Search Results for author:
Found 14 papers, 7 papers with code
Diffusion-Inspired Cold Start with Sufficient Prior in Computerized Adaptive Testing
Redundant and extraneous cognitive states can lead to limited transfer and negative transfer effects.
DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis
To tackle the issues, this paper suggests a meta multigraph-assisted disentangled graph learning framework for CD (DisenGCD), which learns three types of representations on three disentangled graphs: student-exercise-concept interaction, exercise-concept relation, and concept dependency graphs, respectively.
DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendation
To this end, in this paper, we propose DISCO, a hierarchical Disentanglement based Cognitive diagnosis framework, aimed at flexibly accommodating the underlying representation learning model for effective and interpretable job recommendations.
RIGL: A Unified Reciprocal Approach for Tracing the Independent and Group Learning Processes
To this end, in this paper, we propose RIGL, a unified Reciprocal model to trace knowledge states at both the individual and group levels, drawing from the Independent and Group Learning processes.
Survey of Computerized Adaptive Testing: A Machine Learning Perspective
Computerized Adaptive Testing (CAT) provides an efficient and tailored method for assessing the proficiency of examinees, by dynamically adjusting test questions based on their performance.
ReliCD: A Reliable Cognitive Diagnosis Framework with Confidence Awareness
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.
Enhancing Cognitive Diagnosis using Un-interacted Exercises: A Collaboration-aware Mixed Sampling Approach
Finally, we demonstrate the effectiveness and interpretability of our framework through comprehensive experiments on real-world datasets.
Evolutionary Neural Architecture Search for Transformer in Knowledge Tracing
Knowledge tracing (KT) aims to trace students' knowledge states by predicting whether students answer correctly on exercises.
Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search
Then, we propose multi-objective genetic programming (MOGP) to explore the NAS task's search space by maximizing model performance and interpretability.
SegT: A Novel Separated Edge-guidance Transformer Network for Polyp Segmentation
To address the aforementioned issues, we propose a novel separated edge-guidance transformer (SegT) network that aims to build an effective polyp segmentation model.
Privileged Graph Distillation for Cold Start Recommendation
The teacher model is composed of a heterogeneous graph structure for warm users and items with privileged CF links.
Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing
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.
SWIPENET: Object detection in noisy underwater images
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.
Exploiting Cognitive Structure for Adaptive Learning
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.
