# Meta Transfer Learning for Early Success Prediction in MOOCs

arXiv: https://arxiv.org/abs/2205.01064 · HTML: https://mlsanity.com/paper/2205.01064/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 45 references that are in the map.
- First posted on arXiv 25 Apr 2022.

## Builds on (most cited first, 11 of 11)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Meta-Transfer Learning for Few-Shot Learning](https://mlsanity.com/paper/1812.02391/) (2018)
- [SpotTune: Transfer Learning through Adaptive Fine-tuning](https://mlsanity.com/paper/1811.08737/) (2018)
- [Meta-Transfer Learning for Code-Switched Speech Recognition](https://mlsanity.com/paper/2004.14228/) (2020)
- [Next-Term Student Performance Prediction: A Recommender Systems Approach](https://mlsanity.com/paper/1604.01840/) (2016)
- [Transfer Learning using Representation Learning in Massive Open Online Courses](https://mlsanity.com/paper/1812.05043/) (2018)
- [Delving Deeper into MOOC Student Dropout Prediction](https://mlsanity.com/paper/1702.06404/) (2017)

## Built on by (most cited first, 7 of 7)

- [MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks](https://mlsanity.com/paper/2309.14118/) (2023)
- [Intrinsic User-Centric Interpretability through Global Mixture of Experts](https://mlsanity.com/paper/2402.02933/) (2024)
- [RIPPLE: Concept-Based Interpretation for Raw Time Series Models in Education](https://mlsanity.com/paper/2212.01133/) (2022)
- [Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course Design](https://mlsanity.com/paper/2212.08955/) (2022)
- [Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection](https://mlsanity.com/paper/2407.11979/) (2024)
- [Do Not Trust a Model Because It is Confident: Uncovering and Characterizing Unknown Unknowns to Student Success Predictors in Online-Based Learning](https://mlsanity.com/paper/2212.08532/) (2022)
- [Scalable and Explainable Learner-Video Interaction Prediction using Multimodal Large Language Models](https://mlsanity.com/paper/2604.04482/) (2026)

## Abstract (arXiv)

Despite the increasing popularity of massive open online courses (MOOCs), many suffer from high dropout and low success rates. Early prediction of student success for targeted intervention is therefore essential to ensure no student is left behind in a course. There exists a large body of research in success prediction for MOOCs, focusing mainly on training models from scratch for individual courses. This setting is impractical in early success prediction as the performance of a student is only known at the end of the course. In this paper, we aim to create early success prediction models that can be transferred between MOOCs from different domains and topics. To do so, we present three novel strategies for transfer: 1) pre-training a model on a large set of diverse courses, 2) leveraging the pre-trained model by including meta information about courses, and 3) fine-tuning the model on previous course iterations. Our experiments on 26 MOOCs with over 145,000 combined enrollments and millions of interactions show that models combining interaction data and course information have comparable or better performance than models which have access to previous iterations of the course. With these models, we aim to effectively enable educators to warm-start their predictions for new and ongoing courses.