Quantitative Results Research Articles

An optimized spectral reconstruction method for shift excitation Raman differential spectroscopy

Raman spectroscopy is a powerful analytical method, but when the composition of the test sample is intricate, the original spectral data may contain noise and fluorescence background interference, making it more difficult to extract Raman spectral information from the original spectra. Especially the fluorescence background signal, which is typically several orders of magnitude stronger than the Raman signal, can even overwhelm or obscure the Raman signals, thereby impeding the qualitative or quantitative analysis of the Raman spectra. One effective method for removing the fluorescence background is shift excitation Raman differential spectroscopy (SERDS), which typically involves measuring two raw Raman spectra using slightly different excitation wavelengths, combined with reconstruction algorithms, to obtain Raman spectra free from fluorescence interference. For this purpose, a reconstruction method based on Tikhonov regularized least squares (TRLS) was developed in this study, which mitigated the oscillations caused by the direct unconstrained least squares (DULS) reconstruction method. The method was verified and optimized using four groups of artificial datasets with different characteristics. By selecting an appropriate value for parameter α, the relative standard deviation (RSD) of the reconstructed datasets was lower than that of the artificial datasets in most cases. Additionally, we evaluated the performance of the TRLS reconstruction algorithm based on a quantitative model of real Raman spectral datasets, assessing the algorithm’s performance from three perspectives: the root mean square error (RMSE), the correlation coefficient (R), and the ratio of prediction to deviation (RPD). The quantitative results indicate that using the TRLS method for reconstruction enhances both prediction accuracy and practicality. In summary, findings from both simulated data and actual experiments demonstrate that the TRLS-based reconstruction method substantially improves the stability and reliability of differential Raman spectra reconstruction.

Simultaneous image denoising and completion through convolutional sparse representation and nonlocal self-similarity

Low rank matrix approximation (LRMA) has been widely studied due to its capability of approximating original image from the degraded image. According to the characteristics of degraded images, image denoising and image completion have become research objects. Existing methods are usually designed for a single task. In this paper, focusing on the task of simultaneous image denoising and completion, we propose a weighted low rank sparse representation model and the corresponding efficient algorithm based on LRMA. The proposed method integrates convolutional analysis sparse representation (ASR) and nonlocal statistical modeling to maintain local smoothness and nonlocal self-similarity (NLSM) of natural images. More importantly, we explore the alternating direction method of multipliers (ADMM) to solve the above inverse problem efficiently due to the complexity of simultaneous image denoising and completion. We conduct experiments on image completion for partial random samples and mask removal with different noise levels. Extensive experiments on four datasets, i.e., Set12, Kodak, McMaster, and CBSD68, show that the proposed method prevents the transmission of noise while completing images and has achieved better quantitative results and human visual quality compared to 17 methods. The proposed method achieves (1.9%, 1.8%, 4.2%, and 3.7%) gains in average PSNR and (4.2%, 2.9%, 6.7%, and 6.6%) gains in average SSIM over the sub-optimal method across the four datasets, respectively. We also demonstrate that our method can handle the challenging scenarios well. Source code is available at https://github.com/weimin581/demo_CSRNS.

MABDT: Multi-scale attention boosted deformable transformer for remote sensing image dehazing

Owing to the heterogeneous spatial distribution and non-uniform morphological characteristics of haze in remote sensing images (RSIs), conventional dehazing algorithms struggle to precisely recover the fine-grained details of terrestrial objects. To address this issue, a novel multi-scale attention boosted deformable Transformer (MABDT) tailored for RSI dehazing is proposed. This framework synergizes the multi-receptive field features elicited by convolutional neural network (CNN) with the long-term dependency features derived from Transformer, which facilitates a more adept restitution of texture and intricate detail information within RSIs. Firstly, spatial attention deformable convolution is introduced for computation of multi-head self-attention in the Transformer block, particularly in addressing complex haze scenarios encountered in RSIs. Subsequently, a multi-scale attention feature enhancement (MAFE) block is designed, tailored to capture local and multi-level detailed information features using multi-receptive field convolution operations, thereby accommodating non-uniform haze. Finally, a multi-level feature complementary fusion (MFCF) block is proposed, leveraging both shallow and deep features acquired from all encoding layers to augment each level of reconstructed image. The dehazing performance is evaluated on 6 open-source datasets, and quantitative and qualitative experimental results demonstrate the advancements of the proposed method in both metrical scores and visual quality. The source code is available at https://github.com/ningjin00/MABDT.

Displacement field monitoring of tunnel faces using terrestrial laser scanning data

The instability of tunnel faces is a serious geological hazard, thus requiring monitoring and prediction of the stability of tunnel faces. This paper presents a high-accuracy method for estimating the displacement fields of tunnel faces from terrestrial laser scanning data. The proposed method consists of four parts: i) the development of a stable rigid-body reference under planar strain assumption, ii) the fine alignment of the rigid-body references using the developed local deformation constrained rigid-body assumption (LDC-RBA) and local cross-validation iterative closest point (LCV-ICP) algorithm, iii) the segmentation of tunnel faces using a machine learning classifier, and iv) tracking of tunnel face displacement vectors under the LDC-RBA. Qualitative experimental results demonstrate that the estimated displacement fields of the tunnel face using the proposed method conform to the deformation law. Furthermore, quantitative experimental results indicate that the estimation accuracy of tunnel face displacement fields achieves millimeter-level.

Digital motor intervention effects on motor performance of individuals with developmental disabilities: a systematic review.

Individuals (i.e. children/young adults) with developmental disabilities (DDs) and intellectual disabilities (IDs) often display a variety of physical and motor impairments. It is well known that participation in motor activities can positively impact the development of children's cognitive and social skills. Recently, virtual and digital technologies (e.g. video conferencing applications, virtual reality and video gaming) have been increasingly used to promote better physical/motor outcomes. The efficacy of digital technologies in improving motor outcomes for those with DD/ID varies depending on the technology and population, and the comparative effects of various technologies are unknown. The aim of our study is to conduct a systematic review to comprehensively examine the quantitative and qualitative results of current studies reporting the efficacy of digitally based motor interventions on motor outcomes in individuals with DD/ID. Literature published from 1900 to 2024 was searched in four health sciences databases: PubMed, PsycINFO, Scopus and CINAHL. Articles that examined the effects of gross motor/physical activity training using technologies such as exergaming (i.e. exercise through video gaming such as the Wii and Xbox Kinect), virtual reality or telehealth video conferencing applications (i.e. Zoom, Webex or mobile health apps) on the standardised or game-specific gross motor performance of individuals with DD/ID diagnoses that do not typically experience significant walking challenges using experimental or quasi-experimental study designs were included. Thirty relevant articles were retrieved from a search of the databases PubMed (914), PsycINFO (1201), Scopus (1910) and CINAHL (948). Our quantitative synthesis of this published literature suggests strong and consistent evidence of small-to-large improvements in motor skill performance following digital movement interventions. Our review supports the use of digital motor interventions to support motor skill performance in individuals with DD without ID. Digital technologies can provide a more engaging option for therapists to promote motor skill development in individuals with DD or for caregivers to use as an adjunct to skilled therapy.

Ultraviolet-diethyl sulfate composite mutagenesis of Rhodosporidium toruloides and culture optimization to improve carotenoid production

Rhodosporidium toruloides is a carotenoid-producing oleaginous yeast, and its carotenoids are of considerable value to food, chemical, and medical industries. In this study, to improve carotenoid production capacity, the parent R. toruloides strain (CK) was compound mutagenized using ultraviolet-diethyl sulfate (UV-DES), and cultivation conditions were simultaneously optimized to enhance the carotenoid synthesis ability of the mutant strain (M5). The carotenoid content was significantly increased following UV-DES compound mutagenesis (CK = 249.34 μg/g, M5 = 535.28 μg/g). The carotenoid content of M5 was increased to 763.85 μg/g under optimal culture conditions. In addition, mutagenesis altered the carotenoid components, and torulene and torularhodin levels were also increased following mutation, leading to changes in antioxidant capacity. Real-time quantitative PCR results showed that expression of six genes (CAR1, CAR2, CrtE, CrtI, ME1, HMG1) related to carotenoid accumulation were significantly upregulated by UV-DES composite mutagenesis. In conclusion, combined UV-DES mutagenesis and optimal culture conditions promoted the biosynthesis of carotenoids in R. toruloides.

FNContra: Frequency-domain Negative Sample Mining in Contrastive Learning for limited-data image generation

Substantial training data is necessary to train an effective generative adversarial network(GANs), without which the discriminator is easily overfitting, causing the sub-optimal models. To solve these problems, this work explores the Frequency-domain Negative Sample Mining in Contrastive learning (FNContra) to improve data efficiency, which requires the discriminator to differentiate the definite relationships between the negative samples and real images. Concretely, this work first constructs multiple-level negative samples in the frequency domain and then proposes Discriminated Wavelet-instance Contrastive Learning (DWCL) and Generated Wavelet-prototype Contrastive Learning (GWCL). The former helps the discriminator learn the fine-grained texture features, and the latter impels the generated feature distribution to be close to real. Considering the learning difficulty of multi-level negative samples, this work proposes a dynamic weight driven by self-information, which ensures the resultant force is positive from the multi-level negative samples during the training. Finally, this work performs experiments on eleven datasets with different domains and resolutions. The quantitative and qualitative results demonstrate the superiority and effectiveness of the FNContra trained on limited data, and it indicates that FNContra can synthesize high-quality images. Notably, FNContra achieves the best FID scores on 10 out of 11 datasets, with improvements of 17.90 and 29.24 on Moongate and Shells, respectively, compared to the baseline. The code can be found at https://github.com/YQX1996/FNContra.

SDR2Tr‐GAN: A Novel Medical Image Fusion Pipeline Based on GAN With SDR2 Module and Transformer Optimization Strategy

ABSTRACTIn clinical practice, radiologists diagnose brain tumors with the help of different magnetic resonance imaging (MRI) sequences and judge the type and grade of brain tumors. It is hard to realize the brain tumor computer‐aided diagnosis system only with a single MRI sequence. However, the existing multiple MRI sequence fusion methods have limitations in the enhancement of tumor details. To improve fusion details of multi‐modality MRI images, a novel conditional generative adversarial fusion network based on three discriminators and a Staggered Dense Residual2 (SDR2) module, named SDR2Tr‐GAN, was proposed in this paper. In the SDR2Tr‐GAN network pipeline, the generator consists of an encoder, decoder, and fusion strategy that can enhance the feature representation. SDR2 module is developed with Res2Net into the encoder to extract multi‐scale features. In addition, a Multi‐Head Spatial/Channel Attention Transformer, as a fusion strategy to strengthen the long‐range dependencies of global context information, is integrated into our pipeline. A Mask‐based constraint as a novel fusion optimization mechanism was designed, focusing on enhancing salient feature details. The Mask‐based constraint utilizes the segmentation mask obtained by the pre‐trained Unet and Ground Truth to optimize the training process. Meanwhile, MI and SSIM loss jointly improve the visual perception of images. Extensive experiments were conducted on the public BraTS2021 dataset. The visual and quantitative results demonstrate that the proposed method can simultaneously enhance both global image quality and local texture details in multi‐modality MRI images. Besides, our SDR2Tr‐GAN outperforms the other state‐of‐the‐art fusion methods regarding subjective and objective evaluation.

Advancements and challenges in One Health

The One Health concept emphasizes the interconnectedness of human, animal, and environmental health, advocating for a comprehensive and collaborative approach. Since its inception, various global interventions have addressed complex health challenges such as epidemics and pandemics. In developed countries, numerous collaborative platforms have been established, adopting international strategies to tackle both local and global health issues. Current challenges in One Health include endemic and emerging zoonotic diseases, food safety and security, antimicrobial resistance, and wildlife diseases. Developing new bioanalytical techniques and methods based on biosensors, ‘omics’ approaches, and high-throughput analytical techniques that are rapid, low-cost, and capable of providing robust quantitative results is crucial.

Cardiac motion correction with a deep learning network for perfusion defect assessment in SPECT myocardial perfusion imaging

BackgroundIn myocardial perfusion imaging (MPI) with SPECT ungated studies are used for evaluation of perfusion defects despite motion blur. We investigate the potential benefit of motion correction using a deep-learning (DL) network for evaluating perfusion defects. MethodsWe employed a DL network for cardiac motion correction in ECG-gated SPECT-MPI images, wherein the image data from different cardiac phases are combined with respect to a reference gate to reduce motion blur. For training the DL network 197 cases were used. Given the variability of gated images during the cardiac cycle, we investigated the detectability of perfusion defects in two distinct reference gates. To assess perfusion defect detection, we performed receiver-operating-characteristic (ROC) analyses on the motion-corrected images using a separate test dataset of clinical 194 subjects, in which studies were created from actual patient data with inserted simulated-lesions as ground truth. The reconstructed images were assessed by the quantitative-perfusion SPECT (QPS) software. We also evaluated the performance on reduced-count studies (by two and four folds). ResultsThe quantitative results, measured by area-under-the-ROC curve (AUC), demonstrated that DL motion correction improves the detectability of perfusion defects significantly on both standard- and reduced-count studies, and that the detectability can vary with reference cardiac phases. A joint assessment from two reference-phases achieved AUC=0.841 on the quarter-count data, higher than with ungated full-count data (AUC=0.795, p-value=0.0054). ConclusionsDL motion correction can benefit assessment of perfusion defects in standard- and reduced-count SPECT-MPI studies. It can also be beneficial to evaluate perfusion images over multiple cardiac phases.

Securing Consensus in Fractional-Order Multi-Agent Systems: Algebraic Approaches Against Byzantine Attacks

This paper investigates the behavior of fractional-order nonlinear multi-agent systems subjected to Byzantine assaults, specifically focusing on the manipulations of both sensors and actuators. We employ weighted graphs, both directed and undirected, to illustrate the system's topology. Our methodology combines algebraic graph theory with fractional-order Lyapunov techniques to develop algebraic requirements for leader-following consensus, providing a robust framework for analyzing consensus dynamics in these complex systems. We present quantitative results demonstrating the effectiveness of our approach, including two numerical examples that validate the proposed requirements for consensus evaluation. Notably, our work highlights the novelty of using fractional-order systems to enhance resilience against adversarial conditions, contributing significantly to the field of multi-agent systems. By clarifying key terms and streamlining our language, we ensure accessibility for a broader audience while emphasizing the implications of our findings for real-world applications.

Collaboration Efforts of Special Education Teachers in Turkey

Introduction: The roles of teachers, among the most important stakeholders in schools, have begun to change with changes in the world and the field of education. Legal regulations and inclusive education practices require that teachers collaborate with various stakeholders beyond merely teaching. In this respect, the objective of the present research is to examine the status of collaboration between special education teachers and other stakeholders in detail. Method: To this end, the research was designed as a mixed-method study. First, quantitative data were collected and analyzed, followed by qualitative data collection and analysis to understand the findings better. The study used an explanatory sequential design to determine teachers' collaboration levels, particularly in special education. A specially developed Collaboration Scale in Special Education, which underwent validity and reliability analyses, was used for the descriptive analysis of quantitative data. Qualitative data were collected through semi-structured interviews, observations, and researcher diaries and analyzed using content analysis. Findings: The quantitative results of the study showed no significant differences in collaboration levels among teachers based on gender, age, or professional experience. Qualitative data revealed that teachers engaged in limited and generally basic collaboration with administrators, paraprofessionals, and external stakeholders. Quantitative and qualitative findings indicated that collaboration processes were generally superficial and more structured; therefore, focused processes were needed. Discussion: The results of this study emphasize the necessity of structural arrangements and changes to establish effective collaboration between internal and external school stakeholders. Implementing the necessary regulations to ensure effective collaboration is considered highly important for the education of individuals with special needs.

Exploring the Digital Transformation of Generative AI-Assisted Foreign Language Education: A Socio-Technical Systems Perspective Based on Mixed-Methods

This study investigates the complex dynamics and impacts of generative AI integration in foreign language education through the lens of the Generative AI-assisted Foreign Language Education Socio-Technical System (GAIFL-STS) model. Employing an integrated mixed-methods design, the study combines qualitative case studies and hybrid simulation modeling to examine the affordances, challenges, and implications of AI adoption from a multi-level, multi-dimensional, and multi-stakeholder perspective. The qualitative findings, based on interviews, observations, and document analyses, reveal the transformative potential of generative AI in enhancing language learning experiences, as well as the social, cultural, and ethical tensions that arise in the process. The quantitative results, derived from system dynamics and agent-based modeling, provide a systemic and dynamic understanding of the key variables, feedback loops, and emergent properties that shape the trajectories and outcomes of AI integration. The integrated findings offer valuable insights into the strategies, practices, and policies that can support the effective, equitable, and responsible implementation of AI in language education.

Large unions of generalized integral sections on elliptic surfaces

Let f : X → B f \colon X \to B be a nonisotrivial complex elliptic surface and let D ⊂ X \mathcal {D} \subset X be an integral divisor dominating B B . We study finiteness related properties of generalized ( S , D ) (S, \mathcal {D}) -integral sections σ : B → X \sigma \colon B \to X of X X where S ⊂ B S \subset B is arbitrary. These integral sections σ \sigma are algebraic and satisfy the geometric condition f ( σ ( B ) ∩ D ) ⊂ S f ( \sigma (B) \cap \mathcal {D})\subset S . For S ⊂ B S \subset B finite, the set of ( S , D ) (S, \mathcal {D}) -integral sections of X X is finite by the well-known Siegel theorem. In this article, we establish a general quantitative finiteness result for large unions of ( S , D ) (S, \mathcal {D}) -integral sections in which both the subset S S and the divisor D \mathcal {D} are allowed to vary in families where notably S S is not necessarily finite or countable. Some applications to generalized unit equations over function fields are also obtained.

시나리오기반 시뮬레이션실습 교육의 효과와 경험에 대한 연구

Objectives The purposes of this study were to investigated the effectiveness of scenario-based simulation prac-tice education for nursing students. Methods Data were collected through a questionnaire from 169 senior nursing students enrolled at a university in J city between April 15 and June 30, 2024. Quantitative data were gathered from these students, and qualitative data were collected through focus group interviews with 15 students who consented to participate. The quantita-tive data collection was conducted in two phases, pre- and post-surveys, using self-report questionnaires to as-sess problem-solving skills, critical thinking disposition, self-directed learning ability, communication skills, and grit. The data were then analyzed using descriptive statistics and paired t-tests. Qualitative data collection was conducted after the course grades were fully disclosed, as they could influence the research outcomes. Focus group interviews were held with three groups, each consisting of five students, and each interview lasted between 1.5 to 2 hours. The interview questions were provided in advance to allow participants time to reflect. During the qualitative data collection, four researchers took on roles as a moderator, two recorders, and one observer. All in-terviews were recorded and documented. Results In this study, quantitative research results showed that after scenario-based simulation practice educa-tion, nursing students' problem-solving abilities (t=-8.02, p=<.001), critical thinking disposition (t=-6.86, p=<.001), self-directed learning abilities (t=-6.10, p=<.001), communication skills (t=-3.62, p=<.001), and grit (t=-7.26, p=<.001) significantly improved. The qualitative research results, obtained through focus group inter-views, revealed three themes: the coexistence of positive and negative feelings towards the new educational method, the expansion and growth of knowledge through new educational experiences, and self-reflection through role experiences. Conclusions Through scenario-based simulation practice education, students had diverse experiences and showed improvements in cognitive aspects such as problem-solving abilities, critical thinking disposition, and communication skills. It was also confirmed that the integration of theory and practice and clinical performance abilities were enhanced. This study suggests that there is a continuous need for research comparing scenar-io-based simulation practice education with other teaching methods, involving comparison and control groups.

Detection of organic carbon in shale by laser induced breakdown spectroscopy and Raman spectroscopy combined with partial least squares methods

In this study, data fusion of laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy was used to characterize the total organic carbon content (TOC) of shale samples in Ordos Basin. The powder samples are pressed into pellets for LIBS and Raman analysis. Partial least squares calibration method was used for quantitative analysis. By comparing the quantitative results of the two techniques and the quantitative results of different spectral fusion strategies, it is found that the fusion strategy of LIBS and Raman spectral data can improve the prediction ability of the model. The model based on PLS latent variable fusion strategy has better fitting effect, its coefficient of determination (R2) for the calibration sets is 0.9915, the coefficient of determination for the prediction sets is 0.9574 and the root mean square error of the prediction set is 0.5580 wt%. The model has better generalization ability, and the root-mean-square-error-of-cross-validation (RMSECV) of the model is 0.5252 wt%. This study shows that data fusion of LIBS and Raman spectra can achieve rapid analysis of total organic carbon content in shale samples, and may be beneficial to carbon storage research in depleted gas shale.

Optimization of ride-sharing services: An investigation of the ride-hailing service providers

This study investigates the optimization of ride-sharing services (RSS) on the ride-hailing service (RHS) providers in Bangladesh. This study employed an explanatory sequential mixed method research design- a qualitative study followed by a quantitative one. Qualitative data were collected through focus group discussions and in-depth interviews with twenty (20) riders and drivers in Bangladesh, and quantitative data were collected from 300 respondents consisting of riders and drivers using a convenience sampling technique. Factor analysis and hierarchical cluster analysis were applied to the data analysis. The qualitative analysis reveals several significant factors associated with RSS and RHS, including cost efficiency, fare, fuel consumption, traffic congestion, carbon emissions, environmental pollution, employment opportunities, business growth, and security. The quantitative results indicate that using RSS is associated with more significant benefits than RHS in various aspects, including cost efficiency, fare, fuel consumption, traffic congestion, carbon emissions, environmental pollution, employment opportunities, and expansion of the automobile industry. The findings may assist policymakers in understanding how RSS can yield more incredible economic, environmental, and social benefits than RHS by analyzing fare sharing among passengers, carbon emissions, fuel consumption, and the expansion of the vehicle markets etc. Therefore, the government can formulate distinct policies for RSS holders due to their contributions to economic, social, and environmental concerns. While RHS services are available in many cities in Bangladesh, this study considered only Dhaka and Sylhet cities. Thus, future studies can consider more respondents from other cities for a holistic understanding.

Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges

Deep learning techniques have achieved superior performance in computer-aided medical image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in potential misdiagnosis in clinical practice. Oppositely, recent years have also witnessed remarkable progress in defense against these tailored adversarial examples in deep medical diagnosis systems. In this exposition, we present a comprehensive survey on recent advances in adversarial attacks and defenses for medical image analysis with a systematic taxonomy in terms of the application scenario. We also provide a unified framework for different types of adversarial attack and defense methods in the context of medical image analysis. For a fair comparison, we establish a new benchmark for adversarially robust medical diagnosis models obtained by adversarial training under various scenarios. To the best of our knowledge, this is the first survey paper that provides a thorough evaluation of adversarially robust medical diagnosis models. By analyzing qualitative and quantitative results, we conclude this survey with a detailed discussion of current challenges for adversarial attack and defense in medical image analysis systems to shed light on future research directions. Code is available on GitHub.

An interrogation of family-based care for the elderly in Kaloleni sub-location, Kisumu, Kenya

The aim of this study was to interrogate family based care services for the elderly within Kaloleni Sub-Location, Kisumu, Kenya. The population of the elderly (age 60 and above) is projected to outnumber children (0-9 years) by the year 2030, and by 2050, to exceed the population of adolescents and youth aged between 10 to 14 years. The Kenya government is exploring the development of a community care strategy and the roll-out of an Integrated Care of Older Persons (ICOPE) package. While family-based care has been the policy and cultural preference for offering long term elderly care, the state recognized growing demand for responsive changes to current realities. This research set the following three objectives: First, to establish the state of family based care in Kaloleni sub-location; second, to examine alternatives to family based care that exist in the sub-location, and, lastly, to interrogate policy options as suggested by respondents in the study. Using Krejcie and Morgan sample size determination table, a total of 120 elderly persons aged 70 and above were traced and 92 of them randomly sampled. The methodology deployed was mixed methods involving narrative interviewing and cross sectional survey. Descriptive data analysis and thematic analysis were deployed for quantitative and qualitative results respectively. Narrative interviewing/In depth interviews method was used in collecting data from Kaloleni sub-location. While findings from this research may not be generalizable due to study limitations and scope, the study strongly demonstrated the desperate state of gerontological care in informal settlements in urban centers in Kisumu, where over 90% of respondents sought improvement on care service provision for the elderly. The elderly persons mostly rely on care givers from the family but these care givers also tend to be dependent on the elderly respondents for general upkeep. Results support the literature that contends that older people are vulnerable to poverty. It was recommended that policy must shift to address factors that promote healthy aging and diminish suffering in old age as population aging continues. Further studies of a longitudinal nature are required to lend greater insight and inform policy.

TEACHING COMPUTATIONAL THINKING IN SAUDI ARABIAN SCHOOLS USING SMARTPHONE APPS

This study investigates the efficacy of smartphone applications in teaching computational thinking to students in Saudi Arabian primary and secondary schools. Computational thinking, encompassing problem-solving, computational logic, and algorithmic thinking, is an essential skill in the modern world. The research assesses various educational apps that support the development of computational thinking and examines their integration into the Saudi curriculum. This study incorporates qualitative and quantitative research techniques to provide an inclusive perspective. Participants included primary and secondary school students from various schools in Saudi Arabia. The research employed pre- and post-tests for quantitative analysis and interviews and questionnaires for qualitative data. Quantitative results include pre- and post-tests of the students in computational thinking with the aid of co-varying with Computational thinking, while interviews and questionnaires with teachers and students offer qualitative data. Best-selling educational applications for ScratchJr, Lightbot, Cargo-Bot, and Kodable are compared concerning how efficiently they introduce programming ideas and encourage rational thinking. Results have suggested that smartphone applications have a positive influence on students’ computational skills. An enhanced standard is also noticed regarding the students’ aspects that involve problem analysis, identification of pattern, and formation of algorithm. The use of applications such as ScratchJr and Lightbot is justified regarding an application’s capacity to improve students’ motivation and willingness to learn computational concepts. Teachers indicate that their students have shown more concern and engagement mainly when the teacher integrates these apps. Hence, based on the findings of the study, it is evident that incorporating Smartphone applications into the curriculum will lead to a reforming change in the teaching approaches towards efficiency as well as feasibility of the learners. These findings are valuable for the Saudi context for policy and teaching enhancement, signaling the need to provide more resources in digital tools and invest in the professional development of teachers to capture the virtues of advanced technology in improving learners’ performance.