e-learning-ukr

For AI to support students, education institutions need to ensure employees have the skills and training to leverage new technologies.

Systematic reviews and meta-analyses are crucial for synthesizing evidence but are time-consuming and labor-intensive, especially during data extraction. To address this challenge, we developed MetaMate, an open-access web-based tool leveraging large language models (LLMs) for automated data extraction in educational systematic reviews and meta-analyses. MetaMate utilizes a hierarchical schema and divide-and-conquer approach in its extraction chain, and a from-global-to-local lens and example retriever in its verification chain. We evaluated MetaMate’s performance on 32 empirical studies, extracting 20 data elements related to participants and interventions. MetaMate achieved high precision, recall, and F1 scores, with performance comparable to human coders when benchmarked against an expert-defined gold standard. Notably, MetaMate demonstrated advanced mathematical reasoning and semantic comprehension, surpassing keyword-based approaches and avoiding common human errors. As the first LLM-powered data extraction tool designed specifically for educational research, MetaMate has the potential to significantly streamline the systematic review process and reduce time and effort for researchers. MetaMate is available at https://metamate.streamlit.app.

A community to ensure equitable access and benefits from AI in Education

Людям потрібно перестати конкурувати один з одним. Ми тепер на порозі виживання як вид

With the rise of ChatGPT and other generative AI tools like Gemini by Google, Ernie Bot by Baidu, and even Copilot in Microsoft365, what does this mean for us as educators?

Successful data science education requires a holistic approach that involves modifications in how we teach data sciences and reinforcement of lessons throughout the course, writes Ellen Bell

Making data open access has become a cornerstone of transparent and collaborative research practices. Here, Jon Petters provides a brief guide on how to do it, emphasising accessibility, reproducibility and interoperability

In the new report, foundation models dominate, benchmarks fall, prices skyrocket, and on the global stage, the U.S. overshadows.

Despite viral claims, Americans buy over a billion books a year

There is a myth. Cue the string section. That there was once a place for all to gather, share, be festive, develop new connections, every course a hashtag, topple a few governments, people power. T…

Wondering if a pro account is worth the $20? Let's explore the similarities and differences between Gemini Advanced and Perplexity Pro.

Do you teach one of those courses that promotes lots of student anxiety? Nowadays that seems to apply to all sorts of courses.

Systematic reviews and meta-analyses are crucial for synthesizing evidence but are time-consuming and labor-intensive, especially during data extraction. To address this challenge, we developed MetaMate, an open-access web-based tool leveraging large language models (LLMs) for automated data extraction in educational systematic reviews and meta-analyses. MetaMate utilizes a hierarchical schema and divide-and-conquer approach in its extraction chain, and a from-global-to-local lens and example retriever in its verification chain. We evaluated MetaMate’s performance on 32 empirical studies, extracting 20 data elements related to participants and interventions. MetaMate achieved high precision, recall, and F1 scores, with performance comparable to human coders when benchmarked against an expert-defined gold standard. Notably, MetaMate demonstrated advanced mathematical reasoning and semantic comprehension, surpassing keyword-based approaches and avoiding common human errors. As the first LLM-powered data extraction tool designed specifically for educational research, MetaMate has the potential to significantly streamline the systematic review process and reduce time and effort for researchers. MetaMate is available at https://metamate.streamlit.app.

An introduction to individual research metrics and how to discover how your own profile and data are represented online, in part one of Ioannis Glinavos’ advice on managing metrics

Leading education and policy organizations in the TeachAI initiative launched Foundational Policy Ideas for AI in Education, a resource to help leaders understand and consider policies that responsibly address AI in teaching and learning.

У відео використано сервіс https://gemini.google.com/app/

This Education Spotlight, the second in a series of three, presents key lessons and inspiring examples of policy and practice to inform how traditional higher education programmes, like bachelor’s degrees, can effectively cultivat

Happy One-year Anniversary, L&D In Action! That's right: We've been delivering L&D insights from the greatest minds in learning for 365 days now--366 thanks to the leap year. In this special-edition episode, host Tyler takes the reins and delivers a brief reflection on the power of... reflection! It's a topic that many guests have covered, the impact of reflection on the learning process. Science supports it, and we've all seen it in action, even if we have to look as far back as our school years. So, use the time saved from listening to this concise episode to reflect on yourself and your own experiences. Thanks for listening and supporting the show!