Learning Specialists Research Articles

Why is learning from patient safetyincidents (still) so hard? Asociocultural perspective onlearningfrom incidents inhealthcare organizations.

Despite robust quality improvement efforts in healthcare, learning from patient safety incidents remains difficult. Our study explores counter-vailing powers shaping learning processes and possibilities in healthcare organizations, with a focus on social, political and organizational dynamics of learning. Deploying concepts of situated curriculum, boundary work and interconnected knowledge practices, we interviewed staff and physicians (n=15) in a large Academic Health Science Centre in Canada about their experiences of incident investigations and resultant information sharing. Our analytical strategy was abductive, drawing connections to sociology of the professions and learning sciences literature. Incident investigation and follow-up processes are relatively robust for learning about incidents in the organization. However, learning from incidents remains difficult, complicated by the professional politics of incident classification, counter-vailing policies related to privacy, the organization of improvement work towards reporting, and an organizational focus on incidents with severe outcomes. Participants advocated for a broader view of incidents, moving beyond classification and investigation based on severity of outcome to also include incidents that are "learning rich". To that end, we argue for more research on the role of Patient Safety Specialists in organizational learning and more collaborations with learning sciences. This paper illuminates ways in which robust information dissemination structures are an important but insufficient condition for learning from incidents. The argument goes beyond a prescriptive approach to learning from incidents to instead explore the competing visions and values implicated with improvement practices.

Avoiding compassion fatigue in disability services

I recently attended a conference for medical education learning specialists. The opening speaker, Dr. Nicole Piemonte, spoke specifically to individuals who support medical students. However, her wise words can be expanded to all professionals, specifically those in the helping professions.

Generative AI tools in Teaching and Learning: An Enabler to Creating Work-Ready Students in the 4IR.

Higher education is experiencing a paradigm shift with the advent of generative artificial intelligence (AI) tools and large language models. This transformative approach is revolutionising education and the world of work and, if harnessed correctly, has the propensity to shape future minds. The study sought to understand academics and teaching and learning (T&L) specialists' perceptions at a private higher education institution (PHEI) in South Africa on how Generative AI tools should be used in T&L to prepare students adequately for the world of work. This qualitative exploratory study purposively selected 40 academics and T&L specialists across ten sites from a PHEI in South Africa. Three themes emerged from the data: curriculum integration, discourse, partnerships and collaboration and training and awareness. To prepare students for the world of work that uses AI, it is important to integrate Generative AI into the curriculum and teaching and learning, considering industry-specific requirements through continuous discourse, partnerships and collaboration between stakeholders. The training and awareness of Generative AI tools are necessary for lecturers and students to understand better how to use Generative AI tools correctly and responsibly.

Sex differences in brain MRI using deep learning toward fairer healthcare outcomes.

This study leverages deep learning to analyze sex differences in brain MRI data, aiming to further advance fairness in medical imaging. We employed 3D T1-weighted Magnetic Resonance images from four diverse datasets: Calgary-Campinas-359, OASIS-3, Alzheimer's Disease Neuroimaging Initiative, and Cambridge Center for Aging and Neuroscience, ensuring a balanced representation of sexes and a broad demographic scope. Our methodology focused on minimal preprocessing to preserve the integrity of brain structures, utilizing a Convolutional Neural Network model for sex classification. The model achieved an accuracy of 87% on the test set without employing total intracranial volume (TIV) adjustment techniques. We observed that while the model exhibited biases at extreme brain sizes, it performed with less bias when the TIV distributions overlapped more. Saliency maps were used to identify brain regions significant in sex differentiation, revealing that certain supratentorial and infratentorial regions were important for predictions. Furthermore, our interdisciplinary team, comprising machine learning specialists and a radiologist, ensured diverse perspectives in validating the results. The detailed investigation of sex differences in brain MRI in this study, highlighted by the sex differences map, offers valuable insights into sex-specific aspects of medical imaging and could aid in developing sex-based bias mitigation strategies, contributing to the future development of fair AI algorithms. Awareness of the brain's differences between sexes enables more equitable AI predictions, promoting fairness in healthcare outcomes. Our code and saliency maps are available at https://github.com/mahsadibaji/sex-differences-brain-dl.

3R-Based Teaching Module for Biology Education Students: A Research Development

The teaching modules are structured learning packages that encompass lesson unit concepts, designed to align with the semester learning plan (RPS), and aimed at achieving specific competency standards. This study explores the application of a 3R-Based Teaching Module for Biology Education through a validation process. The research follows a Research and Development (RD) methodology, utilizing the ADDIE model, which includes three stages: Analysis, Design, and Development. The study was conducted in the Biology Education programs of Riau Islamic University, Muhammadiyah University, and Lancang Kuning University from July to December 2023. The sample included 15 third-semester students as respondents, along with three experts—specialists in material, learning, and media—selected through purposive sampling techniques. These students had previously completed Environmental Science courses at the three universities. Validation data were collected using questionnaires distributed via Google Forms to both student respondents and expert validators. The validation results revealed high validity scores from media experts (94.44), learning experts (96.59), and material experts (100), with an overall validation score of 96.67. Student readability was also rated highly at 91.16. Based on these results, the 3R-Based Teaching Module was deemed valid and practical for use in biology education programs.

“Constantly Fighting a Battle”

Learning specialists within athletic academic support units on college campuses support the most fragile, neurodiverse learners. Learning specialists provide individualized academic support to prioritized collegiate student-athletes who are most at-risk for academic difficulties (Steinberg et al., 2018). While previous research has emphasized the value this role contributes to the academic success (McCarthy, 2019) and winning success (Stokowski et al., 2020) of college student- athletes, individuals who served as learning specialists have decided to leave the athletic academic support field. The purpose of this study was to identify the primary factors that influenced this decision. Using snowball sampling method, an online questionnaire was disseminated to former learning specialists (N = 22). Descriptive statistics were used to analyze closed-ended responses. Open-ended results were coded for common themes. Six themes emerged related to the dimensions of burnout—workload, control, reward, fairness, values, and community—which all encompass the three domains: emotional exhaustion, depersonalization, and inefficacy (Maslach et al., 2001). The implications of this study provide a critical opportunity to reflect and invest in learning specialist resources to minimize burnout and maximize retention efforts within collegiate athletics academic support units across the nation. Suggestions for additional education and development opportunities focused on learning specialists are offered along with specific retention strategies.

Reconceptualizing assessment design in the age of AI: Is it genuine or faux?

AbstractWhile AI has the potential to streamline assessment development in higher education, there are concerns about its reliability, fairness, and potential to perpetuate bias. This study sought to understand the perceptions of academics and teaching and learning (T&L) specialists on the use of AI tools and large language models on assessment development, whether the private HE institution (PHEI) needs to change how it assesses students, and the types of assessments to be created. This topic is extremely relevant, as many higher education institutions (HEIs) are entering unchartered territory, and there is scant credible evidence on the prior‐implementation reporting and evaluation of AI integration into assessment practices. Forty academics and T&L specialists at a PHEI in South Africa shared their perceptions. The study revealed two key themes: (a) AI use and (b) reconceptualizing assessments and T&L structures, indicating the need for HEIs to understand the synergies between AI and assessment practices.

Extended Realities for Sensorially Diverse Children.

Learning space for children with different sensory needs, nowadays, can be interactive, multisensory experiences, designed collaboratively by 1) specialists in special-needs learning, 2) extended realities (XR) technologists, and 3) sensorial diverse children, to provide the motivation, challenge, and development of key skills. While traditional audio and visual sensors in XR are challenging for XR applications to meet the needs of visually and hearing impaired sensorial-diverse children, our research goes a step ahead by integrating sensory technologies including haptic, tactile, kinaesthetic, and olfactory feedback that was well received by the children. Our research also demonstrates the protocols for 1) development of a suite of XR-applications; 2) methods for experiments and evaluation; and 3) tangible improvements in XR learning experience. Our research considered and is in compliance with the ethical and social implications and has the necessary approval for accessibility, user safety, and privacy.

Early detection of autism spectrum disorder via deep-learning application of fMRI and machine learning for ASD children identifications

This study unveils an advanced convolutional-neural-network (CNN) algorithm that was meticulously engineered to examine resting-state functional magnetic resonance imaging (fMRI) for early ASD detection in pediatric cohorts. The CNN architecture amalgamates convolutional, pooling, batch-normalization, dropout, and fully connected layers, optimized for high-dimensional data interpretation. Rigorous preprocessing yielded 22,176 two-dimensional echo planar samples from 126 subjects (56 ASD, 70 controls) who were sourced from the Autism Brain Imaging Data Exchange (ABIDE I) repository. The model, trained on 17,740 samples across 50 epochs, demonstrated unparalleled diagnostic metrics – accuracy of 99.39%, recall of 98.80%, precision of 99.85%, and an F1 score of 99.32% – and thereby eclipsed extant computational methodologies. Feature map analyses substantiated the model’s hierarchical feature extraction capabilities. This research elucidates a deep learning framework for computer-assisted ASD screening via fMRI, with transformative implications for early diagnosis and intervention. And, this study addresses the critical need for early detection and intervention in autism spectrum disorder (ASD) using machine learning. Specific therapies are needed for ASD, a neurodevelopmental disease that affects social interaction and communication. To find trends in ASD, our research uses a variety of early childhood screening tests as training sets for machine learning algorithms. The methodology that has been suggested utilizes methods of machine learning to compute the ASD spectrum, considering its many expressions. By using multidisciplinary methods and sophisticated screening instruments, we want to create an accurate system for early ASD detection. Algorithmic transparency, data protection, and ethical considerations are essential. This study seeks to build precise instruments for early ASD detection by promoting collaboration between specialists in neurodevelopment, psychology, and machine learning. A robust instrument that enhances the knowledge of medical practitioners is machine learning. Results show how innovation may transform early interventions and help people on the autistic spectrum achieve enhanced results.

The database makes the poison: How the selection of datasets in QSAR models impacts toxicant prediction of higher tier endpoints

As the United States and the European Union continue their steady march towards the acceptance of new approach methodologies (NAMs), we need to ensure that the available tools are fit for purpose. Critics will be well-positioned to caution against NAMs acceptance and adoption if the tools turn out to be inadequate. In this paper, we focus on Quantitative Structure Activity-Relationship (QSAR) models and highlight how the training database affects quality and performance of these models. Our analysis goes to the point of asking, “are the endpoints extracted from the experimental studies in the database trustworthy, or are they false negatives/positives themselves?” We also discuss the impacts of chemistry on QSAR models, including issues with 2-D structure analyses when dealing with isomers, metabolism, and toxicokinetics. We close our analysis with a discussion of challenges associated with translational toxicology, specifically the lack of adverse outcome pathways/adverse outcome pathway networks (AOPs/AOPNs) for many higher tier endpoints. We recognize that it takes a collaborate effort to build better and higher quality QSAR models especially for higher tier toxicological endpoints. Hence, it is critical to bring toxicologists, statisticians, and machine learning specialists together to discuss and solve these challenges to get relevant predictions.

Predicting cervical cancer risk probabilities using advanced H20 AutoML and local interpretable model-agnostic explanation techniques

Background Cancer is positioned as a major disease, particularly for middle-aged people, which remains a global concern that can develop in the form of abnormal growth of body cells at any place in the human body. Cervical cancer, often known as cervix cancer, is cancer present in the female cervix. In the area where the endocervix (upper two-thirds of the cervix) and ectocervix (lower third of the cervix) meet, the majority of cervical cancers begin. Despite an influx of people entering the healthcare industry, the demand for machine learning (ML) specialists has recently outpaced the supply. To close the gap, user-friendly applications, such as H2O, have made significant progress these days. However, traditional ML techniques handle each stage of the process separately; whereas H2O AutoML can automate a major portion of the ML workflow, such as automatic training and tuning of multiple models within a user-defined timeframe. Methods Thus, novel H2O AutoML with local interpretable model-agnostic explanations (LIME) techniques have been proposed in this research work that enhance the predictability of an ML model in a user-defined timeframe. We herein collected the cervical cancer dataset from the freely available Kaggle repository for our research work. The Stacked Ensembles approach, on the other hand, will automatically train H2O models to create a highly predictive ensemble model that will outperform the AutoML Leaderboard in most instances. The novelty of this research is aimed at training the best model using the AutoML technique that helps in reducing the human effort over traditional ML techniques in less amount of time. Additionally, LIME has been implemented over the H2O AutoML model, to uncover black boxes and to explain every individual prediction in our model. We have evaluated our model performance using the findprediction() function on three different idx values (i.e., 100, 120, and 150) to find the prediction probabilities of two classes for each feature. These experiments have been done in Lenovo core i7 NVidia GeForce 860M GPU laptop in Windows 10 operating system using Python 3.8.3 software on Jupyter 6.4.3 platform. Results The proposed model resulted in the prediction probabilities depending on the features as 87%, 95%, and 87% for class ‘0’ and 13%, 5%, and 13% for class ‘1’ when idx_value=100, 120, and 150 for the first case; 100% for class ‘0’ and 0% for class ‘1’, when idx_value= 10, 12, and 15 respectively. Additionally, a comparative analysis has been drawn where our proposed model outperforms previous results found in cervical cancer research.

APPROACH TO THE AUTOMATIC CREATION OF AN ANNOTATED DATASET FOR THE DETECTION, LOCALIZATION AND CLASSIFICATION OF BLOOD CELLS IN AN IMAGE

Context. The paper considers the problem of automating the creation of an annotated dataset for further use in a system for detecting, localizing and classifying blood cells in an image using deep learning. The subject of the research is the processes of digital image processing for object detection and localization.
 Objective. The aim of this study is to create a pipeline of digital image processing methods that can automatically generate an annotated set of blood smear images. This set will then be used to train and validate deep learning models, significantly reducing the time required by machine learning specialists.
 Method. The proposed approach for object detection and localization is based on digital image processing methods such as filtering, thresholding, binarization, contour detection, and filling. The pipeline for detection and localization includes the following steps: The given fragment of text describes a process that involves noise reduction, conversion to the HSV color model, defining a mask for white blood cells and platelets, detecting the contours of white blood cells and platelets, determining the coordinates of the upper left and lower right corners of white blood cells and platelets, calculating the area of the region inside the bounding box, saving the obtained data, and determining the most common color in the image; filling the contours of leukocytes and platelets with said color; defining a mask for red blood cells; defining the contours of red blood cells; determining the coordinates of the upper left and lower right corners of red blood cells; calculating the area of the region within the bounding box; entering data about the found objects into the dataframe; saving to a .csv file for future use. With an unlabeled image dataset and a generated .csv file using image processing libraries, any researcher should be able to recreate a labeled dataset.
 Results. The developed approach was implemented in software for creating an annotated dataset of blood smear images
 Conclusions. The study proposes and justifies an approach to automatically create a set of annotated data. The pipeline is tested on a set of unlabelled data and a set of labelled data is obtained, consisting of cell images and a .csv file with the attributes “file name”, “type”, “xmin”, “ymin”, “xmax”, “ymax”, “area”, which are the coordinates of the bounding box for each object. The number of correctly, incorrectly, and unrecognised objects is calculated manually, and metrics are calculated to assess the accuracy and quality of object detection and localisation.

Generative Artificial Intelligence (AI) Tools in Higher Education: A Moral Compass for the Future?

Higher education is experiencing a paradigm shift with the advent of Generative Artificial Intelligence (AI) tools and large language models. This transformative approach is revolutionising education and the world of work and, if harnessed correctly, has the propensity to shape future minds. The study sought to understand academics' and teaching and learning (TandL) specialists' perceptions at a private higher education institution (PHEI) in South Africa (SA) regarding the ethical implications of using Generative AI tools in TandL prior to the institution taking a formal stance on the use of these tools. This qualitative exploratory study purposively selected 59 academics, researchers and TandL specialists across nine delivery sites at a PHEI in South Africa. Data was collected anonymously using an open-ended online questionnaire, and thematic analysis was performed. Five key themes emerged from the data: Academic integrity while maintaining agility; monitoring responsible ethical use and investments; protection of privacy; accessibility and equality and tools and systems for AI identification. The study also highlights the importance of considering the moral implications of Generative AI in education and how it can be used to shape the future of learning. This study contributes to the limited literature available on the use of AI in higher education TandL and its ethical implications in the global south.

A/part: Writing retreats as spaces fostering a sense of belonging for academic staff

In June 2022, I attended a teaching and learning writing retreat with other early career researchers, the deputy dean of teaching and learning of the faculty, and the teaching and learning specialist for our faculty. I had recently completed my teaching portfolio as part of my probationary requirements and had been asked to join the retreat to guide and assist the other early career researchers who were working on their teaching portfolios at the time. At this specific retreat, we had quite a few frank discussions about our teaching and learning background experiences and we were given the space to be vulnerable and open with one another. At the time, I was working on a paper on student belonging, and I noted the parallel between what I was reading about student belonging and what I was experiencing at the retreat: a profound sense of being a part of a group, of belonging.

Wizja nowoczesnej szkoły w cyfrowym środowisku uczenia się

The article presents an issue indicating the vision of a modern school in a digital learning environment. We are faced with the need to include in the modern vision of the school aspects of individualization and personalization of teaching, as well as lifelong education, the formation of practical skills, the development of online education and the preparation of teachers for theprofession as learning specialists. In the context of the new cultural and didactic situation, the school should abandon the model of knowledge treated as an increment of information, anddevelop a model of education that defines knowledge as a rich set of procedures for dealing with information reality and growing uncertainty.

Development of Geomobile App-Based Outdoor Study to Improve Critical-Social Abilities and Collaborative in the 21st Century

Geography learning in high school is built through field and contextual experiences to verify the theories. The learning method used by the geography teacher is not quite effective in improving students’ critical-social and collaborative abilities. This study aims to 1) develop a Geomobile app-based outdoor study that is valid and practical, and 2) determine its effectiveness in improving students’ critical-social and collaborative abilities in the 21st century. This study used research and development with a modified version of the Borg and Gall model. This research utilized validity tests conducted by material experts, media professionals, and learning specialists. The authentic instruments used to measure critical-social and collaboration abilities. The data analyst used descriptive statistics to assess the quality of the product and conducted an independent sample t-test to evaluate the effectiveness of the Geomobile app for outdoor study. The research was conducted on 216 eleventh-grade Social Science students in five high schools in East Java Province during the academic year 2021–2022. Research findings show that students need tools to enhance essential skills in the 21st century through the use of Geomobile app-based outdoor study. The development results indicate that expert validation feedback on the Geomobile app is very positive, with an average rating of 93%. Additionally, the Geomobile app has been found to effectively enhance critical-social abilities by 59% and collaborative abilities by 63%. Therefore, this development effectively improves students’ abilities in the 21st century. The research has provided support for students to become future social agents, especially in terms of building a better working place and social community in the 21st century.

Kapin (Smart Card) Media: Evaluating Learning Tools Efficacy to Improve Reading Skills of Second Grads

In the context of classroom learning, particularly at the elementary school level, various methods are employed. This study aims to achieve three key research objectives: (1) to establish the reliability of Chapin learning media, (2) to ensure its effective implementation, and (3) to assess its overall efficacy. The research methodology utilized the Research and Development (R&D) approach, following the Addie development model, consisting of five stages: Analysis, Design, Development, Implementation, and Evaluation. The study's population consisted of 12 students from Class II at SDN Tiron III and 23 students from Class II at SDN Tiron IV. Data collection involved the use of validation questionnaires for media, content, and learning specialists, supplemented by both small- and large-scale experiments. Data analysis encompassed validity testing, practicality assessment through teacher response questionnaires (involving 2 teachers and 35 students), and effectiveness evaluation based on students' learning outcomes, analyzed using the N-Gain Score. The results of this study indicate that the smart card learning media achieved a high level of validity, with percentages of 90%, 84%, and 84% for content, media, and overall validity, respectively. Furthermore, the practicality assessment revealed that students' responses averaged 88%, while expert teacher responses averaged 84%. The N-Gain score, with values of 0.77 for the wider class scale and 0.79 for the limited class scale, both fall into the high category, demonstrating the efficacy of the learning media.

Not off to a flying start

Often, when presented with a task or a problem, we jump in and start work immediately. This approach can lead to solutions that do not meet the needs of the people they are designed for. This paper outlines a less straightforward approach using human-centred design. The Accessibility Hub project was conceived as an online self-service resource for students at an Australian tertiary institute. A teaching and learning specialist and an information technology specialist led the project. After initially dividing up the task and getting started on their to-do lists, the two colleagues decided to take a step back. They employed human-centred design, a process that included empathy interviews with students with disability and ideation sessions with colleagues. Out of this grew a community of practice and the idea that the Accessibility Hub could be a resource for all staff and students at the institute. The Accessibility Hub was launched in January 2023 with the tag line ‘Everyone needs a little help sometimes.’

Retooling the Remediation Process: An Overview of One School’s Process Overhaul

ObjectiveThis article describes the process of review and modification of a Doctor of Pharmacy didactic course remediation process. A highlight of the new process includes early intervention with a Learning Specialist, creating the opportunity for students to address learning issues earlier in the courses. MethodsReview of past remediation processes, student success, and pertinent literature related to remediation processes to allow for a new remediation process that supports students’ learning and success. ResultsCreation of a new 2-phase process, including an early intervention and remediation process. The process includes a Learning Specialist position to focus on learning and barriers to academics. Remediation opportunities focus on proficiency in specific areas for each course. ConclusionThe new remediation policy provides greater support to students through the Learning Specialist. The new policy helps students connect with resources earlier in the program and provides multiple opportunities for assisting students during the semester. Over the past 4 years, the Learning Specialist has met with an average of 73 individual students per semester, while only an average of 25 students in the didactic portion of the curriculum (year 1–3) require remediation each semester.

Addressing the Pediatric Mental Health Crisis in Emergency Departments in the US: Findings of a National Pediatric Boarding Consensus Panel.

In 2021, several professional organizations declared a national state of emergency in child and adolescent mental health. Rising volume and acuity of pediatric mental health emergencies, coupled with reduced access to inpatient psychiatric care, has caused tremendous downstream pressures on EDs resulting in long lengths of stay, or "boarding", for youth awaiting psychiatric admission. Nationally, boarding times are highly heterogeneous, with medical / surgical patients experiencing much shorter boarding times compared to patients with primary mental health needs. There is little guidance on best practices in the care of the pediatric patient with significant mental health need "boarding" in the hospital setting. There is a significant increase in the practice of "boarding" pediatric patients within emergency departments and inpatient medical floors while awaiting psychiatric admission. This study aims to provide consensus guidelines for the clinical care of this population. Twenty-three panel participants of fifty-five initial participants (response rate 41.8%) committed to completing four successive rounds of questioning using Delphi consensus gathering methodology. Most (70%) were child psychiatrists and represented 17 health systems. Thirteen participants (56%) recommended maintaining boarded patients in the emergency department, while 78% indicated a temporal limit on boarding in the emergency department should prompt transfer to an inpatient pediatric floor. Of this group, 65% recommended a 24-hour threshold. Most participants (87%) recommended not caring for pediatric patients in the same space as adults. There was unanimous agreement that emergency medicine or hospitalists maintain primary ownership of patient care, while 91% agreed that child psychiatry should maintain a consultative role. Access to social work was deemed most important for staffing, followed by behavioral health nursing, psychiatrists, child life, rehabilitative services, and lastly, learning specialists. There was unanimous consensus that daily evaluation is necessary with 79% indicating vitals should be obtained every 12 hours. All agreed that if a child psychiatric provider is not available on-site, a virtual consultation is sufficient to provide mental health assessment. This study highlights findings of the first national consensus panel regarding the care of youth boarding in hospital-based settings and provides promising beginnings to standardizing clinical practice while informing future research efforts.