{"id":1073,"date":"2015-04-16T15:01:58","date_gmt":"2015-04-16T15:01:58","guid":{"rendered":"https:\/\/www.courses.tegabrain.com\/SS15\/?p=1073"},"modified":"2018-09-04T17:06:54","modified_gmt":"2018-09-04T17:06:54","slug":"javascript-exercise","status":"publish","type":"post","link":"https:\/\/www.courses.tegabrain.com\/SS15\/?p=1073","title":{"rendered":"JavaScript Exercise"},"content":{"rendered":"

For this brief Javascript exercise, I played around with several pieces of text by calculating the Flesch-Kincaid reading level<\/a>. For instance, the following texts are either written essays from different grade levels or comparisons of other literature based on popularity and the time period of when it was written. Overall, this short overview given out numeric results that indicated how readable the text is in a specific reading group; which ranges from at least eleven year old students to university graduates.<\/p>\n

To start off, I have chosen my sister\u2019s recent narrative in which she had to produce for her English class based on a given topic. I ended comparing it off with two essays I\u2019ve written from my first year in college. Overall, I typed out all the needed data in my chosen terminal program; Git Bash, and plugged in the right command line to perform the task.<\/p>\n

\"papers_flesch_index\"<\/a>

Calculating Flesch-Kincaid reading level with school papers through Git Bash, node.js and flesch.js<\/p><\/div>\n

As a result, I first started off calculating my sister\u2019s narrative, \u201cMemory of a Monster\u201d and the test calculated it to be 84 in the Flesch Index. According to the scale, 84 is an easy readability level where at least an\u00a0eleven-year-old student could manage to read. A contrast to the short narrative would be my two college essays, \u201cBernd And Hilla Becher\u201d and \u201cArt Technology Midterm Paper\u201d which fallen into the same scale of 50-53. While going in depth with the numbers, the essays are fairly difficult and a high school senior is still capable to read them. Overall, these two papers were a few increments close to be difficult to read.<\/p>\n

Lastly, for fun, I picked two well-known literatures and compared them of what their Flesch-Kincaid reading level would be. For example, \u201cThe Great Gastby\u201d had at least a 68 Flesch index while the first book of the \u201cHarry Potter\u201d series was scaled to a 75. Both novels either scaled between being having a standard or fairly easy readability level.<\/p>\n

\"books_flesch_index\"

Calculating Flesch-Kincaid reading level with famous literature through Git Bash, node.js and flesch.js<\/p><\/div>\n

Furthermore, whenit came to literature and translating this particular experience on how words can benefit from searching specific information just by developing a program that does it for you. The effort on experimenting this concept was reflected by James Pennebaker during his TED talk on “The Secrets of Pronouns.”<\/a> By going back to the idea of having this formula, which calculates your reading level just from the library of vocabulary a reader or writer, might know.<\/p>\n

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For this brief Javascript exercise, I played around with several pieces of text by calculating the Flesch-Kincaid reading level. For instance, the following texts are either written essays from different grade levels or comparisons of other literature based on popularity and the time period of when it was written. Overall, this short overview given out […]<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/www.courses.tegabrain.com\/SS15\/index.php?rest_route=\/wp\/v2\/posts\/1073"}],"collection":[{"href":"https:\/\/www.courses.tegabrain.com\/SS15\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.courses.tegabrain.com\/SS15\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.courses.tegabrain.com\/SS15\/index.php?rest_route=\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.courses.tegabrain.com\/SS15\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1073"}],"version-history":[{"count":4,"href":"https:\/\/www.courses.tegabrain.com\/SS15\/index.php?rest_route=\/wp\/v2\/posts\/1073\/revisions"}],"predecessor-version":[{"id":1079,"href":"https:\/\/www.courses.tegabrain.com\/SS15\/index.php?rest_route=\/wp\/v2\/posts\/1073\/revisions\/1079"}],"wp:attachment":[{"href":"https:\/\/www.courses.tegabrain.com\/SS15\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1073"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.courses.tegabrain.com\/SS15\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1073"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.courses.tegabrain.com\/SS15\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1073"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}