This paper focuses on "tracing player knowledge" in educational games. Specifically, given a set of concepts or skills required to master a game, the goal is to estimate the likelihood with which the current player has mastery of each of those concepts or skills. The main contribution of the paper is an approach that integrates machine learning and domain knowledge rules to find when the player applied a certain skill and either succeeded or failed. This is then given as input to a standard knowledge tracing module (such as those from Intelligent Tutoring Systems) to perform knowledge tracing. We evaluate our approach in the context of an educational game called "Parallel" to teach parallel and concurrent programming with data collected from real users, showing our approach can predict students skills with a low mean-squared error.
Recently, we have seen a rapid rise in usage of online educational platforms. The personalized education became crucially important in future learning environments. Knowledge tracing (KT) refers to the detection of students' knowledge states and predict future performance given their past outcomes for providing adaptive solution to Intelligent Tutoring Systems (ITS). Bayesian Knowledge Tracing (BKT) is a model to capture mastery level of each skill with psychologically meaningful parameters and widely used in successful tutoring systems. However, it is unable to detect learning transfer across…
Jinan University（暨南大学）；Guangdong Institute of Smart Education（广东省智慧教育研究院）；TAL Education Group（好未来教育集团）；Think Academy International Education（学而思国际教育）
Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems. Recent studies have applied multiple types of deep neural networks to solve the KT problem. However, there are two important factors in real-world educational data that are not well represented. First, most existing works augment input representations with the co-occurrence matrix of questions and knowledge components\footnote{\label{ft:kc}A KC is a generalization of everyday terms like concept, principle, fact, or skill.} (KCs) but fail to …
The recent advances in computer-assisted learning systems and the availability of open educational resources today promise a pathway to providing cost-efficient, high-quality education to large masses of learners. One of the most ambitious use cases of computer-assisted learning is to build a lifelong learning recommendation system. Unlike short-term courses, lifelong learning presents unique challenges, requiring sophisticated recommendation models that account for a wide range of factors such as background knowledge of learners or novelty of the material while effectively maintaining knowled…
Can machines trace human knowledge like humans? Knowledge tracing (KT) is a fundamental task in a wide range of applications in education, such as massive open online courses (MOOCs), intelligent tutoring systems, educational games, and learning management systems. It models dynamics in a student's knowledge states in relation to different learning concepts through their interactions with learning activities. Recently, several attempts have been made to use deep learning models for tackling the KT problem. Although these deep learning models have shown promising results, they have limitations:…
Neural-Symbolic Knowledge Tracing: Injecting Educational Knowledge into Deep Learning for Responsible Learner Modelling
Tallinn University（塔林大学）；University of Jyväskylä（于韦斯屈莱大学）；Czech Technical University（捷克技术大学）；Kangwon National University（江原大学）；University College London（伦敦大学学院）；The University of Hong Kong（香港大学）；Monash University（莫纳什大学）；University of Central Florida（中佛罗里达大学）
Korea University（高丽大学）；Seoul National University（首尔国立大学）；Ewha Womans University（梨花女子大学）
Knowledge tracing (KT) is a field of study that predicts the future performance of students based on prior performance datasets collected from educational applications such as intelligent tutoring systems, learning management systems, and online courses. Some previous studies on KT have concentrated only on the interpretability of the model, whereas others have focused on enhancing the performance. Models that consider both interpretability and the performance improvement have been insufficient. Moreover, models that focus on performance improvements have not shown an overwhelming performance …
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 significant predictive performance compared with traditional models. However, it is difficult to extract psychologically meaningful explanations from the tens of thousands of parameters in neural networks, that would relate to cognitive theory. There are several ways to achieve high …
Future-Proofing Programmers: Optimal Knowledge Tracing for AI-Assisted Personalized Education
College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算机与数据科学学院）；Department of Applied Mathematics, Chung Yuan Christian University, Taiwan（Chung Yuan Christian University 应用数学系）
Learning Management Systems (LMS) and Educational Data Mining (EDM) are two important parts of online educational environment with the former being a centralised web-based information systems where the learning content is managed and learning activities are organised (Stone and Zheng,2014) and latter focusing on using data mining techniques for the analysis of data so generated. As part of this work, we present a literature review of three major tasks of EDM (See section 2), by identifying shortcomings and existing open problems, and a Blumenfield chart (See section 3). The consolidated set of…
Knowledge tracing is a fundamental task in the computer-aid educational system. In this paper, we propose a hierarchical exercise feature enhanced knowledge tracing framework, which could enhance the ability of knowledge tracing by incorporating knowledge distribution, semantic features, and difficulty features from exercise text. Extensive experiments show the high performance of our framework.
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Worcester Polytechnic Institute（伍斯特理工学院）
Knowledge tracing (KT) refers to the problem of predicting future learner performance given their past performance in educational applications. Recent developments in KT using flexible deep neural network-based models excel at this task. However, these models often offer limited interpretability, thus making them insufficient for personalized learning, which requires using interpretable feedback and actionable recommendations to help learners achieve better learning outcomes. In this paper, we propose attentive knowledge tracing (AKT), which couples flexible attention-based neural network mode…
AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；School of Information Technology & Management, University of International Business and Economics（对外经济贸易大学信息技术与管理学院）
TAL Education Group（好未来教育集团）；Guangdong Institute of Smart Education, Jinan University（暨南大学广东智慧教育研究院）
Knowledge tracing (KT) is a crucial technique to predict students' future performance by observing their historical learning processes. Due to the powerful representation ability of deep neural networks, remarkable progress has been made by using deep learning techniques to solve the KT problem. The majority of existing approaches rely on the \emph{homogeneous question} assumption that questions have equivalent contributions if they share the same set of knowledge components. Unfortunately, this assumption is inaccurate in real-world educational scenarios. Furthermore, it is very challenging t…
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 many inherent challenges. In this paper we explore the utility of using Recurrent Neural Networks (RNNs) to model student learning. The RNN family of models have important advantages over previous methods in that they do not require the explicit encoding of human domain knowledge, and can capture more complex representations of student…
Towards Robust Knowledge Tracing Models via k-Sparse Attention
Think Academy International Education（学而思国际教育）；TAL Education Group（好未来教育集团）；Guangdong Institute of Smart Education（广东省智慧教育研究院）；Jinan University（暨南大学）；City University of Hong Kong（香港城市大学）；College of Cyber Security（网络空间安全学院）
University of Science and Technology of China（中国科学技术大学）
With the rapid development of online education system, knowledge tracing which aims at predicting students' knowledge state is becoming a critical and fundamental task in personalized education. Traditionally, existing methods are domain-specified. However, there are a larger number of domains (e.g., subjects, schools) in the real world and the lacking of data in some domains, how to utilize the knowledge and information in other domains to help train a knowledge tracing model for target domains is increasingly important. We refer to this problem as domain adaptation for knowledge tracing (DAK…
RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；Kyoto University（京都大学）
Knowledge tracing is a sequence prediction problem where the goal is to predict the outcomes of students over questions as they are interacting with a learning platform. By tracking the evolution of the knowledge of some student, one can optimize instruction. Existing methods are either based on temporal latent variable models, or factor analysis with temporal features. We here show that factorization machines (FMs), a model for regression or classification, encompasses several existing models in the educational literature as special cases, notably additive factor model, performance factor mod…
Rice University（莱斯大学）；OpenStax（OpenStax（开放教材组织））；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
Knowledge tracing (KT) models, e.g., the deep knowledge tracing (DKT) model, track an individual learner's acquisition of skills over time by examining the learner's performance on questions related to those skills. A practical limitation in most existing KT models is that all questions nested under a particular skill are treated as equivalent observations of a learner's ability, which is an inaccurate assumption in real-world educational scenarios. To overcome this limitation we introduce qDKT, a variant of DKT that models every learner's success probability on individual questions over time.…
Microsoft Corporation（微软公司）；AIXUEXI Education Group Ltd（爱学习教育集团有限公司）；University of Science and Technology of China（中国科学技术大学）
Knowledge tracing (KT) which aims at predicting learner's knowledge mastery plays an important role in the computer-aided educational system. In recent years, many deep learning models have been applied to tackle the KT task, which have shown promising results. However, limitations still exist. Most existing methods simplify the exercising records as knowledge sequences, which fail to explore rich information that existed in exercises. Besides, the existing diagnosis results of knowledge tracing are not convincing enough since they neglect prior relations between exercises. To solve the above …
In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, general-purpose models whose parameters and representations are difficult to interpret. The former typically provide more insight into cognition but the latter often perform better. This tension has recently surfaced in the realm of educational data mining, where a deep learning approach to predicting students' performance as they work through a series of exercises---termed deep knowledge tracing or DKT---has demonstrated a stunni…
KT4EQG: Personalized Exercise Question Generation via Knowledge Tracing
University of California, Santa Barbara（加州大学圣巴巴拉分校）；University of South Alabama（南方大学）；University of Michigan（密歇根大学）；MIT-IBM Watson AI Lab（麻省理工-IBM沃森人工智能实验室）；Harvard University（哈佛大学）
MERIT: Memory-Enhanced Retrieval for Interpretable Knowledge Tracing
School of Computer Science and Technology, East China Normal University（东华师范大学计算机科学与技术学院）；Shanghai Innovation Institute（上海创新研究院）；WeChat AI, Tencent（微信AI，腾讯）
University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；The University of Alabama（阿拉巴马大学）
Interactive Educational Systems (IES) enabled researchers to trace student knowledge in different skills and provide recommendations for a better learning path. To estimate the student knowledge and further predict their future performance, the interest in utilizing the student interaction data captured by IES to develop learner performance models is increasing rapidly. Moreover, with the advances in computing systems, the amount of data captured by these IES systems is also increasing that enables deep learning models to compete with traditional logistic models and Markov processes. However, …
In educational applications, Knowledge Tracing (KT), the problem of accurately predicting students' responses to future questions by summarizing their knowledge states, has been widely studied for decades as it is considered a fundamental task towards adaptive online learning. Among all the proposed KT methods, Deep Knowledge Tracing (DKT) and its variants are by far the most effective ones due to the high flexibility of the neural network. However, DKT often ignores the inherent differences between students (e.g. memory skills, reasoning skills, ...), averaging the performances of all student…
Hong Kong University of Science and Technology（香港科技大学）
The 2017 ASSISTments Data Mining competition aims to use data from a longitudinal study for predicting a brand-new outcome of students which had never been studied before by the educational data mining research community. Specifically, it facilitates research in developing predictive models that predict whether the first job of a student out of college belongs to a STEM (the acronym for science, technology, engineering, and mathematics) field. This is based on the student's learning history on the ASSISTments blended learning platform in the form of extensive clickstream data gathered during t…
Generative Data Imputation for Sparse Learner Performance Data Using Generative Adversarial Imputation Networks
Institute for Intelligent Systems, University of Memphis（孟菲斯大学智能系统研究所）；Faculty of Education, The University of Hong Kong（香港大学教育学院）；Human-Computer Interaction Institute, Carnegie Mellon University（卡内基梅隆大学人机交互研究所）；Centre for Learning Analytics, Faculty of Information Technology, Monash University（莫纳什大学信息技术学院学习分析中心）；Educational Testing Service（教育考试服务中心）；Department of Psychology and Counseling, Arkansas State University（阿肯色州立大学心理学与咨询系）；Department of Applied Social Sciences, Hong Kong Polytechnic University（香港理工大学应用社会科学系）
Guangdong Institute of Smart Education（广东省智慧教育研究院）；Jinan University（暨南大学）；Think Academy International Education（学而思国际教育）；TAL Education Group（好未来教育集团）；Michigan State University（密歇根州立大学）
Knowledge tracing (KT) is the task of using students' historical learning interaction data to model their knowledge mastery over time so as to make predictions on their future interaction performance. Recently, remarkable progress has been made of using various deep learning techniques to solve the KT problem. However, the success behind deep learning based knowledge tracing (DLKT) approaches is still left somewhat unknown and proper measurement and analysis of these DLKT approaches remain a challenge. First, data preprocessing procedures in existing works are often private and custom, which l…
DASKT: A Dynamic Affect Simulation Method for Knowledge Tracing
University of Science and Technology of China（中国科学技术大学）；School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；Liupanshui normal university（六盘水师范学院）
