Knut Neumann

Ehsan Latif, Gyeong-Geon Lee, Knut Neumann et al.

The advancement of natural language processing has paved the way for automated scoring systems in various languages, such as German (e.g., German BERT [G-BERT]). Automatically scoring written responses to science questions in German is a complex task and challenging for standard G-BERT as they lack contextual knowledge in the science domain and may be unaligned with student writing styles. This paper presents a contextualized German Science Education BERT (G-SciEdBERT), an innovative large language model tailored for scoring German-written responses to science tasks and beyond. Using G-BERT, we pre-trained G-SciEdBERT on a corpus of 30K German written science responses with 3M tokens on the Programme for International Student Assessment (PISA) 2018. We fine-tuned G-SciEdBERT on an additional 20K student-written responses with 2M tokens and examined the scoring accuracy. We then compared its scoring performance with G-BERT. Our findings revealed a substantial improvement in scoring accuracy with G-SciEdBERT, demonstrating a 10.2% increase of quadratic weighted Kappa compared to G-BERT (mean difference = 0.1026, SD = 0.069). These insights underline the significance of specialized language models like G-SciEdBERT, which is trained to enhance the accuracy of contextualized automated scoring, offering a substantial contribution to the field of AI in education.

Jennifer Meyer, Olaf Köller, Thorben Jansen et al.

With digital learning environments becoming more prevalent, the ease with which generative AI enables the scalable production of real-time, automated feedback holds the potential to reshape learning and teaching experiences. This meeting report synthesizes the interdisciplinary perspectives of 50 scholars from educational psychology, computer science, science education, and the learning sciences on the use of generative AI for feedback and its promises and risks in educational practice. We highlight points of convergence in the scholarship, identify areas of debate and unresolved challenges, and outline open questions and future directions for research and educational practice that emerged from structured small-group activities designed to bridge disciplinary barriers.

Laís P. V. Vossen, Isabela Gasparini, Elaine H. T. Oliveira et al.

Due to their versatility, concept maps are used in various educational settings and serve as tools that enable educators to comprehend students' knowledge construction. An essential component for analyzing a concept map is its structure, which can be categorized into three distinct types: spoke, network, and chain. Understanding the predominant structure in a map offers insights into the student's depth of comprehension of the subject. Therefore, this study examined 317 distinct concept map structures, classifying them into one of the three types, and used statistical and descriptive information from the maps to train multiclass classification models. As a result, we achieved an 86\% accuracy in classification using a Decision Tree. This promising outcome can be employed in concept map assessment systems to provide real-time feedback to the student.