24 papers · ranked by Valyu relevance
Chris Piech, Jonathan Spencer, Jonathan Huang, Surya Ganguli + 3 more
'Mehran Sahami' 'Leonidas Guibas' 'Jascha Sohl‐Dickstein'] Knowledge tracing—where a machine models the knowledge of a student as they interact with coursework—is a well established problem in computer supported education. Though effectively modeling student knowledge would have high educational impact, the task has…
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…
Qi Liu, Shuanghong Shen, Zhenya Huang, Enhong Chen + 1 more
—High-quality education is one of the keys to achieving a more sustainable world. In contrast to traditional face-to-face classroom education, online education enables us to record and research a large amount of learning data for offering intelligent educational services. Knowledge Tracing (KT), which aims to monitor…
Anirudhan Badrinath, Frédéric Wang, Zachary A. Pardos
Bayesian Knowledge Tracing, a model used for cognitive mastery estimation, has been a hallmark of adaptive learning research and an integral component of deployed intelligent tutoring systems (ITS). In this paper, we provide a brief history of knowledge tracing model research and introduce pyBKT, an accessible and…
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…
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…
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…
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…
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…
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…
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…
Balázs Török, Dávid G. Nagy, Mariann M. Kiss, Karolina Janacsek + 2 more
Internal models capture the regularities of the environment and are central to understanding how humans adapt to environmental statistics. In general, the correct internal model is unknown to observers, instead approximate and transient ones are recruited. However, experimenters assume an ideal observer model, which…
Reneta Kiryakova, Stacey Aston, Ulrik Beierholm, Marko Nardini
Prior knowledge can help observers in various situations. Adults can simultaneously learn two location priors and integrate these with sensory information to locate hidden objects. Importantly, observers weight prior and sensory (likelihood) information differently depending on their respective reliabilities, in line…
Shuji Shinohara, Nobuhito Manome, Kouta Suzuki, Ung-il Chung + 5 more
Bayesian inference is a process of narrowing down hypotheses (causes) to one that best explains observational data (effects). To accurately estimate a cause, a considerable amount of data is required to be observed for as long as possible. However, the object of inference is not always constant. In this case, a method…
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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…
Martin Robinson, Alan Bond, Alexandr Simonov, Jie Zhang + 1 more
Recently, we have introduced the use of techniques drawn from Bayesian statistics to recover kinetic and thermodynamic parameters from voltammetric data, and were able to show that the technique of large amplitude ac voltammetry yielded significantly more accurate parameter values than the equivalent dc approach. In…
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…
Joshua Hesse, Davide Boldini, Stephan Sieber
In the rapidly evolving field of drug discovery, High Throughput Screening (HTS) is a pivotal technique for identifying promising compounds. Despite its wide usage, the primary challenge remains in efficiently sifting through vast chemical libraries to discern true bioactive compounds from false positives. This study…