Engagement in Online Social Networks: The Impact of Self-Disclosure and Humor

This study proposes an engagement model that supports acceptance and use of course-based online social networks for engaging student, hence improving the instructor's credibility. This research demonstrates that instructors who create course-based online social networks can increase student engagement in these online social networks and improve the instructor's credibility. This increase in engagement is seen when the instructor posts private information related to the course and when the instructor makes humorous posts. However, it is not seen when the instructor posts private information unrelated to the course. These results should be useful for instructors who are trying to improve student engagement and to enhance their own credibility. This research utilizes communication privacy management theory and instructional humor processing theory to expand our understanding of how instructor self-disclosure and use of humor via a course-based social network impacts student outcomes. The research also contributes to the theory by providing an engagement model that is unique to online educational settings.     

Anonymity and Self-Disclosure on Weblogs

Bloggers are typically cautious about engaging in self-disclosure because of concerns that what they post may have negative consequences. This article examines the relationship between anonymity (both visual and discursive) and self-disclosure on weblogs through an online survey. The results suggest that increased visual anonymity is not associated with greater self-disclosure, and the findings about the role of discursive anonymity are mixed. Bloggers whose target audience does not include people they know offline report a higher degree of anonymity than those whose audience does. Future studies need to explore the reasons why bloggers visually and discursively identify themselves in particular ways.     

I Know My Professor: Teacher Self-Disclosure in Online Education and a Mediating Role of Social Presence

Acknowledging the importance of teacher–student relationship for effective learning experiences, the present study examined the role of teacher self-disclosure and social presence in online education. An online survey was conducted from a sample of 262 undergraduate students with online class experiences. The findings suggest that students’ perception toward teacher self-disclosure increased students’ feeling of social presence about their teacher. Then, social presence in turn led to teacher–student relationship satisfaction, which ultimately increased perceived knowledge gain. Importantly, the association between teacher self-disclosure and teacher–student relationship satisfaction was mediated by social presence.     

基于在线学习行为分析的个性化学习推荐

随着在线课程和线上学习的普及,大量的在线学习行为数据被积累。如何利用数据挖掘技术分析积累的大数据,从而为教学决策和学习优化提供服务,已经成为新的研究重点。文中分析了在线学习的行为特征,挖掘学习者的性格特征与学习效率的关系,实现个性化学习方法推荐。首先,提取在线学习行为特征,并提出了一种基于BP神经网络的学习成绩预测方法,通过分析在线学习行为特征,预测其相应的线下学习成绩;其次,为了进一步分析学习者的在线学习行为与成绩的关系,提出了基于实际熵的在线学习行为规律性分析,通过分析学习者的在线学习行为,定义并计算相应的实际熵值来评估个体的学习行为规律性,从而分析规律性与最终成绩的关系;再次,基于Felder-Silverman性格分类法获得学习者的性格特征,对学习者实现基于K-means的聚类分析获得相似学习者的类别,将学习成绩较优的学习者的在线学习习惯推荐给同一类别的其他学习者,从而提高学习者的在线学习效率;最终,以某在线课程平台的实际数据为实验对象,分别实现在线学习行为特征提取、线下成绩预测、学习规律性分析和个性化学习推荐,从而验证了所提方法的有效性和应用价值。     

基于上网日志挖掘的用户网购行为分析

移动网络运营商拥有用户全面的上网日志,这些信息蕴含着巨大的价值。通过对用户上网日志的挖掘,运营商不仅能够对网络设施进行维护,辅助进行网络建设,也能为运营商自身和其他行业营销提供依据。对上网日志信息的构成进行了阐述,以网购用户为目标,介绍了数据预处理的方法,利用兴趣模型对用户行为进行分析。     

在线旅游社区用户行为规律的探讨

本文通过抽样采集国内最大的在线旅游社区平台马蜂窝中的用户成员帖子数据,进行预处理并借助数据挖掘技术进行挖掘,从而获得在线旅游社区的用户潜在的行为规律,并对这些行为规律进行相关描述.基于关联规则数据挖掘的在线旅游社区用户行为规律研究结果具有一定的意义,可为在线旅游社区的运营者开展针对性的运营提供有益的参考.     

谈企业应该如何做员工上网行为管理

该文主要讨论了在企业中,如何从技术上实现对员工上网行为的有效管理,加强网络安全,降低运营成本。     

基于网络学习行为的学习者本体构建

随着多媒体技术的发展,教育技术进入到大数据时代,数据挖掘等学习技术对教育领域的各种原理研究十分重要,然而怎样把数据挖掘这种技术有效的应用到教育教学中是问题的关键所在。其中,本体构建就是一个重要的方法,该方法将会对研究行为规律、分析学生的行为产生重大的影响。本文在已有本体构建方法基础上,通过总结本体构建过程,设计网络学习空间中学习者本体构建方法并成功实施,最后对学习者本体进行一致性分析。这将为学习者提供精准的教学支持服务,实现个性化学习。同时,学习者本体的成功构建也将为智慧教育提供重要帮助。     

一种基于判别随机场模型的联机行为识别方法

提出了一种基于判别随机场模型的联机行为识别方法,将传统的随机场模型和隐藏条件随机场模型的特点相结合,构建一个针对于运动序列帧数据建模的帧-隐藏条件随机场模型,并将该模型应用于数据驱动的行为建模,利用传统条件随机场模型对行为间的运动特性进行建模;通过引入隐藏特征函数,设计有效的特征模板来表示行为中子姿态的联系,实现对行为的内在运动特性进行建模.通过对比实验表明,该模型对于联机处理行为序列具有更强的识别能力.     

Gratifications and Seeding Behavior of Online Adolescents

Several studies indicate that only a small minority of Web 2.0 users actively participates, while the minority do not contribute at all. This article investigates whether a similar division applies for adolescents' Internet behavior. Using Szuprowicz’ (1995) typology of interactivity, we distinguish different types of user‐generated content (UGC): media, narrative, and metadata UGC. Our results show a 20%–80% division between high‐ and low‐frequency seeders. Furthermore, we utilize the uses‐and‐gratifications paradigm to investigate how these high‐ and low‐frequency seeders differ in their overall gratifications obtained by WWW use. Although the gratifications' rank orders are identical for all groups, their magnitudes differ significantly. Finally, this article focuses on how these WWW gratifications can predict seeding, while controlling for socio‐demographics and usage frequency.     

The Perils and Promises of Self-Disclosure on Social Media

In addition to their professional social media accounts, individuals are increasingly using their personal profiles and casual posts to communicate their identities to work colleagues. They do this in order to ‘stand out from the crowd’ and to signal attributes that are difficult to showcase explicitly in a work setting. Existing studies have tended to treat personal posts viewed in a professional context as a problem, since they can threaten impression management efforts. These accounts focus on the attempts of individuals to separate their life domains on social media. In contrast, we present the narratives of professional IT workers in India who intentionally disrupt the boundaries between personal and professional profiles in order to get noticed by their employers. Drawing on the dramaturgical vocabulary of Goffman (1959) we shed light on how individuals cope with increased levels of self-disclosure on social media. We argue that their self-presentations can be likened to post-modern performances in which the traditional boundaries between actor and audience are intentionally unsettled. These casual posts communicate additional personal traits that are not otherwise included in professional presentations. Since there are no strict boundaries between formal front-stage and relaxed back-stage regions in these types of performance, a liminal mental state is often used, which enables a better assessment of the type of information to present on social media.     

Effect of Network Quality on Player Departure Behavior in Online Games

Understanding the impact of network conditions on player satisfaction, which is one of the major concerns of network game designers, is a popular research topic. Of the various ways to gauge user satisfaction, in this paper, we focus on how network quality affects a player's decision to leave a game prematurely. To answer this question, we analyze a 1,356-million-packet trace from a large commercial massively multiplayer online role-playing game (MMORPG) called ShenZhou Online. We show that both network delay and network loss significantly affect a player's decision to leave a game prematurely. It is feasible to predict whether players will quit prematurely based on the network conditions they experience. The proposed model can determine the relative impact of different types of network impairment. For our traces, the degrees of player intolerance to network delay, delay jitter, client packet loss, and server packet loss are in the proportion of 1:2:4:3 approximately. The model can also be used to make system design decisions. Through simulations, we show that by prioritizing server processing according to the goodness of network conditions, employing dejitter buffers, or replacing TCP with a more lightweight transport protocol, the probability of premature departure can be significantly reduced. In this way, we demonstrate how our model of players' network experience provides feedback for the design of online games.     

The Influence of Online Search Behavior on Consumers’ Decision-Making Heuristics

Information seeking activities have been broadly categorized into two types: exploratory and directed search. Likewise, product-related information is processed either by alternative or by attribute. The current literature does not specifically describe the effect of online information-seeking activities on the type of information processing employed by a consumer. The present study seeks to investigate this relationship, as well as the effect of repeat visits to a website as an antecedent of information seeking. An eye-tracking experiment was performed to test these relationships. Results indicate that participants with a greater proportion of time spent looking at directed-search elements had longer visual sweeps across the attributes of a song, indicating a greater degree of alternative-based processing. Additionally, repeat website visits resulted in a marginally higher proportion of time spent performing directed search. The implication of the findings in relation to website customization according to users’ information searching behavior is discussed.     

语义增强的在线学习行为预测研究

当前学习者的在线学习行为预测研究未充分利用短文本中的语义数据,导致对学习者的学习状态刻画不够全面,严重影响了行为预测的准确性.针对此问题,本文提出了语义增强的在线学习行为预测方法.首先,利用双向长短时记忆网络得到到短文本的语义向量表示;其次,基于学习者的统计、行为和短文本数据得到学习者的特征表征,并利用长短时记忆网络模型构建其学习状态表征;最后,利用学习状态表征预测学习者的学习行为.在11门真实在线课程数据集上的实验表明,本文方法能过有效提升在线学习行为预测的精确度.     

大学生上网行为管理研究及对策

网络给大学生学习和生活提供了便捷的交流平台,但它是一把"双刃剑",也导致很多学生痴迷于网络.网络中的不良信息除对社会造成影响外,也对学生的生理、心理、行为等方面产生影响.本文通过对大学生上网行为问题研究,提出解决大学生上网行为问题的软硬策略,使上网行为管理得以完善.     

一种基于用户网络签到行为的地点推荐方法

基于位置的地点推荐服务日益强调用户的个性化需求。对此,可通过社交网站上用户与其好友之间、用户与签到地点以及地点与地点之间的关联,来从用户的网络签到行为中总结出用户的出行特点,从而提出一种基于用户网络签到行为的个性化地点推荐方法。该方法通过融合用户对地点的个人偏好程度、地点自身属性对用户的影响程度以及用户好友对地点的推荐程度,来筛选出候选地点中满足用户个性化需求的地点。实验结果验证了该方法在一定场景下的可行性和有效性。     

中国在线语言教育行业及用户行为分析

通过具体数据分析了在线语言教育行业的市场规模和增长率、用户总数、用户规模、用户区域分布情况等多个方面,总结了在线语言教育行业未来的趋势,为行业的发展提供数据支持.     

网络游戏案例研究: 用户行为分析和流失预测过

用户流失预测在很多领域得到关注,目前主流的用户流失预测方法是使用分类法。网络游戏领域发展迅猛,但用户特征选取、特征处理和流失预测的相关研究较少。本文以一款网页网络游戏的用户记录为数据,对用户游戏行为进行分析对比,发现流失用户在游戏投入、博彩热情、玩家互动方面与正常用户存在显著差异;同时发现网络游戏数据存在样本分布不平衡、候选特征库庞大和干扰差异多等难点。在此分析基础上,本文探讨了网游用户的关键特征提取的关注方向,以及归一化和对齐化在特征处理中的关键作用。实验表明,本文提取的特征具有很好的区分度。     

基于数据挖掘的网上证券交易异常行为分析

将数据挖掘技术引入了证券交易异常行为的分析过程,通过对异常交易行为建模,最终给出了基于孤立点挖掘算法的异常行为分析方法.