Search results

The purpose of this study is to explore the determinants of teachers’ continuance intention concerning cloud services.

This study uses a cloud service, namely, Google Sites, to support teacher collaboration and further develop a research model to explore the determinants of their continued usage intention.

The findings reveal that continued intention to use the cloud services is primarily determined by their attitude towards using it; attitude towards using is affected by perceived ease of use and perceived usefulness; and perceived ease of use is influenced by facilitating conditions, while perceived usefulness is influenced by social influence.

This study determines that teachers intend to continue using cloud services once training in using such services has been provided and if they hear favourable opinions from others.

The paper enables us to better understand factors affecting teachers' continuance intention toward cloud services.

The purpose of this paper is to introduce an affective computing-based method of identifying sleeping beauties and their princes in five educational technology journals.

This study develops an information technology-based methodology to analyze sleeping beauties in the field of educational technology. The authors seek to determine the characteristics of studies which trigger the awakening of sleeping beauties (called “princes”). The keywords and Hirsch index (H-index) are used as two essential analysis indicators.

Between 2000 and 2015, these five journals included 7,864 articles with a total of 77,700 citations and 24,312 keywords. This study identified articles as being in deep sleep (75.7 percent), less deep sleep (14.7 percent), and awakening (5.5 percent) states. According to the analytical results, 431 of 7,864 articles are qualified as “sleeping beauties.” Of the 431 sleeping beauties identified, 232 articles were awakened by trend princes, while 286 were awakened by H-index princes. A total of 128 sleeping beauties were awakened by both prince types. Besides, impact factors (IFs) of journals do not have a significant effect on the number of sleeping articles.

There are two main research limitations in this study. The first one is the amount of target journals. Only five well-known educational technology journals are analyzed in this research. There may be more valuable sleeping publications in other journals not been found. The second limitation is that the authors merely pick up the lead author of citing papers as the indicator to determine the H-index prince. The contributions of the rest of authors are not taken are not taken into consideration. These limitations should be further studied.

To the knowledge, this study is the first one reporting the identification of sleeping beauty and princes in educational technology field. Furthermore, the authors devise an informational method to determine sleeping publications, sleeping beauty, and princes. A systematic analysis of five well-known journals in the field of educational technology field confirms the existence of “sleeping beauties.” It is reported that improvements to a journal’s IF are positively correlated to increased numbers of sleeping beauties being awakened. To reduce the number of such articles, or to reduce the overall sleeping duration, journal editors should not only seek to raise the journal’s IF, but also strategically select keywords for maximum visibility, and promote articles to high H-index authors.

In a programming course, students often need tutors' assistance to complete learning activities, as they lack enough background knowledge to complete tasks. A further problem is that without individual tutoring, the knowledge gap between students increases. Therefore, the authors have proposed an instant response learning supplement tool (IRLST) to support students' learning, in order to facilitate students' independent problem-solving skills.

The authors divided the students into two groups according to their learning styles: verbal and visual. The IRLST was used to collect and analyze the information on their usage and provide supplementary resources to facilitate their learning. The proposed system also analyzed the student usage, background knowledge and exam scores to assess their academic performance.

According to the results of statistical analysis, students' learning performance improved significantly, especially low-scoring students. Moreover, as compiler messages were not recognized, students tended to identify the same problems. Thus, it is suggested that teachers not only should focus on improving the students' syntax but also strengthening their background knowledge and debugging skills.

There are two main limitations in this study: (1) as most of the students were in the visual learning group, the size of the groups was impacted, thus it was not possible to establish a control group; (2) one specific version of the IRLST system did not send reliable advice or supplementary content occasionally.

The IRLST developed in this study can be used to provide immediate supplementary resources to help students overcoming programming problems and developing problem-solving skills.

Responding to natural resource depletion and carbon dioxide (CO₂) emission problems, and also the stricter government’s energy regulations, the purpose of this paper is to develop a sustainable waste heat recovery optimal-profit-oriented management model especially targeting on the easily forgotten low- and medium-temperature waste heat in the industry. In the paper, a system is constructed to facilitate converting the low- and medium-grade waste heat in factories into electricity, and yields optimal profit.

This paper integrates an efficient Organic Rankine Cycle (ORC) system from both sustainable energy reservation and cost effectiveness approaches with an optimization model that adopts particle swarm optimization (PSO) algorithm to determine proper installation locations and feasible generator sets. The system is constructed to facilitate converting the low- and medium-grade waste heat in factories into electricity, and yields optimal profit. The model considers the environmental factors: temperature, heat amount, equipment configuration of the number of ORC sets, and detailed investment cost constraints.

The results show that annual investment return rate, annual increase in electricity, power generation efficiency, and annual CO₂ emission reduction are all highly improved, and investment recovery period is shortened. Also, the larger scale of the waste heat emission, the better the performance is achieved. Finally, the study also completes a sensitivity test under dynamic conditions of electricity price, generator sales price and factory budget constraints, and the results are consistently robust. More valuably, this paper demonstrates applications on two different manufacturing industries with various waste heat emission scales to prove the accountability.

The main contributions are in three aspects. First, it proves that applying PSO to a nonlinear mathematical model can help determine the optimal number and style configuration of generators for waste heat sources. Second, different from the prior research works focusing on power generation, this paper also deliberates the cost factors, cost of generators, costs of numerous peripheral components and future maintenance costs to ensure the factories not conflict with the financial limitations. Third, it is not only successfully applied in two industries with different scales, but also robust with various economic tests, electricity price change, generator sales price change, and investment budget adjustments.

The study aims to examine the relationships among personality traits (i.e. the Big Five personality traits and locus of control), self-perceived facial attractiveness, motivations (i.e. intrinsic and extrinsic motivation) and intention toward live stream broadcasting. It also investigates the moderating role of perceived behavioral control in the relationship between motivations and intention.

Data collected from a sample of 637 participants are used to examine the research model and test the hypotheses with the employment of partial least squares structural equation modeling.

The study shows that motivations and perceived behavioral control are significant predictors of intention. Perceived behavioral control has a significant moderating effect between motivations and intention. Intrinsic motivation is positively influenced by self-perceived facial attractiveness, agreeableness, extraversion and internal locus of control, while extrinsic motivation is positively predicted by self-perceived facial attractiveness, conscientiousness and extraversion.

This study enhances our understanding of the determinants of intention toward live stream broadcasting by exploring its relationships with motivations, self-perceived facial attractiveness and personality, as well as the moderating effects of perceived behavioral control.