Five Ways HPC Is Enabling Digital-Based Learning

New interactive and immersive learning methods are entering the education landscape. With high-performance computing at the forefront, education across Asia is experiencing a digital transformation.

Asian Scientist Magazine (Sep. 11, 2023) —The never-ending stream of online content combined with thousands of posts flooding social media have made us a more digitally engaged society today than ever before. However, our use of technology is not just limited to entertainment and socializing. This ability for digital content to engage the new tech-savvy generation is being leveraged to introduce a new form of education delivery: digital-based learning.

Enabled by the massive data processing powers and lightning-fast speeds of high-performance computing (HPC), digital-based learning employs the latest innovations in educational technology (EdTech) applications, online learning platforms, virtual reality and virtual teachers driven by artificial intelligence (AI). Education delivery is poised to enter a new frontier—one that promises to provide students with a highly personalized and engaging learning experience.



TikTok has everyone’s attention these days—from viral dance videos to digital stories. The platform’s predominantly Generation Z users effortlessly navigate the app to splice videos together, generate text, images, audio and music to bring their narratives to life.

This form of digital storytelling (DST) resonates with the new generation and may have applications in the classroom to foster learning. Being a familiar and engaging form of content delivery, DST makes learning a hands-on process by allowing students to create content.

Learning through storytelling is as old as humanity itself. DST is the latest in its evolution and has required HPC to power AI-driven video editors and cloud servers.

For example, back in 2016, the IBM Watson supercomputer helped editors cut together the first AI-made movie trailer for the sci-fi thriller Morgan, shortening a process that typically takes weeks into just 24 hours. Even though the average person doesn’t have direct access to a supercomputer, the latest smartphones’ processing speed of 11–16 teraFLOPS is sufficient to power most AI video editing mobile apps available today.

Leveraging the accessibility of such video editors, researchers from Syarif Hidayatullah State Islamic University Jakarta, Indonesia, used DST to help local seventh grade English as a Foreign Language (EFL) students learn spoken English. In their study, published in IEEE Xplore, the researchers compared the effectiveness of DST with conventional storytelling in teaching students how to describe people, such as sharing stories about themselves and their families.

Teachers used DST videos as pedagogical aids and students practiced by retelling the stories using a self-made video as a guide. The researchers found that giving these DST presentations significantly improved students’ speaking abilities compared to standard teaching methods. Furthermore, students brought their own unique touch when putting their videos together, demonstrating that DST is a holistic educational tool that fosters creativity and promotes both digital and linguistic literacy.



Amidst the background beeping of medical equipment and the buzzing hospital intercom system, a surgeon masterfully inserts a thin-tubed laparoscopic camera through a small incision in the abdomen—a procedure that requires well-honed psychomotor skills. With a virtual reality laparoscopic simulator (VRLS), this operating room (OR) experience can be recreated for surgical trainees.

The idea of virtual reality (VR) surgical simulation was first proposed in the 1990s. Since then, rapid advances in computing power have made true-to-life VR and its medical applications possible. Cutting-edge graphic processing units consistently deliver high frame rates—of at least 90 frames per second—allowing for real-time rendering of realistic VR experiences.

Currently, laparoscopic training requires extensive oversight, first by observing and assisting experienced surgeons then only performing procedures under supervision. However, trainees ideally need more time to practice and become familiar with the procedures. Repeated practice on a VRLS is therefore both an efficient and cost-effective strategy to help trainees perfect their skills while avoiding patient complications.

To help their postgraduate students overcome these steep learning curves, the Department of Surgery at the National University of Malaysia developed a VRLS training program. Working on the LAP MentorTM VRLS system, students practiced basic laparoscopic skills and processes before performing the entire procedure.

They received tactile feedback while handling surgical instruments and were visually presented with a stressful OR environment simulation. After each practice session, the VRLS generated performance reports to allow students to track their progress. Over time, students became faster and more accurate on the VRLS.

Importantly, Dr. Ian Chik, a surgeon at the university, observed that students showed improvement when handling surgical tools in a real OR. Despite their high cost, Chik believes the simulators are a worthwhile investment. “The improvement on patient care far outweighs the cost,” explained Chik in an interview with Supercomputing Asia.



Viewing health as multifaceted is key to delivering high-quality and patient centered care. Beyond developing core medical competencies, VR simulations can help healthcare workers build empathy and compassion for patients. Caring for patients with complex behavioral issues while ensuring personal safety is a difficult situation, especially for a medical trainee with little experience in a clinical setting. Interactive VR offers a potential solution by providing experiential learning in a safe, repeatable and controlled environment. This allows students to develop effective management and interpersonal skills that are crucial for preventing physical and psychological injuries to both healthcare workers and patients.

The National University of Singapore Yong Loo Lin School of Medicine recently announced their newly developed Virtual Reality in Agitation Management (VRAM) program that aims to teach medical and nursing students how to manage agitated patients with empathy.

In the VR simulation, students navigate a difficult real-life scenario in a hospital ward, where a patient experiencing drug-induced psychosis is demanding to be discharged against medical advice. Students are presented with various options to deescalate the situation.

For example, students can choose what to say to patients, when to administer medication, and when to use physical restraints; each of their choices would lead to different outcomes in VR. Importantly, students realistically experience how their decisions can positively or negatively impact the patient, prompting them to empathize with what a real-life patient could be feeling.

Following initial trials, VRAM received positive reception from students, who reported increased confidence in managing and communicating with agitated patients, as well as greater empathy towards them. The school has since rolled out VRAM as part of a compulsory class module for fourth-year medical and second-year nursing students.



School closures during the COVID-19 pandemic disrupted education for students worldwide. By March 2020, 1.5 billion students in over 165 countries had been affected. Using online learning platforms, schools attempted to maintain learning continuity but the abrupt transition from in-person classes to a virtual learning environment (VLE) led to increased failure rates for some students.

For this group of underperforming students, they may have been experiencing inadequate engagement with VLEs and a lack of interaction with peers and teachers—both of which are important for effective learning. To help more students succeed, it is crucial to identify these at-risk students early in the course so that teachers can intervene.

Taiwanese researchers from Asia University and the National Kaohsiung University of Science and Technology have developed an AI framework that can predict a student’s likelihood of passing or failing in the early weeks of an online course with 90 percent accuracy.

To build their explainable student performance prediction (ESPP) model, the researchers collected real- world clickstream data of students participating in a 16-week online course at Gadjah Mada University in Indonesia. Harnessing the power of HPC, the researchers trained their AI model on more than 202,000 logs of 977 students.

Published in Applied Sciences, the results showed that an early prediction deep learning (DL) model outperformed existing models in accurately capturing at-risk students at the sixweek mark. Importantly, the AI could explain why certain students are at risk of failing.  Many machine learning models have a “blackbox” problem, where we see both the inputs and outputs but know little about the decision making process of the model.

In contrast, the ESPP model provides visual representation to highlight the reasons why a student was categorized as at-risk—for example, by not participating in online forum discussions or submitting their assignments. In this way, not only does the newly developed AI framework identify underperforming students, it also provides guidance on how they can improve.



Great teachers make a big difference. They bring a personalized touch to the classroom by paying attention to students’ needs. However, it is not possible for teachers to create an individualized learning path for every student.

With the availability of big data and the astounding processing speeds of HPC, online learning platforms have transformed into AI-driven systems that can serve as virtual assistants to human teachers. As a student learns on these platforms, AI develops an understanding of the student’s strengths and weaknesses, adjusting its content accordingly to provide customized instruction.

By taking over repetitive tasks like reinforcing concepts with practice questions and grading, these AI-enabled systems give teachers more time to address other aspects of students’ overall development. For instance, teachers can focus on fostering curiosity and creativity and help students develop effective communication among other soft skills.

China-based EdTech company Squirrel Ai Learning has built an adaptive learning system that provides personalized K-12 after-school tutoring. A recipient of the UNESCO AI innovation award, Squirrel Ai Learning possesses a unique technology— built with over 10 billion pieces of learning behavior data—that finely breaks down a school subject’s concepts (or knowledge points).

“By splitting 500 knowledge points to 30,000 ultra-fine knowledge points, Squirrel Ai Learning can precisely pinpoint and address weak areas in students’ understanding of study material,” said Tom Mitchell, Chief AI Officer of Squirrel Ai Learning, in an interview with Supercomputing Asia.

Squirrel Ai Learning has over 10 million registered students spread across its 3,000 learning centers in 200 cities in China. The access to massive amounts of student data allows Squirrel Ai Learning to continuously improve its AI models. Moving forward, the Squirrel Ai Learning team hopes to create a more interactive interface by applying large language models—the same type of algorithm that underlies ChatGPT. This would create a more open-ended learning environment that encourages students to be more imaginative.

This article was first published in the print version of Supercomputing Asia, July 2023.
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Copyright: Asian Scientist Magazine.

Disclaimer: This article does not necessarily reflect the views of AsianScientist or its staff.


Pei Ling received her PhD in Biomedical Sciences from the Icahn School of Medicine at Mount Sinai, USA and her BSc in Biochemistry & Molecular Biology from Brown University, USA. She is currently a research fellow at the Institute of Molecular and Cell Biology and a freelance science writer.

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