Jennifer Meyer
Orcid: 0000-0002-5714-3198
According to our database1,
Jennifer Meyer
authored at least 12 papers
between 2023 and 2025.
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Bibliography
2025
(De)motivating Zero-Performing Students With Negative Feedback: Does the Salience of Performance Information Matter?
J. Comput. Assist. Learn., 2025
On the role of engagement in automated feedback effectiveness: Insights from keystroke logging.
Comput. Educ., 2025
Self-assessment accuracy in the age of artificial Intelligence: Differential effects of LLM-generated feedback.
Comput. Educ., 2025
Feedback from Generative AI: Correlates of Student Engagement in Text Revision from 655 Classes from Primary and Secondary School.
Proceedings of the 15th International Learning Analytics and Knowledge Conference, 2025
When LLMs Hallucinate: Examining the Effects of Erroneous Feedback in Math Tutoring Systems.
Proceedings of the 18th International Conference on Educational Data Mining, 2025
Learners' Awareness of AI as Originator of an Academic Text During Revision: An Exploratory Study.
Proceedings of the Artificial Intelligence in Education - 26th International Conference, 2025
2024
Understanding the effectiveness of automated feedback: Using process data to uncover the role of behavioral engagement.
Comput. Educ., 2024
Using LLMs to bring evidence-based feedback into the classroom: AI-generated feedback increases secondary students' text revision, motivation, and positive emotions.
Comput. Educ. Artif. Intell., 2024
Do teachers spot AI? Evaluating the detectability of AI-generated texts among student essays.
Comput. Educ. Artif. Intell., 2024
DARIUS: A Comprehensive Learner Corpus for Argument Mining in German-Language Essays.
Proceedings of the 2024 Joint International Conference on Computational Linguistics, 2024
Fairness in Automated Essay Scoring: A Comparative Analysis of Algorithms on German Learner Essays from Secondary Education.
Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications, 2024
2023
Automated feedback and writing: a multi-level meta-analysis of effects on students' performance.
Frontiers Artif. Intell., February, 2023