A Mixed-Method Approach to Investigating Difficulty in Data Science Education

The purpose of this study was to define a methodology to identify any disconnect between students and instructors in data science classrooms through analyzing qualitative data. A combined qualitative and quantitative approach was used for analysis of survey data from students, faculty/instructors, and teaching assistants across three institutions. Using a manual content analysis paired with a TF-IDF analysis, researchers were able to pull out frequently used terms within responses and encode them into categories and subcategories. Trends were identified from these categories and subcategories to examine general areas of disconnect within the data science classroom. Additionally, a quality analysis was run to determine the effectiveness of the phrasing of the questions posed during the survey. As a whole, the methods used throughout this research process provide direction for researchers in interpretation and analysis of the survey data in an efficient and time-sensitive manner. Furthermore, it allows researchers to analyze the quality of responses to give insight towards rephrasing of survey questions in future analyses. Although the research was applied to data science classrooms, this method has the potential to be applied into other fields and areas of study when performed with coordination between a field expert and a data scientist

Prospective in silico evaluation of cone-beam computed tomography-guided stereotactic adaptive radiation therapy (CT-STAR) for the ablative treatment of ultracentral thoracic disease

PURPOSE: We conducted a prospective, in silico study to evaluate the feasibility of cone-beam computed tomography (CBCT)-guided stereotactic adaptive radiation therapy (CT-STAR) for the treatment of ultracentral thoracic cancers (NCT04008537). We hypothesized that CT-STAR would reduce dose to organs at risk (OARs) compared with nonadaptive stereotactic body radiation therapy (SBRT) while maintaining adequate tumor coverage. METHODS AND MATERIALS: Patients who were already receiving radiation therapy for ultracentral thoracic malignancies underwent 5 additional daily CBCTs on the ETHOS system as part of a prospective imaging study. These were used to simulate CT-STAR, in silico RESULTS: Seven patients were accrued, 6 with intraparenchymal tumors and 1 with a subcarinal lymph node. CT-STAR was feasible in 34 of 35 simulated fractions. In total, 32 dose constraint violations occurred when the P CONCLUSIONS: CT-STAR widened the dosimetric therapeutic index of ultracentral thorax SBRT compared with nonadaptive SBRT. A phase 1 protocol is underway to evaluate the safety of this paradigm for patients with ultracentral early-stage NSCLC

A Mixed-Method Approach to Investigating Difficulty in Data Science Education

The purpose of this study was to define a methodology to identify any disconnect between students and instructors in data science classrooms through analyzing qualitative data. A combined qualitative and quantitative approach was used for analysis of survey data from students, faculty/instructors, and teaching assistants across three institutions. Using a manual content analysis paired with a TF-IDF analysis, researchers were able to pull out frequently used terms within responses and encode them into categories and subcategories. Trends were identified from these categories and subcategories to examine general areas of disconnect within the data science classroom. Additionally, a quality analysis was run to determine the effectiveness of the phrasing of the questions posed during the survey. As a whole, the methods used throughout this research process provide direction for researchers in interpretation and analysis of the survey data in an efficient and time-sensitive manner. Furthermore, it allows researchers to analyze the quality of responses to give insight towards rephrasing of survey questions in future analyses. Although the research was applied to data science classrooms, this method has the potential to be applied into other fields and areas of study when performed with coordination between a field expert and a data scientist