ScholarWorks (California State University)
Not a member yet
87523 research outputs found
Sort by
We Are Scientists: Children's Coloring and Activity Book
No full textMy project aimed to enhance children's learning by creating meaningful and relevant connections to the world of science around them. Through engaging visuals, creative content, and interactive learning, the project supported the development of early STEM curiosity with the potential to lead to future educational and career pathways in science
Bicultural Identity and Bilingualism: Predictors of Perspective-Taking
No full textBicultural individuals and bilinguals share similar characteristics (e.g., enhanced cognitive abilities and broader perspectives), yet there is little research on these groups and their ability to engage in perspective-taking. I described the concept of perspective-taking and its fundamental components to allow readers to understand how perspective-taking might be related to bicultural identity and bilingualism. In this study, I examined bicultural identity, bilingualism, and perspective-taking among students from a public university on the West Coast (N = 771). I investigated whether a stronger bicultural identity or greater bilingual fluency would predict better perspective-taking abilities. The first regression analysis demonstrated a statistically significant small positive relationship between bicultural identity strength and perspective-taking scores, suggesting that a stronger bicultural identity predicts better perspective-taking. The second regression analysis involving bilingualism and perspective-taking revealed a very small non-significant relationship, indicating that bilingual fluency does not significantly contribute to perspective-taking abilities. To compare bicultural identity and bilingualism as predictors of perspective-taking, I conducted a multiple regression analysis. Bicultural identity was found to significantly predict greater perspective-taking, whereas bilingual fluency did not. These findings acknowledge the importance of bicultural identity and its association with perspective-taking skills while suggesting that bilingualism alone may not enhance these abilities significantly. Further research can use this study as a foundation to continue exploring the relationship between biculturalism, bilingualism, and perspective-taking
The Impact of Self-Concept Clarity on Consumer Choice
No full textThis paper examines the effect of higher versus lower self-concept clarity of individuals on consumer choice. Participants from CSUF first participated in an exploratory cross-sectional study by completing multiple scales, including self-concept clarity. Next, an experiment investigated self-concept clarity and identity-linked brands based on the insights from the first survey. The study manipulated self-concept clarity through a randomized essay prompt, followed by choice measures to examine the effect of self-concept clarity on the choice of self-congruent products versus non-self-congruent products. Results showed that self-concept clarity is a predictor of self-congruent product choice. Specifically, consumers are more likely to buy products linked to their identity when self-concept clarity is high. This research offers valuable insights into how self-concept clarity shapes consumer behavior, deepening marketers' understanding of shifting consumer preferences
Quantification of amino acids in Aquilegia nectar by high performance liquid chromatography – tandem mass spectrometry
No full textFlowering plants have evolved nectar production as an adaptation to attract animal pollinators, forming ecologically significant relationships essential for plant reproduction. Understanding metabolic composition of nectar can reveal how genetic and environmental factors influence nectar traits and shape plant-pollinator interactions. Among these metabolites, amino acids serve as an important nutritional resource for pollinators and may influence pollinator preference and behavior. High-performance liquid chromatography - tandem mass spectrometry (HPLC-MS/MS) is a suitable method for quantification of amino acids because it allows for separation while offering high sensitivity. In this thesis, an HPLC-MS/MS method is developed to quantify 23 amino acids in nectar from two Aquilegia species (A. formosa and A. pubescens) collected from three locations (Lundy Canyon, Mosquito Flat, and Morgan Pass). Significant differences in individual amino acid concentrations were found between nectar from Lundy Canyon and Mosquito Flat and Morgan Pass and Mosquito Flat. Total amino acid composition also varied by location with Morgan Pass having the higher amino acid abundance (5,488 µM) and Mosquito Flat having the lowest abundance (861.3 µM). Limits of detection ranged from 0.004907-0.2661 µM and limits of quantification ranged from 0.01402-0.7605 µM, making the method suitably sensitive for analysis of low concentrations of amino acids in low-volume nectar samples
Cultivating belonging: A constructivist antibias teacher training for transforming early childhood education for Black boys
No full textCultivating Belonging: A Constructivist Antibias Teacher Training for Transforming Early Childhood Education for Black Boys explores how early childhood educators engage in reflective practices to disrupt implicit biases and foster equitable learning environments for Black boys. Black boys experience the highest suspension and expulsion rates of any group in preschool settings, a systemic issue rooted in implicit bias and deficit-based narratives. This project examined how educators can cultivate belonging by developing cultural humility, strengthening teacher-child relationships, and critically reflecting on their perceptions and practices. Using a constructivist qualitative approach, this project implemented an antibias teacher training designed to support early childhood educators in identifying and challenging implicit biases in PreK–third grade classrooms.Sources of Data Participants engaged in guided reflections, activities, and collaborative discussions to examine their assumptions and refine their teaching approaches. Data was collected through participant reflections and discussion transcripts and analyzed thematically. Four key themes emerged: Relationship Building, highlighting the role of positive teacher-child interactions in disrupting bias; Realizing the Importance of Reflective Practices, emphasizing the need for ongoing self-examination in teaching; Noticing Diverse Ways of Being and Learning, illustrating how teachers expanded their understanding of student behavior and learning styles; and Biases Around Black Boys in PreK–third Grade Classrooms, revealing how deficit perspectives persist and how educators worked to challenge them.This project demonstrates the power of reflective, constructivist approaches in professional learning to shift teacher's perceptions and practices. It underscores the need for ongoing, intentional teacher development that centers equity and belonging for Black boys. Future research could explore how sustained engagement in antibias training influences long-term changes in classroom interactions, student engagement, and teacher decision-making
Behavioral interventions for skill acquisition following acquired brain injury: A systematic review of single-case design research
No full textThis systematic review evaluated the behavior-analytic single-case experimental design (SCED) literature on skill acquisition interventions for individuals with non-progressive acquired brain injury (ABI). A total of 55 studies, representing 85 cases and 120 participants, met inclusion criteria. A narrative synthesis summarized intervention, design, and participant characteristics, and a preliminary quantitative analysis using nonoverlap of all pairs (NAP) estimated intervention effects. Most interventions involved reinforcement and antecedent strategies and were implemented in home or rehabilitation settings. Cases with lower NAP scores often involved older participants with non-traumatic injuries, longer post-injury durations, and interventions lacking reinforcement. Although this review identified a sufficient number of studies to support a meta-analysis and several interventions demonstrated promising effects, the extent to which these effects would persist following a rigorous quality appraisal remains uncertain due to significant underreporting of critical methodological variables. These findings highlight the capacity and current limitations of behavior-analytic interventions for skill acquisition following ABI. Future research should conduct quality appraisals to assess which treatments qualify as evidence-based practice and examine reinforcer presence, injury characteristics, and setting as potential moderators of treatment effects. Additionally, researchers are encouraged to adopt transparent reporting practices and advocate for interdisciplinary collaboration to advance the visibility, accessibility, and empirical foundation of behavior-analytic rehabilitation for individuals with ABI
Peer Observation: A Practical Toolkit for Strengthening Teacher Practice and Improving Student Learning
No full textThis project addresses the disconnect between the proven benefits of teacher collaboration and the limited opportunities for authentic, teacher-driven collaboration in many K–5 schools. While research demonstrates that collaboration improves teacher satisfaction, retention, and student achievement, professional learning is often structured in ways that feel evaluative, compliance-driven, or disconnected from classroom realities. Peer observation offers a promising alternative by creating a non-evaluative, teacher-centered model of professional learning that emphasizes feedback, reflection, and shared responsibility for student growth. The purpose of this project was to design a practical, research-informed digital toolkit to support teachers, coaches, and administrators in implementing peer observation. Grounded in adult learning theory and research on teacher efficacy, the toolkit includes multimedia text sets, annotated bibliographies, introductory slide decks, editable observation forms and protocols, and coaching tools such as a Matchmaker Form and Collaborative Observation form to support new teachers. Two complementary pathways; Learning Visits and Feedback-Focused Observations, were developed to accommodate different levels of teacher readiness, foster trust, and honor teacher autonomy. The methodology combined a review of the literature with insights from professional coaching practice, resulting in tools that are concise, adaptable, and accessible within the constraints of elementary school schedules. By making observation teacher-centered, practical, and collaborative, this project reframes professional learning as something educators do with one another rather than something done to them. The ultimate goal is to strengthen teacher practice, increase professional fulfillment, and positively impact student learning outcomes
AI-driven deepfake detection using deep learning: A CNN and RNN hybrid model
No full textA new era of hyper-realistic synthetic media has been brought about by the quick development of deep-fake technology, which is powered by artificial intelligence and deep learning. This presents serious problems for online information reliability, personal privacy, and digital security. These media manipulations use speech, motion, and facial emotions to create incredibly realistic photos, movies, and audio that can be used for fraud, disinformation, and public perception modification. Using specialized deep learning architectures, this project seeks to address these issues by creating a comprehensive, multi- modal deepfake detection framework. This includes a hybrid CNN and Long Short-Term Memory (LSTM) model for capturing temporal inconsistencies in video frames, Convolutional Neural Networks (CNN) for detecting spatial artifacts in images, and an Artificial Neural Network (ANN) trained on spectral features and Mel Frequency Cepstral Coefficients (MFCCs) is used to differentiate between synthetic and real audio.The models were trained on curated datasets from credible open-source repositories, each with thousands of annotated real and fake samples to ensure robust generalization. These trained models were effortlessly incorporated into an interactive online application built with Streamlit, allowing users to input media files and instantly receive modality-specific authenticity estimates. The algorithm consistently achieved high accuracy, precision, and recall across all media types. This project combines advanced deep learning with a simple interface to offer a scalable deepfake detection solution
Creating signatures for geo-temporal patterns
No full textFisheries are tasked with the critical responsibility of enforcing management plans and regulations to ensure that fish populations remain sustainable and that marine ecosystems stay healthy. These regulations are designed to balance human needs with the long-term viability of aquatic life. However, despite these efforts, illegal, unreported, and unregulated (IUU) fishing continues to pose a serious threat to sustainable fisheries management. Such activities not only endanger fish stocks but also undermine the efforts of law-abiding fishers and disrupt the ecological balance of marine environments. To address these challenges, this project aims to develop advanced visual analytics tools and data-driven workflows that can assist authorities and stakeholders in detecting, understanding, and mitigating illegal fishing activities. By leveraging data from vessel tracking systems, onboard monitoring devices, and other sources, these tools will help identify the types of fish caught by specific vessels, flag patterns that may suggest illegal behavior, and generate intuitive visualizations that bring hidden activities to light.Ultimately, the project aims to identify the ships that are responsible for illegal fishing and thereby also provide a workflow that will help with catching as well as investigating other suspicious ships that might be involved in illegal activity
ChicMate – An application integrating multi-modal framework for enhanced fashion recommendation
No full textIn today's fast-moving world, many people struggle with selecting outfits that reflect their personal style and current trends. Existing fashion recommendation apps typically offergeneric suggestions that don't fully account for individual tastes or real-life contexts. This project addresses these challenges by providing an interactive, AI-driven mobile application that transforms outfit selection into an engaging, conversational experience.By integrating multi-modal inputs—including text queries, image uploads, and location data—"ChicMate" leverages state-of-the-art deep learning techniques for visual featureextraction and natural language processing. This integration allows the app to generate personalized outfit recommendations that are both context-aware and visually supported,displaying not only descriptive feedback but also images of the suggested items.Overall, ChicMate offers a practical solution to everyday styling dilemmas, making wardrobe management more personal and enjoyable