Predicting student performance is a fundamental task in Intelligent Tutoring Systems (ITSs), by which we can learn about students' knowledge level and provide personalized teaching strategies for them. Researchers have made plenty of efforts on this task. They either leverage educational psychology methods to predict students' scores according to the learned knowledge proficiency, or make full use of Collaborative Filtering (CF) models to represent latent factors of students and exercises. However, most of these methods either neglect the exercise-specific characteristics (e.g., exercise materials), or cannot fully explore the high-order interactions between students, exercises, as well as knowledge concepts. To this end, we propose a Graph-based Exercise- and Knowledge-Aware Learning Network for accurate student score prediction. Specifically, we learn students' mastery of exercises and knowledge concepts respectively to model the two-fold effects of exercises and knowledge concepts. Then, to model the high-order interactions, we apply graph convolution techniques in the prediction process. Extensive experiments on two real-world datasets prove the effectiveness of our proposed Graph-EKLN.
TAL Education Group（好未来教育集团）；The University of Memphis（孟菲斯大学）；Guangdong Institute of Smart Education, Jinan University（暨南大学广东智慧教育研究院）
We propose a simple but effective method to recommend exercises with high quality and diversity for students. Our method is made up of three key components: (1) candidate generation module; (2) diversity-promoting module; and (3) scope restriction module. The proposed method improves the overall recommendation performance in terms of recall, and increases the diversity of the recommended candidates by 0.81\% compared to the baselines.
Korbit Technologies Inc.（Korbit科技公司）；University of Bath（巴斯大学）
We present an adaptive learning Intelligent Tutoring System, which uses model-based reinforcement learning in the form of contextual bandits to assign learning activities to students. The model is trained on the trajectories of thousands of students in order to maximize their exercise completion rates and continues to learn online, automatically adjusting itself to new activities. A randomized controlled trial with students shows that our model leads to superior completion rates and significantly improved student engagement when compared to other approaches. Our approach is fully-automated unl…
Detecting Unsuccessful Students in Cybersecurity Exercises in Two Different Learning Environments
University of Pennsylvania（宾夕法尼亚大学）；Masaryk University（马萨里克大学）；Georgia Institute of Technology（佐治亚理工学院）；The Evergreen State College（常青州立学院）；Lewis & Clark College（刘易斯与克拉克学院）；Northern Kentucky University（北肯塔基大学）
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）
Shanghai Jiao Tong University（上海交通大学）；University of Illinois, Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
With the rapid development in online education, knowledge tracing (KT) has become a fundamental problem which traces students' knowledge status and predicts their performance on new questions. Questions are often numerous in online education systems, and are always associated with much fewer skills. However, the previous literature fails to involve question information together with high-order question-skill correlations, which is mostly limited by data sparsity and multi-skill problems. From the model perspective, previous models can hardly capture the long-term dependency of student exercise…
Australian National University（澳大利亚国立大学）；University of New South Wales（新南威尔士大学）
Online education has gained an increasing importance over the last decade for providing affordable high-quality education to students worldwide. This has been further magnified during the global pandemic as more students switched to study online. The majority of online education tasks, e.g., course recommendation, exercise recommendation, or automated evaluation, depends on tracking students' knowledge progress. This is known as the \emph{Knowledge Tracing} problem in the literature. Addressing this problem requires collecting student evaluation data that can reflect their knowledge evolution …
Zurich University of Applied Sciences（苏黎世应用科学大学）；Kikodo Education Technology Sàrl（基科多教育技术有限公司）
We developed a simulator to quantify the effect of exercise ordering on both student engagement and retention. Our approach combines the construction of neural network representations for users and exercises using a dynamic matrix factorization method. We further created a machine learning models of success and dropout prediction. As a result, our system is able to predict student engagement and retention based on a given sequence of exercises selected. This opens the door to the development of versatile reinforcement learning agents which can substitute the role of private tutoring in exam pr…
Knowledge tracing aims to track students' knowledge status over time to predict students' future performance accurately. Markov chain-based knowledge tracking (MCKT) models can track knowledge concept mastery probability over time. However, as the number of tracked knowledge concepts increases, the time complexity of MCKT predicting student performance increases exponentially (also called explaining away problem. In addition, the existing MCKT models only consider the relationship between students' knowledge status and problems when modeling students' responses but ignore the relationship betw…
The world has transitioned into a new phase of online learning in response to the recent Covid19 pandemic. Now more than ever, it has become paramount to push the limits of online learning in every manner to keep flourishing the education system. One crucial component of online learning is Knowledge Tracing (KT). The aim of KT is to model student's knowledge level based on their answers to a sequence of exercises referred as interactions. Students acquire their skills while solving exercises and each such interaction has a distinct impact on student ability to solve a future exercise. This \te…
Shandong University of Finance and Economics（山东财经大学）；Shandong Jianzhu University（山东建筑大学）；Shandong University（山东大学）；Hong Kong Polytechnic University（香港理工大学）；Education University of Hong Kong（香港教育大学）
Knowledge tracing aims to trace students' evolving knowledge states by predicting their future performance on concept-related exercises. Recently, some graph-based models have been developed to incorporate the relationships between exercises to improve knowledge tracing, but only a single type of relationship information is generally explored. In this paper, we present a novel Dual Graph Ensemble learning method for Knowledge Tracing (DGEKT), which establishes a dual graph structure of students' learning interactions to capture the heterogeneous exercise-concept associations and interaction tr…
We describe two systems for supporting beginner students in acquiring basic skills in expressing statements in the formalism of first-order predicate logic; the first, called "math dictations", presents users with the task of formalizing a given natural-language sentence, while the second, called "Game of Def", challenges users to give a formal description of a set of a geometric pattern displayed to them. In both cases, an automatic checking takes place.
The potential of large language models for improving probability learning: A study on ChatGPT3.5 and first-year computer engineering students
Rey Juan Carlos University（胡安·卡洛斯国王大学）；European Commission, Joint Research Centre（欧盟委员会联合研究中心）；CETINIA: Research Centre for Intelligent Information Technologies（CETINIA智能信息技术研究中心）
This article describes an exemplary robot exercise which was conducted in a class for mechatronics students. The goal of this exercise was to engage students in scientific thinking and reasoning, activities which do not always play an important role in their curriculum. The robotic platform presented here is simple in its construction and is customizable to the needs of the teacher. Therefore, it can be used for exercises in many different fields of science, not necessarily related to robotics. Here we present a situation where the robot is used like an alien creature from which we want to und…
Guangzhou University（广州大学）；School of Computer Science and Cyber Engineering（计算机与网络工程学院）；School of Education（教育学院）
Through the combination of crowdsourcing knowledge graph and teaching system, research methods to generate knowledge graph and its applications. Using two crowdsourcing approaches, crowdsourcing task distribution and reverse captcha generation, to construct knowledge graph in the field of teaching system. Generating a complete hierarchical knowledge graph of the teaching domain by nodes of school, student, teacher, course, knowledge point and exercise type. The knowledge graph constructed in a crowdsourcing manner requires many users to participate collaboratively with fully consideration of t…
A Spatial-Temporal Transformer based Framework For Human Pose Assessment And Correction in Education Scenarios
Fudan-Xiding AI Joint LAB（复旦-西鼎人工智能联合实验室）；Shangshai Xiding AI Research Center Co., Ltd（上海西鼎人工智能研究中心有限公司）；R&D Center Shangshai Xiding AI Research Center Co., Ltd（上海西鼎人工智能研究中心有限公司研发中心）；School of Information Science and Technology Fudan University（复旦大学信息科学与技术学院）
Heilongjiang Institute of Technology（黑龙江工程学院）；Central China Normal University（华中师范大学）
Knowledge tracing (KT) serves as a primary part of intelligent education systems. Most current KTs either rely on expert judgments or only exploit a single network structure, which affects the full expression of learning features. To adequately mine features of students' learning process, Deep Knowledge Tracing Based on Spatial and Temporal Deep Representation Learning for Learning Performance Prediction (DKT-STDRL) is proposed in this paper. DKT-STDRL extracts spatial features from students' learning history sequence, and then further extracts temporal features to extract deeper hidden inform…
Beyond ID Embeddings: Process-Grounded Language Modeling for Cognitive Diagnosis
State Key Laboratory of AI Safety, Institute of Computing Technology, CAS（中国科学院计算技术研究所人工智能安全国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
