An experience with peer assessment in the context of a Computer Systems course

Montagner, Igor S

doi:10.1109/fie43999.2019.9028508

Cited by 2 publications

(3 citation statements)

References 10 publications

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“…Further peer assessment frameworks such as PRAISE (Peer Review Assignments Increase Student Experience), Peer Scholar, Peerwise, are also discussed by their studies to give more support to the peer assessment process. Oral presentation, portfolios, articles, test performances, online discussions, quizzes, etc., are the most common products observed during the survey that are assessed using peer assessment approach [53,54]. Mixed methods survey has been conducted which shows positive and significant relationship between teachers' assessment and peer assessment by using the scoring rubric to collect data for the study [24].…”

Section: Peer Assessment Peer Assessment Has Been Graduallymentioning

confidence: 99%

Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education

Hooda

Rana

Dahiya

et al. 2022

Mathematical Problems in Engineering

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The core focus of this review is to show how immediate and valid feedback, qualitative assessment influence enhances students learning in a higher education environment. With the rising trend of online education especially in this COVID-19 pandemic, the role of assessment and feedback also changes. Earlier the assessment part is not considered the main focus in learning and teaching in HEIs, but now with the increase in online education, it is observed that the paradigm is shifted toward assessing those activities of students that enhance their learning outcomes. A lot of research work has been done on developing assessment strategies and techniques that can support learning and teaching effectively. Yet, there is limited research that looks at how methods applied in learning analytics can be used and possibly constitutes the assessment process. The objective of this work is to provide an exploratory and comparative study of how assessment and feedback practices can enhance students learning outcomes using AI. The key contribution of this study attempts to capture an outline of the most used artificial intelligence and machine learning algorithms for student success. The results showed that I-FCN performed better than other techniques (ANN, XG Boost, SVM, Random Forest, and Decision Trees) in all measured performance metrics. Also, the result of the comparative analysis study will help the educators, instructors, and administrators on how they could take the advantage of a data-driven approach, design less pressurized, more valid, reliable, constructive assessment findings, and connect the power of assessment and feedback to enhance the learning outcomes.

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Section: Peer Assessment Peer Assessment Has Been Graduallymentioning

confidence: 99%

Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education

Hooda

Rana

Dahiya

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…It also helps improve learning through the refinement of programs. Evaluation takes a lot of time for teachers; thus, an alternative is to use peer evaluation, in which students evaluate their peers guided by the teacher (Montagner, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…This questionnaire gives students the opportunity to reflect on their own solutions, not only analyzing the solutions of their peers. Validity was also measured using the correlation between the teacher's qualifications and those given by the students, obtaining r = 0.87, which indicates high validity (Montagner, 2019). In their research, Lee et al (2020) adapted the Quality Matters rubric, which generated the Academic Support rubric, and conducted a study on the internal consistency and reliability of a rubric.…”

Section: Introductionmentioning

confidence: 99%

Design of peer assessment rubrics for ICT topics

et al. 2021

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Peer evaluation consists of the evaluation of students by their peers following criteria or rubrics provided by the teacher, where the way to evaluate students is specified so that they achieve the desired competencies. The quality of the measurement instrument must meet two essential criteria: validity and reliability. In this research, we explored the educational value of peer evaluation rubrics by analyzing the quality of the rubric through the study of content validity, reliability, and internal consistency. Our main purpose was to design an appropriate rubric to grade tasks in the field of information engineering, as well as performing content validation through a group of experts. It was carried out in three phases: 1) construction of a rubric, with its criteria, characteristics, and levels of achievement; 2) content validation by five experts in the field, and 3) application of the rubric to ascertain students' perceptions and satisfaction with its validity. The relevance of the criteria and the definition of their characteristics obtained a score higher than 3.75/4 on a Likert scale. The content validity values (CVR), content validity index (CVI), and general content validity index (GIVC) gave maximum values of + 1. The results obtained indicate that the rubric is adequate, with Aiken's V higher than V 0.87 in all its criteria. The rubric was applied to 326 students of 4 subjects. Cronbach's alpha was used to calculate the reliability of the rubric, obtaining a value of 0.839. The students' perception of validity and satisfaction with the rubric was higher than 0.78. As future work, we intend to design a rubric validation engine according to the applied procedure.

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An experience with peer assessment in the context of a Computer Systems course

Cited by 2 publications

References 10 publications

Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education

Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education

Design of peer assessment rubrics for ICT topics

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