6th International Conference on Libraries (ICOL) 2017 “Towards Lean Libraries”

The International Conference on Libraries (ICOL2017) held in Penang, Malaysia on 2-3 August 2017, was the sixth international ICOL conference, a once-every-two-years opportunity that provides platform for participants and presenters to access the best information, discover new ideas and network with people in the profession. More than 20 abstracts submitted by interested authors, however, after being reviewed, only 18 papers have been accepted. Two accepted papers were withdrawn by their authors by the time of publishing. There were two speakers sponsored by the vendors who gave inputs on topics relevant to the conference but not included in this proceeding. A total of 14 full papers are included in this publication which covers the section of Managing Libraries; Creativity and Innovation; Right Tool at the Right Time and Improve while Reduce

Enhancing User Experience in User-Generated Content Websites by Exploiting Wikipedia

Ph.DDOCTOR OF PHILOSOPH

An Examination of User Detection of Business Email Compromise Amongst Corporate Professionals

With the evolution in technology and increase in utilization of the public Internet, Internet-based mobile applications, and social media, security risks for organizations have greatly increased. While corporations leverage social media as an effective tool for customer advertisements, the abundance of information available via public channels along with the growth in Internet connections to corporate networks including mobile applications, have made cyberattacks attractive for cybercriminals. Cybercrime against organizations is a daily threat and targeting companies of all sizes. Cyberattacks are continually evolving and becoming more complex that make it difficult to protect against with traditional security methods. Cybercriminals utilize email attacks as their most common method to compromise corporations for financial gain. Email attacks on corporations have evolved into very sophisticated scams that specifically target businesses that conduct wire transfers or financial transactions as part of their standard mode of operations. This new evolution of email driven attacks is called Business Email Compromise (BEC) attacks and utilize advanced social engineering, phishing techniques, and email hacking to manipulate employees into conducting fraudulent wire transfers that are intended for actual suppliers and business partners. One of the most common types of BEC attacks is the Chief Executive Officer (CEO) fraud, which are highly customized and targeted attacks aimed to impersonate corporate users that have authority to approve financial transactions and wire transfers in order to influence an employee to unknowingly conduct a fraudulent financial wire transfer. Thus, the main goal of this research study was to assess if there are any significant differences of corporate users’ detection skills of BEC attacks in a simulated test environment based on their personality attributes, using the Myers-Briggs Type Indicator® (MBTI®)’ 16 personalities® framework. BEC attacks have attributed to over $26 billion in corporate financial losses across the globe and are continually increasing. The human aspect in the cybersecurity has been a known challenge and is especially significant in direct interaction with BEC attacks. Furthermore, this research study analyzed corporate users’ attention span levels and demographics to assess if there are any significant differences on corporate users’ BEC attack detection skills. Moreover, this research study analyzed if there are any significant differences for BEC detection skills before and after a BEC awareness training. This research study was conducted by first developing an experiment to measure BEC detection and ensure validity via cybersecurity subject matter experts using the Delphi process. The experiment also collected qualitative and quantitative data for the participants’ performance measures using an application developed for the study. This research was conducted on a group of 45 corporate users in an experimental setting utilizing online surveys and a BEC detection mobile test application. This research validated and developed a BEC detection measure as well as the BEC awareness training module that were utilized in the research experiment. The results of the experiments were analyzed using analysis of variance (ANOVA) and analysis of covariance (ANCOVA) to address the research questions. It was found that there were that no statistically significant mean differences for Business Email Compromise Detection (BECD) skills between personality attributes of corporate professional participants, However, results indicated that there was a significant mean difference for BECD skills and span attention with a p\u3c.0001. Furthermore, there was a significant mean difference for BECD skills and span attention when controlled for gender with a p\u3c0.05. Furthermore, the results indicated that the BEC detection awareness training significantly improved the participant BEC detection skill with a p\u3c.0001. Moreover, following the training, it was found that female BEC detection test scores improved by 45% where the men BECD score improved by 31%. Recommendations for research and industry stakeholders are provided, including to corporations on methods to mitigate BEC attacks

E-portfolio as an alternative assessment approach enhancing self-directed learning in an Open Distance Learning environment

Assessment is an integral part of teaching and learning in higher education. The use of technology in higher education, particularly in the ODL environment, has brought some changes on how we teach and assess students. The traditional assessment practices needed to be reviewed and reconfigured to meet the requirements of the 21st century assessment practices. The purpose of this doctoral study was to design a framework to guide the assessment of an E-portfolio as an alternative assessment approach in an ODL context. The integrated theoretical framework of the learning theories (behaviourism, cognitive and constructivist) and the ODL theories (connectivist, online collaborative and self-directed) underpinned the study. This integrated framework explored lecturer and student experiences in the use of Eportfolio, as an alternative assessment to enhance self-directed learning. In striving to get in-depth insight into this study, the pragmatism paradigm, which calls for the mixed methods research design, was employed for the collection and analysis of data. The sample was drawn from a cohort of six participants and fifty-six respondents in the three colleges of the university. This sequential exploratory mixed methods design employed semi-structured interviews, document analysis for qualitative data collection while a Likert scale of an online questionnaire was used to collect quantitative data. The findings of this research indicated that the E-portfolio can be of greater use as an alternative assessment approach and was able to empower students with higher order thinking skills, critical thinking skills and self-directed learning equipping them with the 21st century skills. Several challenges were experienced during the implementation of the E-portfolio, which included lack of digital literacies and technical assistance, nonsynchronisation of the learning management system for hosting E-portfolio (myUnisa), UNISA’s policies which do not include E-portfolio assessment processes and procedures. In conclusion, the literature study, the findings of the empirical research and the recommendation of this study formed the basis for designing the framework to guide the assessment of an E-portfolio as an alternative assessment strategy for an ODL context.Curriculum and Instructional StudiesPh. D. (Curriculum Studies

The empirical status of text, discourse and genre in the training of English/Arabic translators

Abstract unavailable please refer to PD

EVALUATING MICROLEARNING STRATEGIES IN THE CORPORATE ENVIRONMENT: A COMPARATIVE MIXED METHODS STUDY USING THE KIRKPATRICK MODEL

Corporate training and development, despite their significance as primary learning strategies for adults, have received limited research attention. This study addresses this gap by evaluating and comparing the effectiveness of video-based and digital job aid procedural-based microlearning in a corporate setting, utilizing most of the Kirkpatrick Model: learner satisfaction, knowledge retention, and behavior change. Employing a mixed methods approach to gain a comprehensive understanding of microlearning’s application and potential for improving business outcomes, the study incorporates pre- and post-tests, participant interviews, and self-reported questionnaires. Thirty participants completed the study, with equal representation (15 participants each) for both modalities, and interviews were conducted with 10 participants from each group. The results reveal that the type of microlearning treatment does not significantly impact knowledge retention, while the time elapsed since learning does influence retention. Additionally, the modality of the microlearning treatment may impact behavior change, although further investigations are necessary to examine the role of bias related to treatment preference and individual roles. Further findings indicate that participants favored a mixed modality microlearning approach for corporate training needs, involving initial training through videos and follow-up reference material through job aids. Furthermore, participants preferred knowledge evaluation methods such as quizzes or application-based assessments to apply and evaluate their understanding of the content. Future research should explore the impact of microlearning strategies on Key Performance Indicators (KPIs) in a business context, as well as the relationship between application-based training and knowledge transfer. Additionally, investigating the influence of peers and supervisors on behavioral change, as well as the impact of content management on cognitive load, would be valuable. Ultimately, this research seeks to bridge the gap between educational theory and practical implementation for instructional or learning designers and training, development, and/or enablement leaders, empowering them to make informed decisions regarding business practices and learning modalities

A Systems Theory-Based Framework for Environmental Scanning in Complex System Governance

The purpose of this research was to develop a Systems Theory-based framework for Environmental Scanning (ES) in Complex System Governance (CSG) using an inductive research design. Complexity and uncertainty are normal for external environments in which today’s systems (organizations) exist. These environmental characteristics provide impetus for researchers to focus on organizational planning for disruptive external forces that could threaten system stability and future system existence. The ES function supports the requisite governance metasystemic functions to be enabled, executed, and evolved sufficiently well to promote continuous system viability. In this research the functioning of ES was examined from a diverse literature-based perspective. The literature acknowledges the importance of the ES function, but its consistent development and its impact on system viability in a turbulent environment is not well developed from a Systems Theory-based perspective. This gap in knowledge was addressed in this research. This research examined metasystemic functions performed by ES across a broad literature base encompassing Systems Theory, CSG, Managerial Cybernetics, and ES from several fields of study. This research focused on the lack of explicit use of Systems Theory in ES functionality in metasystemic governance. This research presents a theoretical construct for the expansion of the functionality of ES in CSG that supports enhanced system viability. A rigorous research approach employing a constructivist Grounded Theory Method (GTM) was used to analyze the qualified research literature with a focus on Systems Theory to both consolidate and expand the known functionality of ES in CSG. This research provided a theoretical seventeen-function Systems Theory-based framework for ES in CSG. The overarching theory from this framework is that ES functions support complex system viability through regulation of internal and external variety that is induced by external changes. The literature-based identification of the ES functions demonstrates that ES operates in newly identified mechanisms, beyond the original identification provided by Keating & Katina (2016). A case study was undertaken to demonstrate face validation of the applicability of the emerging Systems Theory-based functions of ES in CSG in an applied setting where possible utility was developed. Topics for future research in ES functionality were identified

A semantic metadata enrichment software ecosystem (SMESE) : its prototypes for digital libraries, metadata enrichments and assisted literature reviews

Contribution 1: Initial design of a semantic metadata enrichment ecosystem (SMESE) for Digital Libraries The Semantic Metadata Enrichments Software Ecosystem (SMESE V1) for Digital Libraries (DLs) proposed in this paper implements a Software Product Line Engineering (SPLE) process using a metadata-based software architecture approach. It integrates a components-based ecosystem, including metadata harvesting, text and data mining and machine learning models. SMESE V1 is based on a generic model for standardizing meta-entity metadata and a mapping ontology to support the harvesting of various types of documents and their metadata from the web, databases and linked open data. SMESE V1 supports a dynamic metadata-based configuration model using multiple thesauri. The proposed model defines rules-based crosswalks that create pathways to different sources of data and metadata. Each pathway checks the metadata source structure and performs data and metadata harvesting. SMESE V1 proposes a metadata model in six categories of metadata instead of the four currently proposed in the literature for DLs; this makes it possible to describe content by defined entity, thus increasing usability. In addition, to tackle the issue of varying degrees of depth, the proposed metadata model describes the most elementary aspects of a harvested entity. A mapping ontology model has been prototyped in SMESE V1 to identify specific text segments based on thesauri in order to enrich content metadata with topics and emotions; this mapping ontology also allows interoperability between existing metadata models. Contribution 2: Metadata enrichments ecosystem based on topics and interests The second contribution extends the original SMESE V1 proposed in Contribution 1. Contribution 2 proposes a set of topic- and interest-based content semantic enrichments. The improved prototype, SMESE V3 (see following figure), uses text analysis approaches for sentiment and emotion detection and provides machine learning models to create a semantically enriched repository, thus enabling topic- and interest-based search and discovery. SMESE V3 has been designed to find short descriptions in terms of topics, sentiments and emotions. It allows efficient processing of large collections while keeping the semantic and statistical relationships that are useful for tasks such as: 1. topic detection, 2. contents classification, 3. novelty detection, 4. text summarization, 5. similarity detection. Contribution 3: Metadata-based scientific assisted literature review The third contribution proposes an assisted literature review (ALR) prototype, STELLAR V1 (Semantic Topics Ecosystem Learning-based Literature Assisted Review), based on machine learning models and a semantic metadata ecosystem. Its purpose is to identify, rank and recommend relevant papers for a literature review (LR). This third prototype can assist researchers, in an iterative process, in finding, evaluating and annotating relevant papers harvested from different sources and input into the SMESE V3 platform, available at any time. The key elements and concepts of this prototype are: 1. text and data mining, 2. machine learning models, 3. classification models, 4. researchers annotations, 5. semantically enriched metadata. STELLAR V1 helps the researcher to build a list of relevant papers according to a selection of metadata related to the subject of the ALR. The following figure presents the model, the related machine learning models and the metadata ecosystem used to assist the researcher in the task of producing an ALR on a specific topic