Texas Digital Library
University of Houston-Clear Lake: UHCL Institutional RepositoryNot a member yet
4292 research outputs found
Sort by
Teacher’s Perception of Humor as a Facilitator of Student Engagement
No full textClassroom engagement is an excellent indicator of students' learning, grades, achievement, test scores, retention, and graduation. However, classroom engagement is heavily dependent on teacher-student interactions. Teachers' role in engaging students in the lesson learned is critical. Using a free and convenient tool, such as teachers' humor, can ensure student engagement in the learning process (Nienaber et al., 2019). The purpose of this study is to determine how schoolteachers perceive humor as a facilitator for student engagement at the classroom level. This study used a sequential mixed methods design to gain insights regarding the teachers' perceptions of the role of humor as a catalyst for student engagement. Survey and interview data were collected from a purposeful sample of K-12 grade teachers in seven private schools in Greater Houston, Texas. The study used an electronic questionnaire from 102 teachers working in grades K-12 and 14 follow-up semi-structured interviews to gather data on teachers' perceptions of positive humor as a facilitator for student engagement. The quantitative data were analyzed using descriptive statistics, Pearson's product-moment correlations, one-way ANOVA test, and independent samples t-test. The qualitative data were analyzed using thematic inductive coding. Results from the quantitative analysis showed no significance. In contrast, the results of the qualitative analysis strongly supported the use of positive humor as a powerful tool for student engagement if utilized appropriately
Identifying STEM Awareness of Secondary Art Educators: A Statewide Assessment
Get PDFThe advent of STEAM programs could be a product of budget cuts and lower student enrollment within the arts. However, art educators, typically, possess minimal training in STEM related fields. The lack of training is especially true for secondary art educators due to the specificity of their degrees required to teach in their respective institutions. The purpose of this mixed method study aims to measure of the extent of STEM awareness in secondary art educators, In the state of Texas, 211 secondary art educators completed the STEM Awareness and Community Survey. The data collected from the secondary art teachers revealed perception differences in regards to teacher educational background and teacher certification training. Follow up interviews with participants revealed teacher’s perceptions of the benefit of art within STEM in terms of creativity, communication, and visualization of concepts. The data concluded the necessity for educational leadership to implement more STEM related training for their arts faculty in order to ensure meaningful integration of STEAM based curriculum
Evaluating Sentiment Analysis Mechanism for Labelled Amazon Reviews
Get PDFSentiment analysis has become increasingly important in understanding customer opinions, feedback, and preferences towards products and services, particularly on marketplaces like Amazon. Researchers have proposed various techniques and algorithms for sentiment analysis. However, there still lacks a good guidance that can systematically direct data scientists to select appropriate algorithms and models, although a few efforts have been made. This thesis aims to fill the gap by presenting a comprehensive evaluation on different sentiment analysis mechanisms for labeled Amazon reviews. To achieve the above goal, we first prepare an accurately labelled Amazon review dataset through manually labeling. This builds a solid foundation for our evaluation. Then, we evaluate the effectiveness of popular mechanisms used in sentiment analysis, including both data preprocessing techniques such as Bag of Words (BOW), Term Frequency- Inverse Document Frequency (TF-IDF) weighting, spell correction, stemming, and lemmatization, and various sentiment analysis models such as K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT). These mechanisms were selected based on their prominence in the field of sentiment analysis, their potential to yield high-accuracy results, and their representation of different designs. We conducted five experiments using a combination of above data preprocessing techniques and analysis models. Through these experiments, we aim to identify a set of optimal combinations of preprocessing techniques and classification models that demonstrate superior performance in sentiment analysis of labeled Amazon reviews.
The experiment results show that the use of BERT with BOW, TF-IDF, Spell Correction, and Lemmatization achieved the highest accuracy of 98.99%, outperforming other combinations. The addition of TF-IDF weighting, spell correction, stemming, and lemmatization improves the accuracy of four analysis models by about 6%, i.e., from 87.34% to 93.4% for KNN, from 86.6% to 94.22% for SVM, from 90.68% to 96.87% for ANN, and from 92.87% to 97.95% for LSTM. However, LR shows a comparatively lower accuracy ranging from 74.32% to 81.09% regardless different preprocessing techniques due to its limitations as a linear model, which may struggle to capture complex patterns and non-linear relationships in the sentiment data. This work provides insights into the effectiveness of different data processing and analysis mechanisms for sentiment analysis of labeled Amazon reviews. The findings can be applied to improve the effectiveness of customer review analysis to help achieve higher level of customer satisfaction, which can be essential in areas such as product and business strategy development
Adopting and Applying a New Assessment Framework for Instruction: A Case Study on Learning Criteria
Get PDFData collection is only one part of the assessment cycle. Oftentimes, libraries collect data but then struggle with the analysis stage. Our academic library has taught a first-year experience instructional program for over five years where little data analysis was conducted to improve outcomes. As part of a pilot project to identify a new assessment framework, in Spring 2022, a team of librarians adopted ACRL Project Outcome, a free resource toolkit available to both public and academic libraries to guide their assessment.
This poster will highlight how utilizing ACRL Project Outcome created an opportunity for cross-departmental collaboration to improve survey structure and standardization where results would lead the library to make more deliberate and confident data-informed decisions for student success. It will also report the initial results of a case study, what has been achieved, and what remains to be accomplished in the future. This poster is intended for academic and public librarians who are interested in learning about a new assessment tool in order to measure the impact of their outcomes for instruction, events, spaces, and more
Numbers Don’t Lie: Increasing Engagement on TLA District 8’s Facebook Page
No full textSocial media platforms like Facebook, Instagram, and TikTok provide easily accessible, free marketing tools for many small organizations like libraries. TLA district officers use these platforms to communicate and market district events to members. However, the limited number of events means that social media communication and marketing often occur infrequently. And if social media platforms are only being used to promote the occasional district event, then members may not have good enough reasons to regularly follow these district communication platforms. This infrequent usage presents a lost opportunity for districts to build richly engaging social spaces where members may learn, inspire, and support one another regarding library initiatives, programming, and pressures.
In 2021, District 8 members elected a new web administrator who chose to focus on increasing community engagement on the district’s Facebook page. The goal was to provide a more interactive experience for anyone choosing to view or follow the page by increasing the number of posts. The administrator achieved this by following and regularly sharing posts from the Facebook pages of every library within the District 8 region. Represented communities range from large urban cities to small rural towns, and the libraries include public, school, university/college, and specialized institutions. Since implementing this change, engagement, and followers have increased. The purpose of this presentation is to share this easy-to-implement strategy along with supporting Facebook data demonstrating the strategy’s effectiveness
Finding Aid for the John D. Holt Papers (HSF-67)
No full textThe John D. Holt Papers include charters, correspondence, directories, handbooks, manuals, handouts, memorandums, training materials, presentations, photographs, organizational charts, reports, newsletters, budgets, CD discs, promotional materials, and miscellaneous materials, documenting the professional career of John D. Holt at NASA Johnson Space Center (JSC) in coastal Houston, Texas. Holt worked at NASA from 1967 to 2001. Holt worked as a NASA contractor for several aerospace companies after his retirement until 2010. He would serve at JSC as a branch chief of Guidance and Propulsion Systems in the Systems Division of the Mission Operations Directorate (MOD); Branch Chief of Payload and Operations Support Branch; Chief of the Production Integration Management Office; and a series of management positions for the Space Shuttle Program
Examining the ecological function of small-scale living shorelines in Galveston Bay
No full textLiving shorelines are an increasingly popular shoreline protection strategy. In contrast to traditional armoring techniques such as concrete bulkheads, living shorelines are designed to provide the many ecological functions and benefits of natural coastal wetlands. Despite a wealth of knowledge on coastal wetland restoration, studies verifying ecological function in living shorelines are limited. The objective of this study was to provide a comprehensive ecological assessment of three living shoreline projects in the Galveston Bay system. This study collected data on stem density, percent cover, and root-biomass to characterize plant communities. Data was also collected on the abundance and community structure of benthic and nekton organisms. Additionally, sediment heavy metal concentrations were examined. Living shoreline data was compared to both natural and armored shorelines ultimately confirming the suspected hypothesis that living shoreline sites function similarly to natural shorelines and improved over armored shorelines
From Their Perspective: A Qualitative Study Examining Black Boys' Relationship with Reading in Grades 3-5
Get PDFThe purpose of this study was to examine the reading experiences of Black boys in grades three through five, gain insight into what they felt contributed to their reading success, explore the relationships that influenced their reading lives, the challenges they faced, and their perspectives of the relationship they had with their reading teacher. A purposeful sample of 3rd-5th grade students who identified as Black boys from an elementary school located within a large suburban school district in the Southeast region of Texas were chosen to participate in interviews. This study used the grounded theory analysis approach (Saldana, 2016). An analysis of the interviews revealed that the Black boys had mostly positive experiences within their reading classrooms. The participants in this study mostly felt that they had positive relationships with their reading teachers. They found that support from their teachers, parents, and school administrators contributed to their reading achievement, while comprehension and unknown advanced vocabulary hindered them from feeling successful in reading
Finding Aid for the Robert V. Grilli Papers (HSF-63)
Get PDFThe Robert V. Grilli Papers is composed of reports, memos, notes, technical manuals and handbooks, checklists, guides, schedules, technical requirements records, technical documents, directories, training manuals, meeting logs, telephone directories, plans, charts, reel-to-reel audiotapes, and other materials, documenting the career of Robert V. Grilli as a contractor with Philco-Ford, Ford Aerospace and Communications Corporation, Rockwell Space Operations Company, and United Space Alliance at Johnson Space Center in coastal Houston, Texas. Grilli worked as a contractor at Johnson Space Center from 1962 to 2011. Most of this collection covers his work on the Space Shuttle Program from 1978 to 1997. Most of the records and manuals document Grilli’s work as a Shuttle program engineer in program requirements, payloads system, telemetry, command data, and communications between 1980 and 1995. There are also Grilli’s personal original reel-to-reel audiotapes of portions of the Apollo 15 mission, Skylab mission, and Skylab 4 mission
Reviewtag: Tagging Amazon Negative Product Review with Deep Learning
Get PDFThe success of Amazon sellers hinges on high ratings and meeting customer needs with exceptional products and services. However, the large scale of negative reviews pose significant challenges that require careful analysis to identify underlying reasons of buyers concerns. We aim to develop an automated tagging system named ReviewTag to address this challenge. The system uses deep learning models and Natural Language Processing (NLP) techniques to swiftly categorize negative reviews into two broader categories i.e., product issues and seller issues.
The system provides further insight into customers' specific issues using subtopic tagging, allowing Amazon sellers to identify areas for improvement and make data-driven decisions to meet evolving customer expectations.
We employ five deep learning models to perform topic and subtopic tagging. These models include Bidirectional Encoder Representations from Transformers (BERT), Distilled Bidirectional Encoder Representations from Transformers (DistilBERT), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). Based on the evaluation with a prototype implementation of ReviewTag, the BERT model demonstrates high precision, recall, and F1-scores of 0.97, 0.96, and 0.96, respectively, for topic tagging. Additionally, the BERT and CNN models show impressive precision, recall, and F1-scores of about 0.92, for subtopic tagging. These results demonstrate the effectiveness of deep learning models for automatically tagging negative product reviews on Amazon. It helps Amazon sellers take action to improve their product ratings