</td><td align="center" rowspan="1" colspan="1">5%</td></tr></tbody></table> </ephtml> </p> <p>Table 4c: Breakdown of correct answers by question type</p> <p> <ephtml> <table><colgroup span="1"><col align="left" span="1" /><col align="char" char="." span="1" /><col align="center" span="1" /><col align="center" span="1" /></colgroup><thead><tr><th align="left" rowspan="1" colspan="1">Question Type</th><th align="center" rowspan="1" colspan="1">N</th><th align="center" rowspan="1" colspan="1">% correct</th><th align="center" rowspan="1" colspan="1">% incorrect</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">Multiple Choice</td><td align="center" rowspan="1" colspan="1">12839</td><td align="center" rowspan="1" colspan="1">82%</td><td align="center" rowspan="1" colspan="1">18%</td></tr><tr><td align="left" rowspan="1" colspan="1">True/False</td><td align="center" rowspan="1" colspan="1">5552</td><td align="center" rowspan="1" colspan="1">77%</td><td align="center" rowspan="1" colspan="1">23%</td></tr></tbody></table> </ephtml> </p> <p>Table 4d: Breakdown of correct answers by difficulty</p> <p> <ephtml> <table><colgroup span="1"><col align="left" span="1" /><col align="center" span="1" /><col align="center" span="1" /><col align="char" char="." span="1" /></colgroup><thead><tr><th align="left" rowspan="1" colspan="1">Difficulty</th><th align="center" rowspan="1" colspan="1">N</th><th align="center" rowspan="1" colspan="1">% correct</th><th align="center" rowspan="1" colspan="1">% incorrect</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">Easy</td><td align="center" rowspan="1" colspan="1">5899</td><td align="center" rowspan="1" colspan="1">98%</td><td align="center" rowspan="1" colspan="1">3%</td></tr><tr><td align="left" rowspan="1" colspan="1">Moderate</td><td align="center" rowspan="1" colspan="1">6593</td><td align="center" rowspan="1" colspan="1">86%</td><td align="center" rowspan="1" colspan="1">14%</td></tr><tr><td align="left" rowspan="1" colspan="1">Hard</td><td align="center" rowspan="1" colspan="1">5899</td><td align="center" rowspan="1" colspan="1">56%</td><td align="center" rowspan="1" colspan="1">44%</td></tr></tbody></table> </ephtml> </p> <hd id="AN0180361925-14">Incomplete survey analysis</hd> <p>Analysis for those participants who started the survey but did not complete it are presented here separately from those who did complete the survey. Overall, there were 466 survey respondents, with 119 (25%) who did not complete the survey, meaning they did not have a response recorded for the last self-estimate question after the VLAT. Of the 119 incomplete surveys, 59 participants exited the survey during the demographics question, before the first page of the VLAT. Another 25 exited the survey on the first page of the VLAT but did not answer any questions. These responses were not recorded as incorrect and were simply omitted from the analysis. Thirty-five participants started a survey and filled out at least five questions, ranging up to 49 questions, from the 53-item test. Figure 2 shows the number of questions answered as compared to what percentage of answered questions were correct. There is a linear trend line to help show that as participants answered more questions, the overall percentage of correct answers was lower. Additionally, it is worth noting that of the 17 "hard" test items, 12 are in the second half of the survey (after question 26.)</p> <p>Graph: Figure 2: Uncompleted surveys, % of answered questions that were correct with linear trend line</p> <hd id="AN0180361925-15">Recommendations for practical application</hd> <p>Previous research has outlined the importance of data visualization skills within the information science profession ([<reflink idref="bib10" id="ref51">10</reflink>]; [<reflink idref="bib12" id="ref52">12</reflink>], [<reflink idref="bib13" id="ref53">13</reflink>]; [<reflink idref="bib16" id="ref54">16</reflink>]; [<reflink idref="bib31" id="ref55">31</reflink>]; [<reflink idref="bib36" id="ref56">36</reflink>]). The results of this study indicate that IS students may possess data visualization literacy skills but have gaps in relation to specific types of data visualizations. The first practical application recommendation is for data visualization literacy instruction to include these specific types of data visualizations (stacked bar charts and stacked area charts). The second recommendation is to include difficult data visualizations within instruction. The study findings showed a steep decline in correct answers as the difficulty of interpreting data visualizations increased, so including more complicated data visualizations within instruction would increase exposure. The third recommendation is that information science curriculum planners and others involved in instruction and professional development include data visualization in general. While this study included no participants who scored in the lowest absolute quartile, based on the analysis of those who ceased participation, there is reason to believe there are information science students within the population who do not possess data visualization literacy skills.</p> <hd id="AN0180361925-16">Recommendations for future research</hd> <p>While many studies have examined the information literacy skills of student populations ([<reflink idref="bib9" id="ref57">9</reflink>]; [<reflink idref="bib15" id="ref58">15</reflink>]; [<reflink idref="bib18" id="ref59">18</reflink>]; [<reflink idref="bib21" id="ref60">21</reflink>], [<reflink idref="bib22" id="ref61">22</reflink>], [<reflink idref="bib23" id="ref62">23</reflink>]; [<reflink idref="bib26" id="ref63">26</reflink>]; [<reflink idref="bib28" id="ref64">28</reflink>]), very few studies have been devoted to information science students, and none specific to data visualization literacy skills. The first recommendation for future research is to expand this preliminary study to determine if the statistical results can be replicated. If another study achieves similar results, it would support the statistical strength of the findings of this study. Alternatively, if another study with a lower response drop-out rate finds information science students scoring in the lowest absolute quartile, it would support the limited findings of this study specific to the incomplete survey responses and would reinforce the importance for broad data visualization literacy instruction for information science students. This recommendation to expand the research could be executed in multiple ways:</p> <p></p> <ulist> <item> Conduct a replication study based on this one, but do the survey within the classroom setting and/or employ other strategies to increase the rate of completion.</item> <p></p> <item> Within a student population from two specific information science programs, with one university that does include data visualization as part of the curriculum and one that does not.</item> <p></p> <item> Within a different population of students from a different degree program. Currently there is very little research at all about data visualization literacy skills within any kind of population.</item> </ulist> <hd id="AN0180361925-17">Recommendations based on the use of the VLAT</hd> <p>One recommendation for further VLAT research would be to create a validated and reliable abbreviated version of the tool. This might help alleviate issues related to the high incompletion rate of VLAT responses and the average expected duration for survey completion. The expected time for survey completion was 12–18 minutes, and while this did include 14 questions that comprised the consent, demographics and quartile questions, the bulk of the survey instrument was dedicated to the VLAT in its entirety. Asking participants to donate 18 minutes of their leisure time, or alternatively if the VLAT were used in a classroom setting as recommended above, could be an undue burden on participants' and researchers' time.</p> <p>The VLAT does not supply a specific number of questions answered correctly to determine what skill level a participant possesses. This presents challenges and limitations in using this tool within research, in that while it can be clear if a participant is at either extreme end of the novice–expert spectrum, it is unclear if participants who do not score at those extremes possess the necessary skills to adequately read and interpret data visualizations. From this perspective, it would be helpful if the VLAT did indicate what score level represents skills at the novice, beginner, competent, proficient, and expert levels.</p> <p>The VLAT was designed to measure the data visualization literacy skills of non-expert users ([<reflink idref="bib24" id="ref65">24</reflink>], p. 559). However, the authors of the VLAT did not define what constitutes a "non-expert user," and 11 of the 12 visualizations are used frequently within K–12 curriculum and published in news outlets. The news outlets surveyed were <emph>The New York Times</emph> (from 2003 to 2015), <emph>The Guardian</emph> (from 2008 to 2015), and <emph>The Washington Post</emph> (from 2014 to 2015), across 494 articles (Lee et al., p. 553). According to [<reflink idref="bib32" id="ref66">32</reflink>], the Flesch-Kincaid readability test score for each of those publications is between an 11th- and 12th-grade reading level. With these factors in mind, we might assume that students engaged in any post-secondary education, and certainly those in Master's degree programs, could (or should) be considered "expert users" when testing their skills using the VLAT, as that population has obviously completed the K–12 curriculum and beyond. This issue could be addressed by creating another version of the VLAT especially for "expert users" or for those who are engaged in post-secondary education. This would likely require the VLAT to consist of visualizations surveyed from sources beyond those in the K–12 curriculum and in news outlets, and instead focus on visualizations used in post-secondary education materials, used in poster presentations at professional conferences, and published in professional journals.</p> <hd id="AN0180361925-18">Conclusions</hd> <p>This study surveyed participants recruited from 56 institutions with ALA-accredited information programs using the Visualization Literacy Assessment Tool (VLAT). The results of this study indicate that while information science students may possess data visualization literacy skills, there are still gaps in their understanding of specific types of data visualizations, and with more difficult visualizations. This highlights the need for further research and work in this subject area.</p> <p>To build upon or challenge the findings of this study, further research is needed. First, this research should include replication studies that aim to increase participation rate and decrease dropout rates, as well as in other student populations to get a better understanding of data visualization literacy skills more broadly. Second, as data visualization is a growing competency within the information science workforce, there is a need for replication studies that examine professionals within the field who are not concurrently students.</p> <p>The VLAT could be improved or expanded upon by including a scoring system to better understand where participants fall on the novice–expert spectrum, having an abbreviated version created to increase ease of use, and having a version created targeting expert-users in order to increase utility of the VLAT. This is especially important within the context of understanding data visualization literacy skills within the field of information science.</p> <ref id="AN0180361925-19"> <title> References </title> <blist> <bibl id="bib1" idref="ref18" type="bt">1</bibl> <bibtext> ACRL. (2011). Visual Literacy Competency Standards for Higher Education. [Online] Available from: <ulink href="http://www.ala.org/acrl/standards/visualliteracy">http://www.ala.org/acrl/standards/visualliteracy</ulink></bibtext> </blist> <blist> <bibl id="bib2" idref="ref22" type="bt">2</bibl> <bibtext> ACRL. (2015). Framework for Information Literacy for Higher Education. [Online] Available from: <ulink href="http://www.ala.org/acrl/standards/ilframework">http://www.ala.org/acrl/standards/ilframework</ulink></bibtext> </blist> <blist> <bibl id="bib3" idref="ref27" type="bt">3</bibl> <bibtext> ACRL. (2022). The Framework for Visual Literacy in Higher Education. 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Her favorite data visualization is a packed bubble chart because it can communicate four dimensions of data in a single visual.</p> <p>SaBrina O. Jeffcoat is a data-driven problem-solver. Jeffcoat is an artist-turned-information scientist passionate about making complex data plain, whose research encompasses both qualitative and quantitative methods that have led to a track record of excelling in nonprofit environments. Jeffcoat has more than a decade of experience as an art and culture professional, including working with SCAD, SC Arts Commission, South Arts, Knight Foundation, and Bloomberg Philanthropies. Jeffcoat is currently a PhD candidate and professor at the University of Albany and teaches on social media practices for nonprofit leaders. Jeffcoat thrives on using data to assess improvement areas and create human-centered solutions for nonprofit stakeholders. 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  Data: <searchLink fieldCode="DE" term="%22Visual+Aids%22">Visual Aids</searchLink><br /><searchLink fieldCode="DE" term="%22Information+Science+Education%22">Information Science Education</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+Literacies%22">Multiple Literacies</searchLink><br /><searchLink fieldCode="DE" term="%22Data%22">Data</searchLink><br /><searchLink fieldCode="DE" term="%22Accreditation+%28Institutions%29%22">Accreditation (Institutions)</searchLink><br /><searchLink fieldCode="DE" term="%22Graduate+Students%22">Graduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Library+Schools%22">Library Schools</searchLink><br /><searchLink fieldCode="DE" term="%22Skill+Development%22">Skill Development</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.3138/jelis-2023-0024
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0748-5786<br />2328-2967
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Data visualization literacy is "the ability and skill to read and interpret visually represented data in and to extract information from data visualizations" and is an emerging literacy type. Even though support exists for data literacy and data visualization use within the academic professions, limited research assessing data visualization literacy skills has been published. This study surveys participants recruited from the 56 institutions with ALA-accredited information programs using Visualization Literacy Assessment Tool (VLAT) content directly from the original test instrument. The results of this study indicate that information science students may possess data visualization literacy skills but have gaps in relation to specific types of data visualizations.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2024
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1444739
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1444739
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3138/jelis-2023-0024
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 410
    Subjects:
      – SubjectFull: Visual Aids
        Type: general
      – SubjectFull: Information Science Education
        Type: general
      – SubjectFull: Multiple Literacies
        Type: general
      – SubjectFull: Data
        Type: general
      – SubjectFull: Accreditation (Institutions)
        Type: general
      – SubjectFull: Graduate Students
        Type: general
      – SubjectFull: Library Schools
        Type: general
      – SubjectFull: Skill Development
        Type: general
    Titles:
      – TitleFull: Data Visualization Literacy Skills of Information Science Students
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Monica Rogers
      – PersonEntity:
          Name:
            NameFull: SaBrina Jeffcoat
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 10
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 0748-5786
            – Type: issn-electronic
              Value: 2328-2967
          Numbering:
            – Type: volume
              Value: 65
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Journal of Education for Library and Information Science
              Type: main
ResultId 1