{"id":26113,"date":"2024-04-09T13:59:25","date_gmt":"2024-04-09T11:59:25","guid":{"rendered":"https:\/\/www.prodabi.de\/?page_id=26113"},"modified":"2024-10-23T11:37:35","modified_gmt":"2024-10-23T09:37:35","slug":"codap-toolkit-prototyp","status":"publish","type":"page","link":"https:\/\/www.prodabi.de\/en\/codap-toolkit-prototyp\/","title":{"rendered":"CODAP Toolkit &#8211; Entscheidungsb\u00e4ume Prototyp"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"26113\" class=\"elementor elementor-26113\" data-elementor-post-type=\"page\">\n\t\t\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-f82f891 e-flex e-con-boxed e-con e-parent\" data-id=\"f82f891\" 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-1a48ee2 elementor-extension-display-condition-disabled elementor-widget elementor-widget-heading\" data-id=\"1a48ee2\" 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\">Entscheidungsb\u00e4ume mit CODAP<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-02eafa1 e-n-tabs-mobile elementor-extension-display-condition-disabled elementor-widget elementor-widget-n-tabs\" data-id=\"02eafa1\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;horizontal_scroll&quot;:&quot;disable&quot;}\" data-widget_type=\"nested-tabs.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"e-n-tabs\" data-widget-number=\"3059617\" aria-label=\"Registerkarten. \u00d6ffnen Sie Elemente mit [Enter] oder [Leertaste], schlie\u00dfen Sie sie mit [Esc] und navigieren Sie mit den Pfeiltasten.\">\n\t\t\t<div class=\"e-n-tabs-heading\" role=\"tablist\">\n\t\t\t\t\t<button id=\"e-n-tab-title-30596171\" data-tab-title-id=\"e-n-tab-title-30596171\" class=\"e-n-tab-title\" aria-selected=\"true\" data-tab-index=\"1\" role=\"tab\" tabindex=\"0\" aria-controls=\"e-n-tab-content-30596171\" style=\"--n-tabs-title-order: 1;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tHauptfunktionen \t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-30596172\" data-tab-title-id=\"e-n-tab-title-30596172\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"2\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-30596172\" style=\"--n-tabs-title-order: 2;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tVorbereitung eigener Umgebungen\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-30596173\" data-tab-title-id=\"e-n-tab-title-30596173\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"3\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-30596173\" style=\"--n-tabs-title-order: 3;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tFachlicher Hintergrund\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-30596174\" data-tab-title-id=\"e-n-tab-title-30596174\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"4\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-30596174\" style=\"--n-tabs-title-order: 4;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tTeste  dich selbst\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t\t<\/div>\n\t\t\t<div class=\"e-n-tabs-content\">\n\t\t\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" id=\"e-n-tab-content-30596171\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-30596171\" data-tab-index=\"1\" style=\"--n-tabs-title-order: 1;\" class=\"e-active elementor-element elementor-element-19d6d92 e-con-full e-flex e-con e-child\" data-id=\"19d6d92\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a363662 elementor-extension-display-condition-disabled elementor-widget elementor-widget-heading\" data-id=\"a363662\" 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\">1. Entscheidungsb\u00e4ume mit bin\u00e4ren Daten<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-44d61f6 elementor-extension-display-condition-disabled elementor-widget elementor-widget-n-accordion\" data-id=\"44d61f6\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;default_state&quot;:&quot;expanded&quot;,&quot;max_items_expended&quot;:&quot;one&quot;,&quot;n_accordion_animation_duration&quot;:{&quot;unit&quot;:&quot;ms&quot;,&quot;size&quot;:400,&quot;sizes&quot;:[]}}\" data-widget_type=\"nested-accordion.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"e-n-accordion\" aria-label=\"Accordion. Open links with Enter or Space, close with Escape, and navigate with Arrow Keys\">\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-7210\" class=\"e-n-accordion-item\" open>\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"1\" tabindex=\"0\" aria-expanded=\"true\" aria-controls=\"e-n-accordion-item-7210\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Basisfunktionen -  Entscheidungsb\u00e4ume mit arbor erstellen und interpretieren <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><i aria-hidden=\"true\" class=\"fas fa-minus\"><\/i><\/span>\n\t\t\t<span class='e-closed'><i aria-hidden=\"true\" class=\"fas fa-plus\"><\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" role=\"region\" aria-labelledby=\"e-n-accordion-item-7210\" class=\"elementor-element elementor-element-ebaad91 e-con-full e-flex e-con e-child\" data-id=\"ebaad91\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6765ce9 elementor-widget__width-initial elementor-extension-display-condition-disabled elementor-widget elementor-widget-text-editor\" data-id=\"6765ce9\" 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>In diesem Video wird gezeigt, wie mit Hilfe von CODAP ganz einfach datenbasierte Entscheidungsb\u00e4ume per Drag &amp; Drop erstellt werden k\u00f6nnen.<\/p><p>Mit folgendem Link gelangst du in die im Video genutzte CODAP Umgebung: <a href=\"https:\/\/tinyurl.com\/CODAPEntscheidungsbaum\">https:\/\/tinyurl.com\/CODAPEntscheidungsbaum<\/a><\/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-c5e028d elementor-widget__width-initial elementor-extension-display-condition-disabled elementor-widget elementor-widget-video\" data-id=\"c5e028d\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=TKd51DjsOHA&amp;t=7s&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-7211\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"2\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-7211\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Maschine spielen - Entscheidungsb\u00e4ume systematisch erstellen und dokumentieren <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><i aria-hidden=\"true\" class=\"fas fa-minus\"><\/i><\/span>\n\t\t\t<span class='e-closed'><i aria-hidden=\"true\" class=\"fas fa-plus\"><\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" role=\"region\" aria-labelledby=\"e-n-accordion-item-7211\" class=\"elementor-element elementor-element-f2a24cb e-con-full e-flex e-con e-child\" data-id=\"f2a24cb\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d0f06c5 elementor-widget__width-initial elementor-extension-display-condition-disabled elementor-widget elementor-widget-text-editor\" data-id=\"d0f06c5\" 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>In diesem Video wird gezeigt, wie der in den vorherigen Items beschriebene Algorithmus in CODAP umgesetzt werden kann, sodass man gewisserma\u00dfen &#8220;Maschine spielt&#8221;.<\/p><p>Der Ansatz dabei ist, dass der Algorithmus durch Sch\u00fclerinnen und Sch\u00fcler semi-autmatisch durchgef\u00fchrt wird, sodass sie die systematische Vorgehensweise handelnd verinnerlichen k\u00f6nnen.<\/p><p>Mit folgendem Link gelangst du in die im Video genutzte CODAP Umgebung: <a href=\"https:\/\/tinyurl.com\/CODAPEntscheidungsbaum\">https:\/\/tinyurl.com\/CODAPEntscheidungsbaum<\/a><\/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-578c03b elementor-widget__width-initial elementor-extension-display-condition-disabled elementor-widget elementor-widget-video\" data-id=\"578c03b\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=OZ7PyVYJ8-Y&amp;t=2s&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-7212\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"3\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-7212\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Testen mit Testdaten - Entscheidungsb\u00e4ume systematisch evaluieren  <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><i aria-hidden=\"true\" class=\"fas fa-minus\"><\/i><\/span>\n\t\t\t<span class='e-closed'><i aria-hidden=\"true\" class=\"fas fa-plus\"><\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" role=\"region\" aria-labelledby=\"e-n-accordion-item-7212\" class=\"elementor-element elementor-element-5bd9488 e-con-full e-flex e-con e-child\" data-id=\"5bd9488\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7e82b74 elementor-extension-display-condition-disabled elementor-widget elementor-widget-text-editor\" data-id=\"7e82b74\" 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>Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.<\/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-f530b31 elementor-widget__width-initial elementor-extension-display-condition-disabled elementor-widget elementor-widget-video\" data-id=\"f530b31\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=XfeumzxUsHY&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-7213\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"4\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-7213\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Pruning - Entscheidungsb\u00e4ume systematisch optimieren <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><i aria-hidden=\"true\" class=\"fas fa-minus\"><\/i><\/span>\n\t\t\t<span class='e-closed'><i aria-hidden=\"true\" class=\"fas fa-plus\"><\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" role=\"region\" aria-labelledby=\"e-n-accordion-item-7213\" class=\"elementor-element elementor-element-73cd149 e-flex e-con-boxed e-con e-child\" data-id=\"73cd149\" 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-64bb307 elementor-extension-display-condition-disabled elementor-widget elementor-widget-text-editor\" data-id=\"64bb307\" 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>Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.<\/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-fec47d7 elementor-widget__width-initial elementor-extension-display-condition-disabled elementor-widget elementor-widget-video\" data-id=\"fec47d7\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=lqKY-1Y6Ph4&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\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\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-268925d elementor-extension-display-condition-disabled elementor-widget elementor-widget-heading\" data-id=\"268925d\" 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\">2. Entscheidungsb\u00e4ume mit beliebigen Daten<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" id=\"e-n-tab-content-30596172\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-30596172\" data-tab-index=\"2\" style=\"--n-tabs-title-order: 2;\" class=\" elementor-element elementor-element-3346065 e-con-full e-flex e-con e-child\" data-id=\"3346065\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c33449b elementor-extension-display-condition-disabled elementor-widget elementor-widget-n-accordion\" data-id=\"c33449b\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;default_state&quot;:&quot;expanded&quot;,&quot;max_items_expended&quot;:&quot;one&quot;,&quot;n_accordion_animation_duration&quot;:{&quot;unit&quot;:&quot;ms&quot;,&quot;size&quot;:400,&quot;sizes&quot;:[]}}\" data-widget_type=\"nested-accordion.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"e-n-accordion\" aria-label=\"Accordion. Open links with Enter or Space, close with Escape, and navigate with Arrow Keys\">\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-2040\" class=\"e-n-accordion-item\" open>\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"1\" tabindex=\"0\" aria-expanded=\"true\" aria-controls=\"e-n-accordion-item-2040\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Einen eigenen Datensatz in CODAP importieren <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><i aria-hidden=\"true\" class=\"fas fa-minus\"><\/i><\/span>\n\t\t\t<span class='e-closed'><i aria-hidden=\"true\" class=\"fas fa-plus\"><\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" role=\"region\" aria-labelledby=\"e-n-accordion-item-2040\" class=\"elementor-element elementor-element-9bf42a8 e-con-full e-flex e-con e-child\" data-id=\"9bf42a8\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-2041\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"2\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-2041\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Das Arbor Plugin f\u00fcr Entscheidungsb\u00e4ume importieren <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><i aria-hidden=\"true\" class=\"fas fa-minus\"><\/i><\/span>\n\t\t\t<span class='e-closed'><i aria-hidden=\"true\" class=\"fas fa-plus\"><\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" role=\"region\" aria-labelledby=\"e-n-accordion-item-2041\" class=\"elementor-element elementor-element-383350c e-con-full e-flex e-con e-child\" data-id=\"383350c\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a74ee9f elementor-extension-display-condition-disabled elementor-widget elementor-widget-text-editor\" data-id=\"a74ee9f\" 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>Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.<\/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-5a084f8 elementor-widget__width-initial elementor-extension-display-condition-disabled elementor-widget elementor-widget-video\" data-id=\"5a084f8\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=2d7pLnDSFSc&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-2042\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"3\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-2042\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Ein CODAP Dokument per Link teilen  <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><i aria-hidden=\"true\" class=\"fas fa-minus\"><\/i><\/span>\n\t\t\t<span class='e-closed'><i aria-hidden=\"true\" class=\"fas fa-plus\"><\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" role=\"region\" aria-labelledby=\"e-n-accordion-item-2042\" class=\"elementor-element elementor-element-e2beca8 e-con-full e-flex e-con e-child\" data-id=\"e2beca8\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" id=\"e-n-tab-content-30596173\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-30596173\" data-tab-index=\"3\" style=\"--n-tabs-title-order: 3;\" class=\" elementor-element elementor-element-9576b3e e-flex e-con-boxed e-con e-child\" data-id=\"9576b3e\" 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-16ac6dc elementor-extension-display-condition-disabled elementor-widget elementor-widget-text-editor\" data-id=\"16ac6dc\" 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-size: 16px; font-style: normal; font-weight: 400;\">2.1 Elementarisation of machine learning with data-based DTs<\/p><p style=\"font-size: 16px; font-style: normal; font-weight: 400;\">ML, as described by Shalev-Shwartz &amp; Ben-David (2014) and Hastie et al. (2009), is a heterogeneous field that includes different methods and learning algorithms for solving different types of tasks automatically in a statistical learning framework. The unifying element between all methods is that they are based on training data. We focus on the subspecies of supervised ML, specifically on classification tasks that can be addressed with DTs. The first contribution of this paper is the elementarised presentation of a data-based DT construction process that simplifies selected aspects of professional DT algorithms so that it is suitable for teaching in school and still serves as an example for ML. Our teaching unit and the research approach will be outlined later based on this elementarisation.<\/p><p style=\"font-size: 16px; font-style: normal; font-weight: 400;\">2.1.1 The statistical learning framework for creating classifiers<\/p><p style=\"font-size: 16px; font-style: normal; font-weight: 400;\">Classification involves the task of providing objects or individuals of a population with (ideally) correct labels concerning a certain question. In statistics, a population is a set of individuals, objects, or events that are of interest to a particular question or statistical investigation. (Kauermann &amp; K\u00fcchenhoff, 2011, p. 5). Typical classification problems are assigning a patient with a diagnosis or classifying emails as &#8220;spam&#8221; or &#8220;not spam.&#8221; The possible labels come from a label set, depending on which we speak of a binary classification problem (two possible labels) or a multiclass classification problem (a finite set of more than two labels). The statistical learning framework is constituted by two main aspects that are described in the following (F1 and F2):<\/p><ul style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><li style=\"font-size: 16px;\"><em>F1 \u2013 Creating a classifier based on training data<\/em><\/li><\/ul><p style=\"font-size: 16px; font-style: normal; font-weight: 400;\">In a statistical learning framework, the task of a learning algorithm is to create a classifier that predicts a label for any given object in the population. The labels are values of a so-called target variable. To make an informed prediction, an object is represented by a set of characteristics displayed as a vector (called instance) from an input space. Since the characteristics inform the choice of the predicted label, they are called predictor variables. The creation of a classifier is based on training examples, i.e., objects from the population from which predictor variables&#8217; values and correct labels are known. A set of training examples is called training data that can be represented in a data table, as illustrated below (Figure 2).<\/p><ul style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><li style=\"font-size: 16px;\"><em>F2 \u2013 Evaluating a classifier based on test data<\/em><\/li><\/ul><p style=\"font-size: 16px; font-style: normal; font-weight: 400;\">As a measure of success in a statistical learning framework, the aim is to quantify the error of a classifier, which is the probability that it does not predict the correct label on a randomly chosen object from the population. Practically, the error is estimated by using test data to calculate the misclassification rate. Test data is structurally identical to training data but was not used to create the classifier.<\/p><p style=\"font-size: 16px; font-style: normal; font-weight: 400;\">2.1.2 Data-based construction of DTs as classifiers<\/p><p style=\"font-size: 16px; font-style: normal; font-weight: 400;\">As characterized by Shalev-Shwartz &amp; Ben-David (2014, p. 212), a DT is a type of classifier that predicts the label of an object by using a hierarchical structure of decision rules that progress from a root node of a tree to leaf nodes. At each node on the root-to-leaf path, the successor child node is chosen on the basis of a splitting of the input space. The internal nodes of the decision tree (excluding the leaf nodes) are referred to as split nodes. Each split node utilizes one of the predictor variables to divide the data. A leaf node always contains a label. This is exemplified by the simple DT in Figure 1 for the context of online platforms predicting whether users play online games frequently or rarely based on ownership of digital devices. The data-based creation of DTs is described below.<\/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-5903091 elementor-extension-display-condition-disabled elementor-widget elementor-widget-image\" data-id=\"5903091\" 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=\"607\" src=\"https:\/\/www.prodabi.de\/wp-content\/uploads\/Fig1-768x607.png\" class=\"attachment-medium_large size-medium_large wp-image-26409\" alt=\"\" srcset=\"https:\/\/www.prodabi.de\/wp-content\/uploads\/Fig1-768x607.png 768w, https:\/\/www.prodabi.de\/wp-content\/uploads\/Fig1-512x405.png 512w, https:\/\/www.prodabi.de\/wp-content\/uploads\/Fig1-1024x809.png 1024w, https:\/\/www.prodabi.de\/wp-content\/uploads\/Fig1-15x12.png 15w, https:\/\/www.prodabi.de\/wp-content\/uploads\/Fig1.png 1026w\" 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-a48e92f elementor-extension-display-condition-disabled elementor-widget elementor-widget-text-editor\" data-id=\"a48e92f\" 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-size: 16px; font-style: normal; font-weight: 400;\">Figure 1: DT for predicting the frequency of online gaming<\/p><p style=\"font-size: 16px; font-style: normal; font-weight: 400;\">In the following, we consider the case of binary classification problems with binary predictor variables. Subsequent generalization to any form of categorical and numerical predictor variables is not difficult. We stay with the example of an online platform, which has a population of users about which some characteristics (types of digital devices used, frequency of platform use, etc.) are known. The platform recommends content to its users based on the topics they are interested in, one of which may be online gaming. Therefore, the task is to classify a user as &#8220;frequently&#8221; or &#8220;rarely&#8221; playing online games based on the characteristics known to the platform. For illustration, we use a mini sample of the JIM-PB data on adolescents&#8217; media behaviour collected in the ProDaBi project (Podworny et al., 2022). The data sample in Figure 2 contains 14 cases and four binary variables.<\/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-f33645a elementor-extension-display-condition-disabled elementor-widget elementor-widget-image\" data-id=\"f33645a\" 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=\"464\" height=\"512\" src=\"https:\/\/www.prodabi.de\/wp-content\/uploads\/Fig2-464x512.png\" class=\"attachment-medium size-medium wp-image-26410\" alt=\"\" srcset=\"https:\/\/www.prodabi.de\/wp-content\/uploads\/Fig2-464x512.png 464w, https:\/\/www.prodabi.de\/wp-content\/uploads\/Fig2-11x12.png 11w, https:\/\/www.prodabi.de\/wp-content\/uploads\/Fig2.png 739w\" sizes=\"(max-width: 464px) 100vw, 464px\" \/>\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-df322c3 elementor-extension-display-condition-disabled elementor-widget elementor-widget-text-editor\" data-id=\"df322c3\" 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-size: 16px; font-style: normal; font-weight: 400;\">Figure 2: JIM-PB Data sample 1<\/p><p style=\"font-size: 16px; font-style: normal; font-weight: 400;\">Characteristics include\u00a0<em>GameConsole<\/em>\u00a0and\u00a0<em>Computer<\/em>\u00a0ownership and the frequency of use of\u00a0<em>Instagram<\/em>\u00a0and\u00a0<em>OnlineGames<\/em>, coded binary as frequently (at least once a week) or rarely.<\/p><p style=\"font-size: 16px; font-style: normal; font-weight: 400;\">Zero-level DT<\/p><p style=\"font-size: 16px; font-style: normal; font-weight: 400;\">The easiest possible DT would be just one node, which is both the root node and the leaf node, receiving the majority label of all 14 training examples. The frequency distribution in the training data in Figure 2\u00a0is &#8220;9 to 5&#8221;, i.e., for the target variable, there are nine occurrences of the value\u00a0<em>frequently\u00a0<\/em>and 5 of the value<em>\u00a0rarely<\/em>. A \u201czero-level\u201d DT simply provides every instance with the majority label (OnlineGames =\u00a0<em>frequently<\/em>). The misclassification rate (MCR), referring to the training data, is calculated as the proportion of minority labels, which is 5 of 14 (35.7%) and can be used as a reference for the evaluation of one-level and multi-level DTs.<\/p><p style=\"font-size: 16px; font-style: normal; font-weight: 400;\">One-level DT<\/p><p style=\"font-size: 16px; font-style: normal; font-weight: 400;\">To create, evaluate, and represent a one-level DT based on training data, four process components (C1 &#8211; C4) can be distinguished.<\/p><ul style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><li style=\"font-size: 16px;\"><em>C1 &#8211; Performing a data split<\/em><\/li><\/ul><p style=\"font-size: 16px; font-style: normal; font-weight: 400;\">First, a so-called data split (C1) is done, producing two subsets of the training data by using a predictor variable and assigning every training example to a subset depending on the value of this predictor variable, as exemplified in Figure 3.<\/p><ul style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><li style=\"font-size: 16px;\"><em>C2 &#8211; Deriving a one-level DT from a data split<\/em><\/li><\/ul><p style=\"font-size: 16px; font-style: normal; font-weight: 400;\">In the second step, based on the data split, for both subsets, a predicted label is specified to create a decision rule (C2) using the majority label for each subset. Each (new) instance is provided with the majority label of the subset to which the instance is assigned. In Figure 3, this would be &#8220;If GameConsole = Yes, then OnlineGames = frequently\u201d and \u201cIf GameConsole = No, then OnlineGames = rarely&#8221;.<\/p><ul style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><li style=\"font-size: 16px;\"><em>C3 &#8211; Evaluating a DT<\/em><\/li><\/ul><p style=\"font-size: 16px; font-style: normal; font-weight: 400;\">For evaluating a one-level DT (C3), the MCR referring to the training data is calculated, which is 4 of 14 (28.6%) in this example.<\/p><ul style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><li style=\"font-size: 16px;\"><em>C4 \u2013 Representing a DT<\/em><\/li><\/ul><p style=\"font-size: 16px; font-style: normal; font-weight: 400;\">Then, the one-level DT can be represented (C4) in different forms, one of which is a DT diagram or the verbal form as the &#8220;if, then&#8221; rule specified 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-ff261ec elementor-extension-display-condition-disabled elementor-widget elementor-widget-image\" data-id=\"ff261ec\" 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=\"381\" src=\"https:\/\/www.prodabi.de\/wp-content\/uploads\/Fig3-1024x381.png\" class=\"attachment-large size-large wp-image-26411\" alt=\"\" srcset=\"https:\/\/www.prodabi.de\/wp-content\/uploads\/Fig3-1024x381.png 1024w, https:\/\/www.prodabi.de\/wp-content\/uploads\/Fig3-512x191.png 512w, https:\/\/www.prodabi.de\/wp-content\/uploads\/Fig3-768x286.png 768w, https:\/\/www.prodabi.de\/wp-content\/uploads\/Fig3-18x7.png 18w, https:\/\/www.prodabi.de\/wp-content\/uploads\/Fig3.png 1439w\" 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<div class=\"elementor-element elementor-element-11ed60d elementor-extension-display-condition-disabled elementor-widget elementor-widget-text-editor\" data-id=\"11ed60d\" 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>\u00a0<\/p><p>\u00a0<\/p><p>Figure 3: Example of a data split<\/p><p>What was exemplified in Figure 3 for the predictor variable <em>GameConsole<\/em> can be done similarly for the other two predictor variables, and the evaluation can be documented for each, as shown in Table 1.<\/p><p>Table <em>1<\/em><em>: <\/em>Evaluation of three different predictor variables used for a data split<\/p><table><tbody><tr style=\"mso-yfti-irow: 0; mso-yfti-firstrow: yes; height: 12.35pt; mso-prop-change: 'Yannik Fleischer' 20240725T1439;\"><td width=\"123\"><p>predictor variable<\/p><\/td><td width=\"109\"><p>number of misclassifications<\/p><\/td><td width=\"104\"><p>misclassification rate (MCR)<\/p><\/td><\/tr><tr style=\"mso-yfti-irow: 1; height: 17.0pt; mso-prop-change: 'Yannik Fleischer' 20240725T1439;\"><td width=\"123\"><p>GameConsole<\/p><\/td><td width=\"109\"><p>4<\/p><\/td><td width=\"104\"><p>28,6 %<\/p><\/td><\/tr><tr style=\"mso-yfti-irow: 2; height: 17.0pt; mso-prop-change: 'Yannik Fleischer' 20240725T1439;\"><td width=\"123\"><p>Computer<\/p><\/td><td width=\"109\"><p>5<\/p><\/td><td width=\"104\"><p>35,7 %<\/p><\/td><\/tr><tr style=\"mso-yfti-irow: 3; mso-yfti-lastrow: yes; height: 17.0pt; mso-prop-change: 'Yannik Fleischer' 20240725T1439;\"><td width=\"123\"><p>Instagram<\/p><\/td><td width=\"109\"><p>5<\/p><\/td><td width=\"104\"><p>35,7 %<\/p><\/td><\/tr><\/tbody><\/table><p>Based on this documentation, we can conclude that using GameConsole yields the best-performing one-level DT for this training data with respect to MCR.<\/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 data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" id=\"e-n-tab-content-30596174\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-30596174\" data-tab-index=\"4\" style=\"--n-tabs-title-order: 4;\" class=\" elementor-element elementor-element-8297815 e-con-full e-flex e-con e-child\" data-id=\"8297815\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-9400db3 elementor-extension-display-condition-disabled elementor-widget elementor-widget-text-editor\" data-id=\"9400db3\" 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>Die Seite befindet sich im Aufbau. Hier werden in Zukunft noch Inhalte erg\u00e4nzt.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7009ff5 elementor-extension-display-condition-disabled elementor-widget elementor-widget-text-editor\" data-id=\"7009ff5\" 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>Hier geht es zu einer Unterrichtsreihe, die die gezeigten Inhalten behandelt: LINK<\/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\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Entscheidungsb\u00e4ume mit CODAP Hauptfunktionen Vorbereitung eigener Umgebungen Fachlicher Hintergrund Teste dich selbst 1. Entscheidungsb\u00e4ume mit bin\u00e4ren Daten Basisfunktionen &#8211; Entscheidungsb\u00e4ume mit arbor erstellen und interpretieren In diesem Video wird gezeigt, wie mit Hilfe von CODAP ganz einfach datenbasierte Entscheidungsb\u00e4ume per Drag &amp; Drop erstellt werden k\u00f6nnen. Mit folgendem Link gelangst du in die im Video [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":0,"parent":0,"menu_order":26,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"page-builder","ast-site-content-layout":"full-width-container","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-26113","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>CODAP Toolkit - Entscheidungsb\u00e4ume Prototyp - ProDaBi<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.prodabi.de\/en\/codap-toolkit-prototyp\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"CODAP Toolkit - Entscheidungsb\u00e4ume Prototyp - ProDaBi\" \/>\n<meta property=\"og:description\" content=\"Entscheidungsb\u00e4ume mit CODAP Hauptfunktionen Vorbereitung eigener Umgebungen Fachlicher Hintergrund Teste dich selbst 1. 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