Qualitative findings of students’ perception on practice of self-regulated strategies in online community discussion

With rapid emergence to the birth of the knowledge era, an active and interactive learning environment is fundamental to any teaching and learning process. Over time, negative implications of traditional learning environment are mounting, and measures must be taken to challenge it. With extensive research on available learning tools, it has become more feasible to provide our students with better learning opportunities. Such was the anticipation in the Malaysian educational context. Hence, this paper aims to address qualitative findings of a study on self-regulated learning strategies conveyed through the proposed interactive e-learning community (iELC). In duration of eight weeks, students (n = 50) from regular national secondary schools were subjected to online learning tasks aimed to advance strategies on self-regulated learning. To date, strategies of self-regulated learning have been perceived as key to learner to succeed academically and after the schooling years. By means of semi-structured interview, data were collected from small group and one-to-one perception. Results were directed at the overall development of self-regulated learning strategies and its diffusion in the iELC discussion platform. Conclusively, these findings add significantly to the understanding of self-regulated learning strategies in an online learning environment.     

Community of inquiry as a theoretical framework to foster “epistemic engagement” and “cognitive presence” in online education

In this paper, several recent theoretical conceptions of technology-mediated education are examined and a study of 2159 online learners is presented. The study validates an instrument designed to measure teaching, social, and cognitive presence indicative of a community of learners within the community of inquiry (CoI) framework [Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2, 1–19; Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence, and computer conferencing in distance education. American Journal of Distance Education, 15(1), 7–23]. Results indicate that the survey items cohere into interpretable factors that represent the intended constructs. Further it was determined through structural equation modeling that 70% of the variance in the online students’ levels of cognitive presence, a multivariate measure of learning, can be modeled based on their reports of their instructors’ skills in fostering teaching presence and their own abilities to establish a sense of social presence. Additional analysis identifies more details of the relationship between learner understandings of teaching and social presence and its impact on their cognitive presence. Implications for online teaching, policy, and faculty development are discussed.     

A note on on-line broadcast scheduling with deadlines

In this paper, we study an on-line broadcast scheduling problem with deadlines, in which the requests asking for the same page can be satisfied simultaneously by broadcasting this page, and every request is associated with a release time, deadline and a required page with a unit size. The objective is to maximize the number of requests satisfied by the schedule. In this paper, we focus on an important special case where all the requests have their spans (the difference between release time and deadline) less than 2. We give an optimal online algorithm, i.e., its competitive ratio matches the lower bound of the problem.     

Online phenotype discovery based on minimum classification error model

Identifying and validating novel phenotypes from images inputting online is a major challenge against high-content RNA interference (RNAi) screening. Newly discovered phenotypes should be visually distinct from existing ones and make biological sense. An online phenotype discovery method featuring adaptive phenotype modeling and iterative cluster merging using improved gap statistics is proposed. Clustering results based on compactness criteria and Gaussian mixture models (GMM) for existing phenotypes iteratively modify each other by multiple hypothesis test and model optimization based on minimum classification error (MCE). The method works well on discovering new phenotypes adaptively when applied to both of synthetic datasets and RNAi high content screen (HCS) images with ground truth labels.     

The provision of online public goods: Examining social structure in an electronic network of practice

Electronic networks of practice are computer-mediated social spaces where individuals working on similar problems self-organize to help each other and share knowledge, advice, and perspectives about their occupational practice or common interests. These interactions occur through message postings to produce an on-line public good of knowledge, where all participants in the network can then access this knowledge, regardless of their active participation in the network. Using theories and concepts of collective action and public goods, five hypotheses are developed regarding the structural and social characteristics that support the online provision and maintenance of knowledge in an electronic network of practice. Using social network analysis, we examine the structure of message contributions that produce and sustain the public good. We then combine the results from network analysis with survey results to examine the underlying pattern of exchange, the role of the critical mass, the quality of the ties sustaining participation, the heterogeneity of resources and interests of participants, and changes in membership that impact the structural characteristics of the network. Our results suggest that the electronic network of practice chosen for this study is sustained through generalized exchange, is supported by a critical mass of active members, and that members develop strong ties with the community as a whole rather than develop interpersonal relationships. Knowledge contribution is significantly related to an individual's tenure in the occupation, expertise, availability of local resources and a desire to enhance one's reputation, and those in the critical mass are primarily responsible for creating and sustaining the public good of knowledge. Finally, we find that this structure of generalized exchange is stable over time although there is a high proportion of member churn in the network.     

Using online simulation in Holonic manufacturing systems

This paper deals with the use of online simulation on Holonic manufacturing systems. Concepts needed for the use of online simulation in a classical hierarchical system were already defined, the observer being the central one. The behavior's differences between both classes of systems are studied to determine the best way to adapt these concepts to this new environment. In the product resource order staff approach (PROSA) reference architecture, staff holons were chosen to welcome the simulation models and the observer. An application on an industrial sized Holonic manufacturing system is described to demonstrate the validity of the approach.     

在线矩形特征选择的压缩跟踪算法

针对压缩跟踪算法无法选择合适的矩形特征,易出现目标漂移、丢失现象,提出了一种基于在线矩形特征选择的压缩跟踪算法。首先,在初始化阶段生成投影矩阵,利用该投影矩阵提取特征来构造候选特征池,在特征池中使用矩形特征来表示目标特性,并去除与目标差异较大的矩形特征,最后计算分类分数最大的窗口,并将其作为目标窗口,从而实现跟踪。实验结果表明,该算法特征总数量比压缩跟踪算法特征总数量减少了13%,且跟踪精度和鲁棒性方面得到了改善,对于320pixel×240pixel大小的视频平均处理帧速为20frame/s,满足实时性要求。     

基于多元线性回归的雾霾预测方法研究

提出了一种在线样本更新的多元线性回归分析的雾霾预测方法。首先搜集了北京市天气状况,包括平均气温、湿度、风级等气象数据以及PM2.5、CO、NO₂、SO₂等大气成分浓度数据,然后通过散点图对这些因素进行主要影响因素分析,筛选出对雾霾影响比较明显的因素作为雾霾预测的依据。通过在线样本更新的多元线性回归建立了PM2.5含量预测模型,并将气象要素作为雾霾的判断标准。最后给出实际例子,利用多元线性回归对北京未来一天、三天及一周的PM2.5含量进行较为精确的预测。     

Online collaborative learning in dyads: Effects of knowledge distribution and awareness

Research on collaborative learning traditionally assumes a certain degree of symmetry between the learning partners in terms of both their learning-relevant traits and their individual learning outcomes. However, if one collaborative partner is clearly more able, skilled, or knowledgeable than the other partner, then it remains unclear who profits more from the collaboration. The present study aimed to explore this issue by manipulating symmetry in prior knowledge within small groups of online learners (dyads) and measuring their problem-solving efficiency and incidental learning gain on an individual and dyad level. Awareness of this symmetry/asymmetry was manipulated, too, to discern it as a potential moderator. Dyads with symmetrical and asymmetrical prior knowledge performed equally well on most measures. Moreover, on average, the more and the less knowledgeable partners in the asymmetrical conditions had equal learning gains. However, while in dyads with symmetrical knowledge learning gains were correlated between the partners, in the asymmetrical dyads they were not. Awareness of symmetry/asymmetry did not act as a moderator, but, overall, dyads with awareness of each other's knowledge learned more from each other than dyads without such awareness. The benefit of awareness was, however, specific to the learning content exposed via awareness. We conclude that researchers and practitioners should be careful when choosing or assigning collaborative partners to each other, as only for partners with symmetrical prior knowledge can a symmetrical increase in knowledge be expected. We further discuss the implications of these findings for research on knowledge awareness and collaboration.