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eScholarship
Open Access Publications from the University of California

Aims and Scope

TISE welcomes papers that advance our understanding of how to better teach statistics and data science using technology or how to better teach technology to future data scientists and statisticians. "Statistics" and "Data science" should be understood to include the broader category of data literacy, data fluency, data acumen and all other incarnations that apply to the preK-12, undergraduate, graduate and professional educational contexts.

There are four categories of manuscripts that are of interest to TISE: Statistical Investigations (theoretical and empirical research), Statistical Thinking (position papers), Technology Innovations and Notes. Authors must identify the appropriate category when they submit their manuscript within their cover letter.

1. Statistical Investigations:Manuscripts submitted under the category of statistical investigations present research based on an empirical study or that constitute new or interesting conceptual/theoretical work.

Empirical Research:This type of statistical investigation might contribute to a theory of learning or a design of technology or it might address the effectiveness of a particular technological tool or design feature for teaching or learning statistics and/or data science. Empirical studies use appropriate, well-documented research methods and data analyses (whether qualitative or quantitative) that support sound conclusions. An investigation of whether alternative methods of teaching of statistical tools lead to better results than another such method without a connection to theory and/or previous research would not generally be acceptable. Papers should include a discussion of the broad impact of the research. Quantitative measurements of student outcomes should be aligned with the theoretical foundations of the study and evidence given as to their reliability and validity. Student performance on a final exam or end of course grade would not generally be an acceptable quantitative measure of student learning.

Conceptual/Theoretical Research:This type of statistical investigation would provide a new perspective on a problem, technology, or body of work. Manuscripts should provide evidence that the study is based on and situated in prior work, include a discussion of the broader impact of the study, use sound argumentation and reasoning, and if appropriate, provide empirical examples to illustrate. Conceptual studies might, for example, develop a critical context for evaluating software, summarize recent innovations in the research literature that are under-appreciated or poorly understood, or point out critical gaps in the research literature and propose approaches for filling these gaps.

2. Statistical Thinking:Manuscripts submitted under the category of statistical thinking, including position papers, describe a timely issue in learning or teaching statistics or data science related to technology. These manuscripts should also propose novel solutions or perspectives. Issues discussed should be of interest to a broad audience. Ideally, manuscripts will include a discussion of and comparison with alternative solutions and propose a solution that is feasible in a variety of settings. For example, a position paper might argue that changes in technology require a change in the curriculum, either to remove or add topics. Or one might argue that new technology allows a new and better approach to teaching fundamental concepts. Position papers may be published with discussion at the editors' prerogative.

3. Technology Innovations: Manuscripts submitted under the category of technology innovations are of two types: discussions of new technology created by the author(s) or case studies of innovative uses of existing technology.

New Technologies: This type of technology innovation should solve educational problems, provide infrastructure to assist statistics and data science educators, or provide infrastructure to assist developers of statistics or data science education technology. These manuscripts need to include an explanation of why the technology is innovative and a pedagogical context for the technology described. This would likely address how the technology is meant to be used, who the intended users are, and what skills or concepts it is meant to help students learn. These manuscripts should also provide a high-level description of the technology that includes a discussion of how and why particular features of the technology were designed. If there are similar or competing technologies that already exist, the manuscript should also compare/contrast these with the new technology. Examples of new technologies include, but are not be limited to, web-based data analysis software, applets designed to teach fundamental concepts, or technology that harvests data for classroom use.

Technology Case Studies: This type of technology innovation manuscript describes either a particular innovative use of existing technology to improve statistics or data science education or methods for teaching the use of technology to solve problems of general statistical interest. Technology case studies should be feasible in a variety of settings, and authors should discuss implementation and access issues (e.g. cost, maintenance). Authors must also provide a pedagogical context that includes a description of the problem solved by the use of technology and, more particularly, a description of the setting in which the authors implemented the technology. Examples might include descriptions of using data collection problems to collect "live" data, an example of using XML for data exchange, or demonstrations of teaching technologies that allow students to access unusual or complex data formats.

4. Notes: This is a broad category that includes Letters to the Editors, Book and Software Reviews, substantial discussions of previous publications, and brief discussions or descriptions of technology or case studies meant to be provocative or informative, but not collected with the rigor of a research study. These manuscripts are brief (3–5 pages), and must be related to technology innovations or innovative use of technology in statistics or data science education. In most cases, submissions to this category will be reviewed by a single reviewer, and the review will focus more on the appropriateness of the submission for this journal and the level of innovation. Potential authors should consult with the editor (rgould@stat.ucla.edu) prior to submitting the manuscript to ensure that the manuscript is appropriate for the Notes category.