# Improving Students Performance in Small-Scale Online Courses -- A Machine Learning-Based Intervention

**Authors:** Sepinoud Azimi, Carmen-Gabriela Popa, Tatjana Cucić  
**arXiv:** [2012.01187](https://arxiv.org/abs/2012.01187)  
**Date:** 2020-11-23  
**Summary page:** https://www.summarizepaper.com/en/paper/2012.01187v1/

## Key points

- Massive open online courses (MOOCs) have revolutionized education delivery
- Many universities are transitioning towards offering online courses to meet demand
- Online courses have challenges such as higher dropout rates and lack of face-to-face interaction
- Machine learning (ML) techniques can improve students' performance in small-scale online courses
- ML has shown success in various domains but is challenging in smaller educational settings with limited data
- Data collected from an online learning management system can effectively predict students' overall performance and propose timely intervention strategies
- Effective intervention strategies can be suggested as early as the middle of the course to positively influence students' progress
- ML-based approaches and student data analysis can help identify challenging students and provide tailored interventions
- An assistive pedagogical tool is presented to support educators in identifying struggling students and suggesting appropriate interventions
- ML-based interventions have the potential to enhance students' performance in small-scale online courses

## Summary

The birth of massive open online courses (MOOCs) has revolutionized the way education is delivered, particularly among the younger generation who value flexibility and autonomy in their learning. As a result, many universities are transitioning towards offering online courses to cater to this demand. However, while online courses have gained popularity, they come with certain challenges. The dropout rate for online courses tends to be higher compared to traditional in-person classes, and the lack of face-to-face interaction with instructors can lead to delayed guidance and intervention. To address these issues, this study explores the potential of machine learning (ML) techniques in improving students' performance in small-scale online courses. ML has shown remarkable success in various domains, but its application in smaller educational settings with limited data remains a challenge. In this research, the authors demonstrate that data collected from an online learning management system can be effectively utilized to predict students' overall performance and propose timely intervention strategies. The findings of this study reveal that effective intervention strategies can be suggested as early as the middle of the course to positively influence students' progress. By leveraging ML-based approaches and analyzing student data, educators can identify challenging students and provide early interventions tailored to their specific needs. Additionally, based on the outcomes of this research, an assistive pedagogical tool is presented to further support educators in identifying struggling students and suggesting appropriate intervention strategies. Overall, this study highlights the potential of ML-based interventions in enhancing students' performance in small-scale online courses. By utilizing data-driven insights and timely interventions, educators can better support learners throughout their educational journey and improve overall course outcomes.