27,329 research outputs found
Latent Skill Embedding for Personalized Lesson Sequence Recommendation
Students in online courses generate large amounts of data that can be used to
personalize the learning process and improve quality of education. In this
paper, we present the Latent Skill Embedding (LSE), a probabilistic model of
students and educational content that can be used to recommend personalized
sequences of lessons with the goal of helping students prepare for specific
assessments. Akin to collaborative filtering for recommender systems, the
algorithm does not require students or content to be described by features, but
it learns a representation using access traces. We formulate this problem as a
regularized maximum-likelihood embedding of students, lessons, and assessments
from historical student-content interactions. An empirical evaluation on
large-scale data from Knewton, an adaptive learning technology company, shows
that this approach predicts assessment results competitively with benchmark
models and is able to discriminate between lesson sequences that lead to
mastery and failure.Comment: Under review by the ACM SIGKDD Conference on Knowledge Discovery and
Data Minin
Adaptive Learning Material Recommendation in Online Language Education
Recommending personalized learning materials for online language learning is
challenging because we typically lack data about the student's ability and the
relative difficulty of learning materials. This makes it hard to recommend
appropriate content that matches the student's prior knowledge. In this paper,
we propose a refined hierarchical knowledge structure to model vocabulary
knowledge, which enables us to automatically organize the authentic and
up-to-date learning materials collected from the internet. Based on this
knowledge structure, we then introduce a hybrid approach to recommend learning
materials that adapts to a student's language level. We evaluate our work with
an online Japanese learning tool and the results suggest adding adaptivity into
material recommendation significantly increases student engagement.Comment: The short version of this paper is published at AIED 201
E-Gotsky: Sequencing Content using the Zone of Proximal Development
Vygotsky's notions of Zone of Proximal Development and Dynamic Assessment
emphasize the importance of personalized learning that adapts to the needs and
abilities of the learners and enables more efficient learning. In this work we
introduce a novel adaptive learning engine called E-gostky that builds on these
concepts to personalize the learning path within an e-learning system. E-gostky
uses machine learning techniques to select the next content item that will
challenge the student but will not be overwhelming, keeping students in their
Zone of Proximal Development.
To evaluate the system, we conducted an experiment where hundreds of students
from several different elementary schools used our engine to learn fractions
for five months. Our results show that using E-gostky can significantly reduce
the time required to reach similar mastery. Specifically, in our experiment, it
took students who were using the adaptive learning engine less time to
reach a similar level of mastery as of those who didn't. Moreover, students
made greater efforts to find the correct answer rather than guessing and class
teachers reported that even students with learning disabilities showed higher
engagement.Comment: A short version of this paper was accepted for publication
Educational Data Mining (EDM) conference 201
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
Time-varying Learning and Content Analytics via Sparse Factor Analysis
We propose SPARFA-Trace, a new machine learning-based framework for
time-varying learning and content analytics for education applications. We
develop a novel message passing-based, blind, approximate Kalman filter for
sparse factor analysis (SPARFA), that jointly (i) traces learner concept
knowledge over time, (ii) analyzes learner concept knowledge state transitions
(induced by interacting with learning resources, such as textbook sections,
lecture videos, etc, or the forgetting effect), and (iii) estimates the content
organization and intrinsic difficulty of the assessment questions. These
quantities are estimated solely from binary-valued (correct/incorrect) graded
learner response data and a summary of the specific actions each learner
performs (e.g., answering a question or studying a learning resource) at each
time instance. Experimental results on two online course datasets demonstrate
that SPARFA-Trace is capable of tracing each learner's concept knowledge
evolution over time, as well as analyzing the quality and content organization
of learning resources, the question-concept associations, and the question
intrinsic difficulties. Moreover, we show that SPARFA-Trace achieves comparable
or better performance in predicting unobserved learner responses than existing
collaborative filtering and knowledge tracing approaches for personalized
education
Optimal Hierarchical Learning Path Design with Reinforcement Learning
E-learning systems are capable of providing more adaptive and efficient
learning experiences for students than the traditional classroom setting. A key
component of such systems is the learning strategy, the algorithm that designs
the learning paths for students based on information such as the students'
current progresses, their skills, learning materials, and etc. In this paper,
we address the problem of finding the optimal learning strategy for an
E-learning system. To this end, we first develop a model for students'
hierarchical skills in the E-learning system. Based on the hierarchical skill
model and the classical cognitive diagnosis model, we further develop a
framework to model various proficiency levels of hierarchical skills. The
optimal learning strategy on top of the hierarchical structure is found by
applying a model-free reinforcement learning method, which does not require
information on students' learning transition process. The effectiveness of the
proposed framework is demonstrated via numerical experiments
From Social to Individuals: a Parsimonious Path of Multi-level Models for Crowdsourced Preference Aggregation
In crowdsourced preference aggregation, it is often assumed that all the
annotators are subject to a common preference or social utility function which
generates their comparison behaviors in experiments. However, in reality
annotators are subject to variations due to multi-criteria, abnormal, or a
mixture of such behaviors. In this paper, we propose a parsimonious
mixed-effects model, which takes into account both the fixed effect that the
majority of annotators follows a common linear utility model, and the random
effect that some annotators might deviate from the common significantly and
exhibit strongly personalized preferences. The key algorithm in this paper
establishes a dynamic path from the social utility to individual variations,
with different levels of sparsity on personalization. The algorithm is based on
the Linearized Bregman Iterations, which leads to easy parallel implementations
to meet the need of large-scale data analysis. In this unified framework, three
kinds of random utility models are presented, including the basic linear model
with L2 loss, Bradley-Terry model, and Thurstone-Mosteller model. The validity
of these multi-level models are supported by experiments with both simulated
and real-world datasets, which shows that the parsimonious multi-level models
exhibit improvements in both interpretability and predictive precision compared
with traditional HodgeRank.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligence as a regular paper. arXiv admin note: substantial text overlap
with arXiv:1607.0340
From evaluation to learning: Some aspects of designing a cyber-university
Research is described on a system for web-assisted education and how it is
used to deliver on-line drill questions, automatically suited to individual
students. The system can store and display all of the various pieces of
information used in a class-room (slides, examples, handouts, drill items) and
give individualized drills to participating students. The system is built on
the basic theme that it is for learning rather than evaluation.
Experimental results shown here imply that both the item database and the
item allocation methods are important and examples are given on how these need
to be tuned for each course. Different item allocation methods are discussed
and a method is proposed for comparing several such schemes. It is shown that
students improve their knowledge while using the system. Classical statistical
models which do not include learning, but are designed for mere evaluation, are
therefore not applicable.
A corollary of the openness and emphasis on learning is that the student is
permitted to continue requesting drill items until the system reports a grade
which is satisfactory to the student. An obvious resulting challenge is how
such a grade should be computed so as to reflect actual knowledge at the time
of computation, entice the student to continue and simultaneously be a clear
indication for the student. To name a few methods, a grade can in principle be
computed based on all available answers on a topic, on the last few answers or
on answers up to a given number of attempts, but all of these have obvious
problems.Comment: First presented at EduLearn1
Tag-Aware Ordinal Sparse Factor Analysis for Learning and Content Analytics
Machine learning offers novel ways and means to design personalized learning
systems wherein each student's educational experience is customized in real
time depending on their background, learning goals, and performance to date.
SPARse Factor Analysis (SPARFA) is a novel framework for machine learning-based
learning analytics, which estimates a learner's knowledge of the concepts
underlying a domain, and content analytics, which estimates the relationships
among a collection of questions and those concepts. SPARFA jointly learns the
associations among the questions and the concepts, learner concept knowledge
profiles, and the underlying question difficulties, solely based on the
correct/incorrect graded responses of a population of learners to a collection
of questions. In this paper, we extend the SPARFA framework significantly to
enable: (i) the analysis of graded responses on an ordinal scale (partial
credit) rather than a binary scale (correct/incorrect); (ii) the exploitation
of tags/labels for questions that partially describe the question{concept
associations. The resulting Ordinal SPARFA-Tag framework greatly enhances the
interpretability of the estimated concepts. We demonstrate using real
educational data that Ordinal SPARFA-Tag outperforms both SPARFA and existing
collaborative filtering techniques in predicting missing learner responses
A Personalized System for Conversational Recommendations
Searching for and making decisions about information is becoming increasingly
difficult as the amount of information and number of choices increases.
Recommendation systems help users find items of interest of a particular type,
such as movies or restaurants, but are still somewhat awkward to use. Our
solution is to take advantage of the complementary strengths of personalized
recommendation systems and dialogue systems, creating personalized aides. We
present a system -- the Adaptive Place Advisor -- that treats item selection as
an interactive, conversational process, with the program inquiring about item
attributes and the user responding. Individual, long-term user preferences are
unobtrusively obtained in the course of normal recommendation dialogues and
used to direct future conversations with the same user. We present a novel user
model that influences both item search and the questions asked during a
conversation. We demonstrate the effectiveness of our system in significantly
reducing the time and number of interactions required to find a satisfactory
item, as compared to a control group of users interacting with a non-adaptive
version of the system
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