Applying A Stem Engagement Framework To Examine Short-Term Retention Of Latinx And Other Underrepresented Groups In An Undergraduate Stem Scholar Program

Studying STEM Intervention Program (SIP) retention, particularly what distinguishes those students who remain in the program from those that leave, may be a key to better understand how to keep students on track towards STEM degree completion. This study focuses on the participation of Latinx and other underrepresented racial/ethnic minoritized (URM) groups in a STEM intervention and support program. Applying London, Rosenthal, Levy, and Lobel’s (2011) STEM Engagement Framework on five cohorts of participants in a SIP, this study found that maintaining higher levels of scientific identity was related to program retention. Therefore, intentionally designing programs that address systemic inequities and celebrate and affirm minoritized groups’ experiences can facilitate adjustment and success. Moreover, women-identified participants were also more likely to remain in the SIP relative to their men-identified counterparts. For practitioners and institutions alike, these results indicate the need to create and implement support programs for women in STEM that go beyond the traditional components of academic support

Examining Latina/o STEM degree aspirations

With the proliferation of Web 2.0, social tag is widely used in various applications. Online bookstores (like Amazon) and online bibliographic community Websites (like LibraryThing) have quickly accumulated a large amount of user-generated information. INEX (INitiative for the Evaluation of XML retrieval) have been using the Amazon/LibraryThing corpus for its Social Book Search Track since 2011. The purpose of the INEX Social Book Search Track is to develop novel algorithms leveraging professional metadata and user-generated metadata for effectively retrieving books. This thesis uses INEX 2013 Social Book Search Track test data set to conduct book search experiments and evaluate the retrieval results. Indices based on professional metadata, user-generated metadata and both are created respectively. The results of this study are summarized as follows: Using social data in the probabilistic retrieval model for Book Search outperforms using traditional bibliographic data. Using all book data including reviews in the probabilistic retrieval model for Book Search can get the best retrieval performance. Using social tag information in the probabilistic retrieval model for Book Search has no significant difference with traditional bibliographic data, but using the number of times a tag used as weight to retrieval can improve the retrieval performance. Using reviews data for re-ranking can achieve the best search results in this study; it can improve 3.1% of the nDCG scores. Using tag data for reranking can improve 25% of the nDCG score. Practically, the results of this thesis can be used as a clue for the design of a book search system and a book recommendations system