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This paper describes how a pre-service teacher’s knowledge and pedagogy changed as she documented her reflective practice while teaching arts-integrated lessons in a fifth-grade classroom during her pre-service teacher preparation program. The pre-service teacher spent three-months conducting an action research project in collaboration with a university mentor.

This paper explores what she and her mentor learned as she prepared arts-integrated lesson plans based on the four studio structures for learning and analyzed them along with identifying and documenting evidence of deep learning through field notes and video recordings.

Analysis of field notes, video recordings and lesson plans led the authors to take a deeper look at where the four studio structures for learning overlapped in the teaching event. In the data the intersections of the four studio structures shared a pattern of increased evidence of deep learning for the students. This paper describes the phenomenon in the classroom at various points of intersection.

This action research study is preliminary, and the findings are suggestive of further research that would require indexing what deep learning looks like and gathering and analyzing student data.

It is recommended that teachers use the four studio structures to integrate the arts in their classrooms and to enhance and encourage creativity, communication, critical thinking, collaboration, character and culture and as teachers work toward deep learning for students.

This case shows how a university partnership provides fertile ground for educators of all skills and experience to participate in the expansion of the field of education as well as personal and professional development.

This paper aims to Test the capabilities/accuracies of four deep learning pre trained convolutional neural network (CNN) models to detect and classify types of highway cracks, as well as developing a new CNN model to maximize the accuracy at different learning rates.

A sample of 4,663 images of highway cracks were collected and classified into three categories of cracks, namely, “vertical cracks,” “horizontal and vertical cracks” and “diagonal cracks,” subsequently, using “Matlab” to classify the sample to training (70%) and testing (30%) to apply the four deep learning CNN models and compute their accuracies. After that, developing a new deep learning CNN model to maximize the accuracy of detecting and classifying highway cracks and testing the accuracy using three optimization algorithms at different learning rates.

The accuracies result of the four deep learning pre-trained models are above the averages between top-1 and top-5 and the accuracy of classifying and detecting the samples exceeded the top-5 accuracy for the pre-trained AlexNet model around 3% and by 0.2% for the GoogleNet model. The accurate model here is the GoogleNet model as the accuracy is 89.08% and it is higher than AlexNet by 1.26%. While the computed accuracy for the new created deep learning CNN model exceeded all pre-trained models by achieving 97.62% at a learning rate of 0.001 using Adam’s optimization algorithm.

The created deep learning CNN model will enable users (e.g. highway agencies) to scan a long highway and detect types of cracks accurately in a very short time compared to traditional approaches.

A new deep learning CNN-based highway cracks detection was developed based on testing four pre-trained CNN models and analyze the capabilities of each model to maximize the accuracy of the proposed CNN.

Fishbein and Ajzen's 1975 Theory of Reasoned Action (TRA), updated by Ajzen and Fishbein in 1980, is advanced in this paper as an appropriate theory for measuring student's intentions to adopt deep or surface processing and to adopt specific learning strategies. TRA is a decision theory that explains motivation by emphasising the specific processes that individuals use to make choices. TRA captures an individual's motivation by using the concept of intention to perform a behaviour. A TRA model was constructed based on a four‐latent‐variable (deep, surface, strategic and intention) framework and empirically assessed for model data fit. The survey items showed loadings on the constructs of deep, surface and strategic processing under this framework, indicating strong construct validity for the three learning factors. The TRA model was found to strongly positively influence the adoption of the deep processing construct, and to strongly negatively influence the adoption of the surface processing construct. In addition, it was found to strongly positively influence the adoption of positive learning strategies and weakly discourage the use of negative learning strategies.

The introduction of International Financial Reporting Standards (IFRS) has brought about renewed calls for the learning environment to foster a deep approach to learning by students. Given this, the purpose of this paper is to determine what aspects of the learning environment, as measured by the Course Experiences Questionnaire, created in two semester-long financial accounting classes, influence students’ approaches to learning, as perceived by Chinese accounting students.

A logistic regression model based on responses from 497 accounting students across two universities in China is used to address this issue.

The findings provide original empirical evidence of the Chinese accounting students’ expectations of deep learning. The main results showed that teaching quality and clear goals and standards were significantly associated with a deep approach to learning.

As two universities are included in the study, the findings are not necessarily generalisable to all accounting degree courses across China. There are practical implications for the teaching of IFRS in the financial accounting unit in China, and particularly for the two universities. Specifically, instructors need to foster students’ learning environment and inspire an enhanced approach to deep learning by focusing more on communicating their expected academic standards and improving their quality of teaching to reverse the passive approach taken by the vast majority of Chinese accounting students.

As one of the few studies from a Chinese accounting classroom context with respect to the learning approaches to teaching IFRS, this study will contribute to extend the existing knowledge of the learning environment of Chinese universities.

The purpose of this paper is to propose a distributed deep learning architecture for smart cities in big data systems.

We have proposed an architectural multilayer to describe the distributed deep learning for smart cities in big data systems. The components of our system are Smart city layer, big data layer, and deep learning layer. The Smart city layer responsible for the question of Smart city components, its Internet of things, sensors and effectors, and its integration in the system, big data layer concerns data characteristics 10, and its distribution over the system. The deep learning layer is the model of our system. It is responsible for data analysis.

We apply our proposed architecture in a Smart environment and Smart energy. 10; In a Smart environment, we study the Toluene forecasting in Madrid Smart city. For Smart energy, we study wind energy foresting in Australia. Our proposed architecture can reduce the time of execution and improve the deep learning model, such as Long Term Short Memory10;.

This research needs the application of other deep learning models, such as convolution neuronal network and autoencoder.

Findings of the research will be helpful in Smart city architecture. It can provide a clear view into a Smart city, data storage, and data analysis. The 10; Toluene forecasting in a Smart environment can help the decision-maker to ensure environmental safety. The Smart energy of our proposed model can give a clear prediction of power generation.

The findings of this study are expected to contribute valuable information to decision-makers for a better understanding of the key to Smart city architecture. Its relation with data storage, processing, and data analysis.

This study, based on self-determination theory (SDT), investigates the effect of business students' future time perspective (FTP), directly and indirectly (mediated by deep learning approaches), on quality of university life.

A sample of 547 business students in Ho Chi Minh City, Vietnam, was surveyed via a two-wave process to collect data to validate the measures and to test the hypotheses using structural equation modeling (SEM).

The results produced by SEM demonstrated that FTP had no direct effect on quality of university life and that deep learning approaches fully mediated the impact of FTP on quality of university life.

The study findings provide business educators with a better understanding of the role that FTP can play for business students. Increased awareness of this issue may help nurture the FTP of business students, which in turn directs them to pursue deep learning approaches to achieve a higher level of quality of university life.

This study is among the first to empirically investigate the overarching role that FTP plays in both deep learning approaches and quality of university life.

Student populations in higher education in the United States have become increasingly diverse as a result of demographic changes. As a result, educators need an understanding of the background and characteristics of these demographic subgroups in order to improve the quality of their education. Students’ approaches to learning affect their quality of learning and are influenced by their perceptions of the learning environment and assessment. The present study extends prior research by examining the approaches to learning, assessment preferences, and the relationship between approaches to learning and assessment preferences of intermediate accounting students enrolled in a public university in the United States with a diverse student population. Students with higher deep approaches to learning had higher preferences for assessment involving higher-order thinking tasks, integrated assessment, and nonconventional assessment. Students with higher surface approaches to learning had lower preferences for assessment involving higher-order thinking tasks. The differences in these relationships for subgroups of students defined by citizenship, age, gender, and race are presented. The implications of the results for teaching and learning in accounting education are discussed.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

The purpose of this paper is to investigate whether learning communities (LCs), defined as a cohort of students jointly enrolled in two distinct courses, increase “deep learning” in either or both courses. This study focuses on the impact of learning communities in quantitative courses.

The hypothesis is tested using a unique data set including individual student performance and characteristics collected from students enrolled in an LC of Principles of Microeconomics and Elementary Statistics. The sample also includes students enrolled in each course separately which allows for testing between groups. The final exam in each course contained questions designed specifically to test deep learning. The design facilitates the use of multivariate regression analysis to examine the correlation between learning in communities and deep learning, holding constant other possible elements of student success.

Despite perceptions among the sample student population that learning increases in both courses as a result of the LC format, the empirical evidence does not reveal any statistically significant increase in deep learning as a result of learning in community. However, the sample is more introverted than the average college student which may meaningfully impact the results.

There are a number of important motivations for implementing an LC program that are not measured here. These include an increased sense of community among students, breadth (rather than depth) of knowledge, and awareness of the interconnectedness of learning across disciplines. However, to the extent that university instructors are motivated to ensure learning in their own discipline, this resource-intensive strategy may not be the most suitable approach in quantitative courses.

Learning communities continue to be a popular pedagogical technique and curriculum requirement, particularly at teaching-focused universities. This research offers an empirical approach to measuring one aspect of their value which is typically left to conceptual or qualitative study.

This paper aims to investigate whether students adopt a “deep” approach to learning, i.e. “seeking meaning”, in the context of the subject of information systems (IS) and hospitality management degree programmes.

A questionnaire that covers the key constructs, i.e. teaching, feedback, assessment, student autonomy and deep learning, is administered to two samples of final year students. Statistical tests indicate the significant differences between the two samples and the relationship between students' perception of their learning environment and “deep” learning in IS.

Results show that, even though there are some significant differences between the two groups, particularly in items of teaching methods, feedback and assessment, “deep learning” is acquired in both contexts.

This research is limited to a comparative study of two institutions and further research is recommended to discover constructs and contexts particular to the hospitality sector.

These results reveal that “constructive alignment” of teaching and learning priorities is needed with resource and training implications for both teachers and educational establishments.

This research investigates information systems subject learning in hospitality management programmes (and the need to see an information system as an integrated, social system). It examines “contexts” as part of the learning environments: this is new. It also marries two different learning measurements (those of ETL and Cope) to quantitatively examine the phenomenon of “deep learning” in the hospitality IS subject context.