CategoriesJournal article

From the Automated Assessment of Student Essay Content to Highly Informative Feedback: a Case Study

How can we provide students with highly informative feedback on their essays using natural language processing?

Check out our new paper, led by Sebastian Gombert, where we present a case study on using GBERT and T5 models to generate feedback for educational psychology students.

In this paper:

➡ We implemented a two-step pipeline that segments the essays and predicts codes from the segments. The codes are used to generate feedback texts that inform the students about the correctness of their solutions and the content areas they need to improve.

➡ We used 689 manually labelled essays as training data for our models. We compared GBERT, T5, and bag-of-words baselines for scoring the segments and the codes. The results showed that the transformer-based models outperformed the baselines in both steps.

➡ We evaluated the feedback using a randomised controlled trial. The control group received essential feedback, while the treatment group received highly informative feedback based on our pipeline. We used a six-item survey to measure the perception of feedback.

➡ We found that highly informative feedback had positive effects on helpfulness and reflection. The students in the treatment group reported higher levels of satisfaction, usefulness, and learning than the students in the control group.

➡ Our paper demonstrates the potential of natural language processing for providing highly informative feedback on student essays. We hope that our work will inspire more research and practice in this area.

You can read the full paper here.

CategoriesJournal article

How to improve Knowledge Tracing with hybrid machine learning techniques


Knowledge Tracing is a well-known problem in AI for Education. It consists of monitoring how the student's knowledge changes during the learning process and accurately predicting their performance in future exercises. But how can we improve the current methods and overcome heir limitations?

In recent years, many advances have been made thanks to various machine learning and deep learning techniques. However, they have some pitfalls, such as modelling one skill at a time, ignoring the relationships between different skills, or inconsistent predictions, i.e. sudden spikes and falls across time steps.

In our recently published systematic literature review, we aim to illustrate the state of the art in this field. Specifically, we want to identify the potential and the frontiers in integrating prior knowledge sources in the traditional machine learning pipeline to supplement the normally considered data. We propose a taxonomy with three dimensions: knowledge source, knowledge representation, and knowledge integration. We also conduct a quantitative analysis to detect the most common approaches and their advantages and disadvantages.

Our work provides a comprehensive overview of the hybrid machine-learning techniques for Knowledge Tracing and highlights the benefits of incorporating prior knowledge sources in the learning process. We believe this can lead to more accurate and robust predictions of student performance and help design more effective and personalized learning interventions. However, we also acknowledge that many challenges and open questions still need to be addressed, such as how to select the most relevant and reliable knowledge sources, how to represent and integrate them in a meaningful way, and how to evaluate their impact on the learning outcomes.

We hope that our work can inspire more research and innovation in the field of Knowledge Tracing and AI for Education.

Zanellati, A., Di Mitri, D., Gabbrielli, M., & Levrini, O. (2023). Hybrid Models for Knowledge Tracing: A Systematic Literature Review. IEEE Transactions on Learning Technologies, 1–16. doi: 10.1109/TLT.2023.3348690