THE NATURE OF FEEDBACK:HOW DIFFERENT TYPES OF PEER FEEDBACK AFFECT WRITING PERFORMANCE

Although providing feedback is commonly practiced in education, there is general agreement regarding what type of feedback is most helpful and why it is helpful. This study examined the relationship between various types of feedback, potential internal mediators, and the likelihood of implementing feedback. Five main predictions were developed from the feedback literature in writing, specifically regarding feedback features (summarization, identifying problems, providing solutions, localization, explanations, scope, praise, and mitigating language) as they relate to potential causal mediators of problem or solution understand and problem or solution agreement, leading to the final outcome of feedback implementation.To empirically test the proposed feedback model, 1073 feedback segments from writing assessed by peers was analyzed. Feedback was collected using SWoRD, an online peer review system. Each segment was coded for each of the feedback features, implementation, agreement, and understanding. The correlations between the feedback features, levels of mediating variables, and implementation rates revealed several significant relationships. Understanding was the only significant mediator of implementation. Several feedback features were associated with understanding: including solutions, a summary of the performance, and the location of the problem were associated with increased understanding; and explanations to problems were associated with decreased understanding. Implications of these results are discussed

Evaluating a formative feedback intervention for international students

Assessment is too often concerned with measurement, rather than learning; however, there is a growing interest in research into formative assessment, which appears justified by studies into its effects on learning. Changes in higher education have led to increased numbers of students, many of whom are from non-traditional backgrounds. This has highlighted the need for transparency and student involvement in assessment. However, the corresponding pressures on staff and on resources mean that many desirable innovations are not easy to implement. The overall aim of this formative feedback intervention (FFI) was to provide timely and helpful feedback to international students who are final-year direct entrants in a large business school. Timeliness of feedback and the development of academic literacy were key concerns. The study concludes that although the FFI did not have a significant impact on module grades, the intervention was successful in getting students to engage in academic writing at an early stage. Most respondents perceived the feedback to be helpful and the feedback messages were clearly received and internalised. Whether appropriate actions were taken by the students to close the gap between their current and their target level requires further investigation

How international is IEJME?

International Electronic Journal of Mathematics Education (IEJME) is an international journal that has been serving the mathematics education community for the last six years. We at times get a variety of feedback from our readers and authors. Some of these feedback are really positive which motivate us to do more in improving the quality of IEJME, whereas some of the criticism that we get are negative which are also helpful to keep us in the right path. This Editorial is about the kind of feedback and where it puts us in the international arena

An Intervention to Improve the Evaluation of Clerkship Students

Background: Effective feedback is an important part of formative evaluation of clerkship students, improving student performance by increasing awareness to weaknesses and strengths. Aim: The aim of this study was to obtain more helpful feedback. Setting: Internal Medicine third year clerkship rotation at Joan C Edwards School of Medicine, Huntington, WV. Participants: The Internal Medicine department has fifty-nine general and subspecialty faculty physicians. Program Description: We changed the structure of the existing feedback form by requesting written comments at the beginning and asking for specific strengths and areas for improvement, educated faculty, and gave them a milestones card. Three reviewers independently ranked written feedback according to a rubric. We compared the quantity of either helpful or unhelpful feedback obtained during the 2016 and 2017 academic years with that obtained in the first rotation of 2018-2019. Program Evaluation: With our intervention, helpful comments increased from 33.8% to 79.2%. A Kappa statistic revealed a lack of bias of the reviewers. Discussion: A small change in the evaluation form along with an educational intervention and milestones card improved the quantity of helpful feedback given to students in the Internal Medicine clerkship

Guidance on Qualification Titles within the Qualifications and Credit Framework

Feedback on the implementation of the Regulatory arrangements for the Qualifications and Credit Framework (Ofqual/08/3726) governing titling within the QCF indicates that further guidance is necessary on particular issues raised by awarding organisations and/or sector skills councils. The qualifications regulators therefore decided to produce this additional guidance, which is intended to provide helpful and transparent confirmation of the regulators' position on a range of issues that have emerged subsequent to the implementation of the regulatory arrangements

Coffee: Boost Your Code LLMs by Fixing Bugs with Feedback

Code editing is an essential step towards reliable program synthesis to automatically correct critical errors generated from code LLMs. Recent studies have demonstrated that closed-source LLMs (i.e., ChatGPT and GPT-4) are capable of generating corrective feedback to edit erroneous inputs. However, it remains challenging for open-source code LLMs to generate feedback for code editing, since these models tend to adhere to the superficial formats of feedback and provide feedback with misleading information. Hence, the focus of our work is to leverage open-source code LLMs to generate helpful feedback with correct guidance for code editing. To this end, we present Coffee, a collected dataset specifically designed for code fixing with feedback. Using this dataset, we construct CoffeePots, a framework for COde Fixing with FEEdback via Preference-Optimized Tuning and Selection. The proposed framework aims to automatically generate helpful feedback for code editing while minimizing the potential risk of superficial feedback. The combination of Coffee and CoffeePots marks a significant advancement, achieving state-of-the-art performance on HumanEvalFix benchmark. Codes and model checkpoints are publicly available at https://github.com/Lune-Blue/COFFEE.Comment: Work in progres