37 research outputs found
Multi-source Education Knowledge Graph Construction and Fusion for College Curricula
The field of education has undergone a significant transformation due to the
rapid advancements in Artificial Intelligence (AI). Among the various AI
technologies, Knowledge Graphs (KGs) using Natural Language Processing (NLP)
have emerged as powerful visualization tools for integrating multifaceted
information. In the context of university education, the availability of
numerous specialized courses and complicated learning resources often leads to
inferior learning outcomes for students. In this paper, we propose an automated
framework for knowledge extraction, visual KG construction, and graph fusion,
tailored for the major of Electronic Information. Furthermore, we perform data
analysis to investigate the correlation degree and relationship between
courses, rank hot knowledge concepts, and explore the intersection of courses.
Our objective is to enhance the learning efficiency of students and to explore
new educational paradigms enabled by AI. The proposed framework is expected to
enable students to better understand and appreciate the intricacies of their
field of study by providing them with a comprehensive understanding of the
relationships between the various concepts and courses.Comment: accepted by ICALT202
Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk
Though deep reinforcement learning (DRL) has obtained substantial success, it
may encounter catastrophic failures due to the intrinsic uncertainty of both
transition and observation. Most of the existing methods for safe reinforcement
learning can only handle transition disturbance or observation disturbance
since these two kinds of disturbance affect different parts of the agent;
besides, the popular worst-case return may lead to overly pessimistic policies.
To address these issues, we first theoretically prove that the performance
degradation under transition disturbance and observation disturbance depends on
a novel metric of Value Function Range (VFR), which corresponds to the gap in
the value function between the best state and the worst state. Based on the
analysis, we adopt conditional value-at-risk (CVaR) as an assessment of risk
and propose a novel reinforcement learning algorithm of
CVaR-Proximal-Policy-Optimization (CPPO) which formalizes the risk-sensitive
constrained optimization problem by keeping its CVaR under a given threshold.
Experimental results show that CPPO achieves a higher cumulative reward and is
more robust against both observation and transition disturbances on a series of
continuous control tasks in MuJoCo
No Length Left Behind: Enhancing Knowledge Tracing for Modeling Sequences of Excessive or Insufficient Lengths
Knowledge tracing (KT) aims to predict students' responses to practices based
on their historical question-answering behaviors. However, most current KT
methods focus on improving overall AUC, leaving ample room for optimization in
modeling sequences of excessive or insufficient lengths. As sequences get
longer, computational costs will increase exponentially. Therefore, KT methods
usually truncate sequences to an acceptable length, which makes it difficult
for models on online service systems to capture complete historical practice
behaviors of students with too long sequences. Conversely, modeling students
with short practice sequences using most KT methods may result in overfitting
due to limited observation samples. To address the above limitations, we
propose a model called Sequence-Flexible Knowledge Tracing (SFKT).Comment: Accepted by CIKM 2023, 10 pages, 8 figures, 5 table
Counterfactual Monotonic Knowledge Tracing for Assessing Students' Dynamic Mastery of Knowledge Concepts
As the core of the Knowledge Tracking (KT) task, assessing students' dynamic
mastery of knowledge concepts is crucial for both offline teaching and online
educational applications. Since students' mastery of knowledge concepts is
often unlabeled, existing KT methods rely on the implicit paradigm of
historical practice to mastery of knowledge concepts to students' responses to
practices to address the challenge of unlabeled concept mastery. However,
purely predicting student responses without imposing specific constraints on
hidden concept mastery values does not guarantee the accuracy of these
intermediate values as concept mastery values. To address this issue, we
propose a principled approach called Counterfactual Monotonic Knowledge Tracing
(CMKT), which builds on the implicit paradigm described above by using a
counterfactual assumption to constrain the evolution of students' mastery of
knowledge concepts.Comment: Accepted by CIKM 2023, 10 pages, 5 figures, 4 table
Cognition-Mode Aware Variational Representation Learning Framework for Knowledge Tracing
The Knowledge Tracing (KT) task plays a crucial role in personalized
learning, and its purpose is to predict student responses based on their
historical practice behavior sequence. However, the KT task suffers from data
sparsity, which makes it challenging to learn robust representations for
students with few practice records and increases the risk of model overfitting.
Therefore, in this paper, we propose a Cognition-Mode Aware Variational
Representation Learning Framework (CMVF) that can be directly applied to
existing KT methods. Our framework uses a probabilistic model to generate a
distribution for each student, accounting for uncertainty in those with limited
practice records, and estimate the student's distribution via variational
inference (VI). In addition, we also introduce a cognition-mode aware
multinomial distribution as prior knowledge that constrains the posterior
student distributions learning, so as to ensure that students with similar
cognition modes have similar distributions, avoiding overwhelming
personalization for students with few practice records. At last, extensive
experimental results confirm that CMVF can effectively aid existing KT methods
in learning more robust student representations. Our code is available at
https://github.com/zmy-9/CMVF.Comment: Accepted by ICDM 2023, 10 pages, 5 figures, 4 table
Multi-Factors Aware Dual-Attentional Knowledge Tracing
With the increasing demands of personalized learning, knowledge tracing has
become important which traces students' knowledge states based on their
historical practices. Factor analysis methods mainly use two kinds of factors
which are separately related to students and questions to model students'
knowledge states. These methods use the total number of attempts of students to
model students' learning progress and hardly highlight the impact of the most
recent relevant practices. Besides, current factor analysis methods ignore rich
information contained in questions. In this paper, we propose Multi-Factors
Aware Dual-Attentional model (MF-DAKT) which enriches question representations
and utilizes multiple factors to model students' learning progress based on a
dual-attentional mechanism. More specifically, we propose a novel
student-related factor which records the most recent attempts on relevant
concepts of students to highlight the impact of recent exercises. To enrich
questions representations, we use a pre-training method to incorporate two
kinds of question information including questions' relation and difficulty
level. We also add a regularization term about questions' difficulty level to
restrict pre-trained question representations to fine-tuning during the process
of predicting students' performance. Moreover, we apply a dual-attentional
mechanism to differentiate contributions of factors and factor interactions to
final prediction in different practice records. At last, we conduct experiments
on several real-world datasets and results show that MF-DAKT can outperform
existing knowledge tracing methods. We also conduct several studies to validate
the effects of each component of MF-DAKT.Comment: Accepted by CIKM 2021, 10 pages, 10 figures, 6 table
Task Aware Dreamer for Task Generalization in Reinforcement Learning
A long-standing goal of reinforcement learning is to acquire agents that can
learn on training tasks and generalize well on unseen tasks that may share a
similar dynamic but with different reward functions. A general challenge is to
quantitatively measure the similarities between these different tasks, which is
vital for analyzing the task distribution and further designing algorithms with
stronger generalization. To address this, we present a novel metric named Task
Distribution Relevance (TDR) via optimal Q functions of different tasks to
capture the relevance of the task distribution quantitatively. In the case of
tasks with a high TDR, i.e., the tasks differ significantly, we show that the
Markovian policies cannot differentiate them, leading to poor performance.
Based on this insight, we encode all historical information into policies for
distinguishing different tasks and propose Task Aware Dreamer (TAD), which
extends world models into our reward-informed world models to capture invariant
latent features over different tasks. In TAD, we calculate the corresponding
variational lower bound of the data log-likelihood, including a novel term to
distinguish different tasks via states, to optimize reward-informed world
models. Extensive experiments in both image-based control tasks and state-based
control tasks demonstrate that TAD can significantly improve the performance of
handling different tasks simultaneously, especially for those with high TDR,
and demonstrate a strong generalization ability to unseen tasks
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Bridging photochemistry and photomechanics with NMR crystallography: the molecular basis for the macroscopic expansion of an anthracene ester nanorod
Crystals composed of photoreactive molecules represent a new class of photomechanical materials with the potential to generate large forces on fast timescales. An example is the photodimerization of 9-tert-butyl-anthracene ester (9TBAE) in molecular crystal nanorods that leads to an average elongation of 8%. Previous work showed that this expansion results from the formation of a metastable crystalline product. In this article, it is shown how a novel combination of ensemble oriented-crystal solid-state NMR, X-ray diffraction, and first principles computational modeling can be used to establish the absolute unit cell orientations relative to the shape change, revealing the atomic-resolution mechanism for the photomechanical response and enabling the construction of a model that predicts an elongation of 7.4%, in good agreement with the experimental value. According to this model, the nanorod expansion does not result from an overall change in the volume of the unit cell, but rather from an anisotropic rearrangement of the molecular contents. The ability to understand quantitatively how molecular-level photochemistry generates mechanical displacements allows us to predict that the expansion could be tuned from +9% to −9.5% by controlling the initial orientation of the unit cell with respect to the nanorod axis. This application of NMR-assisted crystallography provides a new tool capable of tying the atomic-level structural rearrangement of the reacting molecular species to the mechanical response of a nanostructured sample