NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 4 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Abdelmadjid Benmachiche; Abdelhadi Sahia; Soundes Oumaima Boufaida; Khadija Rais; Makhlouf Derdour; Faiz Maazouzi – Education and Information Technologies, 2025
In the context of massive open online courses (MOOCs), searching and retrieving information can be challenging because there is a huge amount of unstructured content, which creates a problem and makes it difficult for users to quickly find relevant lessons or resources. As a result, learners and teachers face significant barriers to accessing the…
Descriptors: MOOCs, Natural Language Processing, Artificial Intelligence, Search Engines
Peer reviewed Peer reviewed
Direct linkDirect link
Emiko Tsutsumi; Yiming Guo; Ryo Kinoshita; Maomi Ueno – IEEE Transactions on Learning Technologies, 2024
Knowledge tracing (KT), the task of tracking the knowledge state of a student over time, has been assessed actively by artificial intelligence researchers. Recent reports have described that Deep-IRT, which combines item response theory (IRT) with a deep learning method, provides superior performance. It can express the abilities of each student…
Descriptors: Item Response Theory, Academic Ability, Intelligent Tutoring Systems, Artificial Intelligence
Peer reviewed Peer reviewed
Direct linkDirect link
Sebastian Gombert; Aron Fink; Tornike Giorgashvili; Ioana Jivet; Daniele Di Mitri; Jane Yau; Andreas Frey; Hendrik Drachsler – International Journal of Artificial Intelligence in Education, 2024
Various studies empirically proved the value of highly informative feedback for enhancing learner success. However, digital educational technology has yet to catch up as automated feedback is often provided shallowly. This paper presents a case study on implementing a pipeline that provides German-speaking university students enrolled in an…
Descriptors: Automation, Student Evaluation, Essays, Feedback (Response)
Peer reviewed Peer reviewed
Direct linkDirect link
Adnane Ez-zizi; Dagmar Divjak; Petar Milin – Language Learning, 2024
Since its first adoption as a computational model for language learning, evidence has accumulated that Rescorla-Wagner error-correction learning (Rescorla & Wagner, 1972) captures several aspects of language processing. Whereas previous studies have provided general support for the Rescorla-Wagner rule by using it to explain the behavior of…
Descriptors: Error Correction, Second Language Learning, Second Language Instruction, Gender Differences