Adoption Of Feedback Research Articles

What do STEM+ Students from Two Chilean Universities do with their Teachers’ Written Feedback Comments Recorded in their Drafts and how is this Related to their Final Grades?

Feedback is crucial for assessing and learning; thus, teachers usually provide written feedback comments (WCF) to students. The purpose of this study is to identify what students do with the written feedback in tasks from the STEM+ discipline area and how it is related to the result of their grades, identifying the type of genre in each discipline. Participants were 5 university teachers and 57 students studying marine biology, phono audiology, and nutrition who performed 671 actions in authentic materials previously given by teachers from an intact class categorized in a corpus analysis. For this reason, this research follows a correlational longitudinal non-experimental approach. To achieve the objectives, the study uses Gardner and Nesi’s genre theory (2012) for text classification and a modified version of Faigley and Witte’s taxonomy (1981) to compare the decisions made by students concerning feedback comments made by teachers. Thus, the study compares the initial and final versions of each text based on teacher comments and correlates these findings with students’ grades. Results of this study indicate a significant correlation between students’ adoption of feedback and their grades. Furthermore, the two main life sciences genres from Gardner and Nesi’s study (2012) were found in the corpus.

P1464Adoption of feedback to validate a machine learning model for single lead STEMI detection

Abstract Background We have explored the performance of a single lead EKG with Artificial Intelligence (AI) based algorithms in STEMI diagnosis, thus far lead V2 has yielded the best results. Anticipating the performance of the LUMENGT-AI model, we designed a feedback strategy with healthcare centers to expand the validation of our work. Purpose To create a pragmatic alternative to the existing gold standard, a 12-lead EKG, for STEMI diagnosis. Methods An observational, retrospective, case-control study. Sample: 2,543 exclusively STEMI (anterior, inferior and lateral wall) diagnosis, EKG records. Feedback: From healthcare centers, confirming STEMI diagnosis and location, was obtained (thrombolysis, primary Percutaneous Coronary Intervention (PCI), pharmaco invasive therapy or coronary artery bypass surgery). Records excluded other patient and medical information. Sample was derived from the private International Telemedical Systems (ITMS) database. LUMENGT-AI Algorithm was employed. Preprocessing: detection of QRS complexes using the wavelet system, segmentation of each EKG into individual heartbeats (53,667 total beats) with fixed window of 0.4s to the left and 0.9s to the right of main QRS; Classification: A 1-D convolutional neural network was implemented; “STEMI” and “Not-STEMI” classes were considered for each heartbeat per lead; individual probabilities were aggregated to generate the final label for each record. Training & Testing: 90% and 10% of the sample were used, respectively. Experiments: Intel PC i7 8750H processor at 2.21GHz, 16GB RAM, Windows 10 OS with NVidia GTX 1070 GPU, 8GB RAM. Results V2 was the most precise lead with an Accuracy of 93.6%, a Sensitivity of 89%, and a Specificity of 94.7%. Conclusions The strategic adoption of feedback from healthcare centers provided strong validation of our model. The results of AI-augmented, single lead EKG are encouraging. We anticipate that this approach will become a promising methodology in STEMI detection.