670 research outputs found
A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining
Educational Data Mining (EDM) has emerged as a vital field of research, which
harnesses the power of computational techniques to analyze educational data.
With the increasing complexity and diversity of educational data, Deep Learning
techniques have shown significant advantages in addressing the challenges
associated with analyzing and modeling this data. This survey aims to
systematically review the state-of-the-art in EDM with Deep Learning. We begin
by providing a brief introduction to EDM and Deep Learning, highlighting their
relevance in the context of modern education. Next, we present a detailed
review of Deep Learning techniques applied in four typical educational
scenarios, including knowledge tracing, undesirable student detecting,
performance prediction, and personalized recommendation. Furthermore, a
comprehensive overview of public datasets and processing tools for EDM is
provided. Finally, we point out emerging trends and future directions in this
research area.Comment: 21 pages, 5 figure
A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation
Cold-start problem is a fundamental challenge for recommendation tasks. The
recent self-supervised learning (SSL) on Graph Neural Networks (GNNs) model,
PT-GNN, pre-trains the GNN model to reconstruct the cold-start embeddings and
has shown great potential for cold-start recommendation. However, due to the
over-smoothing problem, PT-GNN can only capture up to 3-order relation, which
can not provide much useful auxiliary information to depict the target
cold-start user or item. Besides, the embedding reconstruction task only
considers the intra-correlations within the subgraph of users and items, while
ignoring the inter-correlations across different subgraphs. To solve the above
challenges, we propose a multi-strategy based pre-training method for
cold-start recommendation (MPT), which extends PT-GNN from the perspective of
model architecture and pretext tasks to improve the cold-start recommendation
performance. Specifically, in terms of the model architecture, in addition to
the short-range dependencies of users and items captured by the GNN encoder, we
introduce a Transformer encoder to capture long-range dependencies. In terms of
the pretext task, in addition to considering the intra-correlations of users
and items by the embedding reconstruction task, we add embedding contrastive
learning task to capture inter-correlations of users and items. We train the
GNN and Transformer encoders on these pretext tasks under the meta-learning
setting to simulate the real cold-start scenario, making the model easily and
rapidly being adapted to new cold-start users and items. Experiments on three
public recommendation datasets show the superiority of the proposed MPT model
against the vanilla GNN models, the pre-training GNN model on user/item
embedding inference and the recommendation task
Towards A Massive Open Online Course for Cybersecurity in Smart Grids – A Roadmap Strategy
The major trends and transformations in energy systems have brought many challenges, and cybersecurity and operational security are among the most important issues to consider. First, due to the criticality of the energy sector. Second, due to the lack of smart girds’ cybersecurity professionals. Previous research has highlighted skill gaps and shortage in cybersecurity training and education in this sector. Accordingly, we proceeded by crafting a roadmap strategy to foster cybersecurity education in smart grids. This paper outlines the methodology of teaching cybersecurity in smart grids to a large group of students in selected European universities via implementing a Massive Open Online Course. Unlike other solutions, this one focuses on hands-on practical skills without trading-off theoretical knowledge. Thus, flipped learning methodology and gamification practices were used to maximize retention rate. Also, a remote lab that includes a real-time simulator was established for training. Here, the process, outcome, and obstacles to overcome in future deployments, are presented.©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed
Task-oriented Dialogue System for Automatic Disease Diagnosis via Hierarchical Reinforcement Learning
In this paper, we focus on automatic disease diagnosis with reinforcement
learning (RL) methods in task-oriented dialogues setting. Different from
conventional RL tasks, the action space for disease diagnosis (i.e., symptoms)
is inevitably large, especially when the number of diseases increases. However,
existing approaches to this problem employ a flat RL policy, which typically
works well in simple tasks but has significant challenges in complex scenarios
like disease diagnosis. Towards this end, we propose to integrate a
hierarchical policy of two levels into the dialogue policy learning. The high
level policy consists of a model named master that is responsible for
triggering a model in low level, the low level policy consists of several
symptom checkers and a disease classifier. Experimental results on both
self-constructed real-world and synthetic datasets demonstrate that our
hierarchical framework achieves higher accuracy in disease diagnosis compared
with existing systems. Besides, the datasets
(http://www.sdspeople.fudan.edu.cn/zywei/data/Fudan-Medical-Dialogue2.0) and
codes (https://github.com/nnbay/MeicalChatbot-HRL) are all available now
MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs
Student modeling, the task of inferring a student's learning characteristics
through their interactions with coursework, is a fundamental issue in
intelligent education. Although the recent attempts from knowledge tracing and
cognitive diagnosis propose several promising directions for improving the
usability and effectiveness of current models, the existing public datasets are
still insufficient to meet the need for these potential solutions due to their
ignorance of complete exercising contexts, fine-grained concepts, and cognitive
labels. In this paper, we present MoocRadar, a fine-grained, multi-aspect
knowledge repository consisting of 2,513 exercise questions, 5,600 knowledge
concepts, and over 12 million behavioral records. Specifically, we propose a
framework to guarantee a high-quality and comprehensive annotation of
fine-grained concepts and cognitive labels. The statistical and experimental
results indicate that our dataset provides the basis for the future
improvements of existing methods. Moreover, to support the convenient usage for
researchers, we release a set of tools for data querying, model adaption, and
even the extension of our repository, which are now available at
https://github.com/THU-KEG/MOOC-Radar.Comment: Accepted by SIGIR 202
Reward Shaping for User Satisfaction in a REINFORCE Recommender
How might we design Reinforcement Learning (RL)-based recommenders that
encourage aligning user trajectories with the underlying user satisfaction?
Three research questions are key: (1) measuring user satisfaction, (2)
combatting sparsity of satisfaction signals, and (3) adapting the training of
the recommender agent to maximize satisfaction. For measurement, it has been
found that surveys explicitly asking users to rate their experience with
consumed items can provide valuable orthogonal information to the
engagement/interaction data, acting as a proxy to the underlying user
satisfaction. For sparsity, i.e, only being able to observe how satisfied users
are with a tiny fraction of user-item interactions, imputation models can be
useful in predicting satisfaction level for all items users have consumed. For
learning satisfying recommender policies, we postulate that reward shaping in
RL recommender agents is powerful for driving satisfying user experiences.
Putting everything together, we propose to jointly learn a policy network and a
satisfaction imputation network: The role of the imputation network is to learn
which actions are satisfying to the user; while the policy network, built on
top of REINFORCE, decides which items to recommend, with the reward utilizing
the imputed satisfaction. We use both offline analysis and live experiments in
an industrial large-scale recommendation platform to demonstrate the promise of
our approach for satisfying user experiences.Comment: Accepted in Reinforcement Learning for Real Life (RL4RealLife)
Workshop in the 38th International Conference on Machine Learning, 202
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