<p>Internationalization of higher education is a collaborative responsibility academic and non-academic programs share to facilitate the integration of various student populations within the broader culture of the university. My dissertation project links First Year Writing (FYW) classes of domestic and international students to promote and evaluate their intercultural competence development. My research questions explore the use of reflective writing as a genre for formative assessment in the writing classroom and investigate the data it provides about students’ continuous learning. My research methodology combines qualitative analysis of reflective writing and quantitative analysis of intercultural competence development. Participants come from four sections of FYW courses spanning two semesters – Spring 2016 and Fall 2017. I collected reflective writing data from four embedded reflective journals and a final reflective essay assigned to students in each section. Using a grounded scheme, I applied thematic coding analysis of reflective writing and traced frequencies of codes. I also mapped students’ reflections onto the Developmental Model of Intercultural Sensitivity (DMIS; Bennett, 1993). Results from both coding methods contextualize and interpret students’ development in both intercultural competence and writing skills. I also share pedagogical, assessment, and administrative implications for more effective teaching of reflective writing and better continuous assessment of intercultural competence skills within the context of the linked course model curriculum. </p> <p> </p>

<p>Content about learners with gifts and talents is not necessarily a part of most teacher education programs. Without high quality training and professional development opportunities, preservice and inservice teachers are left with no tools to identify and serve the students with gifts and talents. However, adding more content is not enough. The successful translation of training and professional development into effective practice depends on understanding teacher motivation, debunking misconceptions, building adequate knowledge base, and building teacher identity. I adopted several theoretical perspectives in this study: teacher identity formation (Gardner & Kaplan, 2018), Teacher Efficacy (Tschannen-Moran et al., 1998), Teacher Goal Orientations (Butler, 2007), beliefs about gifted learners and gifted education (Gagne & Nadeau, 1991; McCoach & Siegle, 2007), desire to teach (Watt & Richardson, 2007). My participants were 236 preservice teachers who desire to teach learners with gifts and talents and inservice teachers in gifted education.</p><p>The objectives of this mixed-methods investigation were: (1) identifying the differences between preservice and inservice teachers in measures of identity, beliefs, motivation, and desire to teach learners with gifts and talents, (2) modeling the structural relationships among dimensions of identity, motivation, beliefs, and desire to teach, and (3) understanding how participants experiences and perceptions inform their identity, motivation, and belief systems. I used a combination of Multivariate Analysis of Variance (MANOVA), Structural Equation Modeling (SEM) and qualitative thematic analysis to answer my research questions.</p><p>Findings revealed inservice teachers (n=155) have high levels of relational goals, instructional efficacy, positive beliefs, and teacher identity, while preservice teachers (n=81) have high levels of intrinsic motivation and social value for gifted education. SEM showed that teacher identity, mastery goals, influenced positive beliefs; teacher identity was influenced by efficacy, mastery and relational goals. The strongest predictors of desire to teach learners with gifts and talents were teacher identity, teacher efficacy, and relational goals. Qualitative findings indicated that self-perceptions as gifted played a meaningful role in participants deciding to become teachers, understanding the needs of gifted learners, and advocating for gifted education.</p>

<p>Innovations in technology are changing not only everyday life for many individuals around the world but are also influencing the expansion of online learning opportunities at an accelerated rate (Collins & Halverson, 2<a></a>018; Mah, 2016). Online learning platforms allow for scalability, flexibility, greater global access, and innovative and new ways to deliver education (Goodman, Melkers, & Pallais, 2019; Kizilcec et al., 2019). Enrollments in online learning programs and opportunities have seen significant growth in recent years (Seaman, Allen, & Seaman, 2018; U.S. Department of Education, 2018) with continued and steady growth expected into the future. The ubiquity and newness of new online learning formats present a challenge in linking research and practice. Through three separate academic papers, the following dissertation discusses and considers key questions and topics with regards to the use of digital badges and Massive Open Online Courses (MOOCs), two types of emerging online innovations and platforms, and aspects of their efficacy. The three papers respectively 1) identify and discuss the theoretical and empirical foundations digital badges use in specific learners groups by reviewing current literature; 2) highlight the application of a use case in which digital badges have been implemented as a means to offer training; and 3) explore the perceptions of MOOC instructors toward quality learning in their courses in a case study. Conclusions are drawn and solutions as well as potential future directions for research and practice of discussed. </p>

<p>In an era of widespread automation—from grocery store self-checkout machines to self-driving cars—it is not outrageous to wonder: can teachers be automated? And more specifically, can automated computer teachers instruct students how to write? Automated computer programs have long been used in summative writing evaluation efforts, such as scoring standardized essay exams, ranking placement essays, or facilitating programmatic outcomes assessments. However, new claims about automated writing evaluation’s (AWE) formative educational potential mark a significant shift. My project questions the effectiveness of using AWE technology for formative educational efforts such as improving and teaching writing. Taken seriously, these efforts portend a future embrace of semi, or even fully, automated writing classes, an unprecedented development in writing pedagogy.</p><p>Supported by a summer-long grant from the Purdue Research Foundation, I conducted a small-<i>n </i>quasi-experiment to test claims by online college tutoring site Chegg.com that its EasyBib Plus AWE tool can improve both writing and writers. The experiment involved four college English instructors reading pairs of essays comprising one AWE-treated and untreated version per pair. Using a comparative judgment model, a rubric-free method of writing assessment based on Thurstone’s law, raters read and designated one of each pair “better.” Across four raters and 160 essays, I found that AWE-treated essays were designated better only 30% of the time (95% confidence interval: 20-40%), a statistically significant difference from the null hypothesis of 50%. The results suggest that Chegg’s EasyBib Plus tool offers no discernible improvement to student writing, and potentially even worsens it.</p><p>Finally, I analyze Chegg’s recent partnership with the Purdue Writing Lab and Online Writing Lab (OWL). The Purdue-Chegg partnership offers a useful test case for anticipating the effects of higher education’s embrace of automated educational technology going forward. Drawing on the history of writing assessment and the results of the experiment, I argue against using AWE for formative writing instruction. In an era of growing automation, I maintain that a human-centered pedagogy remains one of the most durable, important, effective, and transformative ingredients of a quality education.</p>

<p dir="ltr">Despite educators implementing bias-reducing strategies, accurately evaluating team members’ individual contributions remains a challenge. This study investigated the extent of bias in team member evaluations within a design thinking course. Self-, peer, and undergraduate teaching assistants (mentors) used the Comprehensive Assessment for Team Member Effectiveness (CATME) tool to rate team members based on their contributions to a capstone project.</p><p dir="ltr">To measure alignment between self-, peer, and mentor ratings, statistical analyses such as Kendall’s W Coefficient of Concordance and Spearman’s rank correlations were performed to assess interrater reliability. Other non-parametric tests were used to analyze the ratings’ correlations with grades and demographic data. The analyses revealed moderate alignment between peer and mentor ratings, which suggested that external perspectives were consistent and reliable measures of contributions. Self-ratings had weaker alignment with peer and mentor ratings, indicating a disconnect between students’ internal self-perception and how their teams and mentor saw them contribute. Although there were no statistically significant relationships between ratings and grades, a negative relationship between self-ratings and grades was found, which suggested that students who inflated self-evaluations performed slightly worse on assignments. Further analyses split by course section exposed how demographic factors influenced mentor ratings, illuminating hidden biases that were veiled in the pooled dataset.</p><p dir="ltr">The findings from this study highlight the importance of refining bias-reducing strategies. The objective was to enhance the fairness and accuracy of team member evaluations, thereby reducing frustration from unfair grading and improving the educational experience and outcomes for students engaged in collaborative projects.</p>

Analyzing the structure of a dataset is a challenging problem in high-dimensions as the volume of the space increases at an exponential rate and typically, data becomes sparse in this high-dimensional space. This poses a significant challenge to machine learning methods which rely on exploiting structures underlying data to make meaningful inferences. This dissertation proposes the <i>n</i>-TARP method as a building block for high-dimensional data analysis, in both supervised and unsupervised scenarios.<div><br></div><div>The basic element, <i>n</i>-TARP, consists of a random projection framework to transform high-dimensional data to one-dimensional data in a manner that yields point separations in the projected space. The point separation can be tuned to reflect classes in supervised scenarios and clusters in unsupervised scenarios. The <i>n</i>-TARP method finds linear separations in high-dimensional data. This basic unit can be used repeatedly to find a variety of structures. It can be arranged in a hierarchical structure like a tree, which increases the model complexity, flexibility and discriminating power. Feature space extensions combined with <i>n</i>-TARP can also be used to investigate non-linear separations in high-dimensional data.<br></div><div><br></div><div>The application of <i>n</i>-TARP to both supervised and unsupervised problems is investigated in this dissertation. In the supervised scenario, a sequence of <i>n</i>-TARP based classifiers with increasing complexity is considered. The point separations are measured by classification metrics like accuracy, Gini impurity or entropy. The performance of these classifiers on image classification tasks is studied. This study provides an interesting insight into the working of classification methods. The sequence of <i>n</i>-TARP classifiers yields benchmark curves that put in context the accuracy and complexity of other classification methods for a given dataset. The benchmark curves are parameterized by classification error and computational cost to define a benchmarking plane. This framework splits this plane into regions of "positive-gain" and "negative-gain" which provide context for the performance and effectiveness of other classification methods. The asymptotes of benchmark curves are shown to be optimal (i.e. at Bayes Error) in some cases (Theorem 2.5.2).<br></div><div><br></div><div>In the unsupervised scenario, the <i>n</i>-TARP method highlights the existence of many different clustering structures in a dataset. However, not all structures present are statistically meaningful. This issue is amplified when the dataset is small, as random events may yield sample sets that exhibit separations that are not present in the distribution of the data. Thus, statistical validation is an important step in data analysis, especially in high-dimensions. However, in order to statistically validate results, often an exponentially increasing number of data samples are required as the dimensions increase. The proposed <i>n</i>-TARP method circumvents this challenge by evaluating statistical significance in the one-dimensional space of data projections. The <i>n</i>-TARP framework also results in several different statistically valid instances of point separation into clusters, as opposed to a unique "best" separation, which leads to a distribution of clusters induced by the random projection process.<br></div><div><br></div><div>The distributions of clusters resulting from <i>n</i>-TARP are studied. This dissertation focuses on small sample high-dimensional problems. A large number of distinct clusters are found, which are statistically validated. The distribution of clusters is studied as the dimensionality of the problem evolves through the extension of the feature space using monomial terms of increasing degree in the original features, which corresponds to investigating non-linear point separations in the projection space.<br></div><div><br></div><div>A statistical framework is introduced to detect patterns of dependence between the clusters formed with the features (predictors) and a chosen outcome (response) in the data that is not used by the clustering method. This framework is designed to detect the existence of a relationship between the predictors and response. This framework can also serve as an alternative cluster validation tool.<br></div><div><br></div><div>The concepts and methods developed in this dissertation are applied to a real world data analysis problem in Engineering Education. Specifically, engineering students' Habits of Mind are analyzed. The data at hand is qualitative, in the form of text, equations and figures. To use the <i>n</i>-TARP based analysis method, the source data must be transformed into quantitative data (vectors). This is done by modeling it as a random process based on the theoretical framework defined by a rubric. Since the number of students is small, this problem falls into the small sample high-dimensions scenario. The <i>n</i>-TARP clustering method is used to find groups within this data in a statistically valid manner. The resulting clusters are analyzed in the context of education to determine what is represented by the identified clusters. The dependence of student performance indicators like the course grade on the clusters formed with <i>n</i>-TARP are studied in the pattern dependence framework, and the observed effect is statistically validated. The data obtained suggests the presence of a large variety of different patterns of Habits of Mind among students, many of which are associated with significant grade differences. In particular, the course grade is found to be dependent on at least two Habits of Mind: "computation and estimation" and "values and attitudes."<br></div>

<p> </p> <p>Traditional kinship systems involve the organization of individuals who are biologically connected. However, such systems have evolved beyond bloodlines to incorporate individuals that are biologically unassociated but operate in familial-like roles due to shared spaces and/or experiences. Historically, kinship systems or cultural networks have functioned as the cornerstone of survival for those of the Black lived experience. From the days of legalized human chattel slavery to present-day movements seeking justice for the minoritized, the foundation of kinship was typically built through the local church, the assumed maternal positions by Black women, Black secret societies and more. They each served, and continue to serve, as a means for survival and success against a systemically oppressive society. This study explores the notion and existence of kinship systems at historically Black colleges and universities (HBCUs). It specifically examines how fictive kinships through the lens of faculty-student dynamics, religion, and social activities, potentially influences the academic experience of Black students at HBCUs that currently have an above average retention rate. As America’s educational institution has lacked diversity, inclusion, justice, and equity for Black people for countless years, the primary mission of this study was to amplify Black student voices which have traditionally been suppressed. A supplemental goal of this study was to offer Black students tools for introspection that will aid them in navigating possible barriers to (post) educational success. In turn, this study gives insight to predominantly white institutions of higher learning on how to positively enhance the experience and retention of Black students, and the overall structure of diversity and inclusion on campus.</p>

<p>In this dissertation, I investigate the authentic learning experiences of students participating in the Engineering Projects in Community Service (EPICS) program at Purdue University within the framework of authentic education. Utilizing a quantitative approach, the study assesses the performance of new and returning students across five key outcomes that measure authentic learning during a single semester. The analysis of variance (ANOVA) revealed significant main effects for time of assessment and type of student on performance, with an overall improvement in all outcomes observed from mid-term to final evaluations and returning students typically outperforming new students. Interaction effects between time and type were also examined, revealing subtle yet complex dynamics in students’ learning trajectories. The findings hold implications for enhancing authentic learning, especially in engineering design contexts, and offer insights to guide future implementation of and improvements to authentic education initiatives, particularly the EPICS program. Despite certain limitations, the research opens avenues for future investigations into diverse aspects of authentic education in STEM and beyond.  </p>

<p dir="ltr">Institutions of higher education within the United States have not had a reputation of inclusivity. The discrimination and oppression of people with disabilities is an important topic of conversation within these educational spaces, not only to change the way that society thinks of disability on a whole, but to incite discussions surrounding the best ways to support students with disabilities and their educational goals. This paper will provide a deconstruction of what ableism is, how it impacts mental health and wellness and how it shows up within institutions of higher education. This paper will also provide details on a training course for higher education faculty members that provides practical applications of the ethical ways of creating a supportive learning environment for students with disabilities. This paper will explore how critical disability theory, the social model and some aspects of the medical model can be utilized to provide faculty and staff with the competency to understand and interact with students with disabilities in ways that not only support their learning but contribute to positive social change and the deconstruction of ableist actions.</p>

<p dir="ltr">Engineering Technology (ET) programs are pivotal in preparing graduates for the demands of the modern workforce, particularly in quality management systems (QMS). This study examines the alignment between QMS knowledge and experience gained by graduates in ABET-accredited ET programs and the expectations of industry. Through a dual-survey approach, targeting both industry leaders and academic educators, the research elucidates current QMS practices in industry, the scope of QMS education, and the resulting preparedness of graduates for professional roles. Findings indicate a discernible gap between industry expectations and current academic offerings in QMS education. While industry professionals rely on established QMS frameworks such as ISO 9001 and Lean Six Sigma, academic programs often limit their coverage to theoretical underpinnings rather than hands-on, practical applications. This discrepancy highlights the need for a more robust, application-oriented curriculum that bridges theoretical knowledge with real-world practice. Recommendations include a call for greater integration of practical QMS training within academic programs and stronger partnerships between academia and industry to foster educational content that aligns with professional QMS applications.</p>