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  1. A diagnostic of thirty questions administered to incoming STEM students in Fall 2013 and Fall 2015 - Fall 2018 reflects that their spatial visualization skills (SVS) need to be improved. Previous studies in the SVS subject [1], [2], [3] report that well-developed SVS skills lead to students’ success in Engineering and Technology, Computer Science, Chemistry, Computer Aided Design and Mathematics. Authors [4], [5] mention that aptitude in spatial skills is gradually becoming a standard assessment of an individual’s likelihood to succeed as an engineer. This research reports the qualitative and quantitative results of a project designed to improve SVS’s formore »STEM students managed under two strategies. The first strategy utilized was a series of face-to-face (FtF), two-hour training sessions taught over six weeks to all majors in STEM. This strategy was offered in Spring 2014 and every semester from Fall 2015 - Spring 2018. The second strategy was an embedded training (ET) implemented by one faculty from Fall 2017- Fall 2018. The faculty embedded the training in the US 1100 freshman seminar and was highly motivated to increase awareness of students on the importance and applicability of SVS in their fields of study. As reported by Swail et al. [6], cognitive, social, and institutional factors are key elements to best support students’ persistence and achievement. Both interventions used in this project encompassed all these factors and were supported by an NSF IUSE grant (2015-2019) to improve STEM retention. The FtF training was taken by 34 students majoring in diverse STEM fields. Its effectiveness was statistically assessed through a t-test to compare the results in the Purdue Spatial Visualization Skills Test - Rotations before and after the training and through analysis of surveys. Results were very positive; 85.29% of the participants improved their scores. The average change in scores was 5.29 (from 16.85 to 22.15; 17.65% improvement) and it was statistically significant (p-value 3.9E-8). On the surveys, 90% of students answered that they were satisfied with the training. Several students reported that they appreciated a connection between SVS, Calculus II and Engineering Graphics classes while others based the satisfaction on perceiving the critical role SVS will play in their careers. Results from the ET strategy were also encouraging. Teaching methods, curriculum and results are discussed in this paper. Adjustments to the teaching methods were done over 3 semesters. In the last semester, the faculty found that covering the modules at a slower pace than in the FtF training, asking the students to complete the pre-and post-diagnostic in class, and introducing the Spatial VisTM app to provide students with additional practice were key elements to assure students success and satisfaction. In conclusion, both strategies were demonstrated to be powerful interventions to increase students’ success because they not only offer students, particularly freshman, a way to refine SVS but also increase motivation in STEM while creating a community among students and faculty. The ET is effective and apt to be institutionalized. Lastly, this experimental research strengthens the literature on SVS.« less
  2. This article presents the research findings of a multidisciplinary team􏰀s collective research effort at one university over a five-year period as funded by the National Science Foundation􏰀s 􏰁mproving 􏰂ndergraduate 􏰃TE􏰄 Education (IUSE) program. A collaborative learning and retention action research effort at a large Hispanic Serving Institution is analyzed using mixed methods to document the power of collective impact as the foundation for a learning support model for students historically underrepresented majoring in science, technology, engineering and mathematics (STEM) academic programs. The actions of the team of researchers are presented to describe the 􏰅ising 􏰃tars Collective 􏰁mpact model and themore »impacts achieved. This is a model that aligns objectives, intervention efforts, and reports collective results. The long-term goals of the Rising 􏰃tars Collective 􏰁mpact multiple programs managed by the funded program team included the following: (a) to improve the campus sense of community for students historically under-represented in STEM, (b) to establish innovative and robust STEM education research-based practices to support critical skill attainment for students, and (c) to support faculty understanding of the funds of knowledge of diverse students. The positive student retention and success impacts of this research effort are measured through quantitative statistical analysis of the changes in second-year STEM undergraduate student retention rates and representation rates of women, Hispanics, and African American STEM majors.« less
  3. Generative modeling is a flavor of machine learning with applications ranging from computer vision to chemical design. It is expected to be one of the techniques most suited to take advantage of the additional resources provided by near-term quantum computers. Here, we implement a data-driven quantum circuit training algorithm on the canonical Bars-and-Stripes dataset using a quantum-classical hybrid machine. The training proceeds by running parameterized circuits on a trapped ion quantum computer and feeding the results to a classical optimizer. We apply two separate strategies, Particle Swarm and Bayesian optimization to this task. We show that the convergence of themore »quantum circuit to the target distribution depends critically on both the quantum hardware and classical optimization strategy. Our study represents the first successful training of a high-dimensional universal quantum circuit and highlights the promise and challenges associated with hybrid learning schemes.« less
  4. Abstract This paper is a write-up of the ideas that were presented, developed and discussed at the third International Workshop on QCD Challenges from pp to A–A, which took place in August 2019 in Lund, Sweden (Workshop link: https://indico.lucas.lu.se/event/1214/ ). The goal of the workshop was to focus on some of the open questions in the field and try to come up with concrete suggestions for how to make progress on both the experimental and theoretical sides. The paper gives a brief introduction to each topic and then summarizes the primary results.
  5. This work in progress is motivated by a self-study conducted at Texas State University. The study revealed that the average second year science, technology, engineering and math (STEM) student retention rate is 56% vs. 67% for all majors, and that 16% of STEM majors are female while 57% of all undergraduate students are female. Using these statistics, the authors identified the need to offer motivating experiences to freshman in STEM while creating a sense of community among other STEM students. This paper reports on the impact of two interventions designed by the authors and aligned with this need. The interventionsmore »are: (1) a one-day multi- disciplinary summer orientation (summer15) to give participants the opportunity to undertake projects that demonstrate the relevance of spatial and computational thinking skills and (2) a subsequent six-week spatial visualization skills training (fall 2015) for students in need to refine these skills. The interventions have spatial skills as a common topic and introduce participants to career applications through laboratory tours and talks. Swail et al.[1] mentions that the three elements to address in order to best support students’ persistence and achievement are cognitive, social, and institutional factors. The interventions address all elements to some extent and are part of an NSF IUSE grant (2015-2018) to improve STEM retention. The summer 2015 orientation was attended by 17 freshmen level students in Physics, Engineering, Engineering Technology, and Computer Science. The orientation was in addition to “Bobcat Preview”, a separate mandatory one-week length freshman orientation that includes academic advising and educational and spirit sessions to acclimate students to the campus. The effectiveness of the orientation was assessed through exit surveys administered to participants. Current results are encouraging; 100% of the participants answered that the orientation created a space to learn about science and engineering, facilitated them to make friends and encouraged peer interaction. Eighty percent indicated that the orientation helped them to build confidence in their majors. Exit survey findings were positively linked to a former exit survey from an orientation given to a group of 18 talented and low-income students in 2013. The training on refining spatial visualization skills connects to the summer orientation by its goals. It offers freshman students in need to refine spatial skills a further way to increase motivation to STEM and create community among other students. It is also an effective approach to support students’ persistence and achievement. Bairaktarova et al.[2] mention that spatial skills ability is gradually becoming a standard assessment of an individual’s likelihood to succeed as an engineer. Metz et al.[3] report that well-developed spatial skills have been shown to lead to success in Engineering and Technology, Computer Science, Chemistry, Computer Aided Design and Mathematics. The effectiveness of the fall 2015 training was assessed through comparison between pre and post tests results and exit surveys administered to participants. All participants improved their pre-training scores and average improvement in students’ scores was 18.334%.« less