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Found 67 papers, 33 papers with code
A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing Models
However, the inherent black-box nature of deep learning techniques often poses a hurdle for teachers to fully embrace the model's prediction results.
Improving Low-Resource Knowledge Tracing Tasks by Supervised Pre-training and Importance Mechanism Fine-tuning
Knowledge tracing (KT) aims to estimate student's knowledge mastery based on their historical interactions.
BiVRec: Bidirectional View-based Multimodal Sequential Recommendation
The integration of multimodal information into sequential recommender systems has attracted significant attention in recent research.
Cumulative Distribution Function based General Temporal Point Processes
Our contributions encompass the introduction of a pioneering CDF-based TPP model, the development of a methodology for incorporating past event information into future event prediction, and empirical validation of CuFun's effectiveness through extensive experimentation on synthetic and real-world datasets.
MLPST: MLP is All You Need for Spatio-Temporal Prediction
To accomplish the above goals, we propose an intuitive and novel framework, MLPST, a pure multi-layer perceptron architecture for traffic prediction.
Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting
Then, we generate representative and common spatio-temporal patterns as global nodes to reflect a global dependency between sensors and provide auxiliary information for spatio-temporal dependency learning.
PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction
We devise a spatio-temporal transformer and a parameter-sharing training scheme to address the common knowledge among different spatio-temporal attributes.
Fairly Adaptive Negative Sampling for Recommendations
Pairwise learning strategies are prevalent for optimizing recommendation models on implicit feedback data, which usually learns user preference by discriminating between positive (i. e., clicked by a user) and negative items (i. e., obtained by negative sampling).
simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing
Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems.
Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations
The results demonstrate that our approach is superior on the KT prediction task, and it outperforms a wide range of deep learning based KT models in terms of prediction accuracy with better model interpretability.
Enhancing Deep Knowledge Tracing with Auxiliary Tasks
In this paper, we proposed \emph{AT-DKT} to improve the prediction performance of the original deep knowledge tracing model with two auxiliary learning tasks, i. e., \emph{question tagging (QT) prediction task} and \emph{individualized prior knowledge (IK) prediction task}.
Probabilistic Categorical Adversarial Attack & Adversarial Training
To tackle this problem, we propose Probabilistic Categorical Adversarial Attack (PCAA), which transfers the discrete optimization problem to a continuous problem that can be solved efficiently by Projected Gradient Descent.
Wide & Deep Learning for Judging Student Performance in Online One-on-one Math Classes
In this paper, we investigate the opportunities of automating the judgment process in online one-on-one math classes.
SC-Ques: A Sentence Completion Question Dataset for English as a Second Language Learners
Sentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options.
DialogID: A Dialogic Instruction Dataset for Improving Teaching Effectiveness in Online Environments
In spite of the popularity and advantages of online learning, the education technology and educational data mining communities still suffer from the lack of large-scale, high-quality, and well-annotated teaching instruction datasets to study computational approaches to automatically detect online dialogic instructions and further improve the online teaching effectiveness.
A Design of A Simple Yet Effective Exercise Recommendation System in K-12 Online Learning
We propose a simple but effective method to recommend exercises with high quality and diversity for students.
pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models
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.
A Knowledge-Based Decision Support System for In Vitro Fertilization Treatment
To improve the IVF success rate, we propose a knowledge-based decision support system that can provide medical advice on the treatment protocol and medication adjustment for each patient visit during IVF treatment cycle.
Graph Neural Networks with Adaptive Residual
Graph neural networks (GNNs) have shown the power in graph representation learning for numerous tasks.
CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations
Existing audio-language task-specific predictive approaches focus on building complicated late-fusion mechanisms.
Temporal-aware Language Representation Learning From Crowdsourced Labels
A challenging aspect of this problem is that the quality of crowdsourced labels suffer high intra- and inter-observer variability.
Automatic Task Requirements Writing Evaluation via Machine Reading Comprehension
A TR writing question may include multiple requirements and a high-quality essay must respond to each requirement thoroughly and accurately.
A Multimodal Machine Learning Framework for Teacher Vocal Delivery Evaluation
The quality of vocal delivery is one of the key indicators for evaluating teacher enthusiasm, which has been widely accepted to be connected to the overall course qualities.
Solving ESL Sentence Completion Questions via Pre-trained Neural Language Models
Sentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options.
Robust Learning for Text Classification with Multi-source Noise Simulation and Hard Example Mining
Despite that pre-trained models achieve state-of-the-art performance in many NLP benchmarks, we prove that they are not robust to noisy texts generated by real OCR engines.
Optical Character Recognition
Optical Character Recognition (OCR)
+2
Multi-Task Learning based Online Dialogic Instruction Detection with Pre-trained Language Models
In this work, we study computational approaches to detect online dialogic instructions, which are widely used to help students understand learning materials, and build effective study habits.
An Educational System for Personalized Teacher Recommendation in K-12 Online Classrooms
In this paper, we propose a simple yet effective solution to build practical teacher recommender systems for online one-on-one classes.
Towards the Memorization Effect of Neural Networks in Adversarial Training
In this work, we study the effect of memorization in adversarial trained DNNs and disclose two important findings: (a) Memorizing atypical samples is only effective to improve DNN's accuracy on clean atypical samples, but hardly improve their adversarial robustness and (b) Memorizing certain atypical samples will even hurt the DNN's performance on typical samples.
OpenMEVA: A Benchmark for Evaluating Open-ended Story Generation Metrics
Automatic metrics are essential for developing natural language generation (NLG) models, particularly for open-ended language generation tasks such as story generation.
Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence
Generating long and coherent text is an important but challenging task, particularly for open-ended language generation tasks such as story generation.
The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification
The results show that the text classification models trained under our proposed framework outperform traditional models significantly in terms of fairness, and also slightly in terms of classification performance.
COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction
In this paper, we study two challenging problems in incomplete multi-view clustering analysis, namely, i) how to learn an informative and consistent representation among different views without the help of labels and ii) how to recover the missing views from data.
Ranked #1 on
Incomplete multi-view clustering
on n-MNIST
Partially View-aligned Representation Learning with Noise-robust Contrastive Loss
To solve such a less-touched problem without the help of labels, we propose simultaneously learning representation and aligning data using a noise-robust contrastive loss.
AdvExpander: Generating Natural Language Adversarial Examples by Expanding Text
In this paper, we present AdvExpander, a method that crafts new adversarial examples by expanding text, which is complementary to previous substitution-based methods.
CLEARER: Multi-Scale Neural Architecture Search for Image Restoration
Different from the existing labor-intensive handcrafted architecture design paradigms, we present a novel method, termed as multi-sCaLe nEural ARchitecture sEarch for image Restoration (CLEARER), which is a specifically designed neural architecture search (NAS) for image restoration.
Node Similarity Preserving Graph Convolutional Networks
Specifically, to balance information from graph structure and node features, we propose a feature similarity preserving aggregation which adaptively integrates graph structure and node features.
Personalized Multimodal Feedback Generation in Education
The automatic evaluation for school assignments is an important application of AI in the education field.
Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations
There is an increasing interest in the use of mathematical word problem (MWP) generation in educational assessment.
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning
Extensive experiments on two real-world conversation datasets show that our framework significantly reduces gender bias in dialogue models while maintaining the response quality.
Representation Learning from Limited Educational Data with Crowdsourced Labels
Extensive experiments conducted on three real-world data sets demonstrate the superiority of our framework on learning representations from limited data with crowdsourced labels, comparing with various state-of-the-art baselines.
Contrastive Clustering
In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning.
Ranked #4 on
Image Clustering
on STL-10
(using extra training data)
Interactive Knowledge Distillation
Knowledge distillation is a standard teacher-student learning framework to train a light-weight student network under the guidance of a well-trained large teacher network.
Self-supervised Learning on Graphs: Deep Insights and New Direction
Thus, we seek to harness SSL for GNNs to fully exploit the unlabeled data.
Neural Multi-Task Learning for Teacher Question Detection in Online Classrooms
Asking questions is one of the most crucial pedagogical techniques used by teachers in class.
Automatic Dialogic Instruction Detection for K-12 Online One-on-one Classes
Online one-on-one class is created for highly interactive and immersive learning experience.
Siamese Neural Networks for Class Activity Detection
Classroom activity detection (CAD) aims at accurately recognizing speaker roles (either teacher or student) in classrooms.
Synchronous Bidirectional Learning for Multilingual Lip Reading
Based on this idea, we try to explore the synergized learning of multilingual lip reading in this paper, and further propose a synchronous bidirectional learning (SBL) framework for effective synergy of multilingual lip reading.
Learning Goal-oriented Dialogue Policy with Opposite Agent Awareness
Most existing approaches for goal-oriented dialogue policy learning used reinforcement learning, which focuses on the target agent policy and simply treat the opposite agent policy as part of the environment.
Identifying At-Risk K-12 Students in Multimodal Online Environments: A Machine Learning Approach
We conduct a wide range of offline and online experiments to demonstrate the effectiveness of our approach.
NeuCrowd: Neural Sampling Network for Representation Learning with Crowdsourced Labels
Representation learning approaches require a massive amount of discriminative training data, which is unavailable in many scenarios, such as healthcare, smart city, education, etc.
Graduate Employment Prediction with Bias
The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide.
Learning Multi-level Dependencies for Robust Word Recognition
Robust language processing systems are becoming increasingly important given the recent awareness of dangerous situations where brittle machine learning models can be easily broken with the presence of noises.
Multimodal Learning For Classroom Activity Detection
The experimental results demonstrate the benefits of our approach on learning attention based neural network from classroom data with different modalities, and show our approach is able to outperform state-of-the-art baselines in terms of various evaluation metrics.
Does Gender Matter? Towards Fairness in Dialogue Systems
In particular, we construct a benchmark dataset and propose quantitative measures to understand fairness in dialogue models.
Automatic Short Answer Grading via Multiway Attention Networks
Automatic short answer grading (ASAG), which autonomously score student answers according to reference answers, provides a cost-effective and consistent approach to teaching professionals and can reduce their monotonous and tedious grading workloads.
Say What I Want: Towards the Dark Side of Neural Dialogue Models
Neural dialogue models have been widely adopted in various chatbot applications because of their good performance in simulating and generalizing human conversations.
A Multimodal Alerting System for Online Class Quality Assurance
To warn the unqualified instructors and ensure the overall education quality, we build a monitoring and alerting system by utilizing multimodal information from the online environment.
Deep Knowledge Tracing with Side Information
Monitoring student knowledge states or skill acquisition levels known as knowledge tracing, is a fundamental part of intelligent tutoring systems.
Hierarchical Bayesian Personalized Recommendation: A Case Study and Beyond
Items in modern recommender systems are often organized in hierarchical structures.
Dolphin: A Spoken Language Proficiency Assessment System for Elementary Education
In our offline experiments, we show that Dolphin improves both phonological fluency and semantic relevance evaluation performance when compared to state-of-the-art baselines on real-world educational data sets.
Learning Effective Embeddings From Crowdsourced Labels: An Educational Case Study
In practice, the crowdsourced labels are usually inconsistent among crowd workers given their diverse expertise and the number of crowdsourced labels is very limited.
Recommender Systems with Heterogeneous Side Information
In modern recommender systems, both users and items are associated with rich side information, which can help understand users and items.
R-Transformer: Recurrent Neural Network Enhanced Transformer
Recurrent Neural Networks have long been the dominating choice for sequence modeling.
Ranked #1 on
Music Modeling
on Nottingham
A Novel ILP Framework for Summarizing Content with High Lexical Variety
Summarizing content contributed by individuals can be challenging, because people make different lexical choices even when describing the same events.
Automatic Summarization of Student Course Feedback
Student course feedback is generated daily in both classrooms and online course discussion forums.
Sparse Linear Dynamical System with Its Application in Multivariate Clinical Time Series
Linear Dynamical System (LDS) is an elegant mathematical framework for modeling and learning multivariate time series.
A Comparative Study on Linguistic Feature Selection in Sentiment Polarity Classification
Sentiment polarity classification is perhaps the most widely studied topic.
