Models for early prediction of at-risk students in a course using standards-based grading

By Farshid Marbouti, H. Diefes‐Dux, Krishna P. C. Madhavan. Comput. Educ., 2016

342 citations recorded by the source corpus.

Using predictive modeling methods, it is possible to identify at-risk students early and inform both the instructors and the students. While some universities have started to use standards-based grading, which has educational advantages over common score-based grading, at-risk prediction models have not been adapted to reap the benefits of standards-based grading in courses that utilize this grading. In this paper, we compare predictive methods to identify at-risk students in a course that used standards-based grading. Only in-semester performance data that were available to the course instructors were used in the prediction methods. When identifying at-risk students, it is important to minimize false negative (i.e., type II) error while not increasing false positive (i.e., type I) error significantly. To increase the generalizability of the models and accuracy of the predictions, we used a feature selection method to reduce the number of variables used in each model. The Naive Bayes Classifier model and an Ensemble model using a sequence of models (i.e., Support Vector Machine, K-Nearest Neighbors, and Naive Bayes Classifier) had the best results among the seven tested modeling methods. Seven different prediction models for identifying at-risk students were compared.Only in-semester performance factors (i.e., grades) were used in the models.Models were created based on standards-based grading.Feature selection method resulted in higher accuracy of the models.

Subfields: Artificial Intelligence, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/0622144aef5c26fde992b18fcb61292c8fe29a9e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Pengcheng Jiao
2. Luyi Zheng
3. Ouyang Fan
4. Salem Alelyani
5. M. Yağcı

In Computer Science Applications

1. Mian Wu
2. Pengcheng Jiao
3. Luyi Zheng
4. Liyin Zhang
5. Ouyang Fan
6. M. Yağcı

Citations in the corpus

• A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques (2021, result): “The work of Marbouti et al. [47] differed from the previous studies in that their investigation analyzed predictive models to identify students at risk in a course that uses standard-based grading.”
• Predicting academic performance of students from VLE big data using deep learning models (2020, methodology): “Logistic Regression was employed as the baseline model (Chui et al., 2018; Marbouti et al., 2016).”
• Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course (2023, methodology): “Naïve Bayes classifier algorithm was used to develop a predictive modelling method to identify at-risk students in a class that used standards-based grading (Marbouti et al., 2016).”