Related papers: The Study of Peer Assessment Impact on Group Learning Activities

The Study of Peer Assessment Impact on Group Learning Activities

URL: http://arxiv.org/abs/2201.02344v1
Date: Fri, 7 Jan 2022 06:54:18 GMT
Title: The Study of Peer Assessment Impact on Group Learning Activities
Authors: Zhiyuan Chen, Soon Boon Lee, Shazia Paras Shaikh and Mirza Rayana Sanzana
Abstract summary: This research work provides a complete and systematic review, increase the practice and quality of the peer assessment process. The results show 37% student will choose individual work over group work if given the choice. In the case study, 82.1% of the total of 28 students have en-joyed working in a group using Facebook as communication tools.
Score: 2.045495982086173
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: Comparing with lecturer marked assessments, peer assessment is a more comprehensive learning process and many of the associated problems have occurred. In this research work, we study the peer-assessment impact on group learning activities in order to provide a complete and systematic review, increase the practice and quality of the peer assessment process. Pilot studies were conducted and took the form of surveys, focus group interviews, and questionnaires. Prelimi-nary surveys were conducted with 582 students and 276 responses were received, giving a response rate of 47.4%. The results show 37% student will choose individual work over group work if given the choice. In the case study, 82.1% of the total of 28 students have en-joyed working in a group using Facebook as communication tools. 89.3% of the students can demonstrate their skills through group-working and most importantly, 82.1% of them agree that peer assess-ment is an impartial method of assessment with the help of Facebook as proof of self-contribution. Our suggestions to make group work a pleasant experience are by identifying and taking action against the freeloader, giving credit to the deserving students, educating students on how to give constructive feedback and making the assessment pro-cess transparent to all.

Related papers

The Impact of Group Discussion and Formation on Student Performance: An Experience Report in a Large CS1 Course [10.04958142789079]
The impact of group discussion and formation mechanisms on student performance remains unclear. We employed both random and expertise-balanced grouping methods to examine the efficacy of different group mechanisms. Our analysis revealed that different grouping methods did not significantly influence discussion engagement or poor-performing students' improvement.
arXiv Detail & Related papers (2024-08-26T20:07:44Z)
PICA: A Data-driven Synthesis of Peer Instruction and Continuous Assessment [0.0]
The work herein combines PI and CA in a deliberate and novel manner to pair students together for a PI session in which they collaborate on a CA task. The motivation for this data-driven collaborative learning is to improve student learning, communication, and engagement.
arXiv Detail & Related papers (2024-07-24T20:50:32Z)
Exploring Factors Affecting Student Learning Satisfaction during COVID-19 in South Korea [0.0]
This study aimed to understand several facets related to student learning preferences and satisfaction during the pandemic. Data gathered included students' gender, study major, satisfaction and motivation levels when learning, perceived performance, emotional state and learning environment.
arXiv Detail & Related papers (2024-07-12T14:28:11Z)
Enhancing Students' Learning Process Through Self-Generated Tests [0.0]
This paper describes an educational experiment aimed at the promotion of students' autonomous learning. The main idea is to make the student feel part of the evaluation process by including students' questions in the evaluation exams. Questions uploaded by students are visible to every enrolled student as well as to each involved teacher.
arXiv Detail & Related papers (2024-03-21T09:49:33Z)
Pro-f-quiz: increasing the PROductivity of Feedback through activating QUIZzes [3.5788754401889022]
We have set up an experiment, titled Pro-f-quiz, in which over two years 236 students participated and in which the feedback is communicated through an online quiz. The results show that approximately 85% of the students appreciate the approach with 60% indicating that they reflect more intensively about their work than when the feedback is transmitted traditionally.
arXiv Detail & Related papers (2023-05-25T12:37:40Z)
Biomedical image analysis competitions: The state of current participation practice [143.52578599912326]
We designed a survey to shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis. The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures.
arXiv Detail & Related papers (2022-12-16T16:44:46Z)
Flexible social inference facilitates targeted social learning when rewards are not observable [58.762004496858836]
Groups coordinate more effectively when individuals are able to learn from others' successes. We suggest that social inference capacities may help bridge this gap, allowing individuals to update their beliefs about others' underlying knowledge and success from observable trajectories of behavior.
arXiv Detail & Related papers (2022-12-01T21:04:03Z)
Ranking Scientific Papers Using Preference Learning [48.78161994501516]
We cast it as a paper ranking problem based on peer review texts and reviewer scores. We introduce a novel, multi-faceted generic evaluation framework for making final decisions based on peer reviews.
arXiv Detail & Related papers (2021-09-02T19:41:47Z)
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering [71.15403434929915]
We show that across 5 models and 4 datasets on the task of visual question answering, a wide variety of active learning approaches fail to outperform random selection. We identify the problem as collective outliers -- groups of examples that active learning methods prefer to acquire but models fail to learn. We show that active learning sample efficiency increases significantly as the number of collective outliers in the active learning pool decreases.
arXiv Detail & Related papers (2021-07-06T00:52:11Z)
Catch Me if I Can: Detecting Strategic Behaviour in Peer Assessment [61.24399136715106]
We consider the issue of strategic behaviour in various peer-assessment tasks, including peer grading of exams or homeworks and peer review in hiring or promotions. Our focus is on designing methods for detection of such manipulations. Specifically, we consider a setting in which agents evaluate a subset of their peers and output rankings that are later aggregated to form a final ordering.
arXiv Detail & Related papers (2020-10-08T15:08:40Z)
Social Interactions Clustering MOOC Students: An Exploratory Study [57.822523354358665]
Comments were categorized based on how students interacted with them, e.g., how a student's comment received replies from peers. Statistical modelling and machine learning were used to analyze comment categorization, resulting in 3 strong and stable clusters.
arXiv Detail & Related papers (2020-08-10T09:32:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.