42 skills found · Page 1 of 2
pykt-team / Pykt ToolkitpyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models
bigdata-ustc / EduKTMThe Model Zoo of Knowledge Tracing Models
hcnoh / Knowledge Tracing Collection PytorchA collection of Knowledge Tracing model implementations with PyTorch
jhljx / GKTGraph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network
seewoo5 / KTKnowledge Tracing Models with PyTorch
ApexEDM / GIKTGIKT: A Graph-based Interaction Model for Knowledge Tracing
ZhijieXiong / PyedmineA library of algorithms for reproducing knowledge tracing, cognitive diagnosis, exercise recommendation and learning path recommendation models.
shivanandmn / SAINT Plus Knowledge Tracing Implementation of [SAINT+: Integrating Temporal Features for EdNet Correctness Prediction](https://arxiv.org/abs/2010.12042)
AiFangzhe / Exercise Recommendation Systemwe build a student simulator with our concept-aware deep knowledge tracing model, and then use it to train a flexible and scalable personalized exercise recommendation policy with deep reinforcement learning
arshadshk / SAKT PytorchImplementation of paper "A Self-Attentive model for Knowledge Tracing"
lingochamp / Tensorflow DktBuild DKT (Deep Knowledge Tracing) model with TensorFlow
ekinakyurek / InfluenceCode for "Tracing Knowledge in Language Models Back to the Training Data"
TianHongZXY / Pytorch SAKTa simple pytorch implemention of paper A Self-Attentive model for Knowledge Tracing
shivanandmn / Knowledge Tracing SAINTTowards an Appropriate Query, Key, and Value Computation for Knowledge Tracing -Paper Implementation on "Riiid! Answer Correctness Prediction", Kaggle Competition
ml-smores / FastFeature Aware Student knowledge Tracing Toolkit. Implements HMMs with features for modeling student performance
robert-lindsey / WCRPWeighted Chinese Restaurant Process model for inferring skill labels in Bayesian Knowledge Tracing
tswsxk / XKTMultiple Knowledge Tracing models implemented by mxnet
juno-hwang / Juno DktScikit-learn style implementation of Deep Knowledge Tracing models based on pytorch.
tswsxk / TKTMultiple Knowledge Tracing models implemented by pytorch
utkarshsrivastava / ParallelSparseMatrixFactorizationSparse Matrix Factorization (SMF) is a key component in many machine learning problems and there exist a verity a applications in real-world problems such as recommendation systems, estimating missing values, gene expression modeling, intelligent tutoring systems (ITSs), etc. There are different approaches to tackle with SMF rooted in linear algebra and probability theory. In this project, given an incomplete binary matrix of students’ performances over a set of questions, estimating the probability of success or fail over unanswered questions is of interest. This problem is formulated using Maximum Likelihood Estimation (MLE) which leads to a biconvex optimization problem (this formulation is based on SPARFA [4]). The resulting optimization problem is a hard problem to deal with due to the existence of many local minima. On the other hand, when the size of the matrix of students’ performances increase, the existing algorithms are not successful; therefore, an efficient algorithm is required to solve this problem for large matrices. In this project, a parallel algorithm (i.e., a parallel version of SPARFA) is developed to solve the biconvex optimization problem and tested via a number of generated matrices. Keywords: parallel non-convex optimization, matrix factorization, sparse factor analysis 1 Introduction Educational systems have witnessed a substantial transition from traditional educational methods mainly using text books, lectures, etc. to newly developed systems which are artificial intelligent- based systems and personally tailored to the learners [4]. Personalized Learning Systems (PLSs) and Intelligent Tutoring Systems (ITSs) are two more well-known instances of such recently developed educational systems. PLSs take into account learners’ individual characteristics then customize the learning experience to the learners’ current situation and needs [2]. As computerized learning environments, ITSs model and track student learning states [1, 6, 7]. Latent Factor Model and Bayesian Knowledge Tracing are main classes in ITSs [3]. These new approaches encompass computational models from different disciplines including cognitive and learning sciences, education, 1 computational linguistics, artificial intelligence, operations research, and other fields. More details can be found in [1, 4–6]. Recently, [4] developed a new machine learning-based model for learning analytics, which approximate a students knowledge of the concepts underlying a domain, and content analytics, which estimate the relationships among a collection of questions and those concepts. This model calculates the probability that a learner provides the correct response to a question in terms of three factors: their understanding of a set of underlying concepts, the concepts involved in each question, and each questions intrinsic difficulty [4]. They proposed a bi-convex maximum-likelihood-based solution to the resulting SPARse Factor Analysis (SPARFA) problem. However, the scalability of SPARFA when the number of questions and students significantly increase has not been studied yet.
