Evaluating topic-word review analysis for understanding student peer review performance

© 2013 International Educational Data Mining Society. All rights reserved. Topic modeling is widely used for content analysis of textual documents. While the mined topic terms are considered as a semantic abstraction of the original text, few people evaluate the accuracy of humans’ interpretation of them in the context of an application based on the topic terms. Previously, we proposed RevExplore, an interactive peer-review analytic tool that supports teachers in making sense of large volumes of student peer reviews. To better evaluate the functionality of RevExplore, in this paper we take a closer look at its Natural Language Processing component which automatically compares two groups of reviews at the topic-word level. We employ a user study to evaluate our topic extraction method, as well as the topic-word analysis approach in the context of educational peer-review analysis. Our results show that the proposed method is better than a baseline in terms of capturing student reviewing/writing performance. While users generally identify student writing/reviewing performance correctly, participants who have prior teaching or peer-review experience tend to have better performance on our review exploration tasks, as well as higher satisfaction towards the proposed review analysis approach

Localized Asymmetric Atomic Matter Waves in Two-Component Bose-Einstein Condensates Coupled with Two Photon Microwave Field

We investigate localized atomic matter waves in two-component Bose-Einstein condensates coupled by the two photon microwave field. Interestingly, the oscillations of localized atomic matter waves will gradually decay and finally become non-oscillating behavior even if existing coupling field. In particular, atom numbers occupied in two different hyperfine spin states will appear asymmetric occupations after some time evolution.Comment: 4 pages, 4 figure

Natural language processing techniques for researching and improving peer feedback

Peer review has been viewed as a promising solution for improving studennts' writing, which still remains a great challenge for educators. However, one core problem with peer review of writing is that potentially useful feedbback from peers is not always presented in ways that lead to revision. Our prior investigations found that whether students implement feedback is significantly correlated with two feedback features: localization information and concrete solutions. But focusing on feedback features is time-intensive for researchers and instructors. We apply data mining and Natural Languagee Processing techniques to automatically code reviews for these feedback features. Our results show that it is feasible to provide intelligent suppport to peer review systems to automatically assess students' reviewing performance with respect to problem localization and solution. We also show that similar research conclusions about helpfulness perceptions of feedback across students and different expert types can be drawn from automatically coded data and from hand-coded data. © Earli

A Hessenberg Markov chain for fast fibre delay line length optimization

In this paper we present an approach to compute the invariant vector of the N + 1 state Markov chain P presented in (Rogiest et al., Lecture Notes in Computer Science, NET-COOP 2007 Special Issue, pp. 4465:185-194) to determine the loss rate of an FDL buffer consisting of N lines, by solving a related Hessenberg system (i.e., a Markov chain skip-free in one direction). This system is obtained by inserting additional time instants in the sample paths of P and allows us to compute the loss rate for various FDL lengths by solving a single system. This is shown to be especially effective in reducing the computation time of the heuristic LRA algorithm presented in (Lambert et al., Proc. NAEC 2005, pp. 545-555) to optimize the FDL lengths, where improvements of several orders of magnitude can be realized

Precision Measurement of the Spin-Dependent Asymmetry in the Threshold Region of ^3He(e, e')

We present the first precision measurement of the spin-dependent asymmetry in the threshold region of ^3He(e,e′) at Q^2 values of 0.1 and 0.2(GeV/c)^2. The agreement between the data and nonrelativistic Faddeev calculations which include both final-state interactions and meson-exchange current effects is very good at Q^2 = 0.1(GeV/c)^2, while a small discrepancy at Q^2 = 0.2(GeV/c)^2 is observed

The Microsoft 2016 Conversational Speech Recognition System

We describe Microsoft's conversational speech recognition system, in which we combine recent developments in neural-network-based acoustic and language modeling to advance the state of the art on the Switchboard recognition task. Inspired by machine learning ensemble techniques, the system uses a range of convolutional and recurrent neural networks. I-vector modeling and lattice-free MMI training provide significant gains for all acoustic model architectures. Language model rescoring with multiple forward and backward running RNNLMs, and word posterior-based system combination provide a 20% boost. The best single system uses a ResNet architecture acoustic model with RNNLM rescoring, and achieves a word error rate of 6.9% on the NIST 2000 Switchboard task. The combined system has an error rate of 6.2%, representing an improvement over previously reported results on this benchmark task