No abstract

Student retention is an essential measurement metric in education, indicated by retention rates, which are accumulated as students re-enroll from one academic year to the next. High retention rates can be obtained if institutions aim to provide appropriate support and teaching methods among the various practices to prevent students from deferring their studies. To address this pressing challenge faced by educational institutions, the underlying factors and the methodological aspects of building robust predictive models are reviewed and scrutinized. Educational Data Mining (EDM) and Learning Analytics (LA) have been widely adopted for knowledge discovery from educational data sources, improving the teaching practice, and identifying at-risk students. Various predictive techniques are applied in LA, such as Machine Learning (ML), Statistical Analysis, and Deep Learning (DL). To gain an in-depth review of these techniques, academic publications have been reviewed to highlight their potential to resolve Student Retention issues in education. Additionally, the paper presents a taxonomy of ML approaches and a comprehensive review of the success factors and the features that are not indicative of student performance in three different learning environments: Traditional Learning, Blended Learning, and Online Learning. The survey reveals that supervised ML and DL techniques are broadly applied in Student Retention. However, the application of ensemble and unsupervised learning clustering techniques supporting the heterogenous and homogenous groups of students is generally lacking. Moreover, static and traditional features are commonly used in student performance, ignoring vital factors such as educators-related, cognitive, and personal data. Furthermore, the paper highlights open challenges for future research directions.

Teachers of urban higher education institutions often explore new methods of teaching using innovative techno-pedagogical approaches. This study reports on postgraduate students’ perceptions of the blended learning mode of delivery, co-taught by two lecturers concurrently during the “Qualitative Research” elective course offered for the Master of Educational Leadership program, in a reputed Malaysian university. A qualitative action research methodology was adopted for this study with students’ comments captured through Padlet. Results indicate that students have very positive perceptions of their experiences gained through blended learning and co-lecturing. The findings of this action research study provide evidence of the meaningful and personalized learning experiences reported by students, gained through the collaborative blended mode of delivery. The results also provide more thoughtful reflections for teachers to draw on students’ feedback and possibly adapt their teaching practices to better accommodate students learning needs.

On Instagram, the number of followers is a common success indicator. Hence, followers selling services become a huge part of the market. Influencers become bombarded with fake followers and this causes a business owner to pay more than they should for a brand endorsement. Identifying fake followers becomes important to determine the authenticity of an influencer. This research aims to identify fake users' behavior, and proposes supervised machine learning models to classify authentic and fake users. The dataset contains fake users bought from various sources, and authentic users. There are 17 features used, based on these sources: 6 metadata, 3 media info, 2 engagement, 2 media tags, 4 media similarity. Five machine learning algorithms will be tested. Three different approaches of classification are proposed, i.e. classification to 2-classes and 4-classes, and classification with metadata. Random forest algorithm produces the highest accuracy for the 2-classes (authentic, fake) and 4-classes (authentic, active fake user, inactive fake user, spammer) classification, with accuracy up to 91.76%. The result also shows that the five metadata variables, i.e. number of posts, followers, biography length, following, and link availability are the biggest predictors for the users class. Additionally, descriptive statistics results reveal noticeable differences between fake and authentic users.

The purpose of this study is to investigate the factors that influence the adoption of Software as Service (SaaS) at Small Medium Enterprises (SMEs) that have adopted online business in Sri Lanka. Prior studies have shown that SMEs significantly benefit due to the adoption of SaaS. The research sough to explain the adoption of SaaS using Awareness, Trust, Cost, Top Management Support, Complexity and Relative advantage. Conceptualization of this researcher’s variables and their interrelationship have been supported by theory of Diffusion of Innovation (DOI) of Rogers’ (1962) and Technological Organizational and Environmental (TOE) Framework of Tornatzky and Fleisher (1990). The study was conducted among 250 randomly selected SMEs adopting online business using questionnaires addressed to managerial and ICT professionals who were capable of making ICT decision at SMEs under study. However only 224 questionnaires were returned with complete data required for the purpose of analysis. The study employed principal component analysis to reduce the data and employed Ordinary Least Square (OLS) to test the relationship between the variables. It is found that Cost (CT), Complexity (CX) and Relative Advantages (RA) are having significant impact on SaaS adoption in SMEs in Sri Lanka. This study extends the existing body of knowledge by providing empirical support for explaining SaaS adoption by SMEs in Sri Lanka. The finding will help various parties engaging in promoting the adoption of SaaS among SMEs with the view of SMEs’ development in Sri Lanka. On this basis, the researchers are able to recommend firstly, that SaaS is playing a significant role for the development of SMEs in this area of study and finally, that software vendors, policy makers, technological consultants and application developers intend to adopt SaaS should consider the validated model tested in this research study.