{"id":97,"date":"2020-06-18T17:43:41","date_gmt":"2020-06-18T17:43:41","guid":{"rendered":"https:\/\/www.terc.edu\/dataclubs\/?page_id=97"},"modified":"2022-12-22T18:45:29","modified_gmt":"2022-12-22T18:45:29","slug":"professional-development","status":"publish","type":"page","link":"https:\/\/www.terc.edu\/dataclubs\/professional-development\/","title":{"rendered":"Professional Development"},"content":{"rendered":"\n<h3 class=\"wp-block-heading\"><strong>Professional Development<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">What is data science? How can my students and I engage in it? Why is it essential in scientific disciplines? How does it contribute to solving societal problems? How it can become an important part of the science or math curriculum that I teach?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In the summer of 2020, the Data Clubs project hosted a virtual workshop for high school teachers to help them answer these questions.\u00a0The workshop introduced teachers,\u00a0especially those whose students are doing research projects, to data science with a 15-hour workshop consisting of several modules that were implemented online.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Teachers worked with multiple data sets curated by the project on topics ranging from climate change to COVID-19. Teachers examined these datasets, asked and iterated their own questions of the data, and explored patterns\u2014much in the same ways that their students will do.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The modules emphasized solving problems involving datasets and data tools and engaging with key data science concepts, including:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>the ubiquity of data and the potential for learning from data;<\/li>\n\n\n\n<li>the complexities of measurement and how to look at data through these complexities;<\/li>\n\n\n\n<li>the case\/attribute structure of data;<\/li>\n\n\n\n<li>describing distributions and distributional shape;<\/li>\n\n\n\n<li>looking at relationships within a dataset by comparing distributions and exploring correlations between attributes; and<\/li>\n\n\n\n<li>determining which questions can and cannot be asked of a dataset.&nbsp;<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Teachers used data as they examined rates, charted changes over time, made comparisons between groups, and learned how multiple attributes can contribute to a particular finding. They also learned to use&nbsp;<a href=\"https:\/\/codap.concord.org\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>CODAP<\/strong><\/a>, an online tool for working with critical data science ideas, and had the opportunity to import, clean, and curate their own data sets.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Finally, teachers learned how to assess their students\u2019 competency with data and figure out what is essential for their students to understand.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Professional Development<\/p>\n<p>What is data science? How can my students and I engage in it? Why is it essential in scientific disciplines? How does it contribute to solving societal problems? How it can become an important part of the science or math curriculum that I teach?<\/p>\n<p>In the summer of 2020, the Data Clubs project hosted a virtual workshop for high school teachers to help them answer these questions.\u00a0The workshop introduced teachers,\u00a0especially those whose students are doing research projects,  <a href=\"https:\/\/www.terc.edu\/dataclubs\/professional-development\/\">&nbsp;&raquo;&nbsp;Read more<\/a><\/p>\n","protected":false},"author":11,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","footnotes":""},"class_list":["post-97","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.terc.edu\/dataclubs\/wp-json\/wp\/v2\/pages\/97","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.terc.edu\/dataclubs\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.terc.edu\/dataclubs\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.terc.edu\/dataclubs\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"https:\/\/www.terc.edu\/dataclubs\/wp-json\/wp\/v2\/comments?post=97"}],"version-history":[{"count":13,"href":"https:\/\/www.terc.edu\/dataclubs\/wp-json\/wp\/v2\/pages\/97\/revisions"}],"predecessor-version":[{"id":670,"href":"https:\/\/www.terc.edu\/dataclubs\/wp-json\/wp\/v2\/pages\/97\/revisions\/670"}],"wp:attachment":[{"href":"https:\/\/www.terc.edu\/dataclubs\/wp-json\/wp\/v2\/media?parent=97"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}