Using social network technology as a peer-mediated learning tool in a computer course

Lin, Fang‐Ling; Lin, Der-Fen

doi:10.1109/tale.2013.6654448

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…It has been theoretically shown that max pooling improves discriminability over average pooling ( Boureau et al , 2010 ) though max pooling causes overfitting easily. Global max pooling ( Lin et al , 2013 ) are mostly utilized in models of motif detecting, because it has a reasonable statistical meaning of choose the biggest score after convolution. However, max pooling which loses some information may be not optimal and lack of the better interpretation of probability ( Alipanahi et al , 2015 ) on motif inference.…”

Section: Introductionmentioning

confidence: 99%

Expectation pooling: an effective and interpretable pooling method for predicting DNA–protein binding

Luo¹,

Ding

et al. 2019

Bioinformatics

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Motivation Convolutional neural networks (CNNs) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. While previous studies have built a connection between CNNs and probabilistic models, simple models of CNNs cannot achieve sufficient accuracy on this problem. Recently, some methods of neural networks have increased performance using complex neural networks whose results cannot be directly interpreted. However, it is difficult to combine probabilistic models and CNNs effectively to improve DNA–protein binding predictions. Results In this article, we present a novel global pooling method: expectation pooling for predicting DNA–protein binding. Our pooling method stems naturally from the expectation maximization algorithm, and its benefits can be interpreted both statistically and via deep learning theory. Through experiments, we demonstrate that our pooling method improves the prediction performance DNA–protein binding. Our interpretable pooling method combines probabilistic ideas with global pooling by taking the expectations of inputs without increasing the number of parameters. We also analyze the hyperparameters in our method and propose optional structures to help fit different datasets. We explore how to effectively utilize these novel pooling methods and show that combining statistical methods with deep learning is highly beneficial, which is promising and meaningful for future studies in this field. Availability and implementation All code is public in https://github.com/gao-lab/ePooling. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 99%

Expectation pooling: an effective and interpretable pooling method for predicting DNA–protein binding

Luo¹,

Ding

et al. 2019

Bioinformatics

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“…Moreover, in order to obtain a more generalized use of them, teachers must consider social networks as an opportunity for tutorials and educational communication (Roblyer, McDaniel, Webb, Herman & Witty, 2010). An example of how well this technology can be used is shown in a study done by Fang -Lin y Lin (2013), in which the eff ectiveness of using social media technology in the classroom is proved; the study shows how students improve signifi cantly their learning and also their academic responsibilities.…”

Section: Introductionmentioning

confidence: 99%

Acceptance factors on the use of social networks for academic purposes of management sciences students

Valencia¹,

Salazar²,

Correa-Díaz³

et al. 2015

tner

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Th is study examines factors involving the acceptance of social networks for academic purposes. A survey was applied to 290 graduate students as a quantitative methodological design. Th e main fi nding is that students thought that greater use of social networks for academic purposes would improve interaction and feedback among peers and also would improve teaching-learning processes. Also, students considered their skills as suffi cient to use social networks for academic purposes, expressing their intention to incorporate them into the subjects dynamics.

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Using social network technology as a peer-mediated learning tool in a computer course

Cited by 2 publications

References 27 publications

Expectation pooling: an effective and interpretable pooling method for predicting DNA–protein binding

Expectation pooling: an effective and interpretable pooling method for predicting DNA–protein binding

Acceptance factors on the use of social networks for academic purposes of management sciences students

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