21 papers · ranked by Valyu relevance
Ghodai Abdelrahman, Qing Wang, Bernardo Pereira Nunes
Humans' ability to transfer knowledge through teaching is one of the essential aspects for human intelligence. A human teacher can track the knowledge of students to customize the teaching on students' needs. With the rise of online education platforms, there is a similar need for machines to track the knowledge of…
Siddhartha Pradhan, Yanping Pei, Morgan Lee, Puyuan Zhang + 2 more
Bayesian Knowledge Tracing (BKT) is a widely used and interpretable student modeling approach in intelligent tutoring systems and educational data mining. However, most implementations rely on expectation-maximization or related optimization methods that yield only point estimates, limiting uncertainty quantification…
Nicole Salomons, Brian Scassellati
Creating an accurate model of a user’s skills is an essential task for Intelligent Tutoring Systems (ITS) and robotic tutoring systems. This allows the system to provide personalized help based on the user’s knowledge state. Most user skill modeling systems have focused on simpler tasks such as arithmetic or…
Denis Shchepakin, Sreecharan Sankaranarayanan, Dawn Zimmaro
Bayesian Knowledge Tracing (BKT) is a probabilistic model of a learner's state of mastery corresponding to a knowledge component. It considers the learner's state of mastery as a "hidden" or latent binary variable and updates this state based on the observed correctness of the learner's response using parameters that…
Kai Zhang, Zhengchu Qin, Ying Kuang
The knowledge tracing model takes students' learning behaviours data as input to determine their current knowledge status and predict their future answers. The learning behaviours data describes three main types of learning behaviours: learning process, learning end, and learning interval. The classical knowledge…
Sein Minn, Jill-Jênn Vie, Koh Takeuchi, Hisashi Kashima + 1 more
Intelligent Tutoring Systems have become critically important in future learning environments. Knowledge Tracing (KT) is a crucial part of that system. It is about inferring the skill mastery of students and predicting their performance to adjust the curriculum accordingly. Deep Learning-based KT models have shown…
Ailian Gao, Zenglei Liu, Muhammad Anwar
Knowledge tracing can reveal students’ level of knowledge in relation to their learning performance. Recently, plenty of machine learning algorithms have been proposed to exploit to implement knowledge tracing and have achieved promising outcomes. However, most of the previous approaches were unable to cope with long…
Yanhong Bai, Jiabao Zhao, Tingjiang Wei, Qing Cai + 1 more
With the long-term accumulation of high-quality educational data, artificial intelligence (AI) has shown excellent performance in knowledge tracing (KT). However, due to the lack of interpretability and transparency of some algorithms, this approach will result in reduced stakeholder trust and a decreased acceptance of…
Haoxuan Li, Jifan Yu, Yuanxin Ouyang, Zhuang Liu + 3 more
'Juanzi Li' 'Zhang Xiong'] Knowledge tracing (KT), aiming to mine students' mastery of knowledge by their exercise records and predict their performance on future test questions, is a critical task in educational assessment. While researchers achieved tremendous success with the rapid development of deep learning…
Yanjun Pu, Wenjun Wu, Tianhao Peng, Fang Liu + 4 more
'Ruibo Chen' 'Pu Feng'] Recently, deep neural network-based cognitive models such as deep knowledge tracing have been introduced into the field of learning analytics and educational data mining. Despite an accurate predictive performance of such models, it is challenging to interpret their behaviors and obtain an…
Jianjun Wang, Qianjun Tang, Zongliang Zheng, Hui Li
Knowledge tracing models predict students’ mastery of specific knowledge points by analyzing their historical learning performance. However, existing methods struggle with handling a large number of skills, data sparsity, learning differences, and complex skill correlations. To address these issues, we propose a…
Feng Xu, Kang Chen, Maosheng Zhong, Lei Liu + 4 more
'Xianzeng Luo' 'Lang Zheng' 'Mohammad Amin Fraiwan'] Knowledge tracing is a technology that models students’ changing knowledge state over learning time based on their historical answer records, thus predicting their learning ability. It is the core module that supports the intelligent education system. To address the…
Zhijie Liang, Ruixia Wu, Zhao Liang, Juan Yang + 3 more
'Jianyu Su' 'Xiangjie Kong'] The development of intelligent education has led to the emergence of knowledge tracing as a fundamental task in the learning process. Traditionally, the knowledge state of each student has been determined by assessing their performance in previous learning activities. In recent years, Deep…
Hang Su, Xin Liu, Shanghui Yang, Xuesong Lu
Knowledge tracing (KT) models students' mastery level of knowledge concepts based on their responses to the questions in the past and predicts the probability that they correctly answer subsequent questions in the future. Recent KT models are mostly developed with deep neural networks and have demonstrated superior…
Sophie Seidel, Antoine Zwaans, Samuel Regalado, Junhong Choi + 2 more
CRISPR-based lineage tracing offers a promising avenue to decipher single cell lineage trees, especially in organisms that are challenging for microscopy. A recent advancement in this domain is lineage tracing based on sequential genome editing, which not only records genetic edits but also the order in which they…
Chin-Hsuan Sophie Lin, Trang Thuy Do, Lee Unsworth, Marta I. Garrido
Numerous studies have found that the Bayesian framework, which formulates the optimal integration of the knowledge of the world (i.e. prior) and current sensory evidence (i.e. likelihood), captures human behaviours sufficiently well. However, there are debates regarding whether humans use precise but cognitively…
John Schwarcz, Jan Bauer, Haneen Rajabi, Gabrielle Marmur + 2 more
What seems obvious in one context can take on an entirely different meaning if that context shifts. While context-dependent inference has been widely studied, a fundamental question remains: how does the brain simultaneously infer both the meaning of sensory input and the underlying context itself, especially when the…
Brandon S Coventry, Edward L Bartlett
Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian…
Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, Kevin De Ras + 3 more
By combining machine learning with design of experiments, so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible, and are better at investigating the processes spanning all length scales of chemical engineering. While the active machine…
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Incorporating prior domain knowledge into Bayesian optimization (BO) remains difficult for statistical methods, which also typically suffer from limited interpretability. Large language models (LLMs) offer complementary strengths in reasoning and knowledge integration, but it remains unclear when and how they improve…
Ramsey Issa, Robert Sorenson, Taylor D. Sparks
The discovery of new dental materials is typically a slow process due to high-dimensionality of the formulation space as well as the multiple competing objectives which must be optimized for a given application. Here, we lay out a strategy using active learning and Bayesian optimization that has led to the discovery of…