@article{M2015,
title = {Predicting Students’ Final Exam Scores from their Course Activities},
author = {Mogessie M, Riccardi G. and Ronchetti M.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2015/11/FIE15-PredictingStudentsScores.pdf},
year = {2015},
date = {2015-10-21},
journal = {Proc. IEEE Frontiers in Education, El Paso ( USA), 2015.},
abstract = {A common approach to the problem of predicting students’ exam scores has been to base this prediction on the previous educational history of students. In this paper, we present a model that bases this prediction on students’ performance on several tasks assigned throughout the duration
of the course. In order to build our prediction model, we use data from a semi-automated peer-assessment system implemented in two undergraduate-level computer science courses, where students ask questions about topics discussed in class, answer questions from their peers, and rate answers provided by their peers. We then construct features that are used to build several multiple linear regression models. We use the Root Mean Squared Error (RMSE) of the prediction models to evaluate their performance. Our final model, which has recorded an RMSE of 2.9326 for one course and 3.4383 for another on predicting grades on a scale of 18 to 30, is built using 14 features that capture various activities of students. Our work has possible implications in the MOOC arena and in similar online course administration systems.},
keywords = {Education Analytics, Machine Learning}
}
A common approach to the problem of predicting students’ exam scores has been to base this prediction on the previous educational history of students. In this paper, we present a model that bases this prediction on students’ performance on several tasks assigned throughout the duration
of the course. In order to build our prediction model, we use data from a semi-automated peer-assessment system implemented in two undergraduate-level computer science courses, where students ask questions about topics discussed in class, answer questions from their peers, and rate answers provided by their peers. We then construct features that are used to build several multiple linear regression models. We use the Root Mean Squared Error (RMSE) of the prediction models to evaluate their performance. Our final model, which has recorded an RMSE of 2.9326 for one course and 3.4383 for another on predicting grades on a scale of 18 to 30, is built using 14 features that capture various activities of students. Our work has possible implications in the MOOC arena and in similar online course administration systems.
