scholarly journals Study Partners Recommendation for xMOOCs Learners

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Bin Xu ◽  
Dan Yang
Keyword(s):  
Social Network ◽  
Large Scale ◽  
Topic Model ◽  
Online Courses ◽  
Completion Rate ◽  
Network Information ◽  
Teachers And Students ◽  
Study Partner ◽  
The Relationship ◽  
Content Information

Massive open online courses (MOOCs) provide an opportunity for people to access free courses offered by top universities in the world and therefore attracted great attention and engagement from college teachers and students. However, with contrast to large scale enrollment, the completion rate of these courses is really low. One of the reasons for students to quit learning process is problems which they face that could not be solved by discussing them with classmates. In order to keep them staying in the course, thereby further improving the completion rate, we address the task of study partner recommendation for students based on both content information and social network information. By analyzing the content of messages posted by learners in course discussion forum, we investigated the learners’ behavior features to classify the learners into three groups. Then we proposed a topic model to measure learners’ course knowledge awareness. Finally, a social network was constructed based on their activities in the course forum, and the relationship in the network was then employed to recommend study partners for target learner combined with their behavior features and course knowledge awareness. The experiment results show that our method achieves better performance than recommending method only based on content information.

scholarly journals Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Jun Long ◽  
Lei Zhu ◽  
Zhan Yang ◽  
Chengyuan Zhang ◽  
Xinpan Yuan
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
Multimedia Data ◽  
Attribute Information ◽  
Novel Method ◽  
Relationship Network ◽  
Heterogeneous Social Networks ◽  
The Relationship ◽  
Over Time

Vast amount of multimedia data contains massive and multifarious social information which is used to construct large-scale social networks. In a complex social network, a character should be ideally denoted by one and only one vertex. However, it is pervasive that a character is denoted by two or more vertices with different names; thus it is usually considered as multiple, different characters. This problem causes incorrectness of results in network analysis and mining. The factual challenge is that character uniqueness is hard to correctly confirm due to lots of complicated factors, for example, name changing and anonymization, leading to character duplication. Early, limited research has shown that previous methods depended overly upon supplementary attribute information from databases. In this paper, we propose a novel method to merge the character vertices which refer to the same entity but are denoted with different names. With this method, we firstly build the relationship network among characters based on records of social activities participating, which are extracted from multimedia sources. Then we define temporal activity paths (TAPs) for each character over time. After that, we measure similarity of the TAPs for any two characters. If the similarity is high enough, the two vertices should be considered as the same character. Based on TAPs, we can determine whether to merge the two character vertices. Our experiments showed that this solution can accurately confirm character uniqueness in large-scale social network.

scholarly journals The Attitude of the Academic Community towards Distance Learning: A Lesson from a National Lockdown

2021 ◽  
Vol 19 (4) ◽  
pp. pp262-281
Author(s):  
Marta Migocka-Patrzałek ◽  
Magda Dubińska-Magiera ◽  
Dawid Krysiński ◽  
Stefan Nowicki
Keyword(s):  
Higher Education ◽  
Distance Learning ◽  
Quantitative Analysis ◽  
Large Scale ◽  
Online Courses ◽  
Academic Community ◽  
Teachers And Students ◽  
Willingness To Use ◽  
Academic Year

The number of online courses conducted at universities has been growing steadily worldwide. The demand for this form of education has jumped sharply in the 2019/2020 academic year as a consequence of the COVID-19 pandemic and the national lockdown. The following study uses the case of University of Wrocław and examines how this unprecedented situation would affect the attitude of members of the academic community toward distance learning. The examination, based on quantitative analysis of separated questionnaires distributed among teachers and students, reveals that the previous experience in distance learning strongly correlates with willingness to use it in the future, i.e. after fighting the coronavirus crisis. Thus, the research suggests that the implementation of distance learning may involve the need to put more emphasis on systematic and long-term actions. The results achieved in the study may contribute to improving the ways of implementing distance learning on a large scale in institutions dealing with higher education.  

scholarly journals Event-Based User Classification in Weibo Media

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Liang Guo ◽  
Wendong Wang ◽  
Shiduan Cheng ◽  
Xirong Que
Keyword(s):  
Social Network ◽  
Real Data ◽  
Network Information ◽  
User Classification ◽  
Social Events ◽  
The Social ◽  
Access Information ◽  
Primary Groups ◽  
Event Based ◽  
Content Information

Weibo media, known as the real-time microblogging services, has attracted massive attention and support from social network users. Weibo platform offers an opportunity for people to access information and changes the way people acquire and disseminate information significantly. Meanwhile, it enables people to respond to the social events in a more convenient way. Much of the information in Weibo media is related to some events. Users who post different contents, and exert different behavior or attitude may lead to different contribution to the specific event. Therefore, classifying the large amount of uncategorized social circles generated in Weibo media automatically from the perspective of events has been a promising task. Under this circumstance, in order to effectively organize and manage the huge amounts of users, thereby further managing their contents, we address the task of user classification in a more granular, event-based approach in this paper. By analyzing real data collected from Sina Weibo, we investigate the Weibo properties and utilize both content information and social network information to classify the numerous users into four primary groups: celebrities, organizations/media accounts, grassroots stars, and ordinary individuals. The experiments results show that our method identifies the user categories accurately.

scholarly journals Results of process evaluation of the Unplugged school-based prevention program in Nigeria

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
F Vigna-Taglianti ◽  
I N Emelurumonye ◽  
L Donati ◽  
M Alesina ◽  
I Akanidomo ◽  
...  
Keyword(s):  
Process Evaluation ◽  
Low Income ◽  
Prevention Program ◽  
Large Scale ◽  
Low Income Countries ◽  
School Based ◽  
Class Climate ◽  
Prevention Interventions ◽  
Teachers And Students ◽  
The Relationship

Abstract Background The UNODC with the collaboration of the Government implemented in Nigeria a large-scale project to promote healthy lifestyles in schools, families and communities. Within the project, the prevention program Unplugged was tested through a randomized controlled trial. This study aims to evaluate the implementation of the program in the intervention schools and the satisfaction of teachers and students. Methods 32 schools were randomly extracted from a list of 60 schools provided by the Federal Ministry of Education. 16 schools were randomly allocated to intervention and implemented Unplugged. To monitor program implementation, the teachers filled a form reporting data on fidelity of implementation. To monitor satisfaction, teachers and students filled an anonymous form at the end of the program. Results 69% of intervention schools participated in the process evaluation. The duration of the units was on average 55 min. The implementation rate was very high. All classes implemented six units whilst less than 10% did not implement the other six units. The highest rate of not implementing classes was observed for unit 11 and 12. The highest student interest as declared by the teachers was observed for Unit 1 and 8, the highest interactivity for Unit 9 and 8. Most teachers found the units easy to lead and referred an improvement of teaching skills, knowledge about substances, relationship with the students and class climate, and found very useful the Teacher Handbook. Ninety percent of students considered Unplugged useful for their choices, for 82% it improved the vision of themselves, for 95% their knowledge, for 80% the relationship with mates and for 77% the relationship with teachers. 97.5% of students would like to have a similar program next year. Conclusions Unplugged reached in Nigeria very good results in terms of implementation and satisfaction of teachers and students. Process evaluation is useful to improve the quality of prevention interventions. Key messages School based prevention interventions like Unplugged can be successfully implemented in low income countries, especially when supported by printed Handbook for teachers. Teachers and students participating in Unplugged perceived an improvement of class climate and relationship between teachers and students due to the program.

Set Pair Social Network Analysis Model

2011 ◽  
Vol 50-51 ◽  
pp. 63-67 ◽  
Author(s):  
Hong Mei Yang ◽  
Chun Ying Zhang ◽  
Rui Tao Liang ◽  
Fang Tian
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Dynamic Problems ◽  
Analysis Model ◽  
Network Information ◽  
The Social ◽  
The Relationship

Through the study on social network information, this paper explore that there exists the certain and uncertain phenomena in the process of finding the relationship between individuals by using social networks, and the social networks are constantly changing. In light of there are some uncertainty and dynamic problems for the network, this paper put forward the set pair social network analysis model and set pair social network analysis model and its properties.

scholarly journals Computer-Assisted Reading and Discovery for Student Generated Text in Massive Open Online Courses

2014 ◽  
Vol 2 (1) ◽  
Author(s):  
Justin Reich ◽  
Dustin Tingley ◽  
Jetson Leder-Luis ◽  
Margaret E. Roberts ◽  
Brandon Stewart
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Topic Model ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Course Evaluations ◽  
Computer Assisted ◽  
Discussion Forums ◽  
Massive Open Online ◽  
Daunting Challenge

Dealing with the vast quantities of text that students generate in Massive Open Online Courses (MOOCs) and other large-scale online learning environments is a daunting challenge. Computational tools are needed to help instructional teams uncover themes and patterns as students write in forums, assignments, and surveys. This paper introduces to the learning analytics community the Structural Topic Model, an approach to language processing that can 1) find syntactic patterns with semantic meaning in unstructured text, 2) identify variation in those patterns across covariates, and 3) uncover archetypal texts that exemplify the documents within a topical pattern. We show examples of computationally aided discovery and reading in three MOOC settings: mapping students’ self-reported motivations, identifying themes in discussion forums, and uncovering patterns of feedback in course evaluations. 

Social Networks and Health: New Developments in Diffusion, Online and Offline

2019 ◽  
Vol 45 (1) ◽  
pp. 91-109 ◽  
Author(s):  
Jingwen Zhang ◽  
Damon Centola
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Large Scale ◽  
Social Contagion ◽  
Online Data ◽  
New Developments ◽  
Current State ◽  
The Relationship ◽  
Structural Determinant

The relationship between social networks and health encompasses everything from the flow of pathogens and information to the diffusion of beliefs and behaviors. This review addresses the vast and multidisciplinary literature that studies social networks as a structural determinant of health. In particular, we report on the current state of knowledge on how social contagion dynamics influence individual and collective health outcomes. We pay specific attention to research that leverages large-scale online data and social network experiments to empirically identify three broad classes of contagion processes: pathogenic diffusion, informational and belief diffusion, and behavioral diffusion. We conclude by identifying the need for more research on ( a) how multiple contagions interact within the same social network, ( b) how online social networks impact offline health, and ( c) the effectiveness of social network interventions for improving population health.

Unfolding Sentimental and Behavioral Tendencies of Learners' Concerned Topics From Course Reviews in a MOOC

2018 ◽  
Vol 57 (3) ◽  
pp. 670-696 ◽  
Author(s):  
Sannyuya Liu ◽  
Xian Peng ◽  
Hercy N. H. Cheng ◽  
Zhi Liu ◽  
Jianwen Sun ◽  
...  
Keyword(s):  
Large Scale ◽  
Topic Model ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Video Production ◽  
Feedback Channel ◽  
Massive Open Online ◽  
Behavioral Tendencies ◽  
Negative Sentiment ◽  
Interactive Feedback

Course reviews, which is designed as an interactive feedback channel in Massive Open Online Courses, has promoted the generation of large-scale text comments. These data, which contain not only learners' concerns, opinions and feelings toward courses, instructors, and platforms but also learners' interactions (e.g., post, reply), are generally subjective and extremely valuable for online instruction. The purpose of this study is to automatically reveal these potential information from 50 online courses by an improved unified topic model Behavior-Sentiment Topic Mixture, which is validated and effective for detecting frequent topics learners discuss most, topics-oriented sentimental tendency as well as how learners interact with these topics. The results show that learners focus more on the topics about course-related content with positive sentiment, as well as the topics about course logistics and video production with negative sentiment. Moreover, the distributions of behaviors associated with these topics have some differences.

Relationship between total plasma homocysteine and the risk of aneurysms – a meta-analysis

VASA ◽  
2020 ◽  
pp. 1-6
Author(s):  
Hanji Zhang ◽  
Dexin Yin ◽  
Yue Zhao ◽  
Yezhou Li ◽  
Dejiang Yao ◽  
...  
Keyword(s):  
Large Scale ◽  
Meta Analysis ◽  
Single Species ◽  
Total Plasma ◽  
Control Groups ◽  
Randomized Controlled ◽  
Randomized Controlled Studies ◽  
The Difference ◽  
The Relationship ◽  
Healthy Participants

Summary: Our meta-analysis focused on the relationship between homocysteine (Hcy) level and the incidence of aneurysms and looked at the relationship between smoking, hypertension and aneurysms. A systematic literature search of Pubmed, Web of Science, and Embase databases (up to March 31, 2020) resulted in the identification of 19 studies, including 2,629 aneurysm patients and 6,497 healthy participants. Combined analysis of the included studies showed that number of smoking, hypertension and hyperhomocysteinemia (HHcy) in aneurysm patients was higher than that in the control groups, and the total plasma Hcy level in aneurysm patients was also higher. These findings suggest that smoking, hypertension and HHcy may be risk factors for the development and progression of aneurysms. Although the heterogeneity of meta-analysis was significant, it was found that the heterogeneity might come from the difference between race and disease species through subgroup analysis. Large-scale randomized controlled studies of single species and single disease species are needed in the future to supplement the accuracy of the results.

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