Transition-Aware Multi-Activity Knowledge Tracing
- URL: http://arxiv.org/abs/2301.12916v1
- Date: Thu, 26 Jan 2023 21:49:24 GMT
- Title: Transition-Aware Multi-Activity Knowledge Tracing
- Authors: Siqian Zhao, Chunpai Wang, Shaghayegh Sahebi
- Abstract summary: Knowledge tracing aims to model student knowledge state given the student's sequence of learning activities.
Current KT solutions are not fit for modeling student learning from non-assessed learning activities.
We propose Transition-Aware Multi-activity Knowledge Tracing (TAMKOT)
- Score: 2.9778695679660188
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Accurate modeling of student knowledge is essential for large-scale online
learning systems that are increasingly used for student training. Knowledge
tracing aims to model student knowledge state given the student's sequence of
learning activities. Modern Knowledge tracing (KT) is usually formulated as a
supervised sequence learning problem to predict students' future practice
performance according to their past observed practice scores by summarizing
student knowledge state as a set of evolving hidden variables. Because of this
formulation, many current KT solutions are not fit for modeling student
learning from non-assessed learning activities with no explicit feedback or
score observation (e.g., watching video lectures that are not graded).
Additionally, these models cannot explicitly represent the dynamics of
knowledge transfer among different learning activities, particularly between
the assessed (e.g., quizzes) and non-assessed (e.g., video lectures) learning
activities. In this paper, we propose Transition-Aware Multi-activity Knowledge
Tracing (TAMKOT), which models knowledge transfer between learning materials,
in addition to student knowledge, when students transition between and within
assessed and non-assessed learning materials. TAMKOT is formulated as a deep
recurrent multi-activity learning model that explicitly learns knowledge
transfer by activating and learning a set of knowledge transfer matrices, one
for each transition type between student activities. Accordingly, our model
allows for representing each material type in a different yet transferrable
latent space while maintaining student knowledge in a shared space. We evaluate
our model on three real-world publicly available datasets and demonstrate
TAMKOT's capability in predicting student performance and modeling knowledge
transfer.
Related papers
- SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language Model [64.92472567841105]
Knowledge Tracing (KT) aims to determine whether students will respond correctly to the next question.
Structure-aware Inductive Knowledge Tracing model with large language model (dubbed SINKT)
SINKT predicts the student's response to the target question by interacting with the student's knowledge state and the question representation.
arXiv Detail & Related papers (2024-07-01T12:44:52Z) - COOLer: Class-Incremental Learning for Appearance-Based Multiple Object
Tracking [32.47215340215641]
This paper extends the scope of continual learning research to class-incremental learning for multiple object tracking (MOT)
Previous solutions for continual learning of object detectors do not address the data association stage of appearance-based trackers.
We introduce COOLer, a COntrastive- and cOntinual-Learning-based tracker, which incrementally learns to track new categories while preserving past knowledge.
arXiv Detail & Related papers (2023-10-04T17:49:48Z) - Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph
Propagation [68.13453771001522]
We propose a multimodal intensive ZSL framework that matches regions of images with corresponding semantic embeddings.
We conduct extensive experiments and evaluate our model on large-scale real-world data.
arXiv Detail & Related papers (2023-06-14T13:07:48Z) - Quiz-based Knowledge Tracing [61.9152637457605]
Knowledge tracing aims to assess individuals' evolving knowledge states according to their learning interactions.
QKT achieves state-of-the-art performance compared to existing methods.
arXiv Detail & Related papers (2023-04-05T12:48:42Z) - HiTSKT: A Hierarchical Transformer Model for Session-Aware Knowledge
Tracing [35.02243127325724]
Knowledge tracing (KT) aims to leverage students' learning histories to estimate their mastery levels on a set of pre-defined skills, based on which the corresponding future performance can be accurately predicted.
In practice, a student's learning history comprises answers to sets of massed questions, each known as a session, rather than merely being a sequence of independent answers.
Most existing KT models treat student's learning records as a single continuing sequence, without capturing the sessional shift of students' knowledge state.
arXiv Detail & Related papers (2022-12-23T04:22:42Z) - Responsible Active Learning via Human-in-the-loop Peer Study [88.01358655203441]
We propose a responsible active learning method, namely Peer Study Learning (PSL), to simultaneously preserve data privacy and improve model stability.
We first introduce a human-in-the-loop teacher-student architecture to isolate unlabelled data from the task learner (teacher) on the cloud-side.
During training, the task learner instructs the light-weight active learner which then provides feedback on the active sampling criterion.
arXiv Detail & Related papers (2022-11-24T13:18:27Z) - Learning Data Teaching Strategies Via Knowledge Tracing [5.648636668261282]
We propose a novel method, called Knowledge Augmented Data Teaching (KADT), to optimize a data teaching strategy for a student model.
The KADT method incorporates a knowledge tracing model to dynamically capture the knowledge progress of a student model in terms of latent learning concepts.
We have evaluated the performance of the KADT method on four different machine learning tasks including knowledge tracing, sentiment analysis, movie recommendation, and image classification.
arXiv Detail & Related papers (2021-11-13T10:10:48Z) - Semi-Supervising Learning, Transfer Learning, and Knowledge Distillation
with SimCLR [2.578242050187029]
Recent breakthroughs in the field of semi-supervised learning have achieved results that match state-of-the-art traditional supervised learning methods.
SimCLR is the current state-of-the-art semi-supervised learning framework for computer vision.
arXiv Detail & Related papers (2021-08-02T01:37:39Z) - Bilevel Continual Learning [76.50127663309604]
We present a novel framework of continual learning named "Bilevel Continual Learning" (BCL)
Our experiments on continual learning benchmarks demonstrate the efficacy of the proposed BCL compared to many state-of-the-art methods.
arXiv Detail & Related papers (2020-07-30T16:00:23Z) - Modeling Knowledge Acquisition from Multiple Learning Resource Types [2.9778695679660188]
Students acquire knowledge as they interact with a variety of learning materials, such as video lectures, problems, and discussions.
Current student knowledge modeling techniques mostly rely on one type of learning material, mainly problems, to model student knowledge growth.
We propose a student knowledge model that can capture knowledge growth as a result of learning from a diverse set of learning resource types.
arXiv Detail & Related papers (2020-06-23T23:52:33Z) - Bayesian active learning for production, a systematic study and a
reusable library [85.32971950095742]
In this paper, we analyse the main drawbacks of current active learning techniques.
We do a systematic study on the effects of the most common issues of real-world datasets on the deep active learning process.
We derive two techniques that can speed up the active learning loop such as partial uncertainty sampling and larger query size.
arXiv Detail & Related papers (2020-06-17T14:51:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.