To address this, in this paper, we propose a novel Reliable Cognitive Diagnosis(ReliCD) framework, which can quantify the confidence of the diagnosis feedback and is flexible for different cognitive diagnostic functions.
Finally, we demonstrate the effectiveness and interpretability of our framework through comprehensive experiments on real-world datasets.
Knowledge tracing (KT) aims to trace students' knowledge states by predicting whether students answer correctly on exercises.
Then, we propose multi-objective genetic programming (MOGP) to explore the NAS task's search space by maximizing model performance and interpretability.
To address the aforementioned issues, we propose a novel separated edge-guidance transformer (SegT) network that aims to build an effective polyp segmentation model.
The teacher model is composed of a heterogeneous graph structure for warm users and items with privileged CF links.
In this paper, we study a novel model-agnostic CAT problem, where we aim to propose a flexible framework that can adapt to different cognitive models.
Moreover, inspired by the human education process that drives the learning from easy to hard concepts, we here propose the CMA training paradigm that first trains a clean detector which is free from the influence of noisy data.
Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e. g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items.