OUR@Oakland

OUR@Oakland is Oakland University's institutional repository maintained by the University Libraries. The aim of OUR@Oakland is to collect, organize, and showcase the scholarship, creative work, and archival and special collections created by or affiliated with the Oakland University community.

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Recent Submissions

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    Building Cultural Agility in Future Teachers: Pathways for Multilingual Learners
    (2026-01-01) Walker, Holly Laraine; Smith, Julia B.; Nidiffer, Jana; Martin, Robert
    Increasingly, teachers are called upon to differentiate instruction for Multilingual Learners (MLLs). Typically, the methods of differentiation and scaffolding for MLLs are carried out though ESL programming supports. Many districts that host MLLs have a district ESL coach or coordinator who oversees ESL assessment, placement, progress, and instruction within the standards established by the state. If a district is typical, there will be an ESL support person employed for each school where MLLs attend. In a more progressive scenario, a school may have a team of ESL instructors dedicated to each grade level. At the high school level, there may be sheltered classrooms where MLLs learn content together for their core subjects. There are models that focus on bilingualism, immersion, and transitional instruction where students start their education in the home language and work toward doing academics entirely in English over time. Districts nationwide face funding crises that impact the level of services available for MLLs. In addition, there is an ESL teacher shortage impacting the sustainability of ESL programming. Districts needing attention for MLLs may only have one person in their district overseeing services for all the students. Districts with high numbers of MLLs may have one person in each school overseeing hundreds of students. Due to shortages of ESL support staff, content area teachers are left to their own devices. Many teachers are not skilled in language development and instruction modalities. Teachers are overwhelmed with the task of supporting MLLs in their classrooms and teaching content without sacrificing instructional time and academic rigor. I believe that we can change this landscape by addressing some of these challenges in the pre-service teacher training phase. To explore the pre-service training of currently practicing teachers, I conducted a survey that included items around attitudes toward culture in the classroom, teaching multicultural and multilingual students, and how prepared teachers felt in supporting these MLLs. Respondents indicated the level of support they are getting in the workplace through professional development, available resources, and information that can help them support multilingual learners in their classrooms. The survey included items that measured how well pre-service training prepared teachers in each of these areas. Results of the survey indicated that teachers believe culturally responsive teaching is important. The survey showed that teachers want to increase their cultural competence but are unsure where to find appropriate support materials. Some respondents indicated that support is lacking at the institutional or district level, and professional development would help. In terms of pre-service training, the degree of preparedness for multicultural students varied based on certificate type. Participants with master’s degrees tended to feel more prepared than those with bachelor’s degrees in education or alternative certifications.
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    What We Believe, We Practice: The Impact of Beliefs on Culturally Responsive Teaching
    (2026-01-01) Walroth, Allyson Jean; Smith, Julia; Martin, Robert; Carver, Cynthia
    This dissertation explores the extent to which teachers’ personal beliefs impact their likelihood of using culturally responsive teaching practices in the classroom. With a focus on critical race theory, cognitive dissonance, and teacher agency, this study investigates the degree to which teachers’ multicultural attitudes impact their use of equity-focused instructional methods. A three-part survey was used to gather both demographic data as well as self-reported information related to participants’ attitudes and beliefs and their classroom practices. The findings highlight the various beliefs that act as predictors for the use of culturally responsive practices, as well as those that act as barriers for implementation. This research contributes to the discourse on equity by considering the psychological and structural barriers to inclusive teaching and providing a strategic roadmap for districts to bridge the gap between theoretical multiculturalism and a sustainable classroom transformation.
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    Organizational Change and Digital Learning in K-12 World Language Education: A Case Study
    (2026-01-01) Gardiner, Susan Michelle; Smith, Julia; Flumerfelt, Shannon; Abbott, Christine
    Organizational change in schools is an enduring and persistent evolution through which institutions manage internal needs, external influences, and shifting educational priorities. A large body of literature has investigated the forces that influence how leaders enact change and the impact those changes have on key stakeholders. However, considerably fewer studies have examined organizational change as it pertains to digital learning in K-12 world language (WL) education. This qualitative case study examined how stakeholders interpreted and responded to a proposed digital learning initiative within a K-12 world language program. The research was conducted in a suburban K-12 district during a period of WL program reductions and restructuring and focused on a proposed kindergarten digital learning push-in pilot within an elementary world language program. Data were collected through interviews, meeting transcripts, district documents, field notes, and researcher memos. This dissertation explored a critical but underexamined challenge in systems change management: the disconnect between the conditions necessary for longitudinally meaningful impact in WL education and top-down initiatives designed to align program structures with changes in the broader educational environment.Findings revealed that stakeholders did not merely resist digital innovation; rather, they interpreted the proposed changes through a historical lens of program reduction, instability, and ambiguous communication, which intensified distrust and uncertainty. Digital learning was perceived differently depending on whether it was framed as enrichment, replacement, outsourcing, or cost-saving. Organizational change was influenced by institutional pressures, resource constraints, and competing priorities. Stakeholders navigating systems with such complex dynamics experienced this tension acutely and responded through compliance, resistance, or by stakeholder mobilization in a unified attempt to influence the proposed transformation. The findings suggest that digital learning initiatives in specialized instructional programs require transparent communication, stakeholder inclusion, and attention to the historical conditions through which proposed changes are interpreted. Along with the persistent and continual flux inherent in organizational dynamics, the emergence of digital platforms and artificial intelligence as instruments of second language acquisition, makes this a timely and relevant study.
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    Multi-Domain Machine Learning for Predictive Electric Vehicle Charging Management: A Framework Integrating Behavioral, Infrastructure, and Market Data
    (2026-01-01) Thwany, Hanan; Zohdy, Mohamed A.; Kobus, Christopher J.; Edwards, William; Ruegg, Erica
    The growing popularity of electric vehicles (EVs) is bringing to light several new challenges that the infrastructure of the management of charging and smart grid operation as well as energy demand planning have to cope with. The accuracy of the prediction of the EV charging pattern can significantly increase the charging station utilization, reduce the risk of grid overload and enable the integration of renewable energy sources and also support development of future intelligent transportation systems. However, the majority of the EV charging prediction models being used nowadays are developed on the basis of a single domain dataset, and they are not able to capture or show the complex web of user-charging behavior, charging station features and market-related factors interaction. These limitations prevent such models from being applied in diverse scenarios and also from being deployed for actual charging purposes. With an aim to counter these issues, we present an innovative Multi-Domain Machine Learning Framework for EV Charging Prediction in this work. By combining datasets of different types, it enhances accuracy, robustness, and readiness for deployment of the predictive models. The proposed framework integrates three publicly available datasets describing EV charging behavior, charging infrastructure, and dynamics of the EV market. A preprocessing pipeline was developed to do data cleanup, filling missing data, removing duplicates, converting timestamps into the same time format, categorical encoding of variables, scaling of numeric features, and treatment of class imbalance. It should be noted that LightGBM was used to make an efficient computation and strong performance model on the data that is in structured tables, while TabNet made use of sequential attention mechanisms for carrying out interpretable deep learning on heterogeneous tabular datasets. Accuracy, Weighted F1-score, Macro F1-score, confusion matrix analysis, confidence calibration, computational efficiency, and ensemble weight optimization were used as evaluation metrics. The results from the experiments show that the combination of different information types, including behavioral, infrastructure, temporal, and market-related, leads to a significant improvement in predicting performance compared to datasets when individual datasets are used. XLM-RoBERTa was the model that delivered the highest classification accuracy of 98.76 when tested against the integrated multi-domain dataset, while TabNet and LightGBM came in second and third at 96.89 and 85.67, respectively. It is further revealed from the study that TabNet offers a good trade-off between predictiveness and computational requirements in a system. In contrast, LightGBM is not only the fastest but also requires minimal computing, which makes it a choice for systems under a tight budget. Through the ensemble weight optimization strategy, prediction reliability was enhanced since it assigned each model the best possible contribution. It is clear from this that transformer-based contextual learning can be successful, even under heterogeneous EV charging environments. These results contribute to the development of ML technologies applied to the electric mobility sector and, most importantly, lay a groundwork for future intelligent EV charging solutions and smart city infrastructures.
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    Wheat Field Fire Smoke Detection from UAV Images Using CNN-CABM
    (2026-01-01) Alsalem, Ahmed Essa; Zohdy, Mohamed A; Edwards, William; Kobus, Christopher; Ruegg, Erica
    Wheatfield fires all seriously threaten farmers income, agricultural resources, and wildlife habitats. These fires have become much more frequent nowadays, due to climate change and activities of humans in farming areas. Since smoke detection is the initial step in early fire detection, action must be taken right away. We proposed an improved CNN model that can detect smoke in wheat field fires to address this problem. Features are extracted from UAV images using CNN. CNN is a deep learning model that automatically adjusts to spatial information in input images. As part of this method, 3900 UAV images were collected to illustrate smoke from wheat field fires. The suggested method divides Wheatfield fire images into four categories: high, medium, low, and no fire. The dataset displays smoke from burning wheat. Included are the following categories of smoke photos:(a) a lot of smoke and fire; (b) a lot of smoke and fire (c) Mild smoke and fire. There are three types of fire and smoke: (d) medium; (e) small; and (f) small.The CNN-CBAM and the model that combines CBAM and CNN were trained and validated on this dataset. One strategy for accomplishing this is to apply the channel along with the pores to the important aspects of the images, which is known as the attention mask method. The proposed task is to extract features for smoke patch characteristics of wheat field fires. Also, the CNN design incorporates disconnected heads that extract relevant data from a variety of sources. Image categorization and feature extraction using convolutional neural networks comprise the two components of the suggested CNN-CABM approach. The usefulness of this suggested method was validated through testing on our wheat field fire smoke dataset, outperforming the prior model and achieving an accuracy of up to 0.92. The study found that the combination of CBAM and CNN is particularly effective for the task of detecting fires in wheat fields, indicating a new bright side to the area of early warning systems. To cross-validate the findings of the same dataset using basic SVM and CNN, we determined that the suggested model CNN-CABM outperforms simple SVM and CNN to produce better results. The next steps will be to improve this versions stability in the face of shifting multiplicative nuisances, as well as to increase speed and precision, according to those who contributed.