ABSTRACT
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.
ABSTRACT
This paper aims to adapt and validate the User Experience Questionnaire Plus (UEQ+) in the Indonesian context. The UEQ+ is a modular extension of the User Experience Questionnaire (UEQ), which has been adapted to the Indonesian context and used in many studies. The UEQ+ was originally developed in German and English. As a modular extension, the UEQ+ has more user experience (UX) scales compared to the UEQ and can be used to evaluate products in special scenarios. Several steps were carried out to adapt and validate the UEQ+: translating the questionnaire into Bahasa by involving UX practitioners, evaluating the translation results by involving a UX expert and practitioners, and conducting face validity and reliability testing through two case studies (Zoom and Learn Quran Tajwid as online learning tools). The results showed that the findings from the open-ended questionnaire were consistent with the results of the six scales. Future work is needed to investigate whether the UEQ+ can capture some of the UX-related themes identified from the two case studies.
ABSTRACT
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.
ABSTRACT
This study proposes an integrated framework that investigates interrelationships between contextual factors that influence e-business use and consequently its impact on enterprise performance among small and medium enterprises (SMEs). This study starts with an extensive systematic review of e-business use factors that are contextualized in the technology, organization and environment (TOE) framework and conceptualized using resource-based view (RBV). Data are obtained through a survey of 325 Indonesian SMEs. The partial least square structural equation modeling technique is used to analyze the data and test the hypotheses. The organizational context emerges as the most significant predictor of e-business use, followed by technological and environmental contexts respectively. In addition, e-business use has stronger positive influence on enterprise performance at operational level, rather than managerial and strategic levels. However, e-business use's influence on performance impact at strategic level is indirect, mediating through operational and managerial levels. While the study has attempted to explain the contextual factors that influence the use of e-business as a whole, it is deficient in explaining contextual factors that influence each of e-business applications being used. This study could help SMEs identify contextual areas that may guide them to successfully use e-business and realize its potential benefits.
ABSTRACT
Many software products are created by considering targeted users from different countries. For such products, it is significant to investigate users' expectations related to cultural aspects to achieve user acceptance as broad as possible in all relevant countries. The researchers of the current study examined whether the cultural background of an individual has an influence on the subjective importance of user experience (UX) aspects for several common product categories. The researchers compared findings from a previous study conducted in Germany and a replication study carried out in Indonesia. Results show significant differences concerning the rated importance of UX aspects for many product categories. However, a detailed analysis of the results also shows that the impact of culture is considerably lower than the impact of interindividual differences between persons of the same culture. In addition, both samples show quite similar rankings of the importance of UX aspects. Thus, the product type has a much bigger impact than cultural differences.
