Understanding MOOCs as an Emerging Online Learning Tool: Perspectives From the Students

This study examined participants’ learning experiences in the context of a six-week massive open online course (MOOC) in journalism with five thousand students from 137 countries. Three research questions were asked: (1) Who are the students and why are they enrolled in this MOOC?, (2) How much time have the students spent in taking this MOOC and have they completed all the assignments?, and, (3) What have they learned and what aspects of this MOOC do the students find most helpful? Four hundred and nine students responded to a survey and forty-four responded to interview questions. The main findings showed 84% of the participants were working professionals and only 28.9% were from a journalism background. Of those who did not complete the course, lack of time was the top reason. Most participants reported a positive learning experience, but lack of feedback and/or poor quality were reported as negative experiences. The discussion forum was the least liked aspect of the course.     

Powerful Actor and Hesitant Institutionalization: The State in the History of Turkish Football

We examine the validity of the assumptions of the new institutional theory about the state over the history of national football league as an institutional field in Turkey. The state is not out of the field, on the contrary, at the centre of the field, and is not an ordinary actor but the most important one although the intensity of the state’s impact changes from time to time. Sometimes, the state founds the football clubs and administrates them directly, decides who will be the champion; and sometimes, authorizes the regulative agency and gives autonomy. However, despite the state’s coercive power on the ground, the Turkish football field is still far from being fully institutionalized with its fluctuating structure.     

WSVR-based fuzzy neural network with annealing robust algorithm for system identification

This paper proposes a fuzzy neural network (FNN) based on wavelet support vector regression (WSVR) approach for system identification, in which an annealing robust learning algorithm (ARLA) is adopted to adjust the parameters of the WSVR-based FNN (WSVR-FNN). In the WSVR-FNN, first, the WSVR method with a wavelet kernel function is used to determine the number of fuzzy rules and the initial parameters of FNN. After initialization, the adjustment for the parameters of FNNs is performed by the ARLA. Combining the self-learning ability of neural networks, the compact support of wavelet functions, the adaptive ability of fuzzy logic, and the robust learning capability of ARLA, the proposed FNN has the superiority among the several existed FNNs. To demonstrate the performance of the WSVR-FNN, two nonlinear dynamic plants and a chaotic system taken from the extant literature are considered to illustrate the system identification. From the simulation results, it shows that the proposed WSVR-FNN has the superiority over several presented FNNs even the number of training parameters is considerably small.     

Asymmetric Missing-data Problems: Overcoming the Lack of Negative Data in Preference Ranking

In certain classification problems there is a strong a asymmetry between the number of labeled examples available for each of the classes involved. In an extreme case, there may be a complete lack of labeled data for one of the classes while, at the same time, there are adequate labeled examples for the others, accompanied by a large body of unlabeled data. Since most classification algorithms require some information about all classes involved, label estimation for the un-represented class is desired. An important representative of this group of problems is that of user interest/preference modeling where there may be a large number of examples of what the user likes with essentially no counterexamples.Recently, there has been much interest in applying the EM algorithm to incomplete data problems in the area of text retrieval and categorization. We adapt this approach to the asymmetric case of modeling user interests in news articles, where only labeled positive training data are available, with access to a large corpus of unlabeled documents. User modeling is here equivalent to that of user-specific document ranking. EM is used in conjunction with the Naive Bayes model while its output is also utilized by a Support Vector Machine and Rocchio's technique.Our findings demonstrate that the EM algorithm can be quite effective in modeling the negative class under a number of different initialization schemes. Although primarily just the negative training examples are needed, a natural question is whether using all of the estimated labels (i.e., positive and negative) would be more (or less) beneficial. This is important considering that, in this context, the initialization of the negative class for EM is likely not to be very accurate. Experimental results suggest that EM output should be limited to negative label estimates only.     

Influence of measurement errors on structural damage identification using artificial neural networks

The effect of measurement errors on structural damage identification using artificial neural networks (ANN) was investigated in this study. By using back-propagation (BP) networks with proper input vectors, numerical simulation tests for damage detection on a six-storey frame were conducted with measurement errors in deterministic as well as probabilistic senses. The identifiability using ANN for damage location and extent was studied for the cases of measurement errors with different degrees. The results showed that there exists a critical level of measurement error beyond which the probability of correct identification is sharply decreased. The identifiability using the neural networks in the presence of modeling and measurement errors is finally verified using experimental data on a two-storey steel frame. Project supported by Hong Kong Polytechnic University.     

Formation of ots self-assembled monolayer on glass surface investigated by AFM

The whole process of octadecyltrichlorosilane (OTS) self-assembled monolayer formation on glass surface was clearly revealed by using Atomic Force Microscope (AFM). The growth regularity pattern of OTS self-assembled monolayer on glass substrate was obtained by analyses of the AFM images and the average surface mean roughness (R _avg) of the sample surfaces. The adsorption feature of OTS molecules on the substrate surface during the initial reaction period was probed through a comparison experiment. Project supported by the Scientific Research Foundation for Retured Overseas Chinese Scholar, State Education Committee.     

The glue that makes it “hang together”: A framework for identifying how metadiscourse facilitates uncertainty navigation during knowledge building discussions

Classroom discussions have become a centerpiece of reform efforts in science education because talk mediates the joint co-constructing of knowledge in science classrooms. Although decades of research underscore the importance of talk in supporting science learning, the science education community continues to grapple with how to support teachers and students in navigating the uncertainty that is associated with doing knowledge building work. To address these challenges, we must examine not just what gets constructed (the scientific ideas), but how knowledge is co-constructed by teachers and students (the process of building those ideas) amidst uncertainty. In this study, we propose a conceptual tool for identifying organizational, epistemic, and interpretive metadiscourse markers (MDMs) in science talk. We highlight how teachers and students use these three types of MDMs as they navigate uncertainty while connecting ideas within and across multiple turns of talk, leveraging resources for knowledge building, and making interpretations about one another's ideas. We conclude with a set of suggestions for how researchers and teachers can utilize this framework to attend to the ways that MDMs index the organizational, epistemic, and interpretive dimensions of uncertainty in the knowledge building process.     

Patterns of interpersonal criticism in Japan and United StatesModes de critique interpersonelle au Japon et aux Etats-UnisNormas de critica interpersonal en el Japon y en los Estados Unidos

To investigate patterns of interpersonal criticism in Japan and United States, preliminary interviews were conducted. Three major variables: sources of dissatisfaction; the status of communicative partners: and modes of giving criticism were identified. These variables were incorporated into an Interpersonal Criticism Questionnaire and then administered to 149 Japanese and 168 American college students of both sexes, after establishing cross-cultural equivalence. The results demonstrated a significant difference between the two cultures, but no significant difference between the sexes. Both cultures favored expressing dissatisfaction in a direct way, but Japanese more frequently employed passive forms of criticism and Americans active forms. While Japanese consistently adapted critical messages to the status of their communicative partners, Americans consistently adapted their critical messages to the character of the provocation.