A Case Study of Post-Workshop Use of Simple Active Learning in an Introductory Computing Sequence

Clark, Renee M.; Dickerson, Samuel J.

doi:10.1109/te.2018.2808274

Cited by 15 publications

(6 citation statements)

References 30 publications

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“…Finally, using [12]- [15], and based on the selected competences, the DT and the INCan defined the challenge as "design and implement computer applications to support the follow-up of patients with breast cancer before and during their treatment". Because of the technology that we planned to use, the i-Sem was registered under the title of "Data Analytic and Cloud Computing vs Breast Cancer: Learning that Helps".…”

Section: The Origin Of the Daandccvsbc Projectmentioning

confidence: 99%

Data Analytics and Cloud Computing vs Breast Cancer: Learning That Helps

Mónica¹,

Sergio²,

Ramona³

et al. 2020

IJIET

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This article presents the deployment of the Semester i (i-Sem) "Data Analytics and Cloud Computing vs Breast Cancer: learning that helps" (DA&CCvsBC). i-Sem is one of the elements of the Tec21 educational model of The Monterrey Tech. DA&CCvsBC is a learning environment where engineering students developed competences, by resolving an inspiring challenge designed from a problem posed by a training partner (TP). The challenge consisted in designing and implementing computer applications to support the follow-up of patients with breast cancer before and during their treatment. The project started with the selection of a list of competences to be developed in the students. From that list and based on a real problem situation posed by two training partners (TPs), a challenge was designed. The challenge was presented to students interested in participating and 11 students were selected. So, in 18 weeks, the students, accompanied by professors, built solution proposals for the challenge. During the construction process, the students developed the established competences. The project had a vision: "learning that helps". The intended outcomes were: A report that evidenced the development of students' competences, and two computer applications for monitoring the treatment of patients with breast cancer.

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Section: The Origin Of the Daandccvsbc Projectmentioning

confidence: 99%

Data Analytics and Cloud Computing vs Breast Cancer: Learning That Helps

Mónica¹,

Sergio²,

Ramona³

et al. 2020

IJIET

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“…With this program, engineering-education institutions, manufacturing industries broadly, and steelmakers in particular will be direct beneficiaries. Project-based learning "refocuses education on the student … rewarding drive, passion, creativity, empathy, and resiliency" [30]; and research has demonstrated that students in project-based learning classrooms attain higher scores than students in traditional classrooms [31][32][33][34]. Active learning also results in greater gains in both learning efficiency and effectiveness, vs traditional lecturing, across STEM disciplines [13].…”

Section: Methodsmentioning

confidence: 99%

Research Experiences for Teachers in Simulation and Visualization for Innovative Industrial Solutions

Moreland,

Okosun,

Silaen

et al.

2022 ASEE Annual Conference &Amp; Exposition Proceedings

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An NSF Research Experiences for Teachers (RET) Site has been established at Purdue University Northwest to involved high school teachers and community college instructors in industrial research involving numeric simulation, such as computational fluid dynamics (CFD) and Finite Element Analysis (FEA), as well as interactive simulator development using the Unity 3D game engine. A cohort of 11 teachers participated during the summer of 2021 and included teachers and instructors from Indiana and Illinois high school teachers and community colleges. Research projects involved industrial collaborations with local steel industry partners.The project kicked off with a 6-week in-person research experience hosted at the Center for Innovation through Visualization and Simulation (CIVS) at Purdue University Northwest. The teachers worked with faculty and research staff mentors and graduate students on research projects in the areas of blast furnace/environmental using computational fluid dynamics (CFD) simulation, reheat furnace efficiency using CFD, overhead crane stress using finite element analysis (FEA), and safety training using interactive visualization. Each research project included research mentor staff, graduate and undergraduate students, as well as collaboration with steel industry partners. In addition to the research outcomes, each teacher also developed a lesson plan and education module which will be hosted online for use by other educators. Lesson plans involved a variety of topics programming activities for computer science and related classes, chemistry and environmental activities, math and statistics analysis, and engineering. Each lesson plan also involved some form of activity involving computer simulation and/or interactive visualization.Surveys measured teacher's self-efficacy in a number of areas including research literature review, design, data collection and analysis, communication of research results, ability to relate real world research problems to teaching, and use of simulation and visualization tools for research and teaching. There was a measured improvement between pre-summer experience and post-summer experience in all categories, with the largest improvements involving ability to discuss research ideas and ability to use simulation/visualization tools for research and teaching. Follow-up activities are ongoing during the teacher's academic school year, including assistance from the RET to carry out implementation of the lesson plans in the classroom, field trips, networking activities, presenting research career information to students, and surveying students on their interest in pursuing STEM activities and related careers.

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“…A content analysis of the student interview responses was completed by two analysts to drive reliability [23]. The analysts independently content-analyzed the responses using coding schemes developed as part of previous research [15], [24]. The analysts then discussed each response and the codes assigned to ensure consensus; thus all responses were double-coded.…”

Section: Table 1: Interview Questionsmentioning

confidence: 99%