{"id":23903,"date":"2024-05-29T13:12:13","date_gmt":"2024-05-29T13:12:13","guid":{"rendered":"https:\/\/datasetup.euc.ac.cy\/framework\/"},"modified":"2024-05-29T13:12:13","modified_gmt":"2024-05-29T13:12:13","slug":"framework","status":"publish","type":"page","link":"https:\/\/datasetup.euc.ac.cy\/de\/framework\/","title":{"rendered":"Framework"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"23903\" class=\"elementor elementor-23903\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d0397fa e-flex e-con-boxed e-con e-parent\" data-id=\"d0397fa\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6591050 elementor-widget elementor-widget-heading\" data-id=\"6591050\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">EDUCATE: The DataSETUP Conceptual Framework<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-5407211 e-flex e-con-boxed e-con e-parent\" data-id=\"5407211\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-910e20c elementor-widget elementor-widget-text-editor\" data-id=\"910e20c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p style=\"font-weight: 400;\">Our EDUCATE (<span lang=\"EN-US\"><strong>E<\/strong>mpowering <strong>D<\/strong>ata Science <strong>U<\/strong>nderstanding for Teacher Edu<strong>cat<\/strong>ion<\/span>) framework development drew on the systematic literature review and cycles of framework development with partners in the Erasmus+ research project.<span>\u00a0 <\/span>The framework is targeted at two audiences:<\/p><ol style=\"font-weight: 400;\"><li>Preservice teachers. Preservice teachers <strong><em>do<\/em><\/strong> data science from a learner\u2019s perspective. For this audience, the framework is about <strong><em>doing<\/em><\/strong> data science.<\/li><li>Instructors of preservice teachers. For this audience, the framework is about <strong><em>teaching <\/em><\/strong>and <strong><em>doing<\/em><\/strong> data science. They are teaching PSTs and bringing them through the activities that involve doing data science.<\/li><\/ol><h2>\u00a0<\/h2><h2 style=\"font-weight: 400;\"><strong>EDUCATE framework: <\/strong>Data science PROCESSES and PRACTICES<\/h2><p>The <strong>EDUCATE framework<\/strong> describes the four-component PROCESS for doing data science and consists of the <strong>data sciences processes<\/strong> and aligned <strong>data science practices<\/strong>.\u00a0 In our DataSETUP modules we want preservice teachers to navigate through the process and practices of doing data science.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c297478 elementor-widget elementor-widget-image\" data-id=\"c297478\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"768\" height=\"459\" src=\"https:\/\/datasetup.euc.ac.cy\/wp-content\/uploads\/2025\/10\/DataSETUP-framework_Sept2025-768x459.png\" class=\"attachment-medium_large size-medium_large wp-image-21343\" alt=\"\" srcset=\"https:\/\/datasetup.euc.ac.cy\/wp-content\/uploads\/2025\/10\/DataSETUP-framework_Sept2025-768x459.png 768w, https:\/\/datasetup.euc.ac.cy\/wp-content\/uploads\/2025\/10\/DataSETUP-framework_Sept2025-300x179.png 300w, https:\/\/datasetup.euc.ac.cy\/wp-content\/uploads\/2025\/10\/DataSETUP-framework_Sept2025-1024x612.png 1024w, https:\/\/datasetup.euc.ac.cy\/wp-content\/uploads\/2025\/10\/DataSETUP-framework_Sept2025.png 1138w\" sizes=\"(max-width: 768px) 100vw, 768px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-77167d9 e-flex e-con-boxed e-con e-parent\" data-id=\"77167d9\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f2ced85 elementor-widget elementor-widget-text-editor\" data-id=\"f2ced85\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p style=\"font-weight: 400;\">The data science process, tailored for pre-service teachers, is a streamlined four component process. Data science is not seen as a cyclical process, but rather as four components that interconnect and do not have a fixed sequential order: Get and explore data \u2013 Formulate problem \u2013 Model\/Analyze data \u2013 Communicate results and action plan<\/p><p style=\"font-weight: 400;\">The nine data science practices are aligned with the four components of the data science process with many of the practices occurring during all of the processes. The practices focus on <em>how<\/em> tasks are carried out on a granular level, <em>which <\/em>techniques and tools are employed, and <em>why<\/em> certain methodologies may be preferred.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dba0d67 elementor-widget elementor-widget-text-editor\" data-id=\"dba0d67\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h5>PROCESSES:<\/h5><ul><li style=\"font-weight: 400;\"><strong>Get and explore the data<\/strong>: This process involves gathering data either firsthand or by sourcing pre-existing data sets (often large and open source). Where pre-existing or secondary data are sourced, exploration of the data set also occurs here to gain insights into the structure of the data (e.g. identification of variables, types of data, distributions, visualizations).<\/li><li style=\"font-weight: 400;\"><strong>Formulate the problem<\/strong>: This process involves defining and framing the specific question or problem that the data science project aims to solve. Problem formulation is usually the first step in the data science process which then drives the gathering of data. Teachers, however, often source publicly available data sets for use in classroom contexts and in these situations, problem formulation may occur after the data have been sourced or explored.<\/li><li style=\"font-weight: 400;\"><strong>Model\/Analyze the data<\/strong>: This process involves exploring data to uncover patterns, trends, and insights relevant to the problem. Where relevant, pre-service teachers are introduced to predictive modeling techniques that can be applied to reveal insights into the data.<\/li><li style=\"font-weight: 400;\"><strong>Communicate results and engage in action plan<\/strong>: The final process involves creating a narrative that explains the key findings and subsequently translating these data insights into actionable recommendations for self and society.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5cb3d5f elementor-widget elementor-widget-text-editor\" data-id=\"5cb3d5f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h5>PRACTICES:<\/h5><table style=\"font-weight: 400;\"><tbody><tr><td width=\"138\"><p><strong>Data preparation and processing<span>\u00a0 <\/span><\/strong><\/p><p><strong>\u00a0<\/strong><\/p><\/td><td width=\"486\"><p>\u2026 involves preparing data for analysis. Depending on the structure and source of the data set, this may involve <em>reformatting <\/em>and <em>cleaning the data <\/em>to remove duplicates, filling in missing values and remove inaccuracies and inconsistencies in the data, <em>transforming the data <\/em>by converting data types or deriving new variables, <em>integrating the data<\/em> to combine data sets.<\/p><\/td><\/tr><tr><td width=\"138\"><p><strong>Big data technologies<\/strong><\/p><p><strong>\u00a0<\/strong><\/p><\/td><td width=\"486\"><p>\u2026 the increasing accessibility and use of big data in schools demands that teachers understand the <em>what<\/em> of big data and its\u2019 potential to gain deeper insights into issues that are relevant to our world and enhance their data science instruction. For example, knowledge of <em>cloud computing <\/em>and associated platforms enables teachers to access big data technologies without significant upfront investment in hardware, allowing for more accessible data science projects.<\/p><\/td><\/tr><tr><td width=\"138\"><p><strong>Developing statistical questions <\/strong><\/p><p><strong>\u00a0<\/strong><\/p><\/td><td width=\"486\"><p>\u2026 access to open, large, messy and complex data sets poses challenges for the development of research questions that are <em>sufficiently focused<\/em> and <em>well defined<\/em>. It can be challenging to formulate questions that <em>can be effectively answered<\/em> given the available data and that account for the <em>complexity <\/em>of the data without oversimplifying or overcomplicating the problem. There is also the need to navigate ethical and the broader impacts of the research.<\/p><\/td><\/tr><tr><td width=\"138\"><p><strong>Data governance and ethics <\/strong><\/p><p><strong>\u00a0<\/strong><\/p><\/td><td width=\"486\"><p>\u2026 where students collect their own data, an understanding of <em>informed consent, anonymization<\/em>, and respecting participants&#8217; <em>privacy <\/em><span>is necessary<\/span><em>.<\/em>When secondary data are sourced, it requires critical evaluation of the sources and consideration of the <em>reliability, biases<\/em>, and <em>ethical implications <\/em>of using data from different sources. Attention to <em>bias<\/em>, both their own personal and cultural bias alongside biases inbuilt into data sets is important. The use of open data requires consideration of the ethical implications of <em>using public data.<\/em>Use of <em>data for social good, <\/em><span>for example <\/span>analyzing public health data or environmental data, can develop appreciation for the projects that use data for positive social impact.<\/p><\/td><\/tr><tr><td width=\"138\"><p><strong>Exploratory data analysis<\/strong><\/p><\/td><td width=\"486\"><p>\u2026 involves the selection, calculation and interpretation of appropriate <em>descriptive statistics to <\/em>summarize and describe the data and <em>correlational analysis <\/em>to explore relationships within the data. Also requires the use of visualizations and statistical methods to <em>identify patterns, trend and outliers<\/em>to facilitate understanding of the underlying structure of the data and answer the original question formulated.<\/p><\/td><\/tr><tr><td width=\"138\"><p><strong>Data visualization <\/strong><\/p><p><strong>\u00a0<\/strong><\/p><\/td><td width=\"486\"><p>\u2026 involves revealing and communicating insights from data through the use of <em>data visualization tools and techniques <\/em>and also supports the communication of findings. This can be facilitated through the creation and interpretation of basic charts and graphs, pivot tables, and interactive visualizations.<\/p><\/td><\/tr><tr><td width=\"138\"><p><strong>Computational Thinking<\/strong><\/p><p><strong>\u00a0<\/strong><\/p><\/td><td width=\"486\"><p>\u2026 involves breaking down complex problems into manageable parts, recognizing patterns, and developing algorithms to process and analyze data efficiently. Key practices include <em>decomposition<\/em>, involving dissecting problems into smaller tasks; <em>pattern recognition<\/em>, which involves identifying trends or anomalies in data; and <em>abstraction<\/em>, which focuses on filtering out irrelevant information to highlight core aspects of the data. Additionally, <em>algorithmic thinking<\/em> is essential for designing step-by-step procedures that enable data analysis, while <em>evaluation<\/em> involves assessing solutions and refining approaches. Programming languages like Python and R play a crucial role in applying these practices, providing tools and libraries that allow us to automate processes, handle large datasets, and implement complex analyses effectively.<\/p><\/td><\/tr><tr><td width=\"138\"><p><strong>Modeling<\/strong><\/p><p><strong>\u00a0<\/strong><\/p><\/td><td width=\"486\"><p>\u2026 is the process of creating a mathematical representation of a real-world situation using data. A model can be a <em>mathematical equation<\/em>, a <em>statistical algorithm<\/em>, or a <em>machine learning algorithm<\/em> that describes how different variables in the data relate to each other. The development of a model helps capture patterns, relationships, or trends within the data, allowing pre-service teachers to make <em>predictions<\/em> and <em>inferences <\/em>and understand underlying processes.<\/p><\/td><\/tr><tr><td width=\"138\"><p><strong>Collaborative and communication skills<\/strong><\/p><p><strong>\u00a0<\/strong><\/p><\/td><td width=\"486\"><p>\u2026 data science is not only a technical field but also one that thrives on effective teamwork and communication. Students equipped with these skills are better positioned to succeed in real-world settings. Consequently, it is essential for teachers to develop students\u2019 collaborative and communication skills in order to prepare them to <em>work with diverse teams<\/em>, to clearly and concisely<em> communicate findings from complex data sets<\/em>, to <em>document<\/em> <em>their data science process<\/em>, to interact with stakeholders to <em>define project goals<\/em> and to communicate clearly, to <em>use the language of uncertainty<\/em> when there is no clear right or wrong and <em>collaborate in making responsible decisions<\/em> when addressing ethical concerns around data privacy, bias, and fairness in data science projects.<\/p><\/td><\/tr><\/tbody><\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-21f6d8e elementor-widget elementor-widget-text-editor\" data-id=\"21f6d8e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2><b><span>Pedagogical considerations for enacting the framework:<\/span><\/b><\/h2><p style=\"font-weight: 400;\"><span>Pedagogical factors become relevant when preparing preservice teachers to later prepare and teach data science activities.<\/span><span> <\/span><span lang=\"EN-US\">The key teaching considerations that teacher educators and pre-service teachers take into account when designing and reflecting on a data science activity are highlighted below, in both the planning stages and after its implementation.<\/span><\/p><ul><li style=\"font-weight: 400;\"><strong>CONTEXT<\/strong>:<span>\u00a0<\/span>The data science modules utilise real-world contexts and scenarios that are engaging and relevant to learners\u2019 everyday lives and help them see the practical applications of data science in various fields. Contexts may draw from sports analytics, environmental data, health and wellness, social media and digital trends, economics, politics and civic data, sustainability, social justice issues, food and agriculture etc.<\/li><li style=\"font-weight: 400;\"><strong>KEY IDEAS IN K-12 CURRICULUM<\/strong>:\u00a0<span>Pre-service teachers are expected to understand and apply key statistical concepts that are included in national or regional K-12 curricula and form the foundation of data science. These may encompass different types of data (categorical and numerical), different data collection methods and sampling, descriptive statistics (mean, median and variability), how to visualise data, distributions and patterns, relationships and correlation, inferential reasoning and prediction, bias and uncertainty. <\/span><\/li><li style=\"font-weight: 400;\"><span>\u00a0<\/span><strong>TOOLS<\/strong>: Depending on the learning objectives, a selection of tools may be used. A wide variety of tools and platforms support data science practices. These include programming languages for statistical analyses and data visualisation, data visualisation tools for constructing graphs and interactive visualisations, data sources housing real-world datasets for machine learning and data analysis, and statistical tools for data analysis and visualisation.<\/li><li style=\"font-weight: 400;\"><strong>ASSESSMENT PRACTICES<\/strong>: Assessments evaluate understanding of key concepts and technical skills, as well as the ability to apply data science methods to real-world problems. These assessments can take many forms, including project-based work, report writing, presentations and visualisations, quizzes, data interpretation exercises, portfolios, peer and self-assessments, and rubric-based tasks.<\/li><\/ul><h5>For a more in-depth introduction of the framework and its genesis, take a look at the following publication:<\/h5><p><span>Leavy, A., Kazak, S., Podworny, S., &amp; Frischemeier, D. (2026). Designing Data Science Learning in Initial Teacher Education: The EDUCATE Conceptual Framework. <\/span><em>Education Sciences<\/em><span>, <\/span><em>16<\/em><span>(2), 307. <\/span><a href=\"https:\/\/doi.org\/10.3390\/educsci16020307\">https:\/\/doi.org\/10.3390\/educsci16020307<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-06e41f1 e-flex e-con-boxed e-con e-parent\" data-id=\"06e41f1\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-120c732 elementor-widget elementor-widget-heading\" data-id=\"120c732\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Refining EDUCATE, the DataSETUP framework<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5a9953c elementor-widget elementor-widget-text-editor\" data-id=\"5a9953c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>As part of the design research approach that DataSETUP follows, we have refined the EDUCATE framework over the course of the project. Building on the first preliminary sketch, a more structured framework was designed that outlines relevant data science processes and practices as the first dimension of the framework (see below). Since processes and practices are closely intertwined, the latest version of the framework integrates both in a single illustration (see above).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ac7179e elementor-widget elementor-widget-image\" data-id=\"ac7179e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"768\" height=\"255\" src=\"https:\/\/datasetup.euc.ac.cy\/wp-content\/uploads\/2025\/10\/DataSETUP-framework_dimension-1-1-768x255.png\" class=\"attachment-medium_large size-medium_large wp-image-21342\" alt=\"\" srcset=\"https:\/\/datasetup.euc.ac.cy\/wp-content\/uploads\/2025\/10\/DataSETUP-framework_dimension-1-1-768x255.png 768w, https:\/\/datasetup.euc.ac.cy\/wp-content\/uploads\/2025\/10\/DataSETUP-framework_dimension-1-1-300x99.png 300w, https:\/\/datasetup.euc.ac.cy\/wp-content\/uploads\/2025\/10\/DataSETUP-framework_dimension-1-1-1024x339.png 1024w, https:\/\/datasetup.euc.ac.cy\/wp-content\/uploads\/2025\/10\/DataSETUP-framework_dimension-1-1.png 1406w\" sizes=\"(max-width: 768px) 100vw, 768px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6f843f3 elementor-widget elementor-widget-text-editor\" data-id=\"6f843f3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><br \/><br \/><br \/><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-71efe1b e-flex e-con-boxed e-con e-parent\" data-id=\"71efe1b\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2b4ac14 elementor-widget elementor-widget-heading\" data-id=\"2b4ac14\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">First draft of a preliminary framework<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e7bc3cd elementor-widget elementor-widget-text-editor\" data-id=\"e7bc3cd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Starting at the end of 2023, the team of DataSETUP committed to browse the literature and connect different bits and pieces found in research articles and projects to create the first draft of the preliminary framework for the project that would initiate its iterative (re-)development. What we came up with in the summer of 2024 was the following sketch that illustrates the first draft of the preliminary framework version.<\/p><p>Since then, we have run several rounds of redesign. You can see the latest version above.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-32128b2 e-flex e-con-boxed e-con e-parent\" data-id=\"32128b2\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c9bac8e elementor-widget elementor-widget-image\" data-id=\"c9bac8e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1024\" height=\"662\" src=\"https:\/\/datasetup.euc.ac.cy\/wp-content\/uploads\/2024\/10\/Preliminary-framework_DataSETUP-1024x662.png\" class=\"attachment-large size-large wp-image-21029\" alt=\"\" srcset=\"https:\/\/datasetup.euc.ac.cy\/wp-content\/uploads\/2024\/10\/Preliminary-framework_DataSETUP-1024x662.png 1024w, https:\/\/datasetup.euc.ac.cy\/wp-content\/uploads\/2024\/10\/Preliminary-framework_DataSETUP-300x194.png 300w, https:\/\/datasetup.euc.ac.cy\/wp-content\/uploads\/2024\/10\/Preliminary-framework_DataSETUP-768x497.png 768w, https:\/\/datasetup.euc.ac.cy\/wp-content\/uploads\/2024\/10\/Preliminary-framework_DataSETUP.png 1092w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>EDUCATE: The DataSETUP Conceptual Framework Our EDUCATE (Empowering Data Science Understanding for Teacher Education) framework development drew on the systematic literature review and cycles of framework development with partners in the Erasmus+ research project.\u00a0 The framework is targeted at two audiences: Preservice teachers. Preservice teachers do data science from a learner\u2019s perspective. For this audience, [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_forum_subforum_count":0,"footnotes":""},"class_list":["post-23903","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/datasetup.euc.ac.cy\/de\/wp-json\/wp\/v2\/pages\/23903","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/datasetup.euc.ac.cy\/de\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/datasetup.euc.ac.cy\/de\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/datasetup.euc.ac.cy\/de\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/datasetup.euc.ac.cy\/de\/wp-json\/wp\/v2\/comments?post=23903"}],"version-history":[{"count":0,"href":"https:\/\/datasetup.euc.ac.cy\/de\/wp-json\/wp\/v2\/pages\/23903\/revisions"}],"wp:attachment":[{"href":"https:\/\/datasetup.euc.ac.cy\/de\/wp-json\/wp\/v2\/media?parent=23903"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}