Programming Environment Research Articles

'We are all bringing, like a unique sort of perspective': The core elements of a strengths-based digital arts mentoring program for autistic adolescents from the perspective of their mentors.

While the adoption of strengths-based approaches to supporting autistic adolescents is growing in popularity, the application of this approach to a digital arts mentoring program has yet to be explored. This study reports on the core elements contributing to the success of a community digital arts mentoring program for autistic adolescents from the mentors' perspective. This paper presents an in-depth exploration of mentors' experiences, comprising a component of a broader line of research investigating a digital arts mentoring program for autistic adolescents emphasising positive youth development. The digital arts mentoring program spanned 20 weeks across two Australian school terms and was attended by two groups of autistic adolescents (N = 18) aged between 11 and 17 years. A qualitative approach was utilised in exploring the perspective of their mentors (N = 6). Qualitative data were collected at the end of each school term for each group with the mentors using an interpretive phenomenological approach and Colaizzi's seven-step analysis method. Thirteen individual interviews were conducted with six mentors. This research was conducted with a disability arts provider to provide a digital arts mentoring program to autistic adolescents. The mentors employed have lived experience with disorders such as attention deficit hyperactivity disorder (ADHD) and anxiety. Five primary themes emerged from the data: positive connections, mentor knowledge and experience, mentoring approaches, autism education, program organisation, resources and environment. Subthemes underpinned the primary themes related to positive connections (three subthemes), mentoring approaches (four subthemes) and program organisation, resources and environment (three subthemes). The findings suggest that prior experience, sufficient training, a supportive environment and a flexible and adaptable approach were essential for success. Understanding the core elements of a strengths-based digitalarts program in occupational therapy provides a comprehensive framework for utilising clients' inherent strengths and creativity as therapeutic tool, creating an empowering environment, fostering meaningful outcomes for clients.

An economic-environmental energy management system design for MT-HVDC networks via a semi-definite programming approximation with robust analysis

This research deals with the economic-environmental dispatch problem (EEDP) in multi-terminal high-voltage direct-current (MT-HVDC) systems by proposing a convex approximation based on semi-definite programming (SDP). The exact formulation of the EEDP corresponds to a non-convex, nonlinear programming problem due to the presence of a nonlinear quadratic constraint regarding the products between voltage variables. The thermal plants' economic and objective functions are modeled using typical quadratic functions. The SDP approach allows reaching a convex approximation that ensures the global optimal solution for each objective function independently or the construction of the optimal Pareto front via the weighting-factor optimization methodology. The proposed SDP approach also considers uncertainties in the demand and in the available power of renewable sources, which makes it robust. The main contribution of this research is a multi-period analysis that includes large-scale renewable generation sources and a robust analysis regarding demand and variations in renewable generation. Numerical results in two MT-HVDC systems demonstrate the effectiveness of the proposed SDP approach when compared to combinatorial optimization algorithms. All numerical simulations were carried out using the CVX convex disciplined tool, with the help of the SEDUMI and SDPT solvers, in the MATLAB programming environment.

Accuracy evaluation of dental CBCT and scanned model registration method based on pulp horn mapping surface: an in vitro proof-of-concept

Background and aim3D fusion model of cone-beam computed tomography (CBCT) and oral scanned data can be used for the accurate design of root canal access and guide plates in root canal therapy (RCT). However, the pose accuracy of the dental pulp and crown in data registration has not been investigated, which affects the precise implementation of clinical planning goals. We aimed to establish a novel registration method based on pulp horn mapping surface (PHMSR), to evaluate the accuracy of PHMSR versus traditional methods for crown-pulp registration of CBCT and oral scan data.Materials and methodsThis vitro study collected 8 groups of oral scanned and CBCT data in which the left mandibular teeth were not missing, No. 35 and No. 36 teeth were selected as the target teeth. The CBCT and scanned model were processed to generate equivalent point clouds. For the PHMSR method, the similarity between the feature directions of the pulp horn and the surface normal vectors of the crown were used to determine the mapping points in the CBCT point cloud that have a great influence on the pulp pose. The small surface with adjustable parameters is reconstructed near the mapping point of the crown, and the new matching point pairs between the point and the mapping surface are searched. The sparse iterative closest point (ICP) algorithm is used to solve the new matching point pairs. Then, in the C + + programming environment with a point cloud library (PCL), the PHMSR, the traditional sparse ICP, ICP, and coherent point drift (CPD) algorithms are used to register the point clouds under two different initial deviations. The root square mean error (RSME) of the crown, crown-pulp orientation deviation (CPOD), and position deviation (CPPD) were calculated to evaluate the registration accuracy. The significance between the groups was tested by a two-tailed paired t-test (p < 0.05).ResultsThe crown RSME values of the sparse ICP method (0.257), the ICP method (0.217), and the CPD method (0.209) were not significantly different from the PHMSR method (0.250). The CPOD and CPPD values of the sparse ICP method (4.089 and 0.133), the ICP method (1.787 and 0.700), and the CPD method (1.665 and 0.718) than for the PHMSR method, which suggests that the accuracy of crown-pulp registration is higher with the PHMSR method.ConclusionCompared with the traditional method, the PHMSR method has a smaller crown-pulp registration accuracy and a clinically acceptable deviation range, these results support the use of PHMSR method instead of the traditional method for clinical planning of root canal therapy.

Timing Side-Channel Mitigation via Automated Program Repair

Side-channel vulnerability detection has gained prominence recently due to Spectre and Meltdown attacks. Techniques for side-channel detection range from fuzz testing to program analysis and program composition. Existing side-channel mitigation techniques repair the vulnerability at the IR/binary level or use runtime monitoring solutions. In both cases, the source code itself is not modified, can evolve while keeping the vulnerability, and the developer would get no feedback on how to develop secure applications in the first place. Thus, these solutions do not help the developer understand the side-channel risks in her code and do not provide guidance to avoid code patterns with side-channel risks. In this paper, we present Pendulum , the first approach for automatically locating and repairing side-channel vulnerabilities in the source code, specifically for timing side channels. Our approach uses a quantitative estimation of found vulnerabilities to guide the fix localization, which goes hand-in-hand with a pattern-guided repair. Our evaluation shows that Pendulum can repair a large number of side-channel vulnerabilities in real-world applications. Overall, our approach integrates vulnerability detection, quantization, localization, and repair into one unified process. This also enhances the possibility of our side-channel mitigation approach being adopted into programming environments.

Computing irregular hypar-based quad-mesh patterns for segmented timber shells

Hyperbolic paraboloids, “hypars,” are special types of ruled surfaces. Their geometric properties provide them with loadbearing and stabilizing capacities, as well as distinct esthetic qualities. These attributes become evident in numerous applications in buildings, in many of which concrete or timber is used for the construction of the hypars. Hypars could also be relevant in the context of circular construction and design for disassembly, and the upcycling of construction waste. Due to the geometric simplicity of straight lines, which generate ruled surfaces, hypar-based structures can be designed and built with relatively simple means. They can consist of self-similar or even identical elements, which could facilitate their reuse.Compared to other types of ruled surfaces, such as conoids, hypars have the advantage of being doubly ruled, meaning that structural grids of straight elements can be formed. This paper investigates another interesting property, which is the possibility of creating flat-quad meshes by diagonally connecting the intersection points of the generatrices. This property has been previously described by other scholars, some of which explored its applicability for glass-clad steel grid shells. In this research, we focus on its potential for segmented timber shells that can serve as stand-alone structures, or as modular and reusable building parts, such as façade or roof components. The reusability of such modular units could be achieved by using reversible joints between them.More specifically, our research investigates the design space of construction systems based on such components via computational design and optimization algorithms, such as the memory limited Broyden–Fletcher–Goldfarb–Shanno (LBFGS) algorithm with automatic computation of the gradient, within the Julia programming environment. By applying principles and methods of differential geometry, we study hypars with irregular tilings, enabling the integration of panels with diverse proportions, shapes and sizes, as they can occur in wood production waste. By reducing construction waste, the work aims at reducing the negative environmental impact of the building construction sector. Moreover, irregular tilings could enable a more customized design of acoustic qualities and offer visual variety in segmented hypar based timber structures.The here presented studies show that the proposed optimization method provides a good fit of many tiles to rhombi, particularly when the steepness is not too large. We also show that optimizing towards rectangles provides better results. Overall, the results support the initial assumption that irregular rulings could be a means of adapting to both homogeneous and diverse material stocks.

MolSym: A Python package for handling symmetry in molecular quantum chemistry.

A consideration of the point group symmetry of molecules is often advantageous from a computational efficiency standpoint and sometimes necessary for the correct treatment of chemical physics problems. Many modern electronic structure software packages include a treatment of symmetry, but these are sometimes incomplete or unusable outside of that program's environment. Therefore, we have developed the MolSym package for handling molecular symmetry and its associated functionalities to provide a platform for including symmetry in the implementation and development of other methods. Features include point group detection, molecule symmetrization, arbitrary generation of symmetry element sets and character tables, and symmetry adapted linear combinations of real spherical harmonic basis functions, Cartesian displacement coordinates, and internal coordinates. We present some of the advantages of using molecular symmetry as achieved by MolSym, particularly with respect to Hartree-Fock theory, and the reduction of finite difference displacements in gradient/Hessian computations. This package is designed to be easily integrated into other software development efforts and may be extended to further symmetry applications.

РЕАЛІЗАЦІЯ ДИФЕРЕНЦІЙОВАНОГО ПІДХОДУ ПРИ НАВЧАННІПРОГРАМУВАННЮ МОВОЮ PYTHON ЗДОБУВАЧІВЗАГАЛЬНОЇ СЕРЕДНЬОЇ ОСВІТИ

Students choose their future path starting from school. The learner can determine their abilities when they genuinely understand their potential and prospects in a specific subject or direction. A differentiated approach helps explore the limits of knowledge and encourages its expansion. The paper discusses the implementation of a differentiated approach in teaching computer science, particularly in programming. Tasks are proposed to students, emphasizing the method of solving. Each learner chooses the method that suits them best. Evaluation criteria are provided in advance, helping students identify the aspects of the task to focus on. An example task demonstrating the implementation of a differentiated approach involves calculating the recommended body weight for a woman and a man using the Broca's Formula. Depending on the complexity chosen by the student, this task can be solved using a linear structure, applying branching, or developing a widget. Widgets allow students to demonstrate the use of libraries and functions in the Python programming language. Various solutions in Python and recommendations for the placement of such tasks in the study of a specific topic are also provided. During the defense, all students become acquainted with different task implementations, allowing each student to understand various execution methods and choose a more challenging one next time. The presented example can be extended to other topics, in the study of other programming languages or programming environments. Prospects for further research include developing a system of multi-level programming tasks for the purpose of implementing differentiated learning

Code Verse

Code Verse features a revolutionary online code editor that aims to revolutionize your coding experience by providing a seamless interface between different programming languages. This innovative platform supports developers working with languages such as JavaScript, Python, Java, php etc. facilitating a versatile programming environment that adapts to the diverse needs of users. Code verse's user-friendly interface removes the traditional barriers associated with language-specific editors and allows developers to seamlessly switch between languages within the same platform.

Διαχείριση ανοιχτών δεδομένων με χρήση της τεχνολογίας API στο περιβάλλον του App Inventor

The contemporary global focus on the evolution and dissemination of the coronavirus (COVID-19) underscores its significance on a worldwide scale. It is imperative to acknowledge the dynamic nature of the pandemic, with ongoing monitoring being crucial for effective response. Our research endeavors to enhance educational approaches in light of these challenges. An innovative educational scenario and learning model have been devised and implemented, rooted in dilemmas and argumentation. This model integrates key educational principles, including a STEM-based interdisciplinary approach, self-regulated learning, and collaborative learning. Furthermore, the study emphasizes the instrumental role of technological tools in fostering active and collaborative participation within the classroom setting. A tangible manifestation of this emphasis is an application developed using the visual programming environment of App Inventor. Students showcased their results in class, engaging in discussions, presenting arguments, and reflecting on the findings that ultimately led to the conclusions presented in class. This multifaceted educational approach seeks to address the challenges posed by the evolving landscape of the COVID-19 pandemic.

Visualization and Segmentation of Lung Tissue in DICOM-format CT Images

Abstract In this project, two tools were developed as a preliminary step towards the creation of future Computer-Aided Diagnosis systems for the automatic detection of lung diseases. The first methodology aimed to extract information from DICOM-format files using Python and the Jupyter Notebook programming environment. The implemented process enables the generation of images in various anatomical planes, using the pixel matrix in Hounsfield units. Additionally, a custom tool was developed for anonymizing and visualizing file information. The techniques employed are independent of the grayscale image generated and are not reliant on the type of window used. The second tool introduces a lung segmentation technique for chest CT images. The segmentation uses the pixel matrix in Hounsfield units to initially identify lung tissue based on its linear X-ray attenuation. Subsequently, the Watershed algorithm based on markers is employed as the primary foundation of the technique. Various morphological operations and transformations on axial slices are applied, resulting in successful lung segmentation.

E-training environment for developing Capstone teaching skills for STEM teachers in Egypt

The research investigated the efficacy of utilizing an e-training environment to enhance capstone teaching skills among STEM educators. Conducted at Obour STEM School, Cairo, Egypt, the study employed a quasi-experimental design with 27 randomly selected participants. Three instruments were utilized: a list of Capstone teaching skills, a pre-post achievement test, and an observation card assessing skill acquisition in two main fields: EDP Field and Process Management Field. These fields encompassed eight main skills, subdivided into 64 subskills. The researchers conducted pre- and post-testing, initially administering a 70-question pre-test followed by a month-long online training via Microsoft Teams. The training content aimed to address identified needs based on pre-test results. After the training, participants completed a post-test, which was repeated after another month. Results indicated a statistically significant difference (p&lt;=0.05) between pre-test and post-achievement test scores, favoring the latter, suggesting improvement in Capstone teaching skills. Similarly, there was a significant difference (p&lt;=0.05) between pre-test and post-observation card scores, indicating enhanced skill development. Conclusively, the study demonstrated the effectiveness of e-training, particularly employing the SOLE technique, in enhancing capstone teaching skills among STEM educators. Consequently, the researchers recommended the integration of e-training environments in STEM teacher development programs in Egypt.

Academic Productivity of Applicant and Program as Predictors of a Future Academic Career in Neurosurgery

BACKGROUND AND OBJECTIVES: Academic productivity is viewed as a critical objective factor for a neurosurgery residency applicant. There has been a consistent rise in academic productivity over the last decade, but a lack of consistent data on the utility of this in helping neurosurgery residency programs identify which applicants will enter academic neurosurgery. This cross-sectional study evaluates the predictiveness of academic productivity before and during residency on career choice, both independent and dependent of training environment. METHODS: The 116 accredited neurosurgery residency programs were split into 4 quartile groups based on their 2022 Doximity rankings. Six neurosurgery residency programs were randomly selected from each quartile. Publicly available information including number and type (before or during residency) of publication and type of employment (academic vs nonacademic) was collected on neurosurgeons who matriculated into residency in the year 2000 or later. Multivariable logistic regression was used to explore the associations among neurosurgeon and program characteristics, and an academic career. RESULTS: A total of 557 neurosurgeons were identified. Group 1 (n = 194) had the highest median publications during residency total (12) and first author (5), as well as the highest percentage of neurosurgeons who attended a top 20 medical school (38.7%), hold a higher educational degree (20.6%), and pursued an academic career (72.2%). Neither attending a top 20 medical school, holding a higher educational degree, nor publications were significant multivariable predictors of an academic career. Being in group 1 was the only significant predictor of entering an academic career across analyses. CONCLUSION: Only residency group ranking, not academic productivity, predicted a future academic career. For residency programs evaluating applicants as future academic neurosurgeons, this suggests that program environment is more predictive than traditionally valued characteristics such as research productivity. Additional work is needed to elucidate characteristics or practices by which future academic neurosurgeons can be identified.

Power of a test for assessing interlaboratory consensus of nominal and ordinal characteristics of a substance, material, or object

A concept of the consensus among different laboratories participating in an interlaboratory comparison, classifying a substance, material, or object according to its nominal and ordinal (i.e. categorical) characteristics, is devised using decomposition of the total variation of the laboratory responses. One of the components of the total variation is caused by the between-laboratory differences, and the second—by conditions associated with the applied experimental design (for example, temperature of test items, technician experience, etc). This decomposition is based on the recently developed two-way CATANOVA for nominal variables and two-way ORDANOVA for ordinal variables. The consensus is tested as hypotheses about homogeneity, i.e. insignificance of the corresponding components of the total variation. The consensus power is taken to be the power of the homogeneity test. A methodology for evaluation of the consensus power and corresponding risks of false decisions versus the dataset size of categorical characteristics obtained in an interlaboratory comparison is detailed. Examples of evaluation of the power and risks are discussed using previously-published datasets of an interlaboratory comparison of identification of weld imperfections, and an examination of the intensity of the odor of drinking water. An example of computer code in the R programming environment is presented for the power calculations in the case of nominal variables, using a chi-square distribution. A newly developed tool for ordinal variables, an Excel spreadsheet with macros, which is based on Monte Carlo draws from a multinomial distribution, is also available.

Understanding the landscape of domestic violence transitional housing: services and housing for rural and non-rural clientele

Housing support is one of the most powerful and in-demand remedies to address domestic violence (DV), with almost 40,000 survivors served daily in U.S. based DV housing programmes. DV transitional housing (DVTH) provides a critical safety net of longer-term housing for survivors beyond emergency shelters. Despite its wide use, little research has documented who is using DVTH, their needs, and how programme environment and location impact service provision. Using five years of data from U.S. government funded DVTH programmes, this study examines client and programme characteristics with attention to rurality. Findings indicate demographic, service engagement, and length of stay differences among DVTH clients within rural and non-rural locations. Programme environments and service provision significantly varied between rural and non-rural programmes. Results highlight substantial heterogeneity in programme structure, client demographics, and services engagement across non-rural and rural programmes. The wide range of DVTH types and service use indicate the need for dynamic and flexible support and funding to address survivor-defined needs.

Cultivating Creativity and Improving Coding Skills in Primary School Students via Domain-General and Domain-Specific Learning Scaffoldings

The transformative wave of generative AI is reshaping the creative thinking processes of learners, posing a significant challenge to education and industry in cultivating technological literacy and creativity. This study delves into the exploration of how learners can effectively tackle new challenges by deconstructing fragments from a macro perspective and generating innovative methods or concepts. In the Scratch visual programming environment, learners in the self-regulated learning mode observed entire functioning projects, facilitating easy disassembly and learning, namely by using Code Decomposed by Learner (CDBL). A total of 104 fifth-grade students were divided into two learning scaffoldings: (1) domain-general, learning from the top down (CDBL-TD), and (2) domain-specific, learning from the bottom up (CDBL-BU). Students in the CDBL-TD group exhibited a high degree of completion, strong exploration abilities, and the willingness to experiment with unlearned functions. Although there was no significant difference in originality between the two groups, students in the CDBL-TD group showcased greater uniqueness in designing characters or items within the game. This study introduces a novel programming learning scaffolding, offering instructors a tool to guide students’ creativity and enhance their programming capabilities.

Fast, accurate and accessible calculations of leaf temperature and its physiological consequences with NicheMapR

Abstract Mechanistic predictions of ecological niches are fundamentally based on energy and mass exchange processes that, to be realistic, must be linked to microclimate and incorporate behaviour and physiology. Estimates of leaf temperature and associated transpiration and net photosynthesis rates are fundamental in ecology, agriculture and global change biology. Equations for calculating leaf energy budgets have been available for over 60 years and complex models of stomatal behaviour have been developed. However, user‐friendly ways of estimating leaf temperature under realistic microclimatic regimes, including soil moisture effects, remain limited. Here we show that the integrated microclimate and ectotherm functions of the NicheMapR package for the R programming environment can be used to make fast and effective estimates of leaf temperature and associated rates of water loss and photosynthesis for a wide range of leaf functional types, root depths and microhabitats. Stomatal conductance may be constant or simulated to change dynamically (a) as a function of vapour pressure deficit and CO2 concentration, (b) as a function of soil water potential and (c) as a thermoregulatory mechanism to avoid heat stress. We tested the approach against hourly leaf temperature observations in summer from an arid shrubland in Australia, a temperate woodland in Montana USA, along an elevational gradient in Colorado USA, and at a botanical garden in Florida, USA. National or global gridded data sets can provide the required forcing data but local observational data or some combination of local and gridded data can be used. The global ERA5 and NCEP datasets allow historical leaf energy budget calculations to be made anywhere in the world and a Shiny app has been developed to facilitate use of the model with these datasets. Situated leaf energy budget calculations can be used to design informative field programs to collect much needed detailed temporal data on leaf temperature under natural settings, enabling better predictions and interpretations of how plants respond to environmental change.

PxBLAT: an efficient python binding library for BLAT

BackgroundWith the surge in genomic data driven by advancements in sequencing technologies, the demand for efficient bioinformatics tools for sequence analysis has become paramount. BLAST-like alignment tool (BLAT), a sequence alignment tool, faces limitations in performance efficiency and integration with modern programming environments, particularly Python. This study introduces PxBLAT, a Python-based framework designed to enhance the capabilities of BLAT, focusing on usability, computational efficiency, and seamless integration within the Python ecosystem.ResultsPxBLAT demonstrates significant improvements over BLAT in execution speed and data handling, as evidenced by comprehensive benchmarks conducted across various sample groups ranging from 50 to 600 samples. These experiments highlight a notable speedup, reducing execution time compared to BLAT. The framework also introduces user-friendly features such as improved server management, data conversion utilities, and shell completion, enhancing the overall user experience. Additionally, the provision of extensive documentation and comprehensive testing supports community engagement and facilitates the adoption of PxBLAT.ConclusionsPxBLAT stands out as a robust alternative to BLAT, offering performance and user interaction enhancements. Its development underscores the potential for modern programming languages to improve bioinformatics tools, aligning with the needs of contemporary genomic research. By providing a more efficient, user-friendly tool, PxBLAT has the potential to impact genomic data analysis workflows, supporting faster and more accurate sequence analysis in a Python environment.

SCREENING OF WHEAT GENOTYPES FOR WATER DEFICIT CONDITIONS UNDER THE SCENARIO OF CLIMATE CHANGE

The top-ranked crop in cereals, wheat feeds people around the globe more than any other food crop, with a diversity of uses in the food industry. The global production of wheat is 250 million tons which is still not enough to address the needs of the ever-increasing human population. Wheat plays a vital role in the human diet because it is the source of protein, carbohydrates, and calories. Due to its wider adaptation wheat is sown as a rain-fed and irrigated crop. Drought upsets the crop during all growth and developmental stages and all these stages of the crop are not equally affected by the drought. At initial growth stages, water stress reduces plant emergence and germination and causes a reduction in plant growth by reducing its height, number of productive tillers, and total leaf surface area. When drought occurs at anthesis it reduces the percentage of fertile spikelets. This research aimed to check the response of wheat genotypes under drought stress for different morphological attributes at the maturity stage. Significant results were obtained under drought and normal conditions. The data were further analyzed under PCA for the selection of best genotypes under normal and stress conditions. Zincol performed well under both conditions. Results obtained from this research will be useful in selecting the best genotypes for rainfed and water-stress environments in future breeding programs.

Extending Jupyter with Multi-Paradigm Editors

Computational notebooks like the Jupyter programming environment have been popular, particularly for developing data-driven applications. One of its main benefits is that it easily supports different programming languages with exchangeable kernels. Thus, it makes the user interface of computational notebooks broadly accessible. While their literate programming paradigm has advantages, we can use this infrastructure to make other paradigms similarly easily and broadly accessible to developers. In our work, we demonstrate how the Jupyter infrastructure can be utilized with different interfaces for different programming paradigms, enabling even greater flexibility for programmers and making it easier for them to adopt different paradigms when they are most suitable. We present a prototype that adds graphical programming and a multi-paradigm editor on top of the Jupyter system. The multi-paradigm editor seamlessly combines the added graphical programming with the familiar notebook interface side-by-side, which can further help developers switch between programming paradigms when desired. A subsequent user evaluation demonstrates the benefits not only of alternate interfaces and paradigms but also of the flexibility of seamlessly switching between them. Finally, we discuss some of the challenges in implementing these systems and how these can enhance the software development process in the future.

Learning through the design, process, artefact, analysis and evaluation of thin-film modules in nanotechnology

The aim of this study is to report on the implementation of the laboratory activities of the thin-film module in the learning-by-doing programme. It takes a course in nanotechnology through the implementation of design, process, artefacts, analysis and evaluation in a thin film module, which equips students with hands-on activities. Based on the evaluation results, over 70% of the students finds the use of hands-on activities to learn the thin film module of the programme is very helpful. It strengthens their skills whilst motivating them to take an interest in nanotechnology. The program further offers the benefit of implementing hands-on film modules with laboratory activities, thus creating a more effective learning environment for the nanotechnology program. In addition, the favourable response to the project indicates that the thin film module is successful in helping students understand the concepts and applications of nanotechnology. Regardless, by applying thin-film modules in ‘learning by doing’ activities, students can enhance their self-confidence in nanotechnology-related fields in terms of professional knowledge, technical skills and learning attitudes. Most of all, students get a good satisfaction and achievement. Overall, the feedback from students, both quantitative and qualitative, indicates that the thin film module in the nanotechnology programme is well received by the students.