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There is a strong need to produce more college graduates with STEM degrees when the world copes with ever changing weather patterns, a global pandemic, and other natural and man-made disasters. These graduates also need come from an ever more diverse community to ensure that the needs of all are addressed with the best possible solutions. This project seeks to attract and retain students who have the ability and with the right support structure are fully capable of being successful in a STEM degree and beyond to a STEM career including engineering. In this poster we showcase successful strategies used in the DuSTEM program at Loras college. This program grants students scholarships and academic support funded by the National Science Foundation as part of the S-STEM program. Thus far, there were 22 students who started in the first year of the program and 14 students who started in the second year. All students are academically high-achieving students who demonstrate financial need. Students major in any STEM discipline, including general engineering. In the first year, the academic supports focus on the transition from high school to college and include seminars and proactive advising. In the second year, the academic supports move improve self-efficacy and does so by incorporating work on a group community-based learning project. Thus far the project has been successful in its goals of increasing enrollment and retention as compared to the control group. In the last year, like all students, this group has been impacted by the SARS-COV2 pandemic and supports within this program have attempted to help these students stay connected to a community despite the necessity for online learning. 

Building on previous work in computer generated jazz solos using probabilistic grammars, this paper describes research extending the capabilities of the current learning process and grammar representation used in the Impro-Visor educational music software with the concepts of motifs and motif patterns. An approach has been developed using clustering, best match search techniques, and probabilistic grammar rules to identify motifs and incorporate them into computer generated solos. The abilities of this technique are further expanded through the use of motif patterns. Motif patterns are used to induce coherence in generated solos by learning the patterns in which motifs were used in a given set of transcriptions. This approach is implemented as a feature of the Impro- Visor software. 

Building on previous work in computer generated jazz solos using probabilistic grammars, this paper describes research extending the capabilities of the current learning process and grammar representation used in the Impro-Visor educational music software with the concepts of motifs and motif patterns. An approach has been developed using clustering, best match search techniques, and probabilistic grammar rules to identify motifs and incorporate them into computer generated solos. The abilities of this technique are further expanded through the use of motif patterns. Motif patterns are used to induce coherence in generated solos by learning the patterns in which motifs were used in a given set of transcriptions. This approach is implemented as a feature of the Impro- Visor software.