Search Results (2)

Knowledge tracing is a crucial task in personalized learning that models student mastery based on historical data to predict future performance. Currently, deep learning models in knowledge tracing predominantly use one-hot encodings of question, knowledge, and student IDs, showing promising results. However, they face a significant limitation: a vocabulary gap that impedes the processing of new IDs not seen during training. To address this, our paper introduces a novel method that incorporates aggregated features, termed ‘side information’, that captures essential attributes such as student ability, knowledge mastery, and question difficulty. Our approach utilizes side information to bridge the vocabulary gap caused by ID-based one-hot encoding in traditional models. This enables the model, once trained on one dataset, to generalize and make predictions on new datasets with unfamiliar students, knowledge, or questions without the need for retraining. This innovation effectively bridges the vocabulary gap, reduces the dependency on specific data representations, and improves the overall performance of the model. Experimental evaluations on five distinct datasets show that our proposed model consistently outperforms baseline models, using fewer parameters and demonstrating seamless adaptability to new contexts. Additionally, ablation studies highlight that including side information, especially regarding students and questions, significantly improves knowledge tracing effectiveness. In summary, our approach not only resolves the vocabulary gap challenge but also offers a more robust and superior solution across varied datasets. Full article

Conductometric semiconducting metal oxide gas sensors have been widely used and investigated in the detection of gases. Investigations have indicated that the gas sensing process is strongly related to surface reactions, so one of the important parameters of gas sensors, the sensitivity of the metal oxide based materials, will change with the factors influencing the surface reactions, such as chemical components, surface-modification and microstructures of sensing layers, temperature and humidity. In this brief review, attention will be focused on changes of sensitivity of conductometric semiconducting metal oxide gas sensors due to the five factors mentioned above. Full article