29,034 research outputs found
Knowledge Tracing with Sequential Key-Value Memory Networks
Can machines trace human knowledge like humans? Knowledge tracing (KT) is a
fundamental task in a wide range of applications in education, such as massive
open online courses (MOOCs), intelligent tutoring systems, educational games,
and learning management systems. It models dynamics in a student's knowledge
states in relation to different learning concepts through their interactions
with learning activities. Recently, several attempts have been made to use deep
learning models for tackling the KT problem. Although these deep learning
models have shown promising results, they have limitations: either lack the
ability to go deeper to trace how specific concepts in a knowledge state are
mastered by a student, or fail to capture long-term dependencies in an exercise
sequence. In this paper, we address these limitations by proposing a novel deep
learning model for knowledge tracing, namely Sequential Key-Value Memory
Networks (SKVMN). This model unifies the strengths of recurrent modelling
capacity and memory capacity of the existing deep learning KT models for
modelling student learning. We have extensively evaluated our proposed model on
five benchmark datasets. The experimental results show that (1) SKVMN
outperforms the state-of-the-art KT models on all datasets, (2) SKVMN can
better discover the correlation between latent concepts and questions, and (3)
SKVMN can trace the knowledge state of students dynamics, and a leverage
sequential dependencies in an exercise sequence for improved predication
accuracy
Towards Interpretable Deep Learning Models for Knowledge Tracing
As an important technique for modeling the knowledge states of learners, the
traditional knowledge tracing (KT) models have been widely used to support
intelligent tutoring systems and MOOC platforms. Driven by the fast
advancements of deep learning techniques, deep neural network has been recently
adopted to design new KT models for achieving better prediction performance.
However, the lack of interpretability of these models has painfully impeded
their practical applications, as their outputs and working mechanisms suffer
from the intransparent decision process and complex inner structures. We thus
propose to adopt the post-hoc method to tackle the interpretability issue for
deep learning based knowledge tracing (DLKT) models. Specifically, we focus on
applying the layer-wise relevance propagation (LRP) method to interpret
RNN-based DLKT model by backpropagating the relevance from the model's output
layer to its input layer. The experiment results show the feasibility using the
LRP method for interpreting the DLKT model's predictions, and partially
validate the computed relevance scores from both question level and concept
level. We believe it can be a solid step towards fully interpreting the DLKT
models and promote their practical applications in the education domain
Exploiting Cognitive Structure for Adaptive Learning
Adaptive learning, also known as adaptive teaching, relies on learning path
recommendation, which sequentially recommends personalized learning items
(e.g., lectures, exercises) to satisfy the unique needs of each learner.
Although it is well known that modeling the cognitive structure including
knowledge level of learners and knowledge structure (e.g., the prerequisite
relations) of learning items is important for learning path recommendation,
existing methods for adaptive learning often separately focus on either
knowledge levels of learners or knowledge structure of learning items. To fully
exploit the multifaceted cognitive structure for learning path recommendation,
we propose a Cognitive Structure Enhanced framework for Adaptive Learning,
named CSEAL. By viewing path recommendation as a Markov Decision Process and
applying an actor-critic algorithm, CSEAL can sequentially identify the right
learning items to different learners. Specifically, we first utilize a
recurrent neural network to trace the evolving knowledge levels of learners at
each learning step. Then, we design a navigation algorithm on the knowledge
structure to ensure the logicality of learning paths, which reduces the search
space in the decision process. Finally, the actor-critic algorithm is used to
determine what to learn next and whose parameters are dynamically updated along
the learning path. Extensive experiments on real-world data demonstrate the
effectiveness and robustness of CSEAL.Comment: Accepted by KDD 2019 Research Track. In Proceedings of the 25th ACM
SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19
Introducing a framework to assess newly created questions with Natural Language Processing
Statistical models such as those derived from Item Response Theory (IRT)
enable the assessment of students on a specific subject, which can be useful
for several purposes (e.g., learning path customization, drop-out prediction).
However, the questions have to be assessed as well and, although it is possible
to estimate with IRT the characteristics of questions that have already been
answered by several students, this technique cannot be used on newly generated
questions. In this paper, we propose a framework to train and evaluate models
for estimating the difficulty and discrimination of newly created Multiple
Choice Questions by extracting meaningful features from the text of the
question and of the possible choices. We implement one model using this
framework and test it on a real-world dataset provided by CloudAcademy, showing
that it outperforms previously proposed models, reducing by 6.7% the RMSE for
difficulty estimation and by 10.8% the RMSE for discrimination estimation. We
also present the results of an ablation study performed to support our features
choice and to show the effects of different characteristics of the questions'
text on difficulty and discrimination.Comment: Accepted at the International Conference of Artificial Intelligence
in Educatio
Enhancing Deep Knowledge Tracing with Auxiliary Tasks
Knowledge tracing (KT) is the problem of predicting students' future
performance based on their historical interactions with intelligent tutoring
systems. Recent studies have applied multiple types of deep neural networks to
solve the KT problem. However, there are two important factors in real-world
educational data that are not well represented. First, most existing works
augment input representations with the co-occurrence matrix of questions and
knowledge components\footnote{\label{ft:kc}A KC is a generalization of everyday
terms like concept, principle, fact, or skill.} (KCs) but fail to explicitly
integrate such intrinsic relations into the final response prediction task.
Second, the individualized historical performance of students has not been well
captured. 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}. Specifically, the
QT task helps learn better question representations by predicting whether
questions contain specific KCs. The IK task captures students' global
historical performance by progressively predicting student-level prior
knowledge that is hidden in students' historical learning interactions. We
conduct comprehensive experiments on three real-world educational datasets and
compare the proposed approach to both deep sequential KT models and
non-sequential models. Experimental results show that \emph{AT-DKT} outperforms
all sequential models with more than 0.9\% improvements of AUC for all
datasets, and is almost the second best compared to non-sequential models.
Furthermore, we conduct both ablation studies and quantitative analysis to show
the effectiveness of auxiliary tasks and the superior prediction outcomes of
\emph{AT-DKT}.Comment: Accepted at WWW'23: The 2023 ACM Web Conference, 202
Bridging global divides with tracking and tracing technology
Product-tracking technology is increasingly available to big players in the value chain connecting producers to consumers, giving them new competitive advantages. Such shifts in technology don't benefit small producers, especially those in developing regions, to the same degree. This article examines the practicalities of leveling the playing field by creating a generic form of tracing technology that any producer, large or small, can use. It goes beyond considering engineering solutions to look at what happens in the context of use, reporting on work with partners in Chile and India and reflecting on the potential for impact on business and community well-being
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