11,751 research outputs found
Evaluating and improving adaptive educational systems with learning curves
Personalised environments such as adaptive educational systems can be evaluated and compared using performance curves. Such summative studies are useful for determining whether or not new modifications enhance or degrade performance. Performance curves also have the potential to be utilised in formative studies that can shape adaptive model design at a much finer level of granularity. We describe the use of learning curves for evaluating personalised educational systems and outline some of the potential pitfalls and how they may be overcome. We then describe three studies in which we demonstrate how learning curves can be used to drive changes in the user model. First, we show how using learning curves for subsets of the domain model can yield insight into the appropriateness of the model’s structure. In the second study we use this method to experiment with model granularity. Finally, we use learning curves to analyse a large volume of user data to explore the feasibility of using them as a reliable method for fine-tuning a system’s model. The results of these experiments demonstrate the successful use of performance curves in formative studies of adaptive educational systems
Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling
Adaptive learning technology solutions often use a learner model to trace
learning and make pedagogical decisions. The present research introduces a
formalized methodology for specifying learner models, Logistic Knowledge
Tracing (LKT), that consolidates many extant learner modeling methods. The
strength of LKT is the specification of a symbolic notation system for
alternative logistic regression models that is powerful enough to specify many
extant models in the literature and many new models. To demonstrate the
generality of LKT, we fit 12 models, some variants of well-known models and
some newly devised, to 6 learning technology datasets. The results indicated
that no single learner model was best in all cases, further justifying a broad
approach that considers multiple learner model features and the learning
context. The models presented here avoid student-level fixed parameters to
increase generalizability. We also introduce features to stand in for these
intercepts. We argue that to be maximally applicable, a learner model needs to
adapt to student differences, rather than needing to be pre-parameterized with
the level of each student's ability
An Online Tutor for Astronomy: The GEAS Self-Review Library
We introduce an interactive online resource for use by students and college
instructors in introductory astronomy courses. The General Education Astronomy
Source (GEAS) online tutor guides students developing mastery of core
astronomical concepts and mathematical applications of general astronomy
material. It contains over 12,000 questions, with linked hints and solutions.
Students who master the material quickly can advance through the topics, while
under-prepared or hesitant students can focus on questions on a certain topic
for as long as needed, with minimal repetition. Students receive individual
accounts for study and course instructors are provided with overview tracking
information, by time and by topic, for entire cohorts of students. Diagnostic
tools support self-evaluation and close collaboration between instructor and
student, even for distance learners. An initial usage study shows clear trends
in performance which increase with study time, and indicates that distance
learners using these materials perform as well as or better than a comparison
cohort of on-campus astronomy students. We are actively seeking new
collaborators to use this resource in astronomy courses and other educational
venues.Comment: 15 pages, 9 figures; Vogt, N. P., and A. S. Muise. 2015. An online
tutor for general astronomy: The GEAS self-review library. Cogent Education,
2 (1
A quantitative evaluation of the AVITEWRITE model of handwriting learning
Much sensory-motor behavior develops through imitation, as during the learning of handwriting by children. Such complex sequential acts are broken down into distinct motor control synergies, or muscle groups, whose activities overlap in time to generate continuous, curved movements that obey an intense relation between curvature and speed. The Adaptive Vector Integration to Endpoint (AVITEWRITE) model of Grossberg and Paine (2000) proposed how such complex movements may be learned through attentive imitation. The model suggest how frontal, parietal, and motor cortical mechanisms, such as difference vector encoding, under volitional control from the basal ganglia, interact with adaptively-timed, predictive cerebellar learning during movement imitation and predictive performance. Key psycophysical and neural data about learning to make curved movements were simulated, including a decrease in writing time as learning progresses; generation of unimodal, bell-shaped velocity profiles for each movement synergy; size scaling with isochrony, and speed scaling with preservation of the letter shape and the shapes of the velocity profiles; an inverse relation between curvature and tangential velocity; and a Two-Thirds Power Law relation between angular velocity and curvature. However, the model learned from letter trajectories of only one subject, and only qualitative kinematic comparisons were made with previously published human data. The present work describes a quantitative test of AVITEWRITE through direct comparison of a corpus of human handwriting data with the model's performance when it learns by tracing human trajectories. The results show that model performance was variable across subjects, with an average correlation between the model and human data of 89+/-10%. The present data from simulations using the AVITEWRITE model highlight some of its strengths while focusing attention on areas, such as novel shape learning in children, where all models of handwriting and learning of other complex sensory-motor skills would benefit from further research.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Institutes of Health (1-R29-DC02952-01); Office of Naval Research (N00014-92-J-1309, N00014-01-1-0624); Air Force Office of Scientific Research (F49620-01-1-0397); National Institute of Neurological Disorders and Stroke (NS 33173
Machine Learning Approach for an Advanced Agent-based Intelligent Tutoring System
Machine Learning Approach for an Advanced Agent-based Intelligent Tutoring System
Roya Aminikia
Learning Management Systems (LMSs) are digital frameworks that provide curriculum, training
materials, and corresponding assessments to guarantee an effective learning process. Although
these systems are capable of distributing the learning content, they do not support dynamic learning
processes and do not have the capability to communicate with human learners who are required to
interact in a dynamic environment during the learning process. To create this process and support
the interaction feature, LMSs are equipped with Intelligent Tutoring Systems (ITSs). The main
objective of an ITS is to facilitate students’ movement towards their learning goals through virtual
tutoring. When equipped with ITSs, LMSs operate as dynamic systems to provide students with
access to a tutor who is available anytime during the learning session. The crucial issues we address
in this thesis are how to set up a dynamic LMS, and how to design the logical structure behind an
ITS. Artificial intelligence, multi-agent technology and machine learning provide powerful theories
and foundations that we leverage to tackle these issues.
We designed and implemented the new concept of Pedagogical Agent (PA) as the main part of
our ITS. This agent uses an evaluation procedure to compare each particular student, in terms of
performance, with their peers to develop a worthwhile guidance. The agent captures global knowledge
of students’ feature measurements during students’ guiding process. Therefore, the PA retains
an updated status, called image, of each specific student at any moment. The agent uses this image
for the purpose of diagnosing students’ skills to implement a specific correct instruction. To develop
the infrastructure of the agent decision making algorithm, we laid out a protocol (decision tree) to
select the best individual direction. The significant capability of the agent is the ability to update
its functionality by looking at a student’s image at run time. We also applied two supervised machine
learning methods to improve the decision making protocol performance in order to maximize
the effect of the collaborating mechanism between students and the ITS. Through these methods,
we made the necessary modifications to the decision making structure to promote students’ performance
by offering prompts during the learning sessions. The conducted experiments showed that
the proposed system is able to efficiently classify students into learners with high versus low performance.
Deployment of such a model enabled the PA to use different decision trees while interacting
with students of different learning skills. The performance of the system has been shown by ROC
curves and details regarding combination of different attributes used in the two machine learning
algorithms are discussed, along with the correlation of key attributes that contribute to the accuracy
and performance of the decision maker components
A Multi-Gene Genetic Programming Application for Predicting Students Failure at School
Several efforts to predict student failure rate (SFR) at school accurately
still remains a core problem area faced by many in the educational sector. The
procedure for forecasting SFR are rigid and most often times require data
scaling or conversion into binary form such as is the case of the logistic
model which may lead to lose of information and effect size attenuation. Also,
the high number of factors, incomplete and unbalanced dataset, and black boxing
issues as in Artificial Neural Networks and Fuzzy logic systems exposes the
need for more efficient tools. Currently the application of Genetic Programming
(GP) holds great promises and has produced tremendous positive results in
different sectors. In this regard, this study developed GPSFARPS, a software
application to provide a robust solution to the prediction of SFR using an
evolutionary algorithm known as multi-gene genetic programming. The approach is
validated by feeding a testing data set to the evolved GP models. Result
obtained from GPSFARPS simulations show its unique ability to evolve a suitable
failure rate expression with a fast convergence at 30 generations from a
maximum specified generation of 500. The multi-gene system was also able to
minimize the evolved model expression and accurately predict student failure
rate using a subset of the original expressionComment: 14 pages, 9 figures, Journal paper. arXiv admin note: text overlap
with arXiv:1403.0623 by other author
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