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Tsinghua Science and Technology 2019年第4期24卷 423-434页

作者： Qing Sun Ji Wu wenge rong Wenbo Liu the School of Computer Science and Engineering BeihangUniversity

In programming courses, the traditional assessment approach tends to evaluate student performance by scoring one or more project-level summative assignments. This approach no longer meets the requirements of a quality programming language education. Based on an upgraded peer code review model, we propose a formative assessment approach to assess the learning of computer programming languages, and develop an online assessment system(OOCourse) to implement this approach. Peer code review and inspection is an effective way to ensure the high quality of a program by systematically checking the source code. Though it is commonly applied in industrial and open-source software development, it is rarely taught and practiced in undergraduate-level programming courses. We conduct a case study using the formative assessment method in a sophomore level Object-Oriented Design and Construction course with more than 240 students. We use Moodle(an online learning system) and some relevant plugins to conduct peer code review. We also conduct data mining on the running data from the peer assessment activities. The case study shows that formative assessment based on peer code review gradually improved the programming ability of students in the undergraduate class.

关键词： peer code review programming language learning dynamic designation formative assessment

A hierarchical similarity based job recommendation service framework for university students

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Frontiers of Computer Science 2017年第5期11卷 912-922页

作者： Rui LIU wenge rong Yuanxin OUYANG Zhang XIONG State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China Engineering Research Center of Advanced Computer Application Technology Ministry of Education Beihang University Beijing 100191 China School of Computer Science and Engineering Beihang University Beijing 100191 China

When people want to move to a new job, it is often difficult since there is too much job information available. To select an appropriate job and then submit a resume is tedious. It is particularly difficult for university students since they normally do not have any work experience and also are unfamiliar with the job market. To deal with the informa- tion overload for students during their transition into work, a job recommendation system can be very valuable. In this research, after fully investigating the pros and cons of current job recommendation systems for university students, we propose a student profiling based re-ranking framework. In this system, the students are recommended a list of potential jobs based on those who have graduated and obtained job offers over the past few years. Furthermore, recommended employers are also used as input for job recommendation result re-ranking. Our experimental study on real recruitment data over the past four years has shown this method＇s potential.

关键词： job recommendation students similarity time,re-ranking

Structural information aware deep semi-supervised recurrent neural network for sentiment analysis

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Frontiers of Computer Science 2015年第2期9卷 171-184页

作者： wenge rong Baolin PENG Yuanxin OUYANG Chao LI Zhang XIONG School of Computer Science and Engineering Beihang University Beijing 100191 China Research Institute of Beihang University in Shenzhen Shenzhen 518057 China

With the development of Internet, people are more likely to post and propagate opinions online. Sentiment analysis is then becoming an important challenge to under- stand the polarity beneath these comments. Currently a lot of approaches from natural language processing＇s perspec- tive have been employed to conduct this task. The widely used ones include bag-of-words and semantic oriented analy- sis methods. In this research, we further investigate the struc- tural information among words, phrases and sentences within the comments to conduct the sentiment analysis. The idea is inspired by the fact that the structural information is play- ing important role in identifying the overall statement＇s po- larity. As a result a novel sentiment analysis model is pro- posed based on recurrent neural network, which takes the par- tial document as input and then the next parts to predict the sentiment label distribution rather than the next word. The proposed method learns words representation simultaneously the sentiment distribution. Experimental studies have been conducted on commonly used datasets and the results have shown its promising potential.

关键词： sentiment analysis recurrent neural network,deep learning machine learning

Enhanced Learning Resource Recommendation Based on Online Learning Style Model

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Tsinghua Science and Technology 2020年第3期25卷 348-356页

作者： Hui Chen Chuantao Yin Rumei Li wenge rong Zhang Xiong Bertrand David the School of Computer Science and Engineering Beihang UniversityBeijing 100191China Sino-French Engineer School Beihang UniversityBeijing 100191China. Laboratoire cHnfoRmatique en Image et Systemes d*information(LIRIS)Lab Ecole Centrale de LyonEcully69134France

Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual *** paper introduces a learning style model to represent features of online *** also presents an enhanced recommendation method named Adaptive Recommendation based on Online Learning Style(AROLS),which implements learning resource adaptation by mining learners’behavioral ***,AROLS creates learner clusters according to their online learning ***,it applies Collaborative Filtering(CF)and association rule mining to extract the preferences and behavioral patterns of each ***,it generates a personalized recommendation set of variable size.A real-world dataset is employed for some *** show that our online learning style model is conducive to the learners’data mining,and AROLS evidently outperforms the traditional CF method.

关键词： smart learning e-learning online learning style adaptive recommendation Collaborative Filtering(CF)

Smart City Architecture： A Technology Guide for Implementation and Design Challenges

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China Communications 2014年第3期11卷 56-69页

作者： rong wenge XIONG Zhang COOPER Dave LI Chao SHENG Hao School of Computer Science and Engineering Beihang University Beijing 100191 China Research Institute of Beihang University in Shenzhen Shenzhen 518057 China

Urban development is becoming increasingly reliant on effective use of intelligent *** the process of providing better services to all citizens and improving the efficiency of administration processes,the concept of a smart city has been lauded as a promising solution for the coming challenge of global ***,due to the broad scope of smart cities their definition has not been standardized,thus proposed architectures are *** work proposes a new architecture from the perspective of the data that underpins all the functionality of smart *** proposed architecture is discussed,outlining design challenges and offering insight into the development of smart cities.

关键词：城市发展架构技术指南智慧设计智能城市智能业务管理流程

TV White Space Spectrum Analysis Based on Machine Learning

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Journal of Communications and Information Networks 2019年第2期4卷 68-80页

作者： Yuan Ma Yue Gao Chen Fu wenge rong Zhang Xiong Shuguang Cui College of Information Engineering Shenzhen UniversityShenzhen 518060China School of Electronic Engineering and Computer Science Queen Mary University of LondonLondon E14NSUnited Kingdom School of Computer Science and Engineering Beihang UniversityBeijing 100191China Shenzhen Research Institute of Big Data and School of Science and Engineering the Chinese University of Hong KongShenzhenChina Department of Electrical and Computer Engineering University of CaliforniaDavisCA95616

Exploration of TV white space(TVWS)is a promising solution to mitigate the spectrum shortage and provide opportunities for new *** this paper,we present a detailed analysis of spectrum utilisation over TVWS at different locations in *** short-term and long-term outdoor measurement campaigns are conducted over large scales to better understand the spectrum features and variations across multiple locations and time *** from most fixed-location-only measurements,we also drive along the main streets of London with a portable moving node to measure the on-route spectrum density along with the corresponding geographical information,which allows us to study the features and variations of spectrum use through a continuous *** better analyse the dynamic spectrum utilisation,a machine learning based analysis algorithm is developed over the real-world *** approach allows us to characterise the similarity and variability in spectrum usage within and among different channels,locations,and time instances,which is critical for the secondary system deployment to efficiently exploit the white space.

关键词： TV white space spectrum utilisation features RFeye node self organising mapping machine learning

基于学习行为的MOOC用户持续学习预测框架

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北京航空航天大学学报 2023年第1期49卷 74-82页

作者：陈辉白骏殷传涛荣文戈熊璋北京航空航天大学计算机学院北京100191 北京航空航天大学中法工程师学院北京100191

大型开放式网络课程(MOOC)的出现虽然极大地改变了人们的学习方式,但用户在MOOC平台开展学习的学习情况及完成率预测仍是目前一个重要的技术挑战。针对预测的需求,从用户的学习行为中对用户和课程进行分析,采用长短时记忆机对学习者的... 详细信息

大型开放式网络课程(MOOC)的出现虽然极大地改变了人们的学习方式,但用户在MOOC平台开展学习的学习情况及完成率预测仍是目前一个重要的技术挑战。针对预测的需求,从用户的学习行为中对用户和课程进行分析,采用长短时记忆机对学习者的学习活动进行建模,采用多头注意力机制对用户和课程之间的交互活动情况进行分析,提出一个基于门控单元的特征融合框架,用于学习情况预测。在公开数据集上的结果表明:所提框架能够提升预测精度,使得MOOC平台能够尽可能早地对用户活动进行干预,从而提升整体的MOOC平台使用体验。

关键词：大型开放式网络课程预测框架用户课程内容学习行为