1 code implementation • 31 Mar 2024 • Qi Liu, Yan Zhuang, Haoyang Bi, Zhenya Huang, Weizhe Huang, Jiatong Li, Junhao Yu, Zirui Liu, Zirui Hu, Yuting Hong, Zachary A. Pardos, Haiping Ma, Mengxiao Zhu, Shijin Wang, Enhong Chen
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.
no code implementations • 29 Dec 2023 • Yunfei Zhang, Chuan Qin, Dazhong Shen, Haiping Ma, Le Zhang, Xingyi Zhang, HengShu Zhu
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.
no code implementations • 15 Dec 2023 • Haiping Ma, Changqian Wang, HengShu Zhu, Shangshang Yang, XiaoMing Zhang, Xingyi Zhang
Finally, we demonstrate the effectiveness and interpretability of our framework through comprehensive experiments on real-world datasets.
1 code implementation • NeurIPS 2023 • Shangshang Yang, Xiaoshan Yu, Ye Tian, Xueming Yan, Haiping Ma, Xingyi Zhang
Knowledge tracing (KT) aims to trace students' knowledge states by predicting whether students answer correctly on exercises.
1 code implementation • 10 Jul 2023 • Shangshang Yang, Haiping Ma, Cheng Zhen, Ye Tian, Limiao Zhang, Yaochu Jin, Xingyi Zhang
Then, we propose multi-objective genetic programming (MOGP) to explore the NAS task's search space by maximizing model performance and interpretability.
no code implementations • 19 Jun 2023 • Feiyu Chen, Haiping Ma, Weijia Zhang
To address the aforementioned issues, we propose a novel separated edge-guidance transformer (SegT) network that aims to build an effective polyp segmentation model.
no code implementations • 31 May 2021 • Shuai Wang, Kun Zhang, Le Wu, Haiping Ma, Richang Hong, Meng Wang
The teacher model is composed of a heterogeneous graph structure for warm users and items with privileged CF links.
no code implementations • 15 Jan 2021 • Haoyang Bi, Haiping Ma, Zhenya Huang, Yu Yin, Qi Liu, Enhong Chen, Yu Su, Shijin Wang
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.
no code implementations • 19 Oct 2020 • Long Chen, Feixiang Zhou, Shengke Wang, Junyu Dong, Ning li, Haiping Ma, Xin Wang, Huiyu Zhou
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.
no code implementations • 23 May 2019 • Qi Liu, Shiwei Tong, Chuanren Liu, Hongke Zhao, Enhong Chen, Haiping Ma, Shijin Wang
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.