His research, better known as Visible Learning, is a culmination of nearly 30 years synthesizing more than 1,500 meta-analyses comprising more than 90,000 studies involving over 300 million students around the world. According to Hattie the story underlying the data has hardly changed over time even though some effect sizes were updated and we have some new entries at the top, at the middle, and at the end of the list.īelow you can find an updated version of our first, second and third visualization of effect sizes related to student achievement.John Hattie, Ph.D., is an award-winning education researcher and best-selling author with nearly 30 years of experience examining what works best in student learning and achievement. His research is now based on nearly 1200 meta-analyses – up from the 800 when Visible Learning came out in 2009. John Hattie updated his list of 138 effects to 150 effects in Visible Learning for Teachers (2011), and more recently to a list of 195 effects in The Applicability of Visible Learning to Higher Education (2015). He further explained this story in his book “ Visible learning for teachers“.
He found that the key to making a difference was making teaching and learning visible. He also tells the story underlying the data. (The updated list also includes the classroom.) But Hattie did not only provide a list of the relative effects of different influences on student achievement. Originally, Hattie studied six areas that contribute to learning: the student, the home, the school, the curricula, the teacher, and teaching and learning approaches.
Therefore he decided to judge the success of influences relative to this ‘hinge point’, in order to find an answer to the question “What works best in education?” Hattie found that the average effect size of all the interventions he studied was 0.40. In his ground-breaking study “ Visible Learning” he ranked 138 influences that are related to learning outcomes from very positive effects to very negative effects. John Hattie developed a way of synthesizing various influences in different meta-analyses according to their effect size (Cohen’s d).
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