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This paper presents the phenomenographic analysis of students' approaches to learning in the first year architectural design coursework; thereby correlating contextualization in the architectural curriculum.

This paper reviews phenomenographic data of first year architecture students' learning experience through a comparative analysis of first- and fourth-year students' approaches to learning in the design studio; further co-relating this analysis to the final classification involving all five years of students' learning approaches in the architecture program.

Five meta-categories of the comparative analysis and nineteen meta-categories of the final classification are evaluated using first-year students' learning approaches – to understand the importance of contextualization in curriculums of architecture.

This phenomenographic analysis of first-year students' learning experience represents the onward journey from surface-to-deep approaches to learning that is encountered in their learning approaches, pertaining to the design process in the design coursework during five years of architectural education.

This paper systematically extends the discussion of first year architecture students' engagement in the design process that leads to deep learning; further delving into the static dimension of knowledge and its extension to the dynamic dimension of knowing architecture.

Many master’s students enrol in coursework-based programmes to improve their professional knowledge and skills for the job market. Most studies of employability in higher education focus on undergraduates rather than master’s students, although the number of master’s students worldwide has increased significantly in recent years. This study explores the factors involved in the perceived employability (PE) of master’s students in Hong Kong.

The authors first proposed a conceptual model of PE based on the social cognitive career theory. Using survey data from 786 master’s students in Hong Kong, the authors applied descriptive statistics and an ordinary least squares (OLS) regression to address the following research questions: How do master’s students gauge their PE? How do person, learning and environment variables influence the PE of master’s students?

The authors found that PE is influenced by students' approaches to learning and their institutional career support.

Few studies examined whether students' learning experiences during the master’s programmes influence their employability. This study highlights the importance of learning experiences and career support in coursework-based master’s programmes for enhancing graduate employability.

Online, distance, and eLearning (ODeL) continue to gain recognition as a mandatory component of delivery of education in institutions of higher learning (IHL) around the world following the outbreak of coronavirus disease (COVID-19). This paradigm shift is informed by the need to ensure uninterrupted, valuable, and safe learning experiences for learners during the pandemic. However, governments ordered the closure of schools and colleges following the declaration of COVID-19 as a world pandemic by the World Health Organization (WHO). A report by United Nations Educational, Scientific and Cultural Organization revealed that there was a significant loss of schooling time following the closure of educational facilities which affected over 1.5 billion learners in 194 nations globally. This study explored the use of online approaches to intensify online learning efficacy in IHL. Data collection was conducted using qualitative methods and data analysis done using themes and sub-themes. Findings from this study indicate that students’ engagements on discussion forums are consistent with collaborative learning. Results further support the view that regular, prompt, and meaningful feedback is critical in promoting constructive learning and reflection among students. Based on the findings of this study, practical implications are discussed for stakeholders interested in establishing and strengthening effective delivery of online learning content to enhance students’ learning experiences.

Online resources can be helpful for students and can augment the content presented in learning environments. A team consisting of four biologists, a graduate student, instructional designer and media developers collaborated on the design, development and evaluation of first-year biology online tutorials in a Canadian University. The tutorials were designed to address knowledge gaps resulting in low success rates and attrition of first-year students in biology. The decrease in the number of students in STEM has alarmed educators, prompting a call for efforts to increase STEM majors in universities. Large class sizes, such as first year biology with ∼900 registrants annually, with detail-oriented, content-heavy loads, can result in low success rates and attrition.

Active learning methods, including online formative assessments, which encourage student engagement in course material, can be effective in large introductory science classes, and thus, the authors provided engagement with tutorial online resources. The authors identified the tutorial topics by analyzing previous years' tests, student feedback and pedagogical research in undergraduate biology. The top five topics identified as common misconceptions or troublesome concepts within the course were selected. Standard instructional design processes were used to produce high-quality online tutorials. Tutorials included learning materials, videos, animations, self-assessments, reflective questions and badges to facilitate deep learning of the topics. Effectiveness of the tutorials was evaluated using quantitative methods and quasi-experimental design to compare the student learning results between the control year (without tutorials) and the year when tutorials were offered. Pre- and posttests measuring conceptual understanding were administered to assess gains in student learning. Additionally, student engagement was measured using the Classroom Survey of Student Engagement (CLASSE), and data from learning management system was collected.

Results of the study show that the tutorials were an effective means of providing supplementary assistance to students as well as fostering a gain in students' levels of engagement with the course. Data analysis indicates that there was a significant increased gain in learning of core concepts in biology. Specifically, using formative online assessments resulted in measurable learning gains in students who participated voluntarily, in comparison to students who chose not to engage in self-paced quiz testing.

As seen from the description earlier, the tutorials, and this project, provide suitable university-level complexity to address specific learning gaps in the first year course. They provide a valuable service to students in terms of representing content in an alternate format and motivating students as they engaged with videos and self-assessment most frequently. The project adds to the teaching and learning environment with respect to program design, mode of delivery and scheduling by providing self-paced tutorials that focus on specific concepts in biology. Students may review these resources whenever and as often as they feel necessary to better master the concepts. This makes the content applicable for the various preferences for approaches to learning and accommodation requirements found in students. Importantly, using formative online assessments resulted in measurable learning gains in students who participated voluntarily, in comparison to students who chose not to engage in self-paced quiz testing.

The goals of this research study included evaluating the outcomes of Interdisciplinary Science Threshold Experience (InSciTE) on student experience of science discipline, level of sense belongingness to a large Faculty of Science (FoS), outcomes in learning science literacy skills and whether a student's background played a role in the differences of effects of the high-impact teaching practices. InSciTE was designed to facilitate the transition from high school to a large research-intensive university, and specifically to a FoS with over 6,000 undergraduate students.

The FoS in a Canadian university engaged in the development of a *9 credit program bundling foundational statistics and chemistry courses with integration of aspects of mathematics and biology or physics to create a new first-year, academic interdisciplinary experience called InSciTE. This project-based curriculum emphasized teamwork and leadership, and presented complex interdisciplinary challenges facing today's world. A team-teaching environment consisting of instructors, a lab coordinator and teaching assistants was instrumental for the core InSciTE courses. In addition, the authors utilized a variety of learning practices with interdisciplinary themes to meet the learning outcomes. Course activities included field experience and tours, blended learning and flipped lectures, guest speakers, discovery-based lab activities, group discussions and projects, a capstone research project, and a combination of formative and summative assessments. The authors proposed two hypotheses for the evaluative study; first that the high-impact practices (HIP) will improve students’ experiences and belongingness to science faculty, and second that InSciTE facilitates learning of scientific literacy skills. To assess the effectiveness of InSciTE, the authors used two surveys, the first being the Test of Scientific Literacy Skills (TOSLS), which measures skills related to major aspects of scientific literacy: recognizing and analysing the use of methods of inquiry that lead to scientific knowledge and the ability to organize, analyse, and interpret quantitative data and scientific information. The second survey examined student belongingness, motivation and autonomous learning, combined with demographic data questions.

The results suggest that InSciTE students reported higher feelings of relatedness, group membership, and career aspirations and performed better on the TOSLS compared to students in other science courses.

As a leader in interdisciplinary science, the FoS at a Canadian university developed a full-year course bundling foundational statistics and chemistry courses with integration of some aspects of mathematics and biology or physics to create a new first-year, academic interdisciplinary experience called InSciTE. This project-based curriculum emphasized teamwork and leadership, and presented complex interdisciplinary challenges facing today's world aiming to facilitate transition from high school to a research-intensive university.

The COVID-19 pandemic has created a significant disruption in the educational systems worldwide. Some institutions opted for emergency remote education due to the need to cancel in-person activities. The aims of this paper were to evaluate the use of asynchronous methodology in health sciences education, determine whether asynchronous methodology was sacrificing overall student satisfaction, and investigate whether satisfaction improved as the program develops.

Initially, there was phase 1 that corresponded to four weeks of activities. Each professor produced a video lesson, and after each video lesson, a weekly educational activity was made available. Next, phase 2 was implemented using the same methodology, however lasting six weeks. Three questionnaires were developed, and a Likert scale was administered to verify the students’ level of satisfaction. Data were analyzed using frequency distributions, mean values, standard deviation and confidence interval. The normality of the sum data (total of the questionnaires) was tested using the Kolmogorov–Smirnov test.

Although the students pointed out that the asynchronous methodology facilitated access to the content and considered this methodology satisfactory, they expressed a reduced level of satisfaction regarding emergency remote education in general when data from the first weeks were compared to those of the previous weeks. It is clear that students became increasingly discouraged and tired over time, which motivated the institution to shift into a combination of synchronous and asynchronous methodology to improve student learning.

Teaching in the field of health care encompasses difficult competencies that sometimes are impossible to be learned remotely, so there is a need to examine and evaluate properly the remote education in this area. With careful planning, educational institutions can evaluate their experiences during the pandemic, allowing those involved to highlight strengths and identify weaknesses to better prepare for future needs to improve remote education.

The purpose of the study is to investigate the impact of perceived value and academic entitlement on the online engagement of university students. The mediating effect of technostress inhibitor and teacher behavior between perceived value, entitlement and student engagement was also examined.

The study used a quantitative research methodology, with data collected through a survey of 304 undergraduate students from a public university in Bahrain.

The findings showed that perceived value and academic entitlement were significant predictors of online student engagement. At the same time, only technostress inhibitor was found to mediate those associations. An unexpected result was entitlement's positive and significant impact on student engagement.

University decision-makers are strongly advised to enhance perceived value and support mechanisms for engagement, address technology-related concerns and improve teacher capacity and students' online learning experience.

The study makes a distinct contribution by investigating how perceived value, academic entitlement, technostress inhibitors and teacher behavior influence student engagement in the online higher education context.

Automation of detecting cracked surfaces on buildings or in any industrially manufactured products is emerging nowadays. Detection of the cracked surface is a challenging task for inspectors. Image-based automatic inspection of cracks can be very effective when compared to human eye inspection. With the advancement in deep learning techniques, by utilizing these methods the authors can create automation of work in a particular sector of various industries.

In this study, an upgraded convolutional neural network-based crack detection method has been proposed. The dataset consists of 3,886 images which include cracked and non-cracked images. Further, these data have been split into training and validation data. To inspect the cracks more accurately, data augmentation was performed on the dataset, and regularization techniques have been utilized to reduce the overfitting problems. In this work, VGG19, Xception and Inception V3, along with Resnet50 V2 CNN architectures to train the data.

A comparison between the trained models has been performed and from the obtained results, Xception performs better than other algorithms with 99.54% test accuracy. The results show detecting cracked regions and firm non-cracked regions is very efficient by the Xception algorithm.

The proposed method can be way better back to an automatic inspection of cracks in buildings with different design patterns such as decorated historical monuments.

In the process of continuous casting and rolling of steel plate, due to the influence of rolling equipment and process, there are scratches, inclusions, patches, scabs and pitted surfaces on the surface of steel plate, which will not only affect the corrosion resistance, wear resistance and fatigue strength of steel plate but also may cause production accidents. Therefore, the detection of steel plate surface defect must be strengthened to ensure the production quality of steel plate and the smooth development of industrial construction.

(1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved Multi-Scale Retinex (MSR) enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

When applied to small dataset, the precision of the proposed method is 94.5% and the time is 23.7 ms. In order to compare with deep learning technology, after expanding the image dataset, the precision and detection time of this paper are 0.948 and 24.2 ms, respectively. The proposed method is superior to other traditional image processing and deep learning methods. And the field recognition precision is 91.7%.

In brief, the steel plate surface defect detection technology based on computer vision is effective, but the previous attempts and methods are not comprehensive and the accuracy and detection speed need to be improved. Therefore, a more practical and comprehensive technology is developed in this paper. The main contributions are as follows: (1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved MSR enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.