7,804 research outputs found
Adaptive Educational Hypermedia based on Multiple Student Characteristics
The learning process in Adaptive Educational Hypermedia (AEH) environments is complex and may be influenced by aspects of the student, including prior knowledge, learning styles, experience and preferences. Current AEH environments, however, are limited to processing only a small number of student characteristics. This paper discusses the development of an AEH system which includes a student model that can simultaneously take into account multiple student characteristics. The student model will be developed to use stereotypes, overlays and perturbation techniques. Keywords: adaptive educational hypermedia, multiple characteristics, student model
QuizMap: Open social student modeling and adaptive navigation support with TreeMaps
In this paper, we present a novel approach to integrate social adaptive navigation support for self-assessment questions with an open student model using QuizMap, a TreeMap-based interface. By exposing student model in contrast to student peers and the whole class, QuizMap attempts to provide social guidance and increase student performance. The paper explains the nature of the QuizMap approach and its implementation in the context of self-assessment questions for Java programming. It also presents the design of a semester-long classroom study that we ran to evaluate QuizMap and reports the evaluation results. © 2011 Springer-Verlag Berlin Heidelberg
Knowledgezoom for java: A concept-based exam study tool with a zoomable open student model
This paper presents our attempt to develop a personalized exam preparation tool for Java/OOP classes based on a fine-grained concept model of Java knowledge. Our goal was to explore two most popular student model-based approaches: open student modeling and problem sequencing. The result of our work is a Java exam preparation tool, Knowledge Zoom. The tool combines an open concept-level student model component, Knowledge Explorer and a concept-based sequencing component, Knowledge Maximizer into a single interface. This paper presents both components of Knowledge Zoom, reports results of its evaluation, and discusses lessons learned. © 2013 IEEE
SNR-Based Teachers-Student Technique for Speech Enhancement
It is very challenging for speech enhancement methods to achieves robust
performance under both high signal-to-noise ratio (SNR) and low SNR
simultaneously. In this paper, we propose a method that integrates an SNR-based
teachers-student technique and time-domain U-Net to deal with this problem.
Specifically, this method consists of multiple teacher models and a student
model. We first train the teacher models under multiple small-range SNRs that
do not coincide with each other so that they can perform speech enhancement
well within the specific SNR range. Then, we choose different teacher models to
supervise the training of the student model according to the SNR of the
training data. Eventually, the student model can perform speech enhancement
under both high SNR and low SNR. To evaluate the proposed method, we
constructed a dataset with an SNR ranging from -20dB to 20dB based on the
public dataset. We experimentally analyzed the effectiveness of the SNR-based
teachers-student technique and compared the proposed method with several
state-of-the-art methods.Comment: Published in 2020 IEEE International Conference on Multimedia and
Expo (ICME 2020
Conditional Teacher-Student Learning
The teacher-student (T/S) learning has been shown to be effective for a
variety of problems such as domain adaptation and model compression. One
shortcoming of the T/S learning is that a teacher model, not always perfect,
sporadically produces wrong guidance in form of posterior probabilities that
misleads the student model towards a suboptimal performance. To overcome this
problem, we propose a conditional T/S learning scheme, in which a "smart"
student model selectively chooses to learn from either the teacher model or the
ground truth labels conditioned on whether the teacher can correctly predict
the ground truth. Unlike a naive linear combination of the two knowledge
sources, the conditional learning is exclusively engaged with the teacher model
when the teacher model's prediction is correct, and otherwise backs off to the
ground truth. Thus, the student model is able to learn effectively from the
teacher and even potentially surpass the teacher. We examine the proposed
learning scheme on two tasks: domain adaptation on CHiME-3 dataset and speaker
adaptation on Microsoft short message dictation dataset. The proposed method
achieves 9.8% and 12.8% relative word error rate reductions, respectively, over
T/S learning for environment adaptation and speaker-independent model for
speaker adaptation.Comment: 5 pages, 1 figure, ICASSP 201
A structure for maturing intelligent tutoring system student models
A special structure is examined for evolving a detached model of the user of an intelligent tutoring system. Tutoring is used in the context of education and training devices. A detached approach to populating the student model data structure is examined in the context of the need for time dependent reasoning about what the student knows about a particular concept in the domain of interest. This approach, to generating a data structure for the student model, allows an inference engine separate from the tutoring strategy determination to be used. This methodology has advantages in environments requiring real-time operation
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