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Project
Description: This project focused on the idea of data-based decision-making (DBDM) at the classroom level, focusing on how teachers use assessment data to adapt their instruction to students’ individual needs. The present project investigated this idea directly and evaluated the effectiveness of teacher support on the different steps of this process.
Project
Description: The analysis of narratives often accompanies comprehensive language assessments of students. While analyzing narratives can be time-consuming and labor-intensive, recent advances in large language models (LLMs) indicate that it may be possible to automate this process.
Dataset
Part of Project: Data-based decision-making in schools: Examining the process and effects of teacher support
Description: This dataset contains the relevant data related to the project "Data-based decision-making in schools: Examining the process and effects of teacher support", that were used in the associated manuscript available at https://doi.org/10.1037/edu0000530. Data and associated codebook/materials are all available at the external dataset link.
Dataset
Part of Project: Automated Narrative Scoring Using Large Language Models
Description: Narrative language samples elicited using the ALPS Oral Narrative Retell and Oral Narrative Generation tasks from diverse K-3 students. The test data set was drawn randomly from the larger corpus of narrative language samples.
Dataset
Part of Project: Automated Narrative Scoring Using Large Language Models
Description: Narrative language samples elicited using the ALPS Oral Narrative Retell and Oral Narrative Generation tasks from diverse K-3 students. The training data set was drawn randomly from the larger corpus of narrative language samples.
Dataset
Part of Project: Automated Narrative Scoring Using Large Language Models
Description: Narrative language samples elicited using the ALPS Oral Narrative Retell and Oral Narrative Generation tasks from diverse K-3 students. The tworaters data set was drawn randomly from the larger corpus of narrative language samples. These samples were scored by two raters for reliability purposes.
