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Elisabeth Bauer; Michael Sailer; Frank Niklas; Samuel Greiff; Sven Sarbu-Rothsching; Jan M. Zottmann; Jan Kiesewetter; Matthias Stadler; Martin R. Fischer; Tina Seidel; Detlef Urhahne; Maximilian Sailer; Frank Fischer – Journal of Computer Assisted Learning, 2025
Background: Artificial intelligence, particularly natural language processing (NLP), enables automating the formative assessment of written task solutions to provide adaptive feedback automatically. A laboratory study found that, compared with static feedback (an expert solution), adaptive feedback automated through artificial neural networks…
Descriptors: Artificial Intelligence, Feedback (Response), Computer Simulation, Natural Language Processing
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Steffen Steinert; Lars Krupp; Karina E. Avila; Anke S. Janssen; Verena Ruf; David Dzsotjan; Christian De Schryver; Jakob Karolus; Stefan Ruzika; Karen Joisten; Paul Lukowicz; Jochen Kuhn; Norbert Wehn; Stefan Küchemann – Education and Information Technologies, 2025
As distance learning becomes increasingly important and artificial intelligence tools continue to advance, automated systems for individual learning have attracted significant attention. However, the scarcity of open-source online tools that are capable of providing personalized feedback has restricted the widespread implementation of…
Descriptors: Higher Education, Open Educational Resources, STEM Education, Feedback (Response)
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Landauer, Thomas K.; Lochbaum, Karen E.; Dooley, Scott – Theory Into Practice, 2009
Advances in assessment technologies are affording teachers and students new ways to efficiently assess and track achievement while also better promoting learning. WriteToLearn is one such technology, a Web-based tool that integrates practice and assessment in reading comprehension with writing about what is learned. Based on the principle of…
Descriptors: Feedback (Response), Reading Comprehension, Formative Evaluation, Educational Technology