Structure-based knowledge tracing
WebGenerally speaking, knowledge tracing aims to observe, represent, and quantify a student’s knowledge state, e.g., the mastery level of skills underlying the teaching materials. To better understand the KT problem, let us consider the learning activity depicted in Figure 1. WebApr 13, 2024 · In recent years, with the development of intelligent tutoring systems, more users choose online education because it is more convenient to provide personalized and high-quality education than traditional classrooms [].Knowledge tracing (KT), which evaluates students’ knowledge mastery based on their performance on coursework, has …
Structure-based knowledge tracing
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WebKnowledge Tracing (KT) refers to the problem of predicting future learner performance given their historical interactions with e-learning platforms. Recent years, Deep Learning- based Knowledge Tracing (DLKT) methods show superior performance than traditional methods due to their strong representational ability. WebJan 1, 2024 · Knowledge tracing is an essential and challenging task in intelligent tutoring systems, whose goal is to estimate students’ knowledge state based on their responses …
WebStructure-based Knowledge Tracing (SKT), which exploits the multiple relations in knowledge structure to model the influence propagation among concepts. In the SKT framework, we consider both the... WebIn this paper, we propose a new framework called Structure-based Knowledge Tracing (SKT), which exploits the multiple relations in knowledge structure to model the influence propagation among concepts. In the SKT framework, we consider both the temporal effect on the exercising sequence and the spatial effect on the knowledge structure.
WebJul 19, 2024 · To improve the performance in prediction, many KT models have been proposed, including Bayesian Knowledge Tracing (BKT) [ 1] and Deep Knowledge Tracing [ 2 ], which are representative models based on traditional methods and … Webhome.ustc.edu.cn
WebNov 24, 2024 · Knowledge tracing (KT) is a fundamental personalized-tutoring technique for learners in online learning systems. Recent KT methods employ flexible deep neural …
http://staff.ustc.edu.cn/~huangzhy/files/papers/ShiweiTong-ICDM2024.pdf#:~:text=Abstract%E2%80%94Knowledge%20Tracing%20%28KT%29%20is%20a%20fundamental%20butchallenging%20task,the%20effectiveness%20and%20interpretabilityof%20SKT%20with%20extensive%20experiments. player adobe flash playerWebABSTRACT. Knowledge tracing is the task of understanding student’s knowledge acquisition processes by estimating whether to solve the next question correctly or not. Most deep learning-based methods tackle this problem by identifying hidden representations of knowledge states from learning histories. However, due to the sparse interactions ... playera dinamoWebABSTRACT. Knowledge tracing, which estimates students' knowledge states by predicting the probability that they correctly answer questions, is an essential task for online learning … primary health nampa caldwell blvdWebOct 14, 2024 · Casting the knowledge structure as a graph enabled us to reformulate the knowledge tracing task as a time-series node-level classification problem in the GNN. As … primary health nampa hoursWebNov 15, 2024 · Graph-based knowledge tracing (GKT) proposed in Nakagawa et al. (2024) uses several ways to cast the knowledge structure as a graph, where nodes correspond to skills and edges correspond to their relationships. These graphs are used as input to the model to predict the students’ responses. player adobe flash free downloadWebInspired by the recent successes of graph neural networks (GNNs), we herein propose a GNN-based knowledge tracing method, i.e., graph-based knowledge tracing. Casting the knowledge structure as a graph enabled us to reformulate the knowledge tracing task as a time-series node-level classification problem in the GNN. player adsWebDec 6, 2024 · A novel model called Dual-Centric Knowledge Tracing (DCKT) is proposed to model knowledge states through two joint tasks of knowledge modeling and learner modeling. In particular, we first generate concept embeddings in abundant knowledge structure information via a pretext task (knowledge-centric): unsupervised graph … playera dry fit negra