Postgraduate Annual Research Seminar 2011 UTM

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Hasnain Zafar

Presented research paper on Activity theory and Mobile Collaborative Leaning Design.

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Postgraduate Annual Research Seminar 2011 UTM

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In the ever-evolving field of medical education, the clinical phase plays a pivotal role in shaping competent and well-rounded healthcare professionals. Enhancing this critical phase through the strategic integration of eLearning can create a transformative learning experience, equipping medical students with the knowledge, skills, and confidence needed to thrive in real-world clinical settings. This comprehensive initiative explores innovative approaches to augmenting the clinical phase of medical education with cutting-edge eLearning solutions. By leveraging digital technologies, interactive simulations, and immersive virtual environments, students can gain exposure to a wide range of clinical scenarios and develop essential competencies in a safe, controlled, and engaging learning environment. Key aspects of this initiative include: Virtual Patient Simulations: Highly realistic virtual patient simulations that mimic real-life clinical encounters, allowing students to practice diagnostic and treatment decision-making processes, enhance communication skills, and receive immediate feedback. Interactive Clinical Case Studies: Engaging and multimedia-rich case studies that challenge students to apply their knowledge, critical thinking abilities, and problem-solving skills in authentic clinical contexts. Augmented Reality (AR) and Virtual Reality (VR) Experiences: Immersive AR and VR experiences that enable students to visualize and interact with anatomical structures, surgical procedures, and clinical environments, enhancing their spatial understanding and preparedness for clinical practice. Online Collaborative Learning: Leveraging digital platforms and tools for online discussions, peer-to-peer learning, and collaboration with clinical instructors and mentors, fostering a supportive and interactive learning community. Personalized Learning Pathways: Adaptive learning technologies that tailor the learning experience to individual student needs, providing targeted feedback, remediation, and personalized learning resources. By seamlessly integrating these eLearning components into the clinical phase, medical students can enhance their clinical competencies, develop essential skills for patient care, and gain valuable exposure to a diverse range of clinical scenarios, better preparing them for the challenges and complexities of real-world medical practice.

Enhancing Medical Education Clinical Phase using eLearning

Hasnain Zafar

Slides of my talk on AI TEL for Medical Education at TUMS. I explore the terms AI,ML,DL and AI, short for Artificial Intelligence. In the context of TEL (which stands for Technology-Enhanced Learning), my purpose is to support and enhance the educational process by offering personalized assistance, answering questions, and providing guidance to learners. Whether you need help with specific topics, seeking resources, or simply looking for a study buddy, I am here to assist you. With the integration of AI in TEL, learners can access learning content anytime and anywhere, receiving personalized feedback and recommendations to further improve their understanding and skills. I can also assist educators in creating and delivering dynamic and interactive lessons, helping them monitor student progress and provide individualized support. Overall, I explore the role of AI in TEL is to enhance and enrich the learning experience, providing learners with personalized support, knowledge, and guidance throughout their educational journey. I am here to assist you whenever you need help, so feel free to ask me anything related to TEL! Share some of the applications and AI enabled tools for Teaching and Learning. I share my experience how we can put pedagogy first and technology as mean to apply and enhance learning expriences.

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Slides of my talk at American University of Barbados on technology enhanced learning and how we could incorporate these technologies to enhanced learning experiences. Exploring latest technologies. I explain eLearning, Metaverse, AR,VR and MixReality. Shared how webconferecning started. Share digital resources. Explore microlearning and interactive video base content. Shred digital blooms taxonomy. Virtual Medical cases and sharing bedside teaching or beside teaching. Explore 3D models and how to use these model for immersive learning content design. How LMS are moving to metaverse and how learning who are digital natives likes immersive environment.

Technology Enhanced Learning Futuristic Approach.pptx

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Slides for my talk at IMI SIVU webinar, exploring Interactive Content Design for Blended Learning. Using different framework and Instructional design Model. I explained How to use different tools to design interactive video based content. Showed H5P and Flipgrid for interactive video design. Share with them some search site for OER content. Shared with participants YouTube and how to use video from YouTube and add interaction using H5P.

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Correct blend to Design online learning activities to Engage Students

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I was invited to give a talk on Open Book Exam (OBE), focus of my talk was on online assessment design tools, at symposium, which was a special virtual parallel event to Imamia Medics International’s (IMI) North American Conference. I shared with participants, H5P and demonstrated how to create one best answer (OBA) and show them other options they could use to design online assessment, showed them how to export the assessment items to LMS and how to create items on H5P platform. SIVU Virtual Symposia in Medical Education,

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Virtual Educational Resources for Medical Education

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5th International Conference on Health Professions Education (ICHPE), this year the theme of the conference is, “TechMed: Changing the landscape of Medical Education through technology Enhanced Learning”. I was invited to conduct pre-conference workshop for about 3hours on, "Interactive Content Design Using Elearning Tools for Developing Online Learning Experiences". I am sharing slides which I used during this workshop.

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Slides of my talk on Blockchain Technology and how it will impact Medical Education. I started with history , background , sharing fundamentals of Blockchain technology. What would be barriers in implementing electronic health record and blockchain technology. Shared case studies of Europe where they have implemented digital health and blockchain and save billions in cost and delivery better healthcare.

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The Department of Emergency Medicine at Carolinas Medical Center is passionate about education! Dr. Michael Gibbs is a world-renowned clinician and educator and has helped guide numerous young clinicians on the long path of Mastery of Emergency Medical Care. With his oversight, the EMGuideWire team aim to help augment our understanding of emergent imaging. You can follow along with the EMGuideWire.com team as they post these educational, self-guided radiology slides or you can also use this section to learn more in-depth about specific conditions and diseases. This Radiology Reading Room pertains to Sternal Fractures and Dislocations and is brought to you by Carrie Bissell, MD, Aaron Fox, MD, Kendrick Lim, MD, Stephanie Jensen, MD, and Olivia Rice, MD. It is has special guest editor: Sean Dieffenbaugher, MD and Laurence Kempton, MD

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Poster to be presented at Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024, Kaust, Saudi Arabia, https://cemse.kaust.edu.sa/stochnum/events/event/snsl-workshop-2024. In this work we have considered a setting that mimics the Henry problem \cite{Simpson2003,Simpson04_Henry}, modeling seawater intrusion into a 2D coastal aquifer. The pure water recharge from the ``land side'' resists the salinisation of the aquifer due to the influx of saline water through the ``sea side'', thereby achieving some equilibrium in the salt concentration. In our setting, following \cite{GRILLO2010}, we consider a fracture on the sea side that significantly increases the permeability of the porous medium. The flow and transport essentially depend on the geological parameters of the porous medium, including the fracture. We investigated the effects of various uncertainties on saltwater intrusion. We assumed uncertainties in the fracture width, the porosity of the bulk medium, its permeability and the pure water recharge from the land side. The porosity and permeability were modeled by random fields, the recharge by a random but periodic intensity and the thickness by a random variable. We calculated the mean and variance of the salt mass fraction, which is also uncertain. The main question we investigated in this work was how well the MLMC method can be used to compute statistics of different QoIs. We found that the answer depends on the choice of the QoI. First, not every QoI requires a hierarchy of meshes and MLMC. Second, MLMC requires stable convergence rates for $\EXP{g_{\ell} - g_{\ell-1}}$ and $\Var{g_{\ell} - g_{\ell-1}}$. These rates should be independent of $\ell$. If these convergence rates vary for different $\ell$, then it will be hard to estimate $L$ and $m_{\ell}$, and MLMC will either not work or be suboptimal. We were not able to get stable convergence rates for all levels $\ell=1,\ldots,5$ when the QoI was an integral as in \eqref{eq:integral_box}. We found that for $\ell=1,\ldots 4$ and $\ell=5$ the rate $\alpha$ was different. Further investigation is needed to find the reason for this. Another difficulty is the dependence on time, i.e. the number of levels $L$ and the number of sums $m_{\ell}$ depend on $t$. At the beginning the variability is small, then it increases, and after the process of mixing salt and fresh water has stopped, the variance decreases again. The number of random samples required at each level was estimated by calculating the decay of the variances and the computational cost for each level. These estimates depend on the minimisation function in the MLMC algorithm. To achieve the efficiency of the MLMC approach presented in this work, it is essential that the complexity of the numerical solution of each random realisation is proportional to the number of grid vertices on the grid levels.

$Poster_density_driven_with_fracture_MLMC.pdf$ $Poster_density_driven_with_fracture_MLMC.pdf$

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