Thematic evolution of smart learning environments, insights and directions from a 20-year research milestones: A bibliometric analysis

Smart learning environments (SLEs) have been developed to create an effective learning environment gradually and sustainably by applying technology. Given the growing dependence on technology daily, SLE will inevitably be incorporated into the teaching and learning process. Without transforming technology-enhanced learning environments into SLE, they are restricted to adding sophistication and lack pedagogical benefits, leading to wasteful educational investments. SLE research has grown over time, particularly during the COVID-19 pandemic in 2020-2021, which fundamentally altered the "landscape" of technology use in education. This study aims to discover how the stages of SLE transform from time to time by applying two bibliometric analysis approaches: publication performance analysis and science mapping. The dataset was created by extracting bibliometric data from Scopus, including 427 articles, 162 publication sources (journals and proceeding), and 1080 authors from 2002 to 2022. Three kinds of SLE research subjects were identified by keyword synthesis: SLE features, technological innovation, and adaptive learning systems. Adaptive learning and personalized learning are consistently used interchangeably to demonstrate the significance of supporting the diversity of student and teacher conditions. Learning analytics, essential to employing big data technology for educational data mining, is a new theme being considered increasingly in the future to achieve adaptive and personalized learning. The 20-year SLE research milestone, broken down into five stages with various focuses on goals and served as the foundation for creating a maturity model of SLE.

Development of a Conceptual Framework for Tuberculosis Management and Control; an Evidence Synthesis Using Text Mining Software: A Review

Background

The use of electronic systems supported by text-mining software applications that support the End TB strategy' needs to be explored. This study aimed to address this knowledge gap, and synthesis of evidence.

Methods

The PubMed database was searched for structured review articles published in English since 2012 on interventions to control and manage TB. Nine hundred twenty-five articles met the inclusion criteria. The included articles were synthesized using the text and content analysis software Leximancer. The themes were chosen based on the hit words that emerged in the frequency and heat maps. After the themes were chosen, the concept built the themes based on likelihood.

Results

The framework resulting in the study focuses on early detection and treatment to minimize the chance of TB transmission in the population, especially for highly susceptable populations. The main area highlighted is the appropriate screening and treatment domains. The framework generated in this study is somewhat in line with the WHO Final TB Strategy. This study highlights the importance of improving TB prevention through a patient-centered approach and protecting susceptible populations.

Conclusion

Our findings will be helpful in guiding TB practice, policy development and future research. Future research can elaborate the framework and elicit feedback from TB management stakeholdesr to assess its utility.

Development of Tuberculosis Vulnerability Assessment Conceptual Framework Using Automatic Content Analysis

The tuberculosis prevention and control model needs to be explored. This study aimed to create a conceptual framework for measuring TB vulnerability to guide the prevention program's effectiveness. SLR method was employed, resulting in 1.060 articles being analyzed with ACA Leximancer 5.0 and facet analysis. The built framework consists of five components: risk of TB transmission, damage caused by TB, health care facility, the burden of TB, and awareness of TB. Future research is required to explore variables in each component to formulate the degree of TB vulnerability.

'Everything is data': towards one big data ecosystem using multiple sources of data on higher education in Indonesia

Big data is increasingly being promoted as a game changer for the future of science, as the volume of data has exploded in recent years. Big data characterized, among others, the data comes from multiple sources, multi-format, comply to 5-V's in nature (value, volume, velocity, variety, and veracity). Big data also constitutes structured data, semi-structured data, and unstructured-data. These characteristics of big data formed "big data ecosystem" that have various active nodes involved. Regardless such complex characteristics of big data, the studies show that there exists inherent structure that can be very useful to provide meaningful solutions for various problems. One of the problems is anticipating proper action to students' achievement. It is common practice that lecturer treat his/her class with "one-size-fits-all" policy and strategy. Whilst, the degree of students' understanding, due to several factors, may not the same. Furthermore, it is often too late to take action to rescue the student's achievement in trouble. This study attempted to gather all possible features involved from multiple data sources: national education databases, reports, webpages and so forth. The multiple data sources comprise data on undergraduate students from 13 provinces in Indonesia, including students' academic histories, demographic profiles and socioeconomic backgrounds and institutional information (i.e. level of accreditation, programmes of study, type of university, geographical location). Gathered data is furthermore preprocessed using various techniques to overcome missing value, data categorisation, data consistency, data quality assurance, to produce relatively clean and sound big dataset. Principal component analysis (PCA) is employed in order to reduce dimensions of big dataset and furthermore use K-Means methods to reveal clusters (inherent structure) that may occur in that big dataset. There are 7 clusters suggested by K-Means analysis: 1. very low-risk students, 2. low-risk students, 3. moderate-risk students, 4. fluctuating-risk students, 5. high risk students, 6. very high-risk students and, 7. fail students. Among the clusters unreveal, (1) a gap between public universities and private universities across the three regions in Indonesia, (2) a gap between STEM and non-STEM programmes of study, (3) a gap between rural versus urban, (4) a gap of accreditation status, (5) a gap of quality human resources distribution, etc. Further study, we will use the characteristics of each cluster to predict students' achievement based on students' profiles, and provide solutions and interventions strategies for students to improve their likely success.