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Ten strategies for a successful transition to remote learning: Lessons learned with a flipped course.

ABSTRACT: Transitioning from in-person to remote learning can present challenges for both the instructional team and the students. Here, we use our course "Biodiversity in the Age of Humans" to describe how we adapted tools and strategies designed for a flipped classroom to a remote learning format. Using anonymous survey data collected from students who attended the course either in-person (2019) or remotely (2020), we quantify student expectations and experiences and compare these between years. We summarize our experience and provide ten "tips" or recommendations for a transition to remote learning, which we divide into three categories: (a) precourse instructor preparation; (b) outside of class use of online materials; and (c) during class student engagement. The survey results indicated no negative impact on student learning during the remote course compared to in-person instruction. We found that communicating with students and assessing specific needs, such as access to technology, and being flexible with the structure of the course, simplified the transition to remote instruction. We also found that short, pre-recorded videos that introduce subject materials were among the most valuable elements for student learning. We hope that instructors of undergraduate ecology and evolution courses can use these recommendations to help establish inclusive online learning communities that empower students to acquire conceptual knowledge and develop scientific inquiry and literacy skills.

SUBMITTER: Garcia-Vedrenne AE

PROVIDER: S-EPMC7679550 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Ten strategies for a successful transition to remote learning: Lessons learned with a flipped course.

Garcia-Vedrenne Ana E AE Orland Chloé C Ballare Kimberly M KM Shapiro Beth B Wayne Robert K RK

Ecology and evolution 20201016 22

Transitioning from in-person to remote learning can present challenges for both the instructional team and the students. Here, we use our course "Biodiversity in the Age of Humans" to describe how we adapted tools and strategies designed for a flipped classroom to a remote learning format. Using anonymous survey data collected from students who attended the course either in-person (2019) or remotely (2020), we quantify student expectations and experiences and compare these between years. We summ ...[more]

PMID: 33250998

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Project description:Due to the rapid increase in the availability of patient data, there is significant interest in precision medicine that could facilitate the development of a personalized treatment plan for each patient on an individual basis. Radiation oncology is particularly suited for predictive machine learning (ML) models due to the enormous amount of diagnostic data used as input and therapeutic data generated as output. An emerging field in precision radiation oncology that can take advantage of ML approaches is radiogenomics, which is the study of the impact of genomic variations on the sensitivity of normal and tumor tissue to radiation. Currently, patients undergoing radiotherapy are treated using uniform dose constraints specific to the tumor and surrounding normal tissues. This is suboptimal in many ways. First, the dose that can be delivered to the target volume may be insufficient for control but is constrained by the surrounding normal tissue, as dose escalation can lead to significant morbidity and rare. Second, two patients with nearly identical dose distributions can have substantially different acute and late toxicities, resulting in lengthy treatment breaks and suboptimal control, or chronic morbidities leading to poor quality of life. Despite significant advances in radiogenomics, the magnitude of the genetic contribution to radiation response far exceeds our current understanding of individual risk variants. In the field of genomics, ML methods are being used to extract harder-to-detect knowledge, but these methods have yet to fully penetrate radiogenomics. Hence, the goal of this publication is to provide an overview of ML as it applies to radiogenomics. We begin with a brief history of radiogenomics and its relationship to precision medicine. We then introduce ML and compare it to statistical hypothesis testing to reflect on shared lessons and to avoid common pitfalls. Current ML approaches to genome-wide association studies are examined. The application of ML specifically to radiogenomics is next presented. We end with important lessons for the proper integration of ML into radiogenomics.

Dataset Information

Ten strategies for a successful transition to remote learning: Lessons learned with a flipped course.

Publications

Ten strategies for a successful transition to remote learning: Lessons learned with a flipped course.

Similar Datasets

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