Methodological Considerations for Predicting At-risk Students-Reference-Cited by-同舟云学术

Methodological Considerations for Predicting At-risk Students

Published:2022-02-14 Issue: Volume: Page:
ISSN:
Container-title:Australasian Computing Education Conference
language:
Short-container-title:

Author:

Koutcheme Charles¹,Sarsa Sami¹,Hellas Arto¹,Haaranen Lassi¹,Leinonen Juho¹

Affiliation:

1. Aalto University, Finland

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3511861.3511873

Reference62 articles.

1. Exploring Machine Learning Methods to Automatically Identify Students in Need of Assistance

2. Performance and Consistency in Learning to Program

3. Mohammad Majid al Rifaie Matthew Yee-King and Mark d’Inverno. 2016. Investigating swarm intelligence for performance prediction. (2016). Mohammad Majid al Rifaie Matthew Yee-King and Mark d’Inverno. 2016. Investigating swarm intelligence for performance prediction. (2016).

4. Course signals at Purdue

5. Increasing Students’ Awareness of Their Behavior in Online Learning Environments with Visualizations and Achievement Badges

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Systematic Literature Review on Performance Prediction in Learning Programming Using Educational Data Mining;2023 IEEE Frontiers in Education Conference (FIE);2023-10-18

2. Machine Learning Models for Predicting Student Dropout—a Review;Proceedings of Eighth International Congress on Information and Communication Technology;2023-09-01

3. G is for Generalisation;Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1;2023-03-02

4. Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic Review;Education Sciences;2022-11-03

5. Intelligent Prediction-Intervention approach to Support Students' Success in Web-based Learning Environments: A Case Study in Higher Education;2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT);2022-11