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Past studies have provided important insights into the relationship between science education and information and communication technologies (ICTs). However, few studies have sought to promote learning by integrating science education with indigenous culture via ICTs. Indigenous culture is a treasure of human civilization, but there is a crisis of rapid disappearance. Integrating indigenous culture into students' science education may help them understand the scientific implications of their culture, and preserve and participate in their cultural activities. This study explores the relationships between science education and indigenous culture through the use of ICTs in the context of Taiwan's Rukai tribe. A research process was proposed with three steps: planning, execution, and evaluation. There were 15 indigenous pre-service teachers from six tribes participating in the study. The results show that ICTs help them understand the scientific principles of their own culture, and develop practical materials and lesson plans that can be used for future teaching. It also makes them aware of the meaning and importance of their own culture. [ABSTRACT FROM AUTHOR]

Information technology has brought about significant changes in enterprises, and new work situations have led to new problems. Employee resistance to new technologies, their ability to learn, and their ability to utilize personal resources to improve work engagement in the face of technological pressure are important factors that companies need to consider when undergoing digital transformation. The influence mechanism of configuration effects on factors around employee work engagement has not been explored, and technostress creators have rarely been included in the configuration as influencing factors in previous studies. On the basis of the job demands-resources (JD-R) model and trait activation theory, this study explored the factors that affect employees' work engagement at the level of job demands and personal resources. The fuzzy-set qualitative comparative analysis (fsQCA) method was used to investigate the influence of technical stressors, self-efficacy, and the Big Five personality traits on employees' work engagement. Through a survey of 225 employees in the context of enterprise digital transformation, the results show three driving paths that promote employees' work engagement: openness to experience conscientiousness, self-efficacy driven, and inhibition to technical stressors. The study also analyzed employees' low work engagement state, which is driven by an inhibition of agreeableness and extraversion. This research enriches the study of factors influencing work engagement in the digital transformation of enterprises. [ABSTRACT FROM AUTHOR]

In Massive Open Online Courses (MOOCs), learners can post both text comments and overall ratings regarding the courses. There is growing interest in assessing the consistency of online reviews and the determinants of learner satisfaction. This study analyses the disconfirmation effect between textual review topics and the determinants of learner satisfaction in MOOCs. The MOOCs are categorised under three disciplines - Social Science, Technical Science, and Humanities & Natural Science. A crawler was employed to collect the corpus, extracting 93,679 reviews of 5,214 online courses from a Chinese university MOOC platform (icourse163.org). Textual analytics was used in the topic extraction. The empirical results suggest a strong disconfirmation effect between textual reviews and the determinants of learner satisfaction, i.e., not all textual review topics affect the overall learner satisfaction. Compared with positive reviews, negative (and neutral) reviews have a stronger disconfirmation effect. Further, the antecedents of learner attention are course-discipline specific. The disconfirmation effect is course-discipline dependent, with the most prominent for Technical Science courses, and the least for Humanities & Natural Science courses. This study provides a framework to guide platform managers and course instructors in better course delivery and enhancing overall learner satisfaction. [ABSTRACT FROM AUTHOR]

Scholarly attention to social innovation has increased rapidly in recent years, but a broad picture to illustrate the structure of the field, tracing its evolution and identifying new research areas has been lacking. Thus, we conduct a three-step method to explore the trajectory of academic research on social innovation and identify potential research opportunities. In the first step, the bibliometric mapping software VOSviewer is used to visualize the network of authors and keyword co-occurrences. Next, SciMAT is applied to illustrate the evolution and importance of the themes. Then, content analysis is used to identify how specific research topics in social innovation in higher education have evolved. The author-keyword occurrence analysis and evolution map results reveal that innovation and design thinking were the most prominent keywords, and the citation analysis indicates that the works of Swyngedouw, Moulaert, and Westley as the leading research works in this field. The most influential countries during the study period from 1996 to July 2021 were the UK, the US, and Italy. Furthermore, the research collaboration network is more active among developed countries than among developing countries. Three research streams in social innovation in higher education have been identified: curriculum transformation, community-university partnership, and helix partnerships. This study supports understanding how higher education is shaped through social innovation. The results from this study can serve as a research reference on the state of the art and point to new research opportunities, notably on the need to engage developing countries in collaborating on social innovation in higher education through design, engagement, and partnership. [ABSTRACT FROM AUTHOR]

Learners engaged in large-scale online learning often pose questions in which their peers or instructors can answer using various means of textual interaction topics. This paper assesses the effects of the text interaction strategies in online learning through the lens of the language expectancy theory at three levels: whether to respond to the questions, the identity of the respondents, and the textual interaction topics. Using 112,680 learning records of 610 courses from 71,948 learners crawled from the online learning programming platform iMOOC as the corpus, text mining is used to identify the interaction strategies. Using grounded theory, the textual interaction topics are divided into 2 groups (providing solutions, and encouragement & evaluation for the learners), and sub-divided into 6 topic clusters (code writing, operation guidance, providing references, encouragement, normative interpretation, and opinion exchange). The responses are classified by text mining. The results of the econometric model suggest that responding to the questions online fosters learning and reduces the dropout rate. The online learner benefits more from peer learning than from the instructors. On the text interaction topics, the topic "providing solutions" is more effective in reducing the learner's dropout rate than the topic "encouragement & evaluation". Further, code writing is more effective over providing references, encouragement, and normative interpretation. This study enriches our understanding of the interaction strategies between learners and instructors in iMOOC, and provides a reference for improving the online learning journey and retain learners. [ABSTRACT FROM AUTHOR]

A multivariate time series is one of the most important objects of research in data mining. Time and variables are two of its distinctive characteristics that add the complication of the algorithms applied to data mining. Reduction in the dimensionality is often regarded as an effective way to address these issues. In this paper, we propose a method based on principal component analysis (PCA) to effectively reduce the dimensionality. We call it "piecewise representation based on PCA" (PPCA), which segments multivariate time series into several sequences, calculates the covariance matrix for each of them in terms of the variables, and employs PCA to obtain the principal components in an average covariance matrix. The results of the experiments, including retained information analysis, classification, and a comparison of the central processing unit time consumption, demonstrate that the PPCA method used to reduce the dimensionality in multivariate time series is superior to the prior methods. [ABSTRACT FROM AUTHOR]

The identification and supervision of systemically important financial institutions (SIFIs) are crucial for regulators to address systemic risks to the financial system. Prevalent identification methods are biased because less attention is paid to the indirect connections among financial institutions. This study proposes a novel method for measuring the systemic importance of financial institutions. The proposed method is an identification algorithm based on adjacency information entropy that integrates the direct and the indirect connections among financial institutions. The sizes of financial institutions will not be investigated, and each financial institution is regarded as a node in the financial system network. The adjacency degree of every node is calculated. Subsequently, the information entropy of each node can be determined. The magnitude of the information entropy is taken as the importance measure of these financial institutions in the financial network. We conduct empirical analysis by employing the data of stock prices of 16 listed banks in the Chinese securities market with a sample period from October 2007 to December 2019 and adopt the method presented in this study to rank the systemic importance of these banks. Analysis shows that the ranking is in accordance with the reality, and the algorithm will promote the accuracy of identifying SIFIs. [ABSTRACT FROM AUTHOR]

Business incubators (BIs) have gradually evolved to focus increasingly on the development of networks and network functions. However, existing literature indicates that much ambiguity remains regarding the importance and role of cooperation within the incubator networks. In particular, few studies divide incubator networks into internal and external networks to explore how these networks influence the enterprise growth performance (EGP). Based on the social network and organizational learning theories, we analyze the role of entrepreneurial learning (exploratory and exploitative) in the relationship between internal and external networks and EGP in dynamic environments. Through a multiple regression analysis of survey data from 205 incubatees in 14 Chinese business incubators, we find that the BIs' internal and external networks positively impact EGP and exploratory and exploitative learning mediate the relationship between them. Moreover, when incubatees are in a highly dynamic environment, internal networks more positively affect exploitative learning, while external networks inhibit exploratory learning. Finally, we discuss the study's implications. [ABSTRACT FROM AUTHOR]

This study examines the role of social connections and network centrality in attracting funders to crowdfunding campaigns. We classify social connections as either external (e.g., Facebook) or internal (e.g., investing in online platforms through resource exchange). Drawing from the 108,463 crowdfunding campaigns on the online platform Kickstarter from April 21, 2009, to July 24, 2019, we apply external linkages and online followers to estimate the effect of external social connections. We construct a digraph network for the internal social connections and use PageRank, HITS, and centrality to obtain the weights of the nodes. Next, we compare the performance change of several prediction algorithms by feeding social connection-related variables. This study has several findings. First, for external social connections, having more online followers improves the funding success rate of a campaign. Second, for internal social connections, only authority and degree in centrality positively affect the number of funders and the campaign's financing progress among the weights of the nodes. Third, using social connection variables improves the prediction algorithms for funding outcomes. Fourth, external social connections exert greater funding outcomes than internal social connections. Fourth, entrepreneurs should extend their external social connections to their internal social connections, and network centrality expedites project financing. Fifth, the effect of social connections on fundraising outcomes varies among the campaign categories. Fundraisers who are online influencers should leverage their online social connections, notably for the project categories that matter. [ABSTRACT FROM AUTHOR]

Social networks can provide massive amounts of information for communication among users and communities. The trust relationships in social networks can be utilized to reveal user preferences for improving the quality of social recommendation, which aims to mitigate information overload and provide users with the most attractive and relevant items or services. However, the data sparsity and cold-start issue degrade recommendation performance significantly. To address these issues, a novel trust-embedded collaborative deep generative model (TCDG) is proposed for exploiting multisource information (content, rating and trust) to predict ratings. TCDG employs deep generative model to unsupervisedly learn deep latent representations for item content through an inference network in latent space instead of observation space. Meanwhile, TCDG adopts probabilistic matrix factorization to map users into low-dimensional latent feature spaces by trust relationships, which can reflect users' mutual influence on the formation of users' opinions more accurately and learn better implicit relationships between items and users from content, rating and trust. In addition, an approach with an annealing parameter to calculate the maximum a posteriori estimates is proposed to learn model parameters. Experiments using four real-world datasets are conducted to evaluate the prediction and top-ranking performance of our model. The results indicate that TDCG has better accuracy and robustness than other methods for making recommendations. [ABSTRACT FROM AUTHOR]

As a new financing model, crowdfunding has been developed rapidly in recent years and has attracted the attention of investors and small- and medium-sized enterprises and entrepreneurs. However, many projects fail to be funded; thus, crowdfunding project fundraising outcomes forecasting and multimodel comparisons are meaningful ways to identify project quality and reduce market risk. It is important to reduce participation risk through automated methods, which is of great significance to the sustainable development of Internet finance. First, based on the data from the Kickstarter, preprocessing and exploratory analysis are conducted. Then, we introduce a deep learning algorithm (multilayer perceptron) and apply it to the prediction of crowdfunding financing performance. We compare deep learning with other commonly used machine learning algorithms, including decision tree, random forest, logistic regression, support vector machine, and K-nearest neighbors algorithm. We tune each machine learning algorithm to get the best parameters. The experimental results show that the deep learning model can obtain the best prediction results, with an accuracy of 92.3% when predicting the fundraising outcomes of crowdfunding financing, followed by the decision tree. Deep learning shows significant advantages in many evaluation criteria, which demonstrates the potential for crowdfunding project financing predictions. This study combines machine learning with Internet finance, providing inspiration for future research and resulting in many practical implications. [ABSTRACT FROM AUTHOR]

This study employed electroencephalography to record event-related potentials to investigate the difference in learning performance between learners with different levels of spatial ability in a traditional learning environment that utilizes presentation slides and a learning environment that incorporates virtual reality (VR). Thirty-two university students participated in the experiment. The N1 and P2 components were results that indicated selective attention at an early stage; their amplitudes were proportional to the cognitive loads. The experiment results revealed that the main effect of learning environment was significant. The N1 and P2 components had larger amplitudes and indicated higher cognitive loads in the presentation slides-based environment than in the VR-based environment. The main effect of spatial ability was also significant. The N1 and P2 amplitudes evoked in the high spatial ability (HSA) learners were smaller than those evoked in the low spatial ability (LSA) learners, indicating that the LSA learners possessed fewer cognitive resources and bore relatively high cognitive loads. The interaction effect of learning environment and spatial ability was significant. Larger P2 amplitude was observed in LSA learners in the presentation slides-based environment than in the VR-based environment, implying that VR facilitates the reduction of cognitive loads in LSA learners. The P2 amplitude detected in HSA learners did not show any significant difference in both learning environments, indicating that the VR-based learning environment did not enhance their learning. This result supports the ability-as-compensator hypothesis to a certain extent. [ABSTRACT FROM AUTHOR]