Person: Trina D. Spencer

Professional Titles
Professor/Senior Scientist/Director
ORCID
https://orcid.org/0000-0002-3531-8276

Dr. Spencer is the director of Juniper Gardens Children's Project and professor at the University of Kansas. She is affiliated with the Applied Behavioral Science and Speech-Language-Hearing Sciences departments.

Full Name
Trina D Spencer
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Contributions

Dataset: AAC Narrative Intervention DATA (Uploaded on )

These are the data for story grammar scores and number of different symbols used that resulted from the AAC narrative intervention. They are organized according to multiple baseline across participants design and pseudonyms are used instead of children's names.

Project: AAC Narrative Intervention for Children with Autism (Uploaded on )

In research, augmentative and alternative communication (AAC) interventions have primarily focused on teaching children to make requests (Logan et al., 2017); however, AAC intervention should not stop there.

Dataset: TwoRaters (Uploaded on )

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.

Dataset: Training Data (Uploaded on )

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: Test Data (Uploaded on )

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.

Project: Automated Narrative Scoring Using Large Language Models (Uploaded on )

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.

Project: Play-based Narrative Elicitation (Uploaded on )

The aim of this research is to create developmentally appropriate, play-based storytelling elicitation procedures to collect language samples of young children aged 18-48 months, tools for evaluating the magnitude and quality of narrative language produced in play-based storytelling sessions, and

Dataset: Discourse Type and Elicitation Task Data from the ALPS Project (Uploaded on )

These are the data for 1037 K-3 students who contributed oral academic language samples. https://doi.org/10.1044/2023_AJSLP-22-00365

Language sampling is a critical component of language assessments. However, there are many ways to elicit language samples that likely impact the results. The purpose of this study was to examine how different discourse types and elicitation tasks affect various language sampling outcomes.

This file contains data according to two research designs: small scale RCT and a Repeated Acquisition Design (single case research). The vocabulary at the pre- and post- collections were untaught words whereas the weekly probes for the RAD were the taught words of that week.