In the past few years, tuition increases and budget cuts have resulted in overcrowded classrooms at some colleges. California’s community college system, for example, faced overcrowding last year after the state’s budget crisis drove students from state universities to community colleges.
Personalized education is difficult to obtain in an overcrowded classroom — unless colleges employ data mining and analytics to improve engagement.
College Stats explains the benefits of educational data mining and learning analytics in the infographic below. Educators may not always be able to provide each student with the one-on-one time needed to truly flourish. However, data mining can provide educators with key insights proved to increase grades and student retention.
Educational data mining methods often differ from methods from the broader data mining literature, in explicitly exploiting the multiple levels of meaningful hierarchy in educational data. Methods from the psychometrics literature are often integrated with methods from the machine learning and data mining literatures to achieve this goal.
For example, in mining data about how students choose to use educational software, it may be worthwhile to simultaneously consider data at the keystroke level, answer level, session level, student level, classroom level, and school level. Issues of time, sequence, and context also play important roles in the study of educational data.
Download Data Mining for Education from Columbia University.
Data mining and analytics make it easier for educators to address the needs of students with dissimilar academic capabilities. Check out the full infographic below to learn how data enhances education.