Author: Michaela Jarvis | MIT Media Lab<\/p>\n
MIT researchers analyzed more than 800,000 online\u00a0school reviews using advanced natural language processing, determining that reviews were largely associated with schools\u2019 test scores \u2014 a measure that correlates closely with race and family income and tends to reinforce inequities in educational opportunity \u2014 rather than measures of student growth, which reflect how well schools actually help students learn.<\/p>\n
\u201cOur hope is that parents who learn about our study will be highly discerning when they read school reviews and take what they are reading with a grain of salt, triangulating subjective assessments with a range of metrics that try to capture what\u2019s really going on at the school,\u201d says Nabeel Gillani, a doctoral student and research assistant in MIT\u2019s Media Lab, and the lead author of the study<\/a>, which was published this week in AERA Open<\/em>, a peer-reviewed journal of the American Educational Research Association.<\/p>\n Gillani and his fellow researchers \u2014 his faculty advisor, Professor Deb Roy; MIT graduate student Eric Chu; Media Lab Research Scientist Doug Beeferman; and Rebecca Eynon of the University of Oxford \u2014 drew on approximately 830,000 reviews of more than 110,000 publicly funded K-12 schools across the United States. The reviews were posted by parents from 2009 to 2019 on the GreatSchools.org<\/a> school information website. GreatSchools, which made the reviews data available for the study, has updated its rating systems in recent years to improve its effectiveness in providing information that minimizes inequity in educational opportunities.<\/p>\n The study characterizing the reviews is the first of its kind. Gillani, whose volunteer work involves helping families who are not familiar with U.S. public education to select high-quality schools for their children, first thought of the concept after a phone call with a mother who had recently immigrated to the United States. As the mother read online reviews to select a school for her daughter, Gillani says he was struck by one school in particular for which the reviews were very positive, \u201cbut based on various quality metrics, the school itself didn\u2019t appear to be a quality school,\u201d where student learning and growth were emphasized.<\/p>\n \u201cEver since then, I\u2019ve been interested in what information reviews contain about different measures of school quality. What are they saying about the quality of education children have access to at their schools?\u201d<\/p>\n Gillani says these questions \u201caligned well with our research group\u2019s focus on using machine learning and natural language processing to understand discourse patterns and human behavior.\u201d<\/p>\n To conduct the study, the authors linked the GreatSchools reviews with the Stanford Educational Data Archive and census data on race and socioeconomic status by neighborhood. Their preliminary analyses revealed that reviews were largely posted by parents at urban schools and those that serve more affluent families. They then developed machine learning models that used the language in reviews to predict different attributes of schools, including test scores, measures of student growth, the percentage of students at the school who are white, and the percentage receiving free or reduced lunch. They found that the models were quite accurate in predicting test scores and school demographics, but were virtually unable to predict student growth \u2014 suggesting the information contained in reviews was closely associated with racial and demographic indicators of schools.\u00a0<\/p>\n To better understand these associations, the researchers then inspected the decision-making processes used by the models, identifying words and phrases most closely associated with the school performance measures and demographics. Many of these words and phrases \u2014 such as \u201cthe PTA,\u201d \u201cemails,\u201d \u201cprivate school,\u201d and \u201cwe\u201d and \u201cus\u201d versus \u201cI\u201d and \u201cmy\u201d \u2014 were more closely associated with higher-performing, whiter, and more affluent schools. These associations reflect documented trends in education, which have revealed that parents at such schools often have more time and comfort to be involved in parent groups, better digital connectivity, more schooling options, and two-parent households, according to Roy, MIT professor of media arts and sciences, director of the MIT\u2019s Center for Constructive Communication,\u00a0and executive director of MIT\u2019s Media Lab. \u201cOur study illustrates how techniques from machine learning, applied to large-scale datasets describing human thought and behavior, can surface subtle patterns that might otherwise be difficult to detect,\u201d Roy says.<\/p>\n The findings led the authors to state that \u201cparents who reference school reviews may be accessing and making decisions based on biased perspectives that reinforce achievement gaps.\u201d<\/p>\n If reviews reflect test scores and demographics, and parents use them to decide where to send their children to school, then such reviews could even push schools to continuously prioritize high test scores instead of student progress and growth, Gillani says.<\/p>\n \u201cIn an education system where test scores are notoriously correlated with race and income, one concern is that reviews primarily associated with test scores could influence parent and school decision-making in ways that increasingly skew school demographics along racial and income lines,\u201d he says. \u201cJust like with any market, consumer reviews and preferences are likely to have a strong influence on what kinds of products are ultimately created.\u201d<\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Author: Michaela Jarvis | MIT Media Lab MIT researchers analyzed more than 800,000 online\u00a0school reviews using advanced natural language processing, determining that reviews were largely […] Read More<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":468,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","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_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[24],"tags":[],"_links":{"self":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/4454"}],"collection":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/comments?post=4454"}],"version-history":[{"count":0,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/4454\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media\/468"}],"wp:attachment":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media?parent=4454"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/categories?post=4454"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/tags?post=4454"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}