Librarianship in the Era of Social Media

The chapter presents the symbiotic relationship between library services and social media and its possible implications for the 21st century librarian. The 21st century librarian has witnessed huge changes in the field of library and information science. These changes in the library and information science domain have altered the forms of information and the nature of services but the basic role of the libraries – to cater for the information needs and demands of the users remains. As the popularity of social media is growing exponentially, librarians cannot stand aloof but ensure they exploit the benefits attached to using social media in libraries. With social media, libraries can attract their users and provide improved services while keeping them abreast on latest developments in the libraries. The chapter discussed how social media can be exploited for the benefit of the library clients. The chapter concluded by providing suggestions that will benefit the librarians and libraries to ensure improved symbiotic relationship between library services and social media.

Amelioration of Big Data Analytics by Employing Big Data Tools and Techniques

This chapter describes how in the digital data era, a large volume of data became accessible to data science engineers. With the reckless growth in networking, communication, storage, and data collection capability, the Big Data science is quickly growing in each engineering and science domain. This paper aims to study many numbers of the various analytics ways and tools which might be practiced to Big Data. The important deportment in this paper is step by step process to handle the large volume and variety of data expeditiously. The rapidly evolving big data tools and Platforms have given rise to numerous technologies to influence completely different Big Data portfolio.In this paper, we debate in an elaborate manner about analyzing tools, processing tools and querying tools for Big datahese tools used for data analysis Big Data tools utilize numerous tasks, like Data capture, storage, classification, sharing, analysis, transfer, search, image, and deciding which might also apply to Big data.

Justification for Adopting Qualitative Research Method, Research Approaches, Sampling Strategy, Sample Size, Interview Method, Saturation, and Data Analysis

Justifying the adoption of the qualitative research method to satisfy the examiners (for thesis) and reviewers (for journal articles) is a challenging task for researchers in business, management, marketing, tourism, hospitality and albeit in social science domain. The difficulty continues in establishing the justification for selecting qualitative research approaches, sample strategy, sample size, data collection methods (i.e. interview methods), saturation, and data analysis. In this guide, we aim to ground brief justifications for researchers and guidance on how to justify the section of qualitative research method in thesis and journal articles. This study also provides brief justification on selecting specific qualitative research approaches, sampling strategies, sample size, interviews, and data analysis methods. Furthermore, this study provides a glimpse of justification regarding when and how to reach saturation point in qualitative research. Keywords: Qualitative research method, Research approaches, Sampling strategy, Sample size, Interview method, Saturation, and Qualitative data analysis (QDA)

Multi-Keyword Classification: A Case Study in Finnish Social Sciences Data Archive

In this paper, we consider the task of assigning relevant labels to studies in the social science domain. Manual labelling is an expensive process and prone to human error. Various multi-label text classification machine learning approaches have been proposed to resolve this problem. We introduce a dataset obtained from the Finnish Social Science Archive and comprised of 2968 research studies’ metadata. The metadata of each study includes attributes, such as the “abstract” and the “set of labels”. We used the Bag of Words (BoW), TF-IDF term weighting and pretrained word embeddings obtained from FastText and BERT models to generate the text representations for each study’s abstract field. Our selection of multi-label classification methods includes a Naive approach, Multi-label k Nearest Neighbours (ML-kNN), Multi-Label Random Forest (ML-RF), X-BERT and Parabel. The methods were combined with the text representation techniques and their performance was evaluated on our dataset. We measured the classification accuracy of the combinations using Precision, Recall and F1 metrics. In addition, we used the Normalized Discounted Cumulative Gain to measure the label ranking performance of the selected methods combined with the text representation techniques. The results showed that the ML-RF model achieved a higher classification accuracy with the TF-IDF features and, based on the ranking score, the Parabel model outperformed the other methods.

Students’ Readiness for STEM Learning within the Context of National Education Reform

STEM (science, technology, engineering, and mathematics) education nowadays becomes more and more topical; however, students’ performance in these subjects is rather low and only a small part of them decide to study these sciences therefore it is important to rouse students’ interest in these subjects already at school. It is important to acquire not only the knowledge of the subject but also the transversal skills, thus, the organization of the teaching/learning process becomes more significant. Schools of Latvia start implementing the teaching/learning content and approach that correspond to the new standards of basic and general secondary education, which incorporates four innovative aspects in the science domain: the promotion of the content acquisition through teachers’ reciprocal collaboration; the use of ICT (Information Communication Technologies) as a platform for engineering technological solutions; learning through doing and engagement in discussions and other activities for making socially responsible decisions. The aim of the study is to find out students’ readiness to learn STEM in the context of innovative approaches of the national education reform. To reach the aim, the research question was set – to what extent and in which way are students ready to learn STEM? Using the QuestionPro e-platform, 257 students of Latvian general comprehensive schools were surveyed and the meta-analysis of factors of thematically respective selected questions was performed. The obtained empirical results were compared with the four aspects of the innovative approach pertaining to the education reform in the science domain. The study resulted in isolating main four factors: the course of the teaching/learning process, the feedback, the use of ICT and technologies little used in the acquisition STEM. The found factors comprised all the innovations of the science domain put forward by the national education reform, which means that students already at the pre-reform stage are ready to acquire STEM subjects in an innovative way.

Atrial Flutter Mechanism Detection Using Directed Network Mapping

Atrial flutter (AFL) is a common atrial arrhythmia typically characterized by electrical activity propagating around specific anatomical regions. It is usually treated with catheter ablation. However, the identification of rotational activities is not straightforward, and requires an intense effort during the first phase of the electrophysiological (EP) study, i.e., the mapping phase, in which an anatomical 3D model is built and electrograms (EGMs) are recorded. In this study, we modeled the electrical propagation pattern of AFL (measured during mapping) using network theory (NT), a well-known field of research from the computer science domain. The main advantage of NT is the large number of available algorithms that can efficiently analyze the network. Using directed network mapping, we employed a cycle-finding algorithm to detect all cycles in the network, resembling the main propagation pattern of AFL. The method was tested on two subjects in sinus rhythm, six in an experimental model of in-silico simulations, and 10 subjects diagnosed with AFL who underwent a catheter ablation. The algorithm correctly detected the electrical propagation of both sinus rhythm cases and in-silico simulations. Regarding the AFL cases, arrhythmia mechanisms were either totally or partially identified in most of the cases (8 out of 10), i.e., cycles around the mitral valve, tricuspid valve and figure-of-eight reentries. The other two cases presented a poor mapping quality or a major complexity related to previous ablations, large areas of fibrotic tissue, etc. Directed network mapping represents an innovative tool that showed promising results in identifying AFL mechanisms in an automatic fashion. Further investigations are needed to assess the reliability of the method in different clinical scenarios.

Investigating the citation advantage of author-pays charges model in computer science research: a case study of Elsevier and Springer

PurposeCitation is an important measure of quality, and it plays a vital role in evaluating scientific research. However, citation advantage varies from discipline to discipline, subject to subject and topic to topic. This study aims to compare the citation advantage of open access and toll access articles from four subfields of computer science.Design/methodology/approachThis research studies the articles published by two prestigious publishers: Springer and Elsevier in the author-pays charges model from 2011 to 2015. For experimentation, four sub-domains of computer science are selected including (a) artificial intelligence, (b) human–computer interaction, (c) computer vision and graphics, and (d) software engineering. The open-access and toll-based citation advantage is studied and analyzed at the micro level within the computer science domain by performing independent sample t-tests.FindingsThe results of the study highlight that open access articles have a higher citation advantage as compared to toll access articles across years and sub-domains. Further, an increase in open access articles has been observed from 2011 to 2015. The findings of the study show that the citation advantage of open access articles varies among different sub-domains of a subject. The study contributed to the body of knowledge by validating the positive movement toward open access articles in the field of computer science and its sub-domains. Further, this work added the success of the author-pays charges model in terms of citation advantage to the literature of open access.Originality/valueTo the best of the authors’ knowledge, this is the first study to examine the citation advantage of the author-pays charges model at a subject level (computer science) along with four sub-domains of computer science.

Elementary students’ attitudes towards STEM and 21st-century skills

<span>Science, Technology/Engineering and Math (STEM) and 21st-century learning are very important to be applied to all students starting from their early age, including at the elementary school level. This study aimed to describe students' attitudes towards STEM and 21st-century skills according to gender and education level. This research was descriptive quantitative by explaining and comparing the results of the questionnaires given to students. Student attitudes were described based on domain of STEM, 21st-century skills, gender, and student grade level. The respondent of this study was 130 elementary students who were obtained randomly from one of the elementary schools in Yogyakarta, Indonesia. The results showed that based on the science domain, the mean score of the students was 3.23 (moderate) while mathematics domain obtained was 3.21 (moderate); technology/engineering domain obtained was 3.68 (moderate) and 21st-century skill domain obtained was 3.65 (moderate). According to gender, there was a significant difference between the attitudes of male and female students towards STEM and 21st-century skills. However, there was no significant difference based on the grade of students. Based on these findings, it is suggested that teachers or policymakers should have comprehended the student attitudes before implemented the STEM-based learning and 21st-century skills. These findings can be used to create STEM and 21st-century learning.</span>

PRIMARY STUDENTS’ PERFORMANCE OF STEM DOMAIN-SPECIFIC SELF-EFFICACY BELIEF AND EXPECTANCY-VALUE BELIEF

Most studies have concentrated in assessing students’ overall attitudes towards science, mathematics, and engineering/technology or the attitude towards individual STEM domain. The present research aims to explore primary students’ gender and grade differences of their STEM domain-specific attitudes including self-efficacy and expectancy-value beliefs, as well as their correlations. The results showed no detected significant effects among these different STEM domains in the overall attitudes, the overall self-efficacy beliefs, and the overall expectancy-value beliefs for primary students. The correlations between self-efficacy and expectancy-value were much stronger for the science domain and engineering/technology domain than the mathematics domain. No gender difference of the self-efficacy beliefs was detected except in the mathematics domain, and the result that lower primary students performed significantly better than upper primary students in the self-efficacy was also mainly contributed by the grade difference in the mathematics domain. Whereas no different expectancy-value beliefs existed across genders and grade levels in various STEM domains. The present results reported some unique performances by the primary school students compared to the elder group. Keywords: expectancy-value, gender differences, grade levels, self-efficacy, STEM attitudes

Artificial Intelligence Revolutionises Weather Forecast, Climate Monitoring and Decadal Prediction

Artificial Intelligence (AI) is an explosively growing field of computer technology, which is expected to transform many aspects of our society in a profound way. AI techniques are used to analyse large amounts of unstructured and heterogeneous data and discover and exploit complex and intricate relations among these data, without recourse to an explicit analytical treatment of those relations. These AI techniques are unavoidable to make sense of the rapidly increasing data deluge and to respond to the challenging new demands in Weather Forecast (WF), Climate Monitoring (CM) and Decadal Prediction (DP). The use of AI techniques can lead simultaneously to: (1) a reduction of human development effort, (2) a more efficient use of computing resources and (3) an increased forecast quality. To realise this potential, a new generation of scientists combining atmospheric science domain knowledge and state-of-the-art AI skills needs to be trained. AI should become a cornerstone of future weather and climate observation and modelling systems.