Bento Box Discovery

Poster presentation at the University of Maryland Libraries Research & Innovative Practice Forum on June 8, 2016. The poster proposes that the UMD Libraries should evaluate adoption of Bento Box Discovery for improved user search experience.

Probabilistic Models for Scalable Knowledge Graph Construction

In the past decade, systems that extract information from millions of Internet documents have become commonplace. Knowledge graphs -- structured knowledge bases that describe entities, their attributes and the relationships between them -- are a powerful tool for understanding and organizing this vast amount of information. However, a significant obstacle to knowledge graph construction is the unreliability of the extracted information, due to noise and ambiguity in the underlying data or errors made by the extraction system and the complexity of reasoning about the dependencies between these noisy extractions. My dissertation addresses these challenges by exploiting the interdependencies between facts to improve the quality of the knowledge graph in a scalable framework. I introduce a new approach called knowledge graph identification (KGI), which resolves the entities, attributes and relationships in the knowledge graph by incorporating uncertain extractions from multiple sources, entity co-references, and ontological constraints. I define a probability distribution over possible knowledge graphs and infer the most probable knowledge graph using a combination of probabilistic and logical reasoning. Such probabilistic models are frequently dismissed due to scalability concerns, but my implementation of KGI maintains tractable performance on large problems through the use of hinge-loss Markov random fields, which have a convex inference objective. This allows the inference of large knowledge graphs using 4M facts and 20M ground constraints in 2 hours. To further scale the solution, I develop a distributed approach to the KGI problem which runs in parallel across multiple machines, reducing inference time by 90%. Finally, I extend my model to the streaming setting, where a knowledge graph is continuously updated by incorporating newly extracted facts. I devise a general approach for approximately updating inference in convex probabilistic models, and quantify the approximation error by defining and bounding inference regret for online models. Together, my work retains the attractive features of probabilistic models while providing the scalability necessary for large-scale knowledge graph construction. These models have been applied on a number of real-world knowledge graph projects, including the NELL project at Carnegie Mellon and the Google Knowledge Graph.

Cultivating Collaborative Relationships: A Case Study of Teacher Collaboration and Professional Development

Students often receive instruction from specialists, professionals other than their general educators, such as special educators, reading specialists, and ESOL (English Speakers of Other Languages) teachers. The purpose of this study was to examine how general educators and specialists develop collaborative relationships over time within the context of receiving professional development. While collaboration is considered essential to increasing student achievement, improving teachers’ practice, and creating comprehensive school reform, collaborative partnerships take time to develop and require multiple sources of support. Additionally, both practitioners and researchers often conflate collaboration with structural reforms such as co-teaching. This study used a retrospective single case study with a grounded theory approach to analysis. Data were collected through semi-structured interviews with thirteen teachers and an administrator after three workshops were conducted throughout the school year. The theory, Cultivating Interprofessional Collaboration, describes how interprofessional relationships grow as teachers engage in a cycle of learning, constructing partnership, and reflecting. As relationships deepen some partners experience a seamless dimension to their work. A variety of intrapersonal, interpersonal, and external factors work in concert to promote this growth, which is strengthened through professional development. In this theory, professional development provides a common ground for strengthening relationships, knowledge about the collaborative process, and a reflective space to create new collaborative practices. Effective collaborative practice can lead to aligned instruction and teachers’ own professional growth. This study has implications for school interventions, professional development, and future research on collaboration in schools.

EXAMINING SECONDARY MATHEMATICS TEACHER CANDIDATES’ LEARNING AND ENACTMENT OF MATHEMATICS TEACHING PRACTICES: A MULTIPLE CASE STUDY

This qualitative case study explored three teacher candidates’ learning and enactment of discourse-focused mathematics teaching practices. Using audio and video recordings of their teaching practice this study aimed to identify the shifts in the way in which the teacher candidates enacted the following discourse practices: elicited and used evidence of student thinking, posed purposeful questions, and facilitated meaningful mathematical discourse. The teacher candidates’ written reflections from their practice-based coursework as well as interviews were examined to see how two mathematics methods courses influenced their learning and enactment of the three discourse focused mathematics teaching practices. These data sources were also used to identify tensions the teacher candidates encountered. All three candidates in the study were able to successfully enact and reflect on these discourse-focused mathematics teaching practices at various time points in their preparation programs. Consistency of use and areas of improvement differed, however, depending on various tensions experienced by each candidate. Access to quality curriculum materials as well as time to formulate and enact thoughtful lesson plans that supported classroom discourse were tensions for these teacher candidates. This study shows that teacher candidates are capable of enacting discourse-focused teaching practices early in their field placements and with the support of practice-based coursework they can analyze and reflect on their practice for improvement. This study also reveals the importance of assisting teacher candidates in accessing rich mathematical tasks and collaborating during lesson planning. More research needs to be explored to identify how specific aspects of the learning cycle impact individual teachers and how this can be used to improve practice-based teacher education courses.