Predicting student performance using data from an auto-grading system

by   Huanyi Chen, et al.

As online auto-grading systems appear, information obtained from those systems can potentially enable researchers to create predictive models to predict student behaviour and performances. In the University of Waterloo, the ECE 150 (Fundamentals of Programming) Instructional Team wants to get an insight into how to allocate the limited teaching resources better to achieve improved educational outcomes. Currently, the Instructional Team allocates tutoring time in a reactive basis. They help students "as-requested". This approach serves those students with the wherewithal to request help; however, many of the students who are struggling do not reach out for assistance. Therefore, we, as the Research Team, want to explore if we can determine students which need help by looking into the data from our auto-grading system, Marmoset. In this paper, we conducted experiments building decision-tree and linear-regression models with various features extracted from the Marmoset auto-grading system, including passing rate, testcase outcomes, number of submissions and submission time intervals (the time interval between the student's first reasonable submission and the deadline). For each feature, we interpreted the result at the confusion matrix level. Specifically for poor-performance students, we show that the linear-regression model using submission time intervals performs the best among all models in terms of Precision and F-Measure. We also show that for students who are misclassified into poor-performance students, they have the lowest actual grades in the linear-regression model among all models. In addition, we show that for the midterm, the submission time interval of the last assignment before the midterm predicts the midterm performance the most. However, for the final exam, the midterm performance contributes the most on the final exam performance.



There are no comments yet.


page 1

page 2

page 3

page 4


Detecting Student’s Educational Performance by Machine Learning

During university life students face so many problems regarding their pe...

Looking Back at Postgres

This is a recollection of the UC Berkeley Postgres project, which was le...

Proactive Intervention to Downtrend Employee Attrition using Artificial Intelligence Techniques

To predict the employee attrition beforehand and to enable management to...

Early Prediction of Course Grades: Models and Feature Selection

In this paper, we compare predictive models for students' final performa...

A Comparison of Value-Added Models for School Accountability

School accountability systems increasingly hold schools to account for t...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.