LAB LOGA page on LabLog, a review site (not the lab's official page)
AI Education Lab
Machine-translated · View original
A lab in educational technology that supports learning with educational big data and AI.
AboutAbout the lab
Supporting learning with educational data and AI.
The AI Education Lab studies support for teaching and learning using ICT and AI.
The lab analyzes educational big data, which are records of learning, and pursues research that leads to generating and recommending learning materials tailored to each learner.
Emphasizing practice in real educational settings, the lab aims to scientifically analyze and solve problems in education and learning.
On the lab's website, it is introduced under the name "Chengjiu YIN Lab."
Key terms
- Educational big data
- Large amounts of data collected in educational settings, such as eBook learning logs
- Pattern mining
- An analysis method that finds frequently occurring sequences and patterns in large amounts of data
- Clustering
- An analysis method that divides data with similar characteristics into several groups
ResearchResearch themes
The lab studies support for education and learning tailored to individual learners, using ICT and generative AI. It works on both analyzing educational big data and developing systems that can be used in real educational settings.
*Illustrative imageGenerating personalized learning materials
Based on learner characteristics and learning history, the lab is building a mechanism that uses generative AI technology to dynamically generate and recommend the best materials for each learner.
*Illustrative imageMaterial recommendation based on learner characteristics
Using learners' behavior patterns and VARK learning styles, the lab developed a system that recommends optimal materials according to timing and individual needs.
*Illustrative imagePattern mining for improving materials
Applying pattern mining to viewing logs of self-made materials, the lab extracted improvement points that go into the content of the materials, and proposed and tested improvement methods.
*Illustrative imageCluster analysis of learning logs
The lab clusters learning behavior based on eBook learning logs and analyzes the characteristics of each learning style. It has found a tendency for students who reread more often to study more efficiently.
MembersFaculty
- ProfessorChengjiu Yin
VoicesStudent voices
Ratings and reviews reflect the experiences of individual current or former students, not the lab's official views. Whether a lab has reviews, or how many, says nothing about the lab itself.
Students who take part in an interview may receive a thank-you gift worth ¥1,000, regardless of what they say.
Student ratings (0 students)
--
--
--
--
--
--
Student voices answer questions like these
- What's great about this lab?
- What did you learn after joining?
- What's the vibe, and what is the professor like?
- Who is this lab for?
- Where do graduates go?
Your experience can help younger students choose their lab. Your name won't be shown.
Write a lab reviewAnonymous · Your draft is saved if you stopRelatedLabs similar to AI Education Lab
Intelligent Software LabFaculty of Engineering, Department of Electrical Engineering and Computer ScienceSoftware engineering (SE) is a research field aimed at impro…
Human Data Interaction LabFaculty of Engineering, Department of Electrical Engineering and Computer ScienceHuman–data interaction: design, methods, and technologies fo…
Image and Media Understanding LabFaculty of Engineering, Department of Electrical Engineering and Computer ScienceCyber-physical systems that create social value by integrati…
Cyber-Physical Computing LabFaculty of Engineering, Department of Electrical Engineering and Computer ScienceWe aim to create new computer system architectures based on …The information on this page is compiled by the LabLog team from the lab's public information and interviews. For corrections or removal requests, please contact LabLog, not the lab.
Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Learning to Code” Preply.com Images / CC BY 2.0 (cropped)
- Generating personalized learning materials: “Learning-Points” Pooh.Penchan / CC BY-SA 4.0 (cropped)
- Material recommendation based on learner characteristics: “Bodo language textbooks” SlowPhoton / CC BY-SA 4.0 (cropped)
- Pattern mining for improving materials: “CRISP DM Data mining management process” Alexander Schröder / CC BY-SA 4.0 (cropped)
- Cluster analysis of learning logs: “Italy, E-Learning, Covid19” Nessma Elaassar / CC BY-SA 4.0 (cropped)
- Related lab (知能ソフトウェア研究室): “Software Testing Life Cycle” Perfect Happiness / CC BY-SA 4.0 (cropped)
- Related lab (ヒューマン・
データ・インタラクション研究室): “APRENDIZAJE SIGNIFICATIVO” SAMMYTQM / CC BY-SA 4.0 (cropped) - Related lab (イメージ・メディア理解研究室): “USB webcam for PC” WrS.tm.pl / CC0 (cropped)
- Related lab (サイバーフィジカル・コンピューティング研究室): “Josephson Traveling Wave Parametric Amplifier (J-TWPA) 002” OJB Quantum / CC BY 4.0 (cropped)
About LabLog
LabLog is a lab review app for students only. Student members at each university interview students in labs and publish what they say. Student voices can only be read by students who sign up with a university email address. Operated by bestiee Inc.
Learn more about LabLog ↗Search engines and AI may reference the basic lab info and rating summaries on this page and the description of LabLog. Student voices are for student members only. Automated collection by programs (scraping, or input to or training of AI) is prohibited under Article 8 of the Terms of Service. If you need data for research or analysis, please contact us.