Experience of distance education for project-based learning in data science.
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ABSTRACT: Supplementary information
The online version contains supplementary material available at 10.1007/s42081-022-00154-2.
SUBMITTER: Sakamaki K
PROVIDER: S-EPMC8994060 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Rationale and objectivesThe coronavirus disease of 2019 (COVID-19) pandemic has challenged the educational missions of academic radiology departments nationwide. We describe a novel cloud-based HIPAA compliant and accessible education platform which simulates a live radiology workstation for continued education of first year radiology (R1) residents, with an emphasis on call preparation and peer to peer resident learning.Materials and methodsThree tools were used in our education model: Pacsbin (Orion Medical Technologies, Baltimore, MD, pacsbin.com), Zoom (Zoom Video Communications, San Jose, CA, zoom.us), and Google Classroom (Google, Mountain View, CA, classroom.google.com). A senior radiology resident (R2-R4) (n = 7) driven workflow was established to provide scrollable Digital Imaging and Communications in Medicine (DICOM) based case collections to the R1 residents (n = 9) via Pacsbin. A centralized classroom was created using Google Classroom for assignments, reports, and discussion where attending radiologists could review content for accuracy. Daily case collections over an 8-week period from March to May were reviewed via Zoom video conference readout in small groups consisting of a R2-R4 teacher and R1 residents. Surveys were administered to R1 residents, R2-4 residents, and attending radiologist participants.ResultsHundred percent of R1 residents felt this model improved their confidence and knowledge to take independent call. Seventy-eight percent of the R1 residents (n = 7/9) demonstrated strong interest in continuing the project after pandemic related restrictions are lifted. Based on a Likert "helpfulness" scale of 1-5 with 5 being most helpful, the project earned an overall average rating of 4.9. Two R2-R4 teachers demonstrated increased interest in pursuing academic radiology.ConclusionIn response to unique pandemic circumstances, our institution implemented a novel cloud-based distance learning solution to simulate the radiology workstation. This platform helped continue the program's educational mission, offered first year residents increased call preparation, and promoted peer to peer learning. This approach to case-based learning could be used at other institutions to educate residents.
| S-EPMC7414323 | biostudies-literature
Project description:Engaging, hands-on design experiences are key for formal and informal Science, Technology, Engineering, and Mathematics (STEM) education. Robotic and video game design challenges have been particularly effective in stimulating student interest, but equivalent experiences for the life sciences are not as developed. Here we present the concept of a "biotic game design project" to motivate student learning at the interface of life sciences and device engineering (as part of a cornerstone bioengineering devices course). We provide all course material and also present efforts in adapting the project's complexity to serve other time frames, age groups, learning focuses, and budgets. Students self-reported that they found the biotic game project fun and motivating, resulting in increased effort. Hence this type of design project could generate excitement and educational impact similar to robotics and video games.
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Project description:Introduction: Scenario-based learning and gamification have many advantages in comparison to traditional didactic teaching methods, including development of many higher-level skills such as analysis and evaluation. It is hoped that these simulations provide a real-world experience in a format accessible to students. Integration of these tools into teaching excelled during the COVID-19 pandemic, an event that completely changed education and initiated the greatest advancement in digital learning to date. We discuss our experiences using Resimion, a novel scenario-based learning tool that was adapted to biomedical science, both for teaching and assessment. Methods: Our cohort included 769 students studying BSc(Hons) Biomedical Science at the University of the West of England from 2020 to 2023. Data was obtained from assessments within four different modules, two at FHEQ level 5 and two at level 6. Students were grouped based on reasonable adjustment (RA) status, including physical issues, specific learning differences and neurodiversity, with differences between student groups and assessment types analysed by ANOVA. Results: Data clearly demonstrate good engagement from students utilising Resimion software, representing 18,436 student interactions in total, across both assessed and non-assessed activities. RAs of any type did not alter submission rates (p = 0.53) or student outcome in any of the assessment types analysed. However, submission rates for Resimion assessments were notably higher than for other assessment types (p = 0.002). Whist outcomes were not significantly different, students with RAs did take significantly longer to complete the Haematology and Transfusion assessments (p = 0.0012). Specifically, neurodiverse students and those with specific learning differences used on average 81% of their allocated time, students with other RAs used 76%, whereas students without RAs used just 56% (p ≤ 0.0001), highlighting the appropriate adjustment of extra time provided for these students. It was further observed that 1.3% of Resimion activities undertaken by students utilised the in-built inclusivity features in the software. Both students with known RAs, and those without, utilised these features, therefore also aiding students without a formal diagnosis. Conclusion: The scenario-based learning tool Resimion was successfully integrated into the teaching of biomedical science and provided an engaging platform for students, with comparable results to other traditional assessment types.
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Project description:There is a clear demand for quantitative literacy in the life sciences, necessitating competent instructors in higher education. However, not all instructors are versed in data science skills or research-based teaching practices. We surveyed biological and environmental science instructors (n = 106) about the teaching of data science in higher education, identifying instructor needs and illuminating barriers to instruction. Our results indicate that instructors use, teach, and view data management, analysis, and visualization as important data science skills. Coding, modeling, and reproducibility were less valued by the instructors, although this differed according to institution type and career stage. The greatest barriers were instructor and student background and space in the curriculum. The instructors were most interested in training on how to teach coding and data analysis. Our study provides an important window into how data science is taught in higher education biology programs and how we can best move forward to empower instructors across disciplines.
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Project description:Animal welfare science features interdisciplinary and collaborative working across fields, spanning behavioural ecology, psychology, veterinary sciences, economics, and fundamental biology. However, education research is not yet prevalent within the animal welfare literature. In a Web of Science topic search there were 188 papers which specifically discussed or explored how to teach animal welfare from 1978 to 2017. Of these, only 34% (n = 61) specifically focused on instructional design or pedagogical research, and these were predominantly within veterinary education (57%). Despite this, the literature is in broad agreement that animal welfare education is an important topic that should be done well. Within the UK, there were a possible 586 animal-related courses within Universities College Admissions Service database for potential students to choose from, highlighting the significance of robust and considered educational practice. The current gaps identified in the literature were discussion of hidden curriculums outside of veterinary degrees, animal-centered education, the blueprinting of assessment, and authentic assessment. Therefore, this review proposes that animal welfare scientists interested in education consider discipline based educational research (DBER) practices, and engage more fully with the educational research literature. A key component of DBER is the recognition that specialist knowledge needs to be taught by specialists, and so it is important that animal welfare scientists begin to access educational research too.
| S-EPMC6997439 | biostudies-literature
Project description:We live in an increasingly data-driven world, where high-throughput sequencing and mass spectrometry platforms are transforming biology into an information science. This has shifted major challenges in biological research from data generation and processing to interpretation and knowledge translation. However, postsecondary training in bioinformatics, or more generally data science for life scientists, lags behind current demand. In particular, development of accessible, undergraduate data science curricula has the potential to improve research and learning outcomes as well as better prepare students in the life sciences to thrive in public and private sector careers. Here, we describe the Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative, which aims to progressively build data science competency across several years of integrated practice. Through EDUCE, students complete data science modules integrated into required and elective courses augmented with coordinated cocurricular activities. The EDUCE initiative draws on a community of practice consisting of teaching assistants (TAs), postdocs, instructors, and research faculty from multiple disciplines to overcome several reported barriers to data science for life scientists, including instructor capacity, student prior knowledge, and relevance to discipline-specific problems. Preliminary survey results indicate that even a single module improves student self-reported interest and/or experience in bioinformatics and computer science. Thus, EDUCE provides a flexible and extensible active learning framework for integration of data science curriculum into undergraduate courses and programs across the life sciences.
| S-EPMC7906378 | biostudies-literature
Project description:Shifting learning to distant formats especially at the higher education level has been unprecedented during the past decade. Diverse digital learning media have been emerging which allow learner autonomy, and at the same time, require the ability of efficient regulation of various aspects of the learning process for sustainable academic progress. In this context, supporting students in self-regulated learning (SRL) in an optimal way becomes an important factor for their academic success. The present study attempts through a systematic review of 38 studies to provide an overview of the interventions identified as supporting all areas of SRL (metacognitive, cognitive, motivational and emotional), in its three phases (preparatory, performance, appraisal) in distance education environments at the higher education level. As the study results show, there are a number of SRL support interventions available with proven positive effect on SRL. However, their distribution has been found to be uneven. Whereas metacognition regulation and the performance phase of learning is vastly investigated, the emotion regulation, and the preparatory and appraisal phases of the SRL cycle are somewhat underexplored. As complex and multi-component as the process of SRL is, the combination of various interventions, and specific features, for more comprehensive support has also been found beneficial. Additionally, it has been revealed that the emotion regulation, in most cases, is closely related to motivation regulation, and similar interventions support these two. Future studies can further explore the efficiency and relevance of the identified interventions, taking closer look at the effects of various digital media, learner characteristics as well as different levels of education on learners' self-regulation needs.
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Project description:Despite recognising the importance of transparency and understanding of predictive models, little effort has been made to investigate the errors made by these models. In this paper, we address this gap by interviewing 12 students whose results and predictions of submitting their assignment differed. Following our previous quantitative analysis of 25,000+?students, we conducted online interviews with two groups of students: those predicted to submit their assignment, yet they did not (False Negative) and those predicted not to submit, yet they did (False Positive). Interviews revealed that, in False Negatives, the non-submission of assignments was explained by personal, financial and practical reasons. Overall, the factors explaining the different outcomes were not related to any of the student data currently captured by the predictive model.
| S-EPMC7334695 | biostudies-literature
Project description:Prior to the Covid-19 global pandemic, we reviewed literature and identified comprehensive evidence of the efficacy of blended learning for pre-registration nursing students who learn across distances and/or via satellite campuses. Following a methodological framework, a scoping literature review was undertaken. We searched six databases (EBSCOHOST (CINHAL plus; Education research Complete; Australia/New Zealand Reference Centre); Google Scholar; EMBASE (Ovid) [ERIC (Ovid); Medline (Ovid)]; PubMed: ProQuest Education Journals & ProQuest Nursing & Allied Health Source) for the period 2005-December 2015. Critical appraisal for critiquing qualitative and quantitative studies was undertaken, as was a thematic analysis. Twenty-eight articles were included for review, which reported nursing research (n = 23) and student experiences of blended learning in higher education (n = 5). Four key themes were identified in the literature: active learning, technological barriers, support, and communication. The results suggest that when delivered purposefully, blended learning can positively influence and impact on the achievements of students, especially when utilised to manage and support distance education. Further research is needed about satellite campuses with student nurses, to assist with the development of future educational practice.
| S-EPMC7195119 | biostudies-literature
Project description:MotivationDirect sequencing of microbes in human ecosystems (the human microbiome) has complemented single genome cultivation and sequencing to understand and explore the impact of commensal microbes on human health. As sequencing technologies improve and costs decline, the sophistication of data has outgrown available computational methods. While several existing machine learning methods have been adapted for analyzing microbiome data recently, there is not yet an efficient and dedicated algorithm available for multiclass classification of human microbiota.ResultsBy combining instance-based and model-based learning, we propose a novel sparse distance-based learning method for simultaneous class prediction and feature (variable or taxa, which is used interchangeably) selection from multiple treatment populations on the basis of 16S rRNA sequence count data. Our proposed method simultaneously minimizes the intraclass distance and maximizes the interclass distance with many fewer estimated parameters than other methods. It is very efficient for problems with small sample sizes and unbalanced classes, which are common in metagenomic studies. We implemented this method in a MATLAB toolbox called MetaDistance. We also propose several approaches for data normalization and variance stabilization transformation in MetaDistance. We validate this method on several real and simulated 16S rRNA datasets to show that it outperforms existing methods for classifying metagenomic data. This article is the first to address simultaneous multifeature selection and class prediction with metagenomic count data.AvailabilityThe MATLAB toolbox is freely available online at http://metadistance.igs.umaryland.edu/.Contactzliu@umm.eduSupplementary informationSupplementary data are available at Bioinformatics online.
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