8,693 research outputs found
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
Survey of data mining approaches to user modeling for adaptive hypermedia
The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the applicatio
A Review of Data Mining in Personalized Education: Current Trends and Future Prospects
Personalized education, tailored to individual student needs, leverages
educational technology and artificial intelligence (AI) in the digital age to
enhance learning effectiveness. The integration of AI in educational platforms
provides insights into academic performance, learning preferences, and
behaviors, optimizing the personal learning process. Driven by data mining
techniques, it not only benefits students but also provides educators and
institutions with tools to craft customized learning experiences. To offer a
comprehensive review of recent advancements in personalized educational data
mining, this paper focuses on four primary scenarios: educational
recommendation, cognitive diagnosis, knowledge tracing, and learning analysis.
This paper presents a structured taxonomy for each area, compiles commonly used
datasets, and identifies future research directions, emphasizing the role of
data mining in enhancing personalized education and paving the way for future
exploration and innovation.Comment: 25 pages, 5 figure
Application of Particle Swarm Optimization to Formative E-Assessment in Project Management
The current paper describes the application of Particle Swarm Optimization algorithm to the formative e-assessment problem in project management. The proposed approach resolves the issue of personalization, by taking into account, when selecting the item tests in an e-assessment, the following elements: the ability level of the user, the targeted difficulty of the test and the learning objectives, represented by project management concepts which have to be checked. The e-assessment tool in which the Particle Swarm Optimization algorithm is integrated is also presented. Experimental results and comparison with other algorithms used in item tests selection prove the suitability of the proposed approach to the formative e-assessment domain. The study is presented in the framework of other evolutionary and genetic algorithms applied in e-education.Particle Swarm Optimization, Genetic Algorithms, Evolutionary Algorithms, Formative E-assessment, E-education
Developing Student Model for Intelligent Tutoring System
The effectiveness of an e-learning environment mainly encompasses on how efficiently the tutor presents the
learning content to the candidate based on their learning capability. It is therefore inevitable for the teaching
community to understand the learning style of their students and to cater for the needs of their students. One
such system that can cater to the needs of the students is the Intelligent Tutoring System (ITS). To overcome
the challenges faced by the teachers and to cater to the needs of their students, e-learning experts in recent times
have focused in Intelligent Tutoring System (ITS). There is sufficient literature that suggested that meaningful,
constructive and adaptive feedback is the essential feature of ITSs, and it is such feedback that helps students
achieve strong learning gains. At the same time, in an ITS, it is the student model that plays a main role in
planning the training path, supplying feedback information to the pedagogical module of the system. Added to
it, the student model is the preliminary component, which stores the information to the specific individual
learner. In this study, Multiple-choice questions (MCQs) was administered to capture the student ability with
respect to three levels of difficulty, namely, low, medium and high in Physics domain to train the neural
network. Further, neural network and psychometric analysis were used for understanding the student
characteristic and determining the student’s classification with respect to their ability. Thus, this study focused
on developing a student model by using the Multiple-Choice Questions (MCQ) for integrating it with an ITS
by applying the neural network and psychometric analysis. The findings of this research showed that even
though the linear regression between real test scores and that of the Final exam scores were marginally weak
(37%), still the success of the student classification to the extent of 80 percent (79.8%) makes this student model
a good fit for clustering students in groups according to their common characteristics. This finding is in line
with that of the findings discussed in the literature review of this study. Further, the outcome of this research is
most likely to generate a new dimension for cluster based student modelling approaches for an online learning
environment that uses aptitude tests (MCQ’s) for learners using ITS. The use of psychometric analysis and
neural network for student classification makes this study unique towards the development of a new student
model for ITS in supporting online learning. Therefore, the student model developed in this study seems to be
a good model fit for all those who wish to infuse aptitude test based student modelling approach in an ITS
system for an online learning environment. (Abstract by Author
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