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The purpose of this paper is to present data that begin to detail the deficiencies of log file analytics reporting methods that are commonly built into institutional repository (IR) platforms. The authors propose a new method for collecting and reporting IR item download metrics. This paper introduces a web service prototype that captures activity that current analytics methods are likely to either miss or over-report.

Data were extracted from DSpace Solr logs of an IR and were cross-referenced with Google Analytics and Google Search Console data to directly compare Citable Content Downloads recorded by each method.

This study provides evidence that log file analytics data appear to grossly over-report due to traffic from robots that are difficult to identify and screen. The study also introduces a proof-of-concept prototype that makes the research method easily accessible to IR managers who seek accurate counts of Citable Content Downloads.

The method described in this paper does not account for direct access to Citable Content Downloads that originate outside Google Search properties.

This paper proposes that IR managers adopt a new reporting framework that classifies IR page views and download activity into three categories that communicate metrics about user activity related to the research process. It also proposes that IR managers rely on a hybrid of existing Google Services to improve reporting of Citable Content Downloads and offers a prototype web service where IR managers can test results for their repositories.

The purpose of this paper is to investigate the influence of behavioural attitudes towards the most popular social medium in the world, Facebook, amongst Millennials in South Africa (SA), and to determine whether various usage and demographic variables have an impact on intention-to-purchase and purchase perceptions.

Quantitative research was conducted by means of a survey among a sample of over 3,500 respondents via self-administered structured questionnaires in SA. A generalised linear model was used to analyse the data.

The results confirm that advertising on Facebook has a positive influence on the behavioural attitudes (intention-to-purchase and purchase) of Millennials who reside in SA. The usage characteristics, log on duration and profile update incidence, as well as the demographic influence of ethnic orientation also resulted in more favourable perceptions of Facebook advertising.

Research on Facebook advertising was only conducted in SA, whereas other emerging countries warrant further investigation to establish if they share the slight positive sentiment towards intention-to-purchase and purchase. This inquiry only provides a “snap shot” of behavioural attitudes, usage and demographic factors towards social media advertising, whereas future research could consider the development of cognitive, affective and behavioural attitudes towards Facebook advertising by employing longitudinal and qualitative research designs.

Organisations and managers should consider that their existing Facebook advertising strategies may only have a limited effect on intention-to-purchase and purchase in SA. However, certain usage characteristics, namely the more time spent logged on to Facebook and the greater frequency of profile update incidence, as well as the demographic variable, namely black and coloured Millennials, resulted in more favourable behavioural attitudes towards Facebook advertising. Hence, organisations and managers should be prepared to alter or adapt their Facebook advertising tactics accordingly when targeting the notoriously fickle Millennials.

This investigation found that Facebook advertising has a nominal positive influence on behavioural attitudes among Millennials, which is in congruence with the communications of the effect pyramid model that was established through traditional advertising research. This paper also makes a noteworthy contribution to attitudinal research in emerging countries where there is a dearth of research in social media advertising.

For aiding computer security experts in their study, log files are a crucial piece of information. Especially the time domain is very important for us because in most cases, timestamps are the only linking points between events caused by attackers, faulty systems or simple errors and their corresponding entries in log files. With the idea of storing and analyzing this log information in graph databases, we need a suitable model to store and connect timestamps and their events. This paper aims to find and evaluate different approaches how to store timestamps in graph databases and their individual benefits and drawbacks.

We analyse three different approaches, how timestamp information can be represented and stored in graph databases. For checking the models, we set up four typical questions that are important for log file analysis and tested them for each of the models. During the evaluation, we used the performance and other properties as metrics, how suitable each of the models is for representing the log files’ timestamp information. In the last part, we try to improve one promising looking model.

We come to the conclusion, that the simplest model with the least graph database-specific concepts in use is also the one yielding the simplest and fastest queries.

Limitations to this research are that only one graph database was studied and also improvements to the query engine might change future results.

In the study, we addressed the issue of storing timestamps in graph databases in a meaningful, practical and efficient way. The results can be used as a pattern for similar scenarios and applications.

The purpose of this paper is to describe a proposal for a data-driven investigation aimed at determining whether students’ learning behavior can be extracted and visualized from action logs recorded by Moodle. The paper also tried to show whether there is a correlation between the activity level of students in online environments and their academic performance with respect to final grade.

The analysis was carried out using log data obtained from various courses dispensed in a university using a Moodle platform. The study also collected demographic profiles of students and compared them with their activity level in order to analyze how these attributes affect students’ level of activity in the online environment.

This work has shown that data mining algorithm like vector space model can be used to aggregate the action logs of students and quantify it into a single numeric value that can be used to generate visualizations of students’ level of activity. The current investigation indicates that there is a lot of variability in terms of the correlation between these two variables.

The value presented in the study can help instructors monitor course progression and enable them to rapidly identify which students are not performing well and adjust their pedagogical strategies accordingly.

A plan to continue the work by developing a complete dashboard style interface that instructors can use is already underway. More data need to be collected and more advanced processing tools are necessary in order to obtain a better perspective on this issue.

Recent advancement in technological development has encouraged distance learning institutions to be more productive and creative in effectively utilizing the Learning Management System (LMS). Among the many measures employed is the integration of federated search engine into the LMS which allows for a more productive and wider scope of information retrieval through the provisions of library resources and services. The purpose of this paper is to report one such case study in Wawasan Open University exploring the integration of federated search engine (EBSCO Discovery Service (EDS) widget) into the learning spaces of LMS. Widgets resemble apps that enable the integration of EDS functionality in providing access for students to retrieve library learning resources from the convenience of the LMS, excluding the need to log onto the library.

This paper presents a discussion that highlights the development and conjectural implementation of a framework on the integration of the EDS widget into the University’s LMS. Data collection includes meta-analysis data from the micro- and macro-level infrastructure that make up the framework, namely, end-user layer, system layer and data management layer.

Findings from this study addressed significant importance to the library in promoting effective search and utilization of information needs. The findings will also make clear recommendations in developing effective collaborations between the library and faculties. Although the implementation of this framework is still in a developmental stage, this study still provides pertinent information in validating the integration of EDS into the University’s LMS.

While serious limitations are not anticipated, possible concerns do exist with programming algorithms in the integration of EDS into the LMS. These challenges will be reported in the paper as reference for future replications of study

One key implication is the increase in the usage of the library resources and the potential to reach a larger audience of remote library users.

The primary advantage is to minimize the need for multiple gateway login while ensuring the library to monitor relevant library databases activities throughout the system check of the LMS.

This study aims to investigate customers’ churning out of Internet Protocol Television (IPTV) service, one of the most prevalent forms of IT convergence.

Based on the review of current literature, a research model is introduced to depict the effects of select independent variables on customer churning behavior. First of all, the two groups are compared in terms of predictor variables, including switching barriers, voice of customer (VOC), membership period and degree of contents usage. Then, a curvilinear regression was applied to understand the association relationship between the level of IPTV contents usage and variables of switching barriers, VOC and membership period. Third, a logit regression was performed to predict customer churning through the variables of switching barriers, VOC, membership period and level of IPTV contents usage.

Through the empirical analysis, this study analyzed the factors affecting customer churning behavior of IPTV service providers based on switching barriers, VOC and contents usage.

Although several studies on IPTV have been undertaken globally, they have largely depended on self-reporting surveys to examine dynamics between antecedent variables and IPTV performance in terms of customer satisfaction, usage intension and customer retention. This empirical study is performed to understand influential factors of IPTV service defection through the weblog analysis of 3,906 service users, who represented both service defectors and non-defectors during a specific month.

This paper aims to examine the influence of interactive social media marketing communications on teenagers’ cognitive, affective and behavioral attitude components in South Africa. The study also considers the impact of a number of additional factors such as usage (access, length of usage, log-on frequency, log-on duration and profile update incidence) and demographic (gender, age and population group) variables on young consumers’ attitudes toward social media marketing communications.

A survey was used via three self-administered questionnaires, which were distributed to over 13,000 learners in the age range of 13-18 years (Generation Z cohort) at colleges and high schools in South Africa. A generalized linear model was used for statistical data analysis.

The study ascertained that social media marketing communications had a positive on each attitude component among adolescents, but on a declining scale, which correlates to the purchase funnel. The results also revealed that teenagers who used social media for long time periods; updated their profiles frequently and were from the Colored and Black population groups, displayed the most favorable attitudinal responses to social media marketing communications.

Social media was collectively analyzed and did not consider the number of different social media types, which could be examined individually. This investigation only considered the Generation Z cohort, but other cohorts to attitudes toward social media marketing communications could also be assessed.

Companies and their brands should consider using and/or adapting their strategies based on the declining impact of social media marketing communications on the hierarchical attitude stages among young consumers and the divergent influence on usage and demographic variables when targeting the lucrative and technologically advanced, but capricious, Generation Z consumers.

This research established that social media marketing communications had a favorable influence on cognitive, affective and behavioral attitude components among young consumers, but on a declining scale, which is in congruence with the purchase funnel model. This investigation also makes an important contribution to attitudinal research in developing countries, where there is a lack of research in social media marketing communications.

The purpose of this study is to design and implement an intelligent online proctoring system (IOPS) by using the advantage of artificial intelligence technology in order to monitor the online exam, which is urgently needed in online learning settings worldwide. As a pilot application, the authors used this system in an authentic university online exam and checked the proctoring result.

The IOPS adopts the B/S (Browser/Server) architecture. The server side is implemented with programming language C and Python and stores the identification data of all examinees and their important behavior change status, including facial expression, eye and mouth movement and speech. The browser side collects and analyzes multimodal data of the examinee writing the online test locally and transfers the examinee’s most important behavior status change data to the server. Real-time face recognition and voice detection are implemented with the support of open-source software.

The system was integrated into a Web-based intelligent tutoring system for school mathematics education. As a pilot application, the system was also used for online proctored exam in an undergraduate seminar in Peking University during the epidemic period in 2020. The recorded log data show that all students concentrated themselves on the exam and did not leave the camera and did not speak.

During the epidemic period of the novel coronavirus outbreak, almost all educational institutions in the world use online learning as the best way to maintain the teaching and learning schedule for all students. However, current online instruction platforms lack the function to prevent the learners from cheating in online exams and cannot guarantee the integrity and equality for all examinees as in traditional classroom exams. The literature review shows that the online proctoring system should become an important component of online exams to tackle the growing online cheating problem. Although such proctoring systems have been developed and put on the market, the practical usage of such systems in authentic exams and its effect have not been reported. Those systems are heavyweight and commercial product and cannot be freely used in education. The light-weight IOPS developed by the authors could meet the need for online exam as a stable and practical approach and could contribute to the growing online learning and distance learning.