Transformation Framework Research Articles

Storytelling as Pedagogical Practice in Support of Transcendence and Well-Being

The purpose of this article is to explore the potential of storytelling within business ethics education for facilitating transcendence and well-being. In doing so, the article focuses on five areas. First, a brief introduction to workplace spirituality and its association with well-being is provided. Second, consideration is given to the nature of storytelling and its benefits for relational-spiritual teaching. Third, the ways that storytelling (and specific examples of stories) can invite and enhance three interrelated dimensions of transcendent connection (Miller, 2015; Reed, 2018) of relevance in business ethics are described. These include the transpersonal, the interpersonal (including interpersonal transcendence across difference) and the intrapersonal. Within this discussion, the ways that each of these dimensions can contribute to well-being are identified. Fourth, a relational-spiritual, transformative learning framework within which storytelling can be practiced is provided. Fifth, directions for future research are discussed.

541 A Framework for Multicultural and Multidisciplinary Near-Peer Mentoring for Artificial Intelligence in Healthcare Education: A University of Florida Friend Group

OBJECTIVES/GOALS: This work aims to explore how citizen science serves as a transformative frame work to bridge scientific knowledge, focusing on its potential to enhance transdisciplinary learning in artificial intelligence (AI) biomedical and clinical sciences by facilitating near-peer mentoring. METHODS/STUDY POPULATION: Our group of eight friends comprise a multicultural and multidisciplinary cohort including students from the USA, Philippines, Indonesia, and Guatemala pursuing PhD degrees in electrical and computer engineering, epidemiology, physics, and MD, PharmD, and DMD degrees. We engage in shared online courses, collaborative projects, and abstract submissions. Employing our collective knowledge, we design interactive learning experiences, support each other’s initiatives, and collaboratively develop lectures and presentations. We in tend to expand collaborations in biomedical AI education while fostering principles of experiential and collaborativelearning, constructivism, and authentic inquiry. RESULTS/ANTICIPATED RESULTS: Our recent successes include submittedconference abstracts on data science and AI education in pharmacy and the facilitation of a guest lecture in health informatics. Additionally, we are currently collaborating on seven biomedical machine learning projects in radio frequency engineering, aiming for conference submissions. Moving forward, our goal is to expand our group, support the formation of similar communities, and promote data science and AI literacy in biomedical and clinical contexts. We aspire to extend this knowledge to families, classmates, and eventually patients, facilitating a broader understanding of the role of AI in healthcare. DISCUSSION/SIGNIFICANCE: We believe diverse expertise and pedagogical theories can help demonstrate the potential of citizen science to democratize scientific experience. By nurturing collaborative networks our efforts aim to bridge gaps between disciplines and enhance the broader public’s understanding of AI in healthcare.

Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks.

Graph convolutional deep learning has emerged as a promising method to explore the functional organization of the human brain in neuroscience research. This paper presents a novel framework that utilizes the gated graph transformer (GGT) model to predict individuals' cognitive ability based on functional connectivity (FC) derived from fMRI. Our framework incorporates prior spatial knowledge and uses a random-walk diffusion strategy that captures the intricate structural and functional relationships between different brain regions. Specifically, our approach employs learnable structural and positional encodings (LSPE) in conjunction with a gating mechanism to efficiently disentangle the learning of positional encoding (PE) and graph embeddings. Additionally, we utilize the attention mechanism to derive multi-view node feature embeddings and dynamically distribute propagation weights between each node and its neighbors, which facilitates the identification of significant biomarkers from functional brain networks and thus enhances the interpretability of the findings. To evaluate our proposed model in cognitive ability prediction, we conduct experiments on two large-scale brain imaging datasets: the Philadelphia Neurodevelopmental Cohort (PNC) and the Human Connectome Project (HCP). The results show that our approach not only outperforms existing methods in prediction accuracy but also provides superior explainability, which can be used to identify important FCs underlying cognitive behaviors.

MRG-T: Mask-Relation-Guided Transformer for Remote Vision-Based Pedestrian Attribute Recognition in Aerial Imagery

Nowadays, with the rapid development of consumer Unmanned Aerial Vehicles (UAVs), utilizing UAV platforms for visual surveillance has become very attractive, and a key part of this is remote vision-based pedestrian attribute recognition. Pedestrian Attribute Recognition (PAR) is dedicated to predicting multiple attribute labels of a single pedestrian image extracted from surveillance videos and aerial imagery, which presents significant challenges in the computer vision community due to factors such as poor imaging quality and substantial pose variations. Despite recent studies demonstrating impressive advancements in utilizing complicated architectures and exploring relations, most of them may fail to fully and systematically consider the inter-region, inter-attribute, and region-attribute mapping relations simultaneously and be stuck in the dilemma of information redundancy, leading to the degradation of recognition accuracy. To address the issues, we construct a novel Mask-Relation-Guided Transformer (MRG-T) framework that consists of three relation modeling modules to fully exploit spatial and semantic relations in the model learning process. Specifically, we first propose a Masked Region Relation Module (MRRM) to focus on precise spatial attention regions to extract more robust features with masked random patch training. To explore the semantic association of attributes, we further present a Masked Attribute Relation Module (MARM) to extract intrinsic and semantic inter-attribute relations with an attribute label masking strategy. Based on the cross-attention mechanism, we finally design a Region and Attribute Mapping Module (RAMM) to learn the cross-modal alignment between spatial regions and semantic attributes. We conduct comprehensive experiments on three public benchmarks such as PETA, PA-100K, and RAPv1, and conduct inference on a large-scale airborne person dataset named PRAI-1581. The extensive experimental results demonstrate the superior performance of our method compared to state-of-the-art approaches and validate the effectiveness of mask-relation-guided modeling in the remote vision-based PAR task.

A Comprehensive Framework for Face age Synthesis, Voice Transformation, and Cartoon Generation

This paper presents a comprehensive framework addressing age-invariant face recognition (AIFR), face age synthesis (FAS), voice transformation, and avatar generation. Traditional AIFR techniques focus on minimizing age-related variations in face recognition but lack visual interpretability, while FAS methods aim to synthesize faces of different ages but often compromise recognition due to artifacts. To address these limitations, we propose MTLFace, a multi-task learning framework that simultaneously handles AIFR and FAS tasks. MTLFace employs attention-based feature decomposition to separate identity and age-related features spatially, improving interpretability. Additionally, we introduce an identity conditional module for fine-grained face age synthesis, enhancing the naturalness of synthesized faces. Unlike conventional methods that achieve age group-level synthesis, our identity conditional module enables identity-level synthesis, resulting in smoother age transitions and preserving individual facial characteristics. Leveraging high-quality synthesized faces, we enhance AIFR performance via selective fine-tuning, where synthesized faces are used to augment training data, leading to improved robustness against age variations. Furthermore, we contribute a large cross-age face dataset with annotations, facilitating research in age-related tasks. In addition to AIFR and FAS, we explore limitations in existing methodologies for voice transformation and propose advancements to improve speech quality and address current challenges. Lastly, we introduce a novel approach for cartoon face generation, utilizing component-based facial feature extraction and template matching to produce diverse and stylistic cartoon faces. Experimental results across various benchmarks demonstrate the effectiveness of our approaches in achieving superior performance in face recognition, voice transformation, and cartoon face generation, thereby contributing to the advancement of multimodal synthesis technologies with potential applications in entertainment, virtual communication, and assistive technologies

Finding Visual Saliency in Continuous Spike Stream

As a bio-inspired vision sensor, the spike camera emulates the operational principles of the fovea, a compact retinal region, by employing spike discharges to encode the accumulation of per-pixel luminance intensity. Leveraging its high temporal resolution and bio-inspired neuromorphic design, the spike camera holds significant promise for advancing computer vision applications. Saliency detection mimic the behavior of human beings and capture the most salient region from the scenes. In this paper, we investigate the visual saliency in the continuous spike stream for the first time. To effectively process the binary spike stream, we propose a Recurrent Spiking Transformer (RST) framework, which is based on a full spiking neural network. Our framework enables the extraction of spatio-temporal features from the continuous spatio-temporal spike stream while maintaining low power consumption. To facilitate the training and validation of our proposed model, we build a comprehensive real-world spike-based visual saliency dataset, enriched with numerous light conditions. Extensive experiments demonstrate the superior performance of our Recurrent Spiking Transformer framework in comparison to other spike neural network-based methods. Our framework exhibits a substantial margin of improvement in capturing and highlighting visual saliency in the spike stream, which not only provides a new perspective for spike-based saliency segmentation but also shows a new paradigm for full SNN-based transformer models. The code and dataset are available at https://github.com/BIT-Vision/SVS.

Benchmarking Cyber Harassment Dialogue Comprehension through Emotion-Informed Manifestations-Determinants Demarcation

In the digital age, cybercrimes, particularly cyber harassment, have become pressing issues, targeting vulnerable individuals like children, teenagers, and women. Understanding the experiences and needs of the victims is crucial for effective support and intervention. Online conversations between victims and virtual harassment counselors (chatbots) offer valuable insights into cyber harassment manifestations (CHMs) and determinants (CHDs). However, the distinction between CHMs and CHDs remains unclear. This research is the first to introduce concrete definitions for CHMs and CHDs, investigating their distinction through automated methods to enable efficient cyber-harassment dialogue comprehension. We present a novel dataset, Cyber-MaD that contains Cyber harassment dialogues manually annotated with Manifestations and Determinants. Additionally, we design an Emotion-informed Contextual Dual attention Convolution Transformer (E-ConDuCT) framework to extract CHMs and CHDs from cyber harassment dialogues. The framework primarily: a) utilizes inherent emotion features through adjective-noun pairs modeled by an autoencoder, b) employs a unique Contextual Dual attention Convolution Transformer to learn contextual insights; and c) incorporates a demarcation module leveraging task-specific emotional knowledge and a discriminator loss function to differentiate manifestations and determinants. E-ConDuCT outperforms the state-of-the-art systems on the Cyber-MaD corpus, showcasing its potential in the extraction of CHMs and CHDs. Furthermore, its robustness is demonstrated on the emotion cause extraction task using the CARES_CEASE-v2.0 dataset of suicide notes, confirming its efficacy across diverse cause extraction objectives. Access the code and data at 1. https://www.iitp.ac.in/~ai-nlp-ml/resources.html#E-ConDuCT-on-Cyber-MaD, 2. https://github.com/Soumitra816/Manifestations-Determinants.

Target Focused Shallow Transformer Framework for Efficient Visual Tracking

Template learning transformer trackers have achieved significant performance improvement recently due to the longdependency learning using the self-attention (SA) mechanism. However, the typical SA mechanisms in transformers adopt a less discriminative design approach which is inadequate for focusing on the most important target information during tracking. Therefore, existing trackers are easily distracted by background information and have constraints in handling tracking challenges. The focus of our research is to develop a target-focused discriminative shallow transformer tracking framework that can learn to distinguish the target from the background and enable accurate tracking with fast speed. Extensive experiments will be performed on several popular benchmarks, including OTB100, UAV123, GOT10k, LaSOT, and TrackingNet, to demonstrate the effectiveness of the proposed framework.

MGQFormer: Mask-Guided Query-Based Transformer for Image Manipulation Localization

Deep learning-based models have made great progress in image tampering localization, which aims to distinguish between manipulated and authentic regions. However, these models suffer from inefficient training. This is because they use ground-truth mask labels mainly through the cross-entropy loss, which prioritizes per-pixel precision but disregards the spatial location and shape details of manipulated regions. To address this problem, we propose a Mask-Guided Query-based Transformer Framework (MGQFormer), which uses ground-truth masks to guide the learnable query token (LQT) in identifying the forged regions. Specifically, we extract feature embeddings of ground-truth masks as the guiding query token (GQT) and feed GQT and LQT into MGQFormer to estimate fake regions, respectively. Then we make MGQFormer learn the position and shape information in ground-truth mask labels by proposing a mask-guided loss to reduce the feature distance between GQT and LQT. We also observe that such mask-guided training strategy has a significant impact on the convergence speed of MGQFormer training. Extensive experiments on multiple benchmarks show that our method significantly improves over state-of-the-art methods.

Mean Teacher DETR with Masked Feature Alignment: A Robust Domain Adaptive Detection Transformer Framework

Unsupervised domain adaptation object detection(UDAOD) research on Detection Transformer(DETR) mainly focuses on feature alignment and existing methods can be divided into two kinds, each of which has its unresolved issues. One-stage feature alignment methods can easily lead to performance fluctuation and training stagnation. Two-stage feature alignment method based on mean teacher comprises a pretraining stage followed by a self-training stage, each facing problems in obtaining reliable pretrained model and achieving consistent performance gains. Methods mentioned above have not yet explore how to utilize the third related domain such as target-like domain to assist adaptation. To address these issues, we propose a two-stage framework named MTM, i.e. Mean Teacher-DETR with Masked Feature Alignment. In the pretraining stage, we utilize labeled target-like images produced by image style transfer to avoid performance fluctuation. In the self-training stage, we leverage unlabeled target images by pseudo labels based on mean teacher and propose a module called Object Queries Knowledge Transfer(OQKT) to ensure consistent performance gains of the student model. Most importantly, we propose masked feature alignment methods including Masked Domain Query-based Feature Alignment(MDQFA) and Masked Token-wise Feature Alignment(MTWFA) to alleviate domain shift in a more robust way, which not only prevent training stagnation and lead to a robust pretrained model in the pretraining stage, but also enhance the model's target performance in the self-training stage. Experiments on three challenging scenarios and a theoretical analysis verify the effectiveness of MTM.

THGFormer: Time-Aware Hypergraph Learning for Multimodal Social Media Popularity Prediction (Student Abstract)

Social media popularity prediction of multimodal user-generated content (UGC) is a crucial task for many real-world applications. However, existing efforts are often limited by missing inter-instance correlations and UGC temporal patterns. To address these issues, we propose a novel time-aware hypergraph Transformer framework, THGFormer. It fully represents inter-instance and intra-instance relations by hypergraphs, captures the temporal dependencies with a time encoder, and enhances UGC's representations via a neighborhood knowledge aggregation. Extensive experiments conducted on two real-world datasets demonstrate that THGFormer outperforms state-of-the-art popularity prediction models across several settings.

Decoupling User Relationships Guides Information Diffusion Prediction (Student Abstract)

Information diffusion prediction is a critical task for many social network applications. However, current methods are mainly limited by the following aspects: user relationships behind resharing behaviors are complex and entangled. To address these issues, we propose MHGFormer, a novel multi-channel hypergraph transformer framework, to better decouple complex user relations and obtain fine-grained user representations. First, we employ designed triangular motifs to decouple user relations into three different level hypergraphs. Second, a position-aware hypergraph transformer is used to refine user relation and obtain high-quality user representations. Extensive experiments conducted on two social datasets demonstrate that MHGFormer outperforms state-of-the-art diffusion models across several settings.

Navigating the Future of Work: Human Capital in the Age of Industry 4.0

The advent of Industry 4.0 signifies a pivotal transition, driven by the integration of digital technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), robotics, and big data analytics. This revolution is reshaping the landscape of both industrial and service sectors, highlighting the need for economic restructuring to pave the way for sustainable development. This paper investigates the profound impact of Industry 4.0 on human capital within the framework of sustainable economic transformation. The analysis embarks on detailing the revolutionary essence of Industry 4.0 and its repercussions on traditional industrial operations. A focal point of this inquiry is the transformation in human capital, deemed essential for achieving sustainable economic targets. This transformation is distinguished by a shift from manual to cognitive capabilities and the imperative for continuous learning, which are crucial for cultivating an adaptable and innovative workforce. Furthermore, the emergence of new job roles demanding advanced digital skills, the obstacles in workforce transformation such as the digital divide, and the need for comprehensive skill development are discussed. Strategies for the development of human capital are proposed, emphasizing the significance of educational, training, and lifelong learning initiatives by organizations and governments, all aligned with sustainable economic goals. Concluding, the paper provides a future outlook on the role of human capital within the sustainable economic development framework in an automated era. Recommendations for navigating the challenges and opportunities of human capital management in the Industry 4.0 epoch are provided, aiming at fostering sustainable economic growth.
 
 Received: 27 February 2024 / Accepted: 22 March 2024 / Published: 23 March 2024

A hybrid convolution transformer for hyperspectral image classification

ABSTRACT Hyperspectral images play a crucial role in remote sensing applications surveillance, environment and precision agriculture, containing abundant object information. However, they often face challenges such as limited labelled data and imbalanced classes. In recent years, convolutional neural networks (CNNs) have shown impressive performance in computer vision tasks, including hyperspectral image classification. The emergence of transformers has also attracted attention for hyperspectral image analysis due to their promising capabilities. Nevertheless, transformers typically demand a substantial amount of training data, making their application challenging in scenarios with limited labelled samples. To overcome this limitation, we propose a hybrid convolution transformer framework. Our method uses a vision transformer and a residual 3D convolutional neural network model. It also uses a sequence aggregation layer to avoid overfitting issues that come up when there isn’t enough training data. Our proposed residual channel attention module captures richer spatial-spectral complementary information and maintains spectral details during the feature extraction process. We conducted experiments on three benchmark datasets. The proposed model achieved state of the art performance 99.75 % , 99.46 % and 99.95 % in terms of overall accuracy (OA) using only 5 % , 5 % and 10 % labelled training samples respectively. Which is better than other state of the art methods.

HiFormer: Hierarchical transformer for grounded situation recognition

The prevalence of monitoring video is critical to public safety, but existing Object Detection and Action Recognition models are overwhelmed by camera operators, unable to identify relevant events. In light of this, Grounding Situation Recognition (GSR) provides a practical solution to recognize the events in a surveillance video. That is, GSR can identify the noun entities (e.g., humans) and their actions (e.g., driving), and provide grounding frames for involved entities. Compared with Action Recognition and Object Detection, GSR is more in line with human cognitive habits, better allowing law enforcement agencies to understand the predictions. However, the crucial issue with most existing frameworks is the neglect of verb ambiguity, that is, superficially similar verbs but have distinct meanings (e.g. buying v.s. giving). Many existing works propose a two-stage model, which first blindly predicts the verb, and then uses this verb information to predict semantic roles. These frameworks ignore the importance of noun information during verb prediction, making them susceptible to misidentifications. To address this problem and better discern between ambiguous verbs, we propose HiFormer, a novel hierarchical transformer framework with direct and comprehensive consideration of similar verbs for each image, to more accurately identify the salient verb, semantic roles, and the grounding frames. Compared with the state-of-the-art models in Grounded Situation Recognition (SituFormer and CoFormer), HiFormer shows an advantage of over 35% and 20% on the Top-1 and Top-5 verb accuracy respectively, as well as 13% on the Top-1 Noun accuracy.

STP: Self-supervised transfer learning based on transformer for noninvasive blood pressure estimation using photoplethysmography

Non-invasive blood pressure (BP) monitoring plays a crucial role in cardiovascular disease prevention, but traditional cuff-based methods lack continuous monitoring capability. Photoplethysmography (PPG) offers a promising alternative by capturing blood volume changes optically. The challenge of effectively capturing fine-grained discriminative features of BP using limited paired signals persists, despite the benefits offered by deep models trained on extensive data. The objective of this study is to create a Transformer framework for continuous monitoring of noninvasive BP through self-supervised transfer learning and BP pattern adaptation. We developed a data preprocessing strategy that integrates eleven physiological signal transformations to perform unsupervised pseudo-labels for PPG signals. Then, we developed a self-supervised learning network based on the Transformer architecture to extract robust signal representations from transformed PPGs during the pretraining phase. Furthermore, we designed a transfer learning approach that incorporates BP pattern adaptation to derive discriminative features for accurate estimation of BP values. The proposed STP model was evaluated on multi-source datasets containing 1,213 subjects based on a subject-wise paradigm. The clinical standard was met with estimation errors of 0.85 ± 4.21 mmHg and 0.49 ± 2.76 mmHg for systolic BP and diastolic BP, respectively. These results indicate that the STP model performs competitively in terms of BP estimation when compared to current state-of-the-art approaches. Our study presents a novel STP approach for continuous monitoring of noninvasive BP using PPG signals. By integrating BP pattern adaptation, our approach achieves a competitive performance in estimating BP values, demonstrating its potential for clinical applications.

Radiance-field holography for high-quality 3D reconstruction

Conventional light field-based computer-generated holograms, such as holographic stereograms, can provide nature parallax and accurate occlusion relation but are restricted by the inherent tradeoff concerning the angular and spatial resolution of the reconstructed optical field. In contrast, Wigner distribution function based light field generated holograms, also called non-hogel-based holograms, release this limitation by using basic ray-to-wave transformation frameworks. However, at present, this transformation framework is accurate only for generating holograms from orthogonal light field images of 3D scenes with a limited depth range and size. Thus, the radiance field holography method is proposed in this paper to generate high-quality 3D holograms for arbitrary scenes that can achieve high-resolution reconstruction within a deep depth range. In our method, a proposed radiance field is constructed from the perspective light field, which consists of multiple reference planes to resample and redistribute the light rays to represent the diffracted optical field of the 3D objects according to our developed sampling criterion. The complex optical field of the 3D object can be synthesized from these reference planes based on inverse Wigner transform and Fresnel propagation. As a result, both the numerical simulations and the optical experiments are implemented, and it is demonstrated that the high-quality 3D reconstruction of the synthesized hologram is achieved, which reflects the highest reconstruction resolution among the existing methods, the large depth range with accurate refocus and defocus, and the natural view-dependent effects.

A comparative analytical investigation for some linear and nonlinear time-fractional partial differential equations in the framework of the Aboodh transformation

This article discusses two simple, complication-free, and effective methods for solving fractional-order linear and nonlinear partial differential equations analytically: the Aboodh residual power series method (ARPSM) and the Aboodh transform iteration method (ATIM). The Caputo operator is utilized to define fractional order derivatives. In these methods, the analytical approximations are derived in series form. We calculate the first terms of the series and then estimate the absolute error resulting from leaving out the remaining terms to ensure the accuracy of the derived approximations and determine the accuracy and efficiency of the suggested methods. The derived approximations are discussed numerically using some values for the relevant parameters to the subject of the study. Useful examples are thought to illustrate the practical application of current approaches. We also examine the fractional order results that converge to the integer order solutions to ensure the accuracy of the derived approximations. Many researchers, particularly those in plasma physics, are anticipated to gain from modeling evolution equations describing nonlinear events in plasma systems.

Perspective study on identification of high-impact processes for advancing Diversity, Equity and Inclusion (DEI) in Indian organizations

BACKGROUND: Inequality in India is quite a common phenomenon, the same is intertwined with social stratification and cultural influences. In a classical Indian setup, occupations are driven by social structure. Discrimination in the workplace in India is primarily also the result of structural inequalities. Organizations today are facing challenges in blending different cultures. This study gathered the views of Diversity, Equity and Inclusion (DEI) implementors and identified the high-impact processes that helped them to advance their work. OBJECTIVE: The primary objective of this study is to understand the high-impact practices which are being followed by employees implementing DEI in Indian organizations and how the leadership is helping them in driving DEI in their organizations. METHOD: To achieve the stated objective, this study used a semi-structured interview technique that involves asking participants a set of questions and following them up with probe questions to further explore their responses. This interview’s target group involves executives currently engaged in DEI implementation activities. The interview questions are based on the research instrument, a modified version of the Museus Context Culturally Engaging Campus Environment (CECE) model. A purposeful snowball sampling technique with predefined criteria was used for sample selection. The semi-structured interview transcripts were open-coded to derive code lists which then were axially coded for the identification of various themes. RESULTS: The study found that employee hiring & retention, engaging and supporting relationships, culturally relevant and responsive programs. and transformation framework are the four dominant high-impact practices which have significantly helped Indian organizations in implementing and advancing DEI initiatives in their organizations. CONCLUSIONS: By identifying the high-impact practices that helped Indian organizations in implementing DEI initiatives this study adds to the current body of knowledge in workplace diversity research. This study helps policymakers and other stakeholders in promoting, supporting and sustaining DEI initiatives, which now form a part of mandatory ESG (Environmental, Social, and Corporate Governance) reporting to be done by the top 1000 listed companies, Furthermore, entrepreneurs and practitioners can utilize the finding of the study as a guide.

Strategies for Reforming College English Teaching in the Context of Educational Digital Transformation

In the new educational landscape, cultivating the abilities and competencies of college students is one of the core objectives. It is crucial to guide them to actively participate in classroom activities and enhance their engagement. With China entering the era of digitalization, various digital technologies have been integrated into teaching practices, fostering innovation in teaching methods and creating an enriched learning atmosphere that encourages student involvement. In this context, students proactiveness has significantly increased, leading to comprehensive improvements in their skills and competencies. This article begins by analyzing the advantages of educational digital transformation and subsequently focuses on exploring reform strategies for college English education within the framework of digital transformation. These insights are intended as a valuable reference for educators and stakeholders in the field of education.