Predicting and preventing student failure &ndash; using the k-nearest neighbour method to predict student performance in an online course environment | Zendy

Tuomas Tanner | Zendy; Hannu Toivonen | Zendy

Open Access

Predicting and preventing student failure – using the k-nearest neighbour method to predict student performance in an online course environment

Author(s) -

Tuomas Tanner,

Hannu Toivonen

Publication year - 2010

Publication title -

international journal of learning technology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.23

H-Index - 9

eISSN - 1741-8119

pISSN - 1477-8386

DOI - 10.1504/ijlt.2010.038772

Subject(s) - computer science , course (navigation) , k nearest neighbors algorithm , feature (linguistics) , typing , online course , focus (optics) , artificial intelligence , machine learning , mathematics education , psychology , speech recognition , engineering , linguistics , philosophy , physics , optics , aerospace engineering

We study the problem of predicting student performance in an online course. Our specific goal is to identify at an early stage of the course those students who have a high risk of failing. We employ the k-nearest neighbour method (KNN) and its many variants on this problem. We present extensive experimental results from a 12-lesson course on touch-typing, with a database of close to 15,000 students. The results indicate that KNN can predict student performance accurately, and already after the very first lessons. We conclude that early tests on skills can also be strong predictors for final scores also in other skill-based courses. Selected methods described in this paper will be implemented as an early warning feature for teachers of the touch-typing course, so they can quickly focus their attention to the students who need help the most.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research