Predicting Student Risks Through Longitudinal Analysis
published: Oct. 7, 2014, recorded: August 2014, views: 44
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Poor academic performance in K-12 is often a precursor to unsatisfactory educational outcomes such as dropout, which are associated with significant personal and social costs. Hence, it is important to be able to predict students at risk of poor performance, so that the right personalized intervention plans can be initiated. In this paper, we report on a large-scale study to identify students at risk of not meeting acceptable levels of performance in one state-level and one national standardized assessment in Grade 8 of a major US school district. An important highlight of our study is its scale - both in terms of the number of students included, the number of years and the number of features, which provide a very solid grounding to the research. We report on our experience with handling the scale and complexity of data, and on the relative performance of various machine learning techniques we used for building predictive models. Our results demonstrate that it is possible to predict students at-risk of poor assessment performance with a high degree of accuracy, and to do so well in advance. These insights can be used to pro-actively initiate personalized intervention programs and improve the chances of student success.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !