Public survey instruments for business administration using social network analysis and big data

Purpose: The subject matter of this research is closely intertwined with the scientific discussion about the necessity of developing and implementing practice-oriented means of measuring social well-being taking into account the intensity of contacts between individuals. The aim of the research is to test the toolkit for analyzing social networks and to develop a research algorithm to identify sources of consolidation of public opinion and key agents of influence. The research methodology is based on postulates of sociology, graph theory, social network analysis and cluster analysis. Design/Methodology/Approach: The basis for the empirical research was provided by the data representing the reflection of social media users on the existing image of Russia and its activities in the Arctic, chosen as a model case. Findings: The algorithm allows to estimate the density and intensity of connections between actors, to trace the main channels of formation of public opinion and key agents of influence, to identify implicit patterns and trends, to relate information flows and events with current information causes and news stories for the subsequent formation of a "cleansed" image of the object under study and the key actors with whom this object is associated. Practical Implications: The work contributes to filling the existing gap in the scientific literature, caused by insufficient elaboration of the issues of applying the social network analysis to solve sociological problems. Originality/Value: The work contributes to filling the existing gap in the scientific literature formed as a result of insufficient development of practical issues of using analysis of social networks to solve sociological problems.peer-reviewe

Cluster Analysis in Online Learning Communities: A Text Mining Approach

This paper presents a theory-informed blueprint for mining unstructured text data using mixed- and multi-methods to improve understanding of collaboration in asynchronous online discussions (AOD). Grounded in a community of inquiry theoretical framework to systematically combine established research techniques, we investigated how AOD topics and individual reflections on those topics affect formation of clusters or groups in a community. The data for the investigation came from 54 participants and 470 messages. Data analysis combined the analytical efficiency and scalability of topic modeling, social network analysis, and cluster analysis with qualitative content analysis. The cluster analysis found three clusters and that members of the intermediate cluster (i.e., middle of three clusters) played a pivotal role in this community by expressing uncertainty statements, which facilitated a collective sense-making process to resolve misunderstandings. Furthermore, we found that participants’ selected discussion topics and how they discussed those topics influenced cluster formations. Theoretical, practical, and methodological implications are discussed in depth

Proceedings of the First European Workshop on Latent Semantic Analysis in Technology Enhanced Learning

Latent Semantic Analysis (LSA) has been successfully deployed in various educational applications to enrich learning and teaching with information-technology. The primary goal of the workshop is to bring together experts in the field in order to share knowledge gained within the scattered research about latent semantic analysis in educational applications, in particular from the context of the IST projects Cooper, iCamp,T enCompetence and ProLearn

Constraint capture and maintenance in engineering design

The Designers' Workbench is a system, developed by the Advanced Knowledge Technologies (AKT) consortium to support designers in large organizations, such as Rolls-Royce, to ensure that the design is consistent with the specification for the particular design as well as with the company's design rule book(s). In the principal application discussed here, the evolving design is described against a jet engine ontology. Design rules are expressed as constraints over the domain ontology. Currently, to capture the constraint information, a domain expert (design engineer) has to work with a knowledge engineer to identify the constraints, and it is then the task of the knowledge engineer to encode these into the Workbench's knowledge base (KB). This is an error prone and time consuming task. It is highly desirable to relieve the knowledge engineer of this task, and so we have developed a system, ConEditor+ that enables domain experts themselves to capture and maintain these constraints. Further we hypothesize that in order to appropriately apply, maintain and reuse constraints, it is necessary to understand the underlying assumptions and context in which each constraint is applicable. We refer to them as “application conditions” and these form a part of the rationale associated with the constraint. We propose a methodology to capture the application conditions associated with a constraint and demonstrate that an explicit representation (machine interpretable format) of application conditions (rationales) together with the corresponding constraints and the domain ontology can be used by a machine to support maintenance of constraints. Support for the maintenance of constraints includes detecting inconsistencies, subsumption, redundancy, fusion between constraints and suggesting appropriate refinements. The proposed methodology provides immediate benefits to the designers and hence should encourage them to input the application conditions (rationales)

Mining Students’ Messages to Discover Problems Associated with Academic Learning

WhatsApp has become the preferred choice of students for sending messages in developing countries. Due to its privacy and the ability to create groups, students are able to express their “feelings” to peers without fear. To obtain immediate feedback on problems hindering effective learning, supervised learning algorithms were applied to mine the sentiments in WhatsApp group messages of University students. An ensemble classifier made up of Naïve Bayes, Support Vector Machines, and Decision Trees outperformed the individual classifiers in predicting the mood of students with an accuracy of 0.76, 0.92 recall, 0.72 precision and 0.80 F-score. These results show that we can predict the mood and emotions of students towards academic learning from their private messages. The method is therefore proposed as one of the effective ways by which educational authorities can cost effectively monitor issues hindering students’ academic learning and by extension their academic progress. Keywords: WhatsApp; Sentiments; Ensemble; Classification; Naïve Bayes; Support Vector Machines.

Access and usability issues of scholarly electronic publications

This chapter looks at the various access and usability issues related to scholarly information resources. It first looks at the various channels through which a user can get access to scholarly electronic publications. It then discusses the issues and studies surrounding usability. Some important parameters for measuring the usability of information access systems have been identified. Finally the chapter looks at the major problems facing the users in getting access to scholarly information through today's hybrid libraries, and mentions some possible measures to resolve these problems