Set Of Videos Research Articles

Changes in facial soft tissue support with and without a maxillary labial flange.

To quantify facial esthetic outcomes of prosthetic upper lip support in both full animation and repose for patients with an existing implant-supported fixed complete dental prosthesis (ISFCDP). Twenty patients had a removable prosthetic labial flange fabricated for their existing maxillary prosthesis. The 3dMDface system was used to capture video sets of each patient going from repose to high smile with and without the labial flange. A single frame representing high smile and repose was selected from each video. Five facial anatomic markers and 21 individual points on the upper lip were evaluated for both image sets using quantitative photometric analysis. To provide a qualitative assessment, the same images were shown to dentists, laypeople, and the research subjects (patients) to determine which they found to be more esthetically pleasing, or if there was no difference. Quantitative data was evaluated using paired t-tests. The two-dimensional analysis showed a difference with and without a labial flange for lip projection and lip thickness. Additionally, 13 out of 21 landmarks in the three-dimensional analysis showed differences when comparing the superimposed labial flange and no flange linear difference values between high smile and repose. Survey results showed that research subjects (54.41%), dentists (51.50%), and laypeople (44.50%), all preferred labial flange versus no flange photos or were not able to tell a difference. Qualitative and quantitative results suggest that a prosthetic labial flange has an impact on upper lip support. Clinicians should consider lip support both in full animation and repose when treatment planning for the maxillary ISFCDP.

Enhancement of helping skills using videos and blended learning in social and health care education

ABSTRACT Students in social and health care education often encounter difficulties translating the concepts in communication into the complexities of interpersonal dynamics and diversified contexts in practicums. Most previous resources were developed in English, and some Chinese materials were not up to date. In this project, social work teachers produced two sets of videos and initiated coursework revamping by engaging students in producing more communication skills videos. An e-learning community has been set up for the storage of teacher’s demonstration videos and quality student videos. The video was applied in skill practice courses and disseminated to social work practicum supervisors and students during the pandemic, as their face-to-face learning opportunities were restricted. Students reported improvements in levels of skills and competence after watching the videos. It shows that videos on communication skills can be further promoted as a blended learning strategy in professional practice training.

Reputation Burning: Analyzing the Impact of Brand Sponsorship on Social Influencers

The growth of the influencer marketing industry warrants an empirical examination of the effect of posting sponsored videos on influencers’ reputations. We collected a novel data set of user-generated YouTube videos created by prominent English-speaking influencers in the beauty and style category. We extracted a rich set of theory-driven video features and used DiNardo-Fortin-Lemieux reweighting to construct comparable treatment and control groups matched at the influencer-video level. The empirical analysis of the matched sample revealed a reputation-burning effect; that is, posting a sponsored video, compared with posting an equivalent organic video, cost the influencers 0.19% of their reputation (operationalized as the number of subscribers). The reputation-burning effect was stronger among the influencers with larger audiences. An analysis of likes, comments, and comment texts revealed a larger gap in audience response between sponsored and organic videos among the influencers with larger (versus smaller) audiences. The reputation-burning effect was mitigated when a high fit existed between the sponsored content and the influencer’s “usual” content and when the promoted brand was less well known. Our study empirically tested the assumption of several theoretical works. Moreover, it contributes to the literature on influencer marketing and celebrity endorsements and provides managerial implications for influencers, brands, and social media platforms. This paper was accepted by D. J. Wu, information systems. Funding: This research was supported by funding provided by Harvard Business School. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00193 .

Automated Classification of Elementary Instructional Activities

Despite a tremendous increase in the use of video for conducting research in classrooms as well as preparing and evaluating teachers, there remain notable challenges to using classroom videos at scale, including time and financial costs. Recent advances in artificial intelligence could make the process of analyzing, scoring, and cataloguing videos more efficient. These advances include natural language processing, automated speech recognition, and deep neural networks. To train artificial intelligence to accurately classify activities in classroom videos, humans must first annotate a set of videos in a consistent way. This paper describes our investigation of the degree of inter-annotator reliability regarding identification of and duration of activities among annotators with and without experience analyzing classroom videos. Validity of human annotations is crucial for research involving temporal analysis within classroom video research. The study reported here represents an important step towards applying methods developed in other fields to validate temporal analytics within learning analytics research for classifying time- and event-based activities in classroom videos.

An unsupervised embedding method based on streaming videos for process monitoring in repetitive production systems

Advanced production systems, such as multi-step assembly processes, predominantly comprise of repetitive operations. The repetitive manual or human–robot integrated production operations call for new real-time process management technologies such as the increasing use of sensors and the development of system intelligence. Conventional process monitoring and management methods, which are often labor-intensive, fall short in providing immediate and actionable insights. To tackle this limitation, we develop an unsupervised embedding method to automatically delineate the process into different stages and predict real-time progress information. We propose a Contrastive Variational Autoencoder as a feature extractor to adeptly embed repetitive processes into a Gaussian Mixture Model. Based on the extracted features, we propose an adaptive change-point detection and an Iterative Dynamic Time Wrapping algorithm to identify and segment multiple standardized process stages automatically. Theoretically, we establish the asymptotic optimality of the detected change-points associated with the given precision of image and feature extractors, ensuring the high-quality process stage separation and labeling. The proposed method autonomously extracts essential features encapsulating progress information from a limited set of unlabelled process videos. Through four diverse case studies including production of an actual aircraft spoiler, our method exhibits very promising performance. Specifically, it achieves an average of 98.14% accuracy in predicting production progress and 0.9202 area under the curve in predicting progress deviation across three distinct production environments. The proposed process monitoring method in repetitive production systems has the potential to significantly improve productivity, promote standardization of repetitive operations, and predict production deviations.

Similarity-based ranking of videos from fixed-size one-dimensional video signature

The amount of information is multiplying, one of the popular and widely used formats is short videos. Therefore, maintaining the copyright protection of this information, preventing it from being disclosed without authorization, is a challenge. This work presents a way to rank a set of short videos based on a video profile similarity metric, finding a set of reference videos, using a self-supervised method, without the need for human tagging. The self-supervised method uses a search based on a Genetic Algorithm, of a subgroup of the most similar videos. Similarities are calculated using the SMAPE metric on video signatures vectors, generated with a fixed size, using Structural Tensor, maximum sub matrix and T-SNE.

Interobserver agreement of an ED PoCUS video training dataset of normal appendix and appendicitis in children

BackgroundEducational video datasets can be an effective method for training in emergency department (ED) point-of-care ultrasound (PoCUS). A video dataset for normal appendix and appendicitis in children using ED PoCUS images was developed to assess interobserver agreement, as measured by Cohen’s Kappa on key sonographic findings.MethodsThree sets of 25 ED PoCUS videos were selected and curated from pediatric patients with normal appendix and acute appendicitis. Four participant ED sonologist-physicians were trained on the first set of 25 videos showing normal appendix or normal bowel in patients without appendicitis to note if normal appendix was seen in any part or in it’s entirety from tip-to-cecum. They were then tested on the second set of similar videos. A third set of 25 videos from patients who had appendicitis where participant sonologists were asked to note if appendicitis was present or absent, with and without appendicolith or perforation. Cohen’s Kappa was calculated in aggregate and stratified by experience vs. novice against a senior sonologist-physician aware of all patient outcomes for visualization of: 1. any part of normal appendix, 2. normal appendix visualized from tip to cecum 3. any part of appendicitis, 4. appendicolith, 5. appendiceal perforation.ResultsCohen’s Kappa for any part of normal appendix, 0.71, 95% CI (0.58–0.85); normal appendix tip-to-cecum, 0.43, 95% CI (0.19–0.67), appendicitis, 0.53, 95%CI (0.34–0.70), appendicolith, 0.63, 95%CI (0.43–0.84), perforated appendicitis, 0.46, 95%CI (0.22–0.70). Stratified by experienced vs. novice: any part of normal appendix, 0.75 vs. 0.68; normal appendix tip-to-cecum, 0.50 vs. 0.36; appendicitis, 0.78 vs. 0.31; appendicolith, 0.75 vs. 0.5; perforated appendicitis, 0.5 vs 0.42.ConclusionsThis educational video dataset may be used to train sonologist-physicians in ED PoCUS scanning for normal appendix and appendicitis in children. Sonologist experience affected interobserver agreement with respect to visualization of entire normal appendix and appendicitis.

Non-polypoid Colorectal Lesions Detection and False Positive Detection by Artificial Intelligence under Blue Laser Imaging and Linked Color Imaging.

Artificial intelligence (AI) with white light imaging (WLI) is not enough for detecting non-polypoid colorectal polyps and it still has high false positive rate (FPR). We developed AIs using blue laser imaging (BLI) and linked color imaging (LCI) to detect them with specific learning sets (LS). The contents of LS were as follows, LS (WLI): 1991 WLI images of lesion of 2-10 mm, LS (IEE): 5920 WLI, BLI, and LCI images of non-polypoid and small lesions of 2-20 mm. LS (IEE) was extracted from videos and included both in-focus and out-of-focus images. We designed three AIs as follows: AI (WLI) finetuned by LS (WLI), AI (IEE) finetuned by LS (WLI)+LS (IEE), and AI (HQ) finetuned by LS (WLI)+LS (IEE) only with images in focus. Polyp detection using a test set of WLI, BLI, and LCI videos of 100 non-polypoid or non-reddish lesions of 2-20 mm and FPR using movies of 15 total colonoscopy were analyzed, compared to 2 experts and 2 trainees. The sensitivity for LCI in AI (IEE) (83%) was compared to that for WLI in AI (IEE) (76%: p=0.02), WLI in AI (WLI) (57%: p<0.01), BLI in AI (IEE) (78%: p=0.14), and LCI in trainees (74%: p<0.01). The sensitivity for LCI in AI (IEE) (83%) was significantly higher than that in AI (HQ) (78%: p<0.01). The FPR for LCI (6.5%) in AI (IEE) were significantly lower than that in AI (HQ) (17.3%: p<0.01). AI finetuned by appropriate LS detected non-reddish and non-polypoid polyps under LCI.

Detecting Temporal shape changes with the Euler Characteristic Transform

Abstract Organoids are multi-cellular structures that are cultured in vitro from stem cells to resemble specific organs (e.g., brain, liver) in their three-dimensional composition. Dynamic changes in the shape and composition of these model systems can be used to understand the effect of mutations and treatments in health and disease. In this paper, we propose a new technique in the field of topological data analysis for DEtecting Temporal shape changes with the Euler Characteristic Transform (DETECT). DETECT is a rotationally invariant signature of dynamically changing shapes. We demonstrate our method on a data set of segmented videos of mouse small intestine organoid experiments and show that it outperforms classical shape descriptors. We verify our method on a synthetic organoid data set and illustrate how it generalizes to 3D. We conclude that DETECT offers rigorous quantification of organoids and opens up computationally scalable methods for distinguishing different growth regimes and assessing treatment effects.

P-645 A machine learning model for optimal trigger day recommendation

Abstract Study question Could AI be used for trigger day recommendations to improve the number of M2 oocytes retrieved in IVF processes? Summary answer The use of AI for trigger day recommendations can improve the number of M2 oocytes retrieved in IVF processes by 1 on average. What is known already One of the most important decisions during ovarian stimulation during IVF cycles is when to trigger follicular maturation. Optimally timed trigger injection should maximize the number of retrieved high-quality oocytes. While these decisions are commonly made based on follicle sizes, hormone levels, and patient parameters, they are subjective and the criteria vary across medical centers and physicians. Numerous research studies are exploring how follicle sizes and other parameters translate to stimulation outcomes, but only in recent years, the use of artificial intelligence in decisions about the day of the trigger has become one of the main fields of interest. Study design, size, duration A retrospective study was made on 1085 IVF cycles conducted between 2019 and 2021 at 6 IVF centers in Poland. Processes were selected to have at least one set of ultrasound cine-loop videos for both ovaries from a maximum of three days before trigger day. Additionally, data on age, AMH, estradiol level, endometrium thickness, and the process outcome (MII oocytes number) was collected. Follicles were measured using the FOLLISCAN system. Participants/materials, setting, methods The recommendation algorithm uses a model predicting the number of MII oocytes retrieved if the trigger was applied on each of the three days following examination. The day with a maximum expected number of oocytes is recommended. The average number of retrieved MII oocytes was compared between two groups: cycles where the recommendation was the same as the actual physician decision, and cycles where it was different. Ten-fold cross-validation and propensity score matching were performed. Main results and the role of chance The model predicting MII oocyte numbers had an R2 of 0.56 and errors of 3.5 (RMSE), and 2.54 (MAE) overall. For the trigger on the day of the USG examination, it was RMSE: 3.33, MAE: 2.39, and later (+1, +2, +3 days respectively) RMSE: 3.6, 3.67, 3.44, MAE: 2.57, 2.71, 2.56. On average, 1.98 more MII oocytes (95% Confidence Interval: 1.22-2.77) were retrieved from processes where the model recommendation was the same as the actual physician decision (on time) compared to those where the model recommendation differed (not on time). Because this was not a randomized trial, and patient parameters vary across compared groups, propensity matching was used to ensure their homogeneity. After this, there were 1.11 more MII oocytes (CI: 0.22-1.99) retrieved on average in the “on time” group than in the “not on time” group. For patients with less than 10 follicles, there were on average 0.3 more MII oocytes in the “on time” group. For patients with 10-20 follicles, on average 0.72 more MII oocytes were observed, and for those with at least 20 follicles 1.32 more on average. Limitations, reasons for caution The study was limited to a few IVF centers in Poland, and its retrospective findings do not necessarily generalize to different centers. Additional evaluation of the model on data from multiple centers should be performed. Also, randomized control trials could be used to evaluate its effectiveness. Wider implications of the findings Results show the potential of using machine learning to optimize ovarian stimulation outcomes. An increased number of MII oocytes results in a higher success rate of in vitro fertilization. Trial registration number Project support was provided by the Polish National Center for Research and Development no. POIR.01.01.01-00-1634/20-00 and ERC Consolidator Grant TUgbOAT no. 772346.

Multi-Task Learning of Object States and State-Modifying Actions From Web Videos.

We aim to learn to temporally localize object state changes and the corresponding state-modifying actions by observing people interacting with objects in long uncurated web videos. We introduce three principal contributions. First, we develop a self-supervised model for jointly learning state-modifying actions together with the corresponding object states from an uncurated set of videos from the Internet. The model is self-supervised by the causal ordering signal, i.e., initial object state → manipulating action → end state. Second, we explore alternative multi-task network architectures and identify a model that enables efficient joint learning of multiple object states and actions, such as pouring water and pouring coffee, together. Third, we collect a new dataset, named ChangeIt, with more than 2600 hours of video and 34 thousand changes of object states. We report results on an existing instructional video dataset COIN as well as our new large-scale ChangeIt dataset containing tens of thousands of long uncurated web videos depicting various interactions such as hole drilling, cream whisking, or paper plane folding. We show that our multi-task model achieves a relative improvement of 40% over the prior methods and significantly outperforms both image-based and video-based zero-shot models for this problem.

Sign Tone: Two Way Sign Language Recognition and Multilingual Interpreter System Using Deep Learning

In this paper, a comparative experimental assessment of computer vision-based methods for sign language reclog- nation is conducted. By implementing the most recent deep neural network methods in this field, a thorough evaluation on multiple publicly available datasets is performed. The aim of the present study is to provide insights on sign language recognition, focusing on mapping non-segmented video streams to glosses. For this task, two new sequence training criteria, known from the fields of speech and scene text recognition, are introduced. Furthermore, a plethora of pretraining schemes is thoroughly discussed. Finally, a new RGB+D dataset for the Greek sign language is created. To the best of our knowledge, this is the first sign language dataset where three annotation levels are provided (individual gloss, sentence and spoken language) for the same set of video captures

Indication and Contraindication of Video-Based Psychotherapy: A Qualitative Delphi Survey to Capture Psychotherapists' Reflection and Deliberation Processes

The Corona pandemic forced many psychotherapists to make a rapid transition to video setting. Even after the acute phase of the pandemic, video-based treatments can be determined to a certain extent in practice. Thus, indication and contraindication recommendations are essential to ensure treatment quality. However, empirical studies are almost completely lacking in this regard. In the context of this work, the decision-making and consideration processes of psychotherapists regarding video-based treatments are to be recorded and those factors that can have an effect on the indication are to be identified. Two-stage, qualitative Delphi survey with psychodynamically oriented psychotherapists (n=9) and behavioural therapists (n=7) who can be assigned to the fields of research and practice. The quality of the therapeutic alliance guides the participants' choice of setting. An indication for video setting results from the feasibility of therapeutic interventions and the respective treatment goal. The therapists weighed the costs and benefits of video setting for various disorders. The changed feeling of control on both sides and the possibility of improving health care are decisive factors. Based on the results, a decision tree for psychotherapists was created, which includes the identified indication criteria.

Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett's esophagus: a tandem randomized and video trial.

This study evaluated the effect of an artificial intelligence (AI)-based clinical decision support system on the performance and diagnostic confidence of endoscopists in their assessment of Barrett's esophagus (BE). 96 standardized endoscopy videos were assessed by 22 endoscopists with varying degrees of BE experience from 12 centers. Assessment was randomized into two video sets: group A (review first without AI and second with AI) and group B (review first with AI and second without AI). Endoscopists were required to evaluate each video for the presence of Barrett's esophagus-related neoplasia (BERN) and then decide on a spot for a targeted biopsy. After the second assessment, they were allowed to change their clinical decision and confidence level. AI had a stand-alone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.3%, respectively. Without AI, BE experts had an overall sensitivity, specificity, and accuracy of 83.3%, 58.1%, and 71.5%, respectively. With AI, BE nonexperts showed a significant improvement in sensitivity and specificity when videos were assessed a second time with AI (sensitivity 69.8% [95%CI 65.2%-74.2%] to 78.0% [95%CI 74.0%-82.0%]; specificity 67.3% [95%CI 62.5%-72.2%] to 72.7% [95%CI 68.2%-77.3%]). In addition, the diagnostic confidence of BE nonexperts improved significantly with AI. BE nonexperts benefitted significantly from additional AI. BE experts and nonexperts remained significantly below the stand-alone performance of AI, suggesting that there may be other factors influencing endoscopists' decisions to follow or discard AI advice.

MULTI-MODAL GRAPHNET LEARNING-BASED FEATURE EXTRACTION FOR SPATIOTEMPORAL SALIENCY DETECTION ON MULTIMEDIA VIDEOS

In multimedia content analysis, spatiotemporal saliency detection plays a crucial role in understanding visual data. However, existing methods often struggle with efficiently capturing complex patterns in videos. To address this, we propose a Multi-modal GraphNet Learning-Based Feature Extraction approach. Our method integrates multi-modal information from both spatial and temporal domains to enhance saliency detection accuracy. By leveraging GraphNet, we effectively model the intricate relationships among video frames. We validate our approach on a diverse set of multimedia videos, demonstrating significant improvements in saliency detection performance. Specifically, our method achieves an average precision of 0.85 and a recall of 0.78, outperforming state-of-the-art techniques. Furthermore, our approach exhibits robustness across various video types and scenarios. Through experimental evaluation, we confirm the efficacy of our proposed method in enhancing spatiotemporal saliency detection. This work contributes to advancing the field of multimedia analysis, offering a promising solution for understanding visual content in videos.

A Deep Approach to Deep Fake Detection

In recent months, the proliferation of free deep learning-based software tools has facilitated the creation of credible face exchanges in videos, resulting in what are known as "DeepFake" (DF) videos. While manipulations of digital videos have been demonstrated for several decades through the use of visual effects, recent advances in deep learning have significantly increased the realism of fake content and the accessibility with which it can be created. These AI-synthesized media, popularly referred to as DF, pose a significant challenge for detection. Detecting DF is a major challenge due to the complexity of training algorithms to spot them. In this work, we propose a detection approach using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Our system leverages a CNN to extract features at the frame level, which are then used to train an RNN. The RNN learns to classify whether a video has been manipulated, detecting temporal inconsistencies between frames introduced by DF creation tools. We evaluate our approach against a large set of fake videos collected from standard datasets and demonstrate competitive results using a simple architecture.

A structural description of pedestrian movement behavior in multiple surveillance videos

This study purpose of recording pedestrians' activities in all surveillance videos and then convert these records into structured descriptions to analyze a large number of surveillance videos. This paper proposes a four-stage processing method. The first stage uses convolutional neural network (CNN) to detect objects in the surveillance video and classify them. The second stage finds the correlation of pedestrians in continuous images and describes multiple objects' trajectories in the surveillance video. The third stage calculates the similarity of the pedestrian object sets of the surveillance video at different times as the basis for pedestrian re-identification. In the fourth stage, extract the walking path of the pedestrian in each surveillance video. Each pedestrian's actions in each surveillance video, including whether to stand, walking path, direction, clothing color and walking time. To construct a correlation table of pedestrian motion in multiple surveillance videos.This experiment uses two image datasets for experimentation and analysis to evaluate this method's effectiveness for pedestrian movement in multiple surveillance videos. The image datasets are the Multi-Camera Object Tracking (MCT) Challenge and PETS 2009 Benchmark Data. This experiment includes pedestrian detection, pedestrian identification, and multi-pedestrian tracking of pedestrians in single and multiple surveillance cameras. The results show that in the pedestrian detection experiment, the convolutional neural network can effectively solve the problems caused by static objects, light changes, similar colors, crowded pedestrians, and incomplete objects. In the pedestrian recognition and re-recognition experiments, the accuracy is reduced due to the influence of low-resolution surveillance videos. This detection method of the pedestrian is more accurate than traditional dynamic object detection methods. After the pedestrian tracking and re-identification are completed, the pedestrian relationships in multiple surveillance cameras are integrated, and a pedestrian behavior association table is built for surveillance videos.

Rating accuracy, leniency, and rater perceptions when using the RPM and BARS

AbstractResearchers have argued that social‐comparative rating formats hold important psychometric advantages over traditional absolute ratings. We asked 152 participants to observe and assess the videotaped performance of individuals completing a task using a social‐comparative (Relative Percentile Method; RPM) and absolute rating (Behaviorally Anchored Rating Scale; BARS) formats. After collecting expert ratings on the same set of videos, we calculated accuracy indices and leniency. Additionally, we collected rater perceptions of accuracy and fairness for both formats. Our study revealed that the BARS was perceived as more accurate and fairer than the RPM. However, the RPM was found to be better at combating rater leniency. We discuss the implications of these findings.

Research on the influence mechanism of short video communication effect of furniture brand: Based on ELM model and regression analysis

The elaboration likelihood model (ELM) and regression analysis were used to investigate the impact of furniture brands’ communication strategies on consumer behavior through short video platforms. The work examined a set of representative short videos, analyzing how key features—such as content theme, duration (16-60 seconds), graphics, subtitles, background music, and title style—affected communication effectiveness. The ELM model uncovered the correlation between these video characteristics and the Communication Effect Index (DCI), with statistical significance confirmed by regression. Findings indicated that live broadcasts, graphical presentations, subtitles/topics, upbeat music, and exclamatory titles significantly enhanced communication efficiency. Limitations, including time-period sampling bias, sample size, and item duplication in the ELM application, were also considered. Based on these findings, the research offers optimization suggestions and future directions for furniture enterprises in leveraging short video marketing.

Cultural Appropriation for Improved Knowledge Acquisition in Medical Education.

Culture is an essential component that governs all aspects of human behavior. Superstition is an irrational belief observed in almost all cultures. It is linked to one or more factors like supernatural powers, good luck, bad omen, fiction, illegitimate activity, absurd narration, folk tales, or practice without any rational basis. A cross-sectional social experiment was conducted to evaluate the effect of cultural appropriation as a tool to enhance medical knowledge acquisition and attitudinal development in medical education. The experiment was designed to target a non-medical population. Four superstition-oriented videos were developed with 20 scientific pieces of information related to forensic medicine. A data collection sheet was developed on Microsoft form with 16 questions was distributed on the participants. Out of the 986 participants, 763 (77.5%) watched the whole set of videos. About 55-95% of responders demonstrated knowledge acquisition of all the questions. There was a statistically significant difference between those who watched the videos and those who did not. When participants were asked about the most important information they remember from the videos, their answers fell into two main categories; information related to core scientific knowledge (80% of participants) and information not related to the core knowledge (16% of respondents). The top three areas for the reasons why people wanted to watch the videos were curiosity, knowledge, and career. A change in attitudes was reported among the participants where 80% of responders demonstrated curiosity to know more about this world, 46% responders reported developing more respect for the forensic physician and 43% revealed their ignorance about this great hidden world. Cultural appropriation could be a needed strategy to accommodate for upscale in education. Learners might validate that learning happens through a door that adopts not only honours their culture and adapts to it.