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The purpose of this papers is to provide an overview of how students and teachers in Taiwan conceptualize learning, especially in technology-enhanced learning environments. Their conceptions of learning reveal the extent to which the prevalence of technological use in education has facilitated students to cultivate a more advanced conception of learning and develop a deeper learning approach.

It reviews a total of nine relevant case studies, covering the contexts of conventional schools (from elementary schools to college, and cram schools) as well as technology-enhanced environments (internet-assisted learning and mobile learning); and participants from Grade 2 students to adult learners as well as teachers. Their conceptions of learning and preferred learning approaches are summarized.

Results of the studies show the Taiwanese students’ and teachers’ conceptions of learning in general and of technology-enhanced learning in particular. The students tended to be passive learners to receive instructions and considered examinations as a short-term goal for their study, with surface learning approaches commonly adopted. Despite technology may help to promote their cultivation of a more sophisticated conception of learning, many of them still opted for rote memorization and practice as the major ways to study. The potentials of technology in enhancing learning thus have not been fully realized.

The results shed light on an Asian-specific educational culture which is exam oriented. They reveal the challenges regarding the use of technology in education, which hinder the promotion of students’ advanced conceptions of learning. They also highlight the directions of future work to create a more accessible and gratifying technology-enhanced environment.

This study aims to introduce a deep neural network (DNN) to estimate the effective thermal conductivity of the flat heat pipe with spreading thermal resistance.

A total of 2,160 computational fluid dynamics simulation cases over up to 2,000 W/mK are conducted to regress big data and predict a wider range of effective thermal conductivity up to 10,000 W/mK. The deep neural networking is trained with reinforcement learning from 10–12 steps minimizing errors in each step. Another 8,640 CFD cases are used to validate.

Experimental, simulational and theoretical approaches are used to validate the DNN estimation for the same independent variables. The results from the two approaches show a good agreement with each other. In addition, the DNN method required less time when compared to the CFD.

The DNN method opens a new way to secure data while predicting in a wide range without experiments or simulations. If these technologies can be applied to thermal and materials engineering, they will be the key to solve thermal obstacles that many longing to overcome.

The role of entrepreneurship education in promoting entrepreneurial actions remains unclear. The purpose of this paper is to investigate the logic of different types of entrepreneurship education and the effect of learning characteristics in promoting entrepreneurial actions among student entrepreneurs in the higher education setting.

The study employs a quantitative approach involving the use of survey data collected via an Internet tool. The constructs of variables are measured using previously tested scales. The data were analysed using partial least squares modelling because it can handle formative and reflective constructs in the same model and is capable of testing for moderation.

The findings illustrate that voluntary entrepreneurship education generates learning outcomes in terms of students' entrepreneurial actions, which is important because without action, a venture will never be launched. This is especially so if students show a deep learning orientation, while mastery motivation showed a significant and negative moderating effect. This is not the case for compulsory entrepreneurship education.

Embedded in construal level theory, this paper offers knowledge that can help to advance entrepreneurship education research (1) by uncovering the role of different types of entrepreneurship education interventions, (2) by considering students' entrepreneurial actions as the dependent variable and (3) by unravelling the role of students' learning characteristics in the efficacy of entrepreneurship education interventions. By doing this, the study addresses recent repeated calls for more fine-grained research focused on how university students learn in entrepreneurship in higher education and its effects.

The purpose of this paper is to investigate the potential integration of deep learning (DL) and digital twins (DT), referred to as (DDT), to facilitate Construction 4.0 through an exploratory analysis.

A mixed approach involving qualitative and quantitative analysis was applied to collect data from global industry experts via interviews, focus groups and a questionnaire survey, with an emphasis on the practicality and interoperability of DDT with decision-support capabilities for process optimization.

Based on the analysis of results, a conceptual model of the framework has been developed. The research findings validate that DL integrated DT model facilitating Construction 4.0 will incorporate cognitive abilities to detect complex and unpredictable actions and reasoning about dynamic process optimization strategies to support decision-making.

The DL integrated DT model will establish an interoperable functionality and develop typologies of models described for autonomous real-time interpretation and decision-making support of complex building systems development based on cognitive capabilities of DT.

The research explores how the technologies work collaboratively to integrate data from different environments in real-time through the interplay of the optimization and simulation during planning and construction. The framework model is a step for the next level of DT involving process automation and control towards Construction 4.0 to be implemented for different phases of the project lifecycle (design–planning–construction).

This article aims to consider teacher's views about intervisitations regarding its application and its usefulness as a community-enhancer. Many educators venture into the world of teaching because they love learning and value learning from their peers (rather than merely from text or administrators); however, teacher reservations or hesitations towards the practice of engaging in intervisitations do exist and can serve as an obstacle.

The findings reported in this study resulted from the analysis of two teacher's perspectives towards classroom intervisitations. The subset of data presented in this study resulted from the surveys and semi-structured interviews that were conducted. Qualitative methodology was used to address the research question as it allows for a greater exploration, description and ideally the emotions of participants/teachers. The coding process consisted of open coding, which then led to axial coding and the elevation of codes to themes.

In this study, teacher buy-in would be enhanced through the protocol feeling more personalized, less-dictated and more flexible in its execution, especially through the support of administrators and district leaders. In addition, teacher mindsets and perceptions also need some reshifting and should be part of the professional development process involving intervisitation roll-outs as any hesitations/limitations/and lack of willingness need to be honed in on and prioritized. Lastly, limiting teachers from an appropriate amount of time to complete such work may also encourage shallow collaboration among teachers instead of in-depth reflexive practice. By prioritizing intervisitations and/or inter-teacher collaboration in the building and allowing teachers to embark on professional development sessions with each other as a means of growing as a teacher and community, all will flourish.

Through examining the narratives of two educators, it was found that teacher willingness to partake in intervisitations is dependent on the school climate, particularly with regards to trust and a yearn-to-learn among inter-school peers and administrators. In addition, providing ample time and educating teachers on the benefits of such practices enhances one's wanting to independently venture into such work.

The purpose of this study is to evaluate the effectiveness of one online PD in PBL. Researchers want to investigate if a five-day international online PBL training will prepare teachers to implement PBL in their classrooms. Secondly, the researchers aim to determine if the training provides teachers with sufficient knowledge and support to ensure successful PBL implementation.

Participants were given a 5-day (20 h) online PBL training created by one of the researchers with three frontline teachers. Seven trainers are divided into four groups for four groups of participants. Group A included Grade 1 and Grade 2 teachers, Group B included Grade 3 and Grade 4 teachers, Group C included Grade 5 and Grade 6 teachers, and Group D consisted of Grades 7 through 9 teachers. All the participants were given exactly the same surveys at the beginning and the end of the training.

Consistent with previous studies comparing in person and virtue PD programs, this five-day interactive PD program was effective in increasing teachers' knowledge of and ability to plan and implement PBL projects. Specifically, results showed that teachers' knowledge level of PBL shifted from a shallow understanding of what the name implies to a deeper, more comprehensive, and more concrete understanding of PBL essential concepts, its pedagogical values, specific process involved in a PBL project. In addition, the PD program increased teachers' comfort level and ability of planning and implementing PBL projects across grade levels and subject areas.

This research study supported the previous study results that virtual PD programs can be as effective as in person programs. Further, this is the study discovered the effectiveness of PBL training between the US and China through online format, which has not been conducted literately before. The positive results will be used to promote the online collaboration internationally in the future.

This paper aims to uncover new factors that influence the spread of malaria.

The historical data related to malaria were collected from government agencies. Later, the data were cleaned and standardized before passing through the analysis process. To obtain the simplicity of these numerous factors, the first procedure involved in executing the factor analysis where factors' groups related to malaria distribution were determined. Therefore, machine learning was deployed, and the confusion matrices are computed. The results from machine learning techniques were further analyzed with logistic regression to study the relationship of variables affecting malaria distribution.

This research can detect 28 new noteworthy factors. With all the defined factors, the logistics model tree was constructed. The precision and recall of this tree are 78% and 82.1%, respectively. However, when considering the significance of all 28 factors under the logistic regression technique using forward stepwise, the indispensable factors have been found as the number of houses without electricity (houses), number of irrigation canals (canals), number of shallow wells (places) and number of migrated persons (persons). However, all 28 factors must be included to obtain high accuracy in the logistics model tree.

This paper may lead to highly-efficient government development plans, including proper financial management for malaria control sections. Consequently, the spread of malaria can be reduced naturally.

The paper aims to address the gap in the literature related to students’ mindsets and learning activities through investigation of the differences in students’ expectations of, feelings towards, and perceptions of an overseas study tour based on their mindset. The study provides an in-depth analysis of students with different mindsets and proposes the use of overseas tours and intercultural learning to foster students’ growth mindset.

An overseas study tour hosted by a self-financing tertiary institution in Hong Kong was selected for investigation. 13 sub-degree students participated in the study tour during the summer term in 2018. Two types of primary data – quantitative (i.e., a questionnaire survey) and qualitative (i.e., in-depth interviews) – of fixed mindset and growth mindset students were collected for analysis.

The findings indicate differences in students’ expectations of, feelings towards, and perceptions of an overseas study tour depending on whether they demonstrate a fixed or growth mindset. The growth mindset students had more and higher expectations of the study tour, all of which were related to personal growth and development. The fixed mindset students did not have as much of a desire for personal development and their expectations were easily met. Both growth and fixed mindset students had positive feelings and perceptions of the tour.

Research on the application value of overseas study tours in helping students from self-financing tertiary institutions develop a growth mindset is scarce, and thus warrants further investigation.

Mouth segmentation is one of the challenging tasks of development in lip reading applications due to illumination, low chromatic contrast and complex mouth appearance. Recently, deep learning methods effectively solved mouth segmentation problems with state-of-the-art performances. This study presents a modified Mobile DeepLabV3 based technique with a comprehensive evaluation based on mouth datasets.

This paper presents a novel approach to mouth segmentation by Mobile DeepLabV3 technique with integrating decode and auxiliary heads. Extensive data augmentation, online hard example mining (OHEM) and transfer learning have been applied. CelebAMask-HQ and the mouth dataset from 15 healthy subjects in the department of rehabilitation medicine, Ramathibodi hospital, are used in validation for mouth segmentation performance.

Extensive data augmentation, OHEM and transfer learning had been performed in this study. This technique achieved better performance on CelebAMask-HQ than existing segmentation techniques with a mean Jaccard similarity coefficient (JSC), mean classification accuracy and mean Dice similarity coefficient (DSC) of 0.8640, 93.34% and 0.9267, respectively. This technique also achieved better performance on the mouth dataset with a mean JSC, mean classification accuracy and mean DSC of 0.8834, 94.87% and 0.9367, respectively. The proposed technique achieved inference time usage per image of 48.12 ms.

The modified Mobile DeepLabV3 technique was developed with extensive data augmentation, OHEM and transfer learning. This technique gained better mouth segmentation performance than existing techniques. This makes it suitable for implementation in further lip-reading applications.

Social networks (SNs) have recently evolved from a means of connecting people to becoming a tool for social engineering, radicalization, dissemination of propaganda and recruitment of terrorists. It is no secret that the majority of the Islamic State in Iraq and Syria (ISIS) members are Arabic speakers, and even the non-Arabs adopt Arabic nicknames. However, the majority of the literature researching the subject deals with non-Arabic languages. Moreover, the features involved in identifying radical Islamic content are shallow and the search or classification terms are common in daily chatter among people of the region. The authors aim at distinguishing normal conversation, influenced by the role religion plays in daily life, from terror-related content.

This article presents the authors' experience and the results of collecting, analyzing and classifying Twitter data from affiliated members of ISIS, as well as sympathizers. The authors used artificial intelligence (AI) and machine learning classification algorithms to categorize the tweets, as terror-related, generic religious, and unrelated.

The authors report the classification accuracy of the K-nearest neighbor (KNN), Bernoulli Naive Bayes (BNN) and support vector machine (SVM) [one-against-all (OAA) and all-against-all (AAA)] algorithms. The authors achieved a high classification F1 score of 83\%. The work in this paper will hopefully aid more accurate classification of radical content.

In this paper, the authors have collected and analyzed thousands of tweets advocating and promoting ISIS. The authors have identified many common markers and keywords characteristic of ISIS rhetoric. Moreover, the authors have applied text processing and AI machine learning techniques to classify the tweets into one of three categories: terror-related, non-terror political chatter and news and unrelated data-polluting tweets.