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Expert judgement of collaborative cloud classroom quality and its criteria using the many-facets rasch model.

ABSTRACT: Collaborative Cloud Classroom (3CR) is an educational application that provides flexibility in the learning process. This specialized tool is designed to improve analytical and teambuilding skills, as well as enable students to document ideas, which are subsequently developed into bubble diagrams and text outlines. This study aims to assess and analyze the menu qualities possessed by the 3CR application using the Many-Facet Rasch Model (MFRM) analysis using 5 raters and 6 menus. A rating assessment was applied to 5 criteria, namely usability, functionality, visual communication, learning design, and security. In this case, a total of 135 from 150 data points were coded into FACETS software. The results showed that the six menus in the 3CR application were valid and reliable, as well as met the quality standards of learning tools. Security criteria were also the most difficult standards for raters to measure in all the application menus. The study also found that Rater A was more severe, and Rater E was more lenient in rating the 3CR menu. Rater B has overfitting and detected bias between rater gender and 3CR's menu (Discussion Forum and Learning Reflection menu) based on MFRM analysis.

SUBMITTER: Ramadhani R

PROVIDER: S-EPMC10570598 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Expert judgement of collaborative cloud classroom quality and its criteria using the many-facets rasch model.

Ramadhani Rahmi R Syahputra Edi E Simamora Elmanani E Soeharto Soeharto S

Heliyon 20231005 10

Collaborative Cloud Classroom (3CR) is an educational application that provides flexibility in the learning process. This specialized tool is designed to improve analytical and teambuilding skills, as well as enable students to document ideas, which are subsequently developed into bubble diagrams and text outlines. This study aims to assess and analyze the menu qualities possessed by the 3CR application using the Many-Facet Rasch Model (MFRM) analysis using 5 raters and 6 menus. A rating assessm ...[more]

PMID: 37842603

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Project description:OBJECTIVES:Sources of bias, such as the examiners, domains and stations, can influence the student marks in objective structured clinical examination (OSCE). This study describes the extent to which the facets modelled in an OSCE can contribute to scoring variance and how they fit into a Many-Facet Rasch Model (MFRM) of OSCE performance. A further objective is to identify the functioning of the rating scale used. DESIGN:A non-experimental cross-sectional design. PARTICIPANTS AND SETTINGS:An MFRM was used to identify sources of error (eg, examiner, domain and station), which may influence the student outcome. A 16-station OSCE was conducted for 329 final year medical students. Domain-based marking was applied, each station using a sample from eight defined domains across the whole OSCE. The domains were defined as follows: communication skills, professionalism, information gathering, information giving, clinical interpretation, procedure, diagnosis and management. The domains in each station were weighted to ensure proper attention to the construct of the individual station. Four facets were assessed: students, examiners, domains and stations. RESULTS:The results suggest that the OSCE data fit the model, confirming that an MFRM approach was appropriate to use. The variable map allows a comparison with and between the facets of students, examiners, domains and stations and the 5-point score for each domain with each station as they are calibrated to the same scale. Fit statistics showed that the domains map well to the performance of the examiners. No statistically significant difference between examiner sensitivity (3.85 logits) was found. However, the results did suggest examiners were lenient and that some behaved inconsistently. The results also suggest that the functioning of response categories on the 5-point rating scale need further examination and optimisation. CONCLUSIONS:The results of the study have important implications for examiner monitoring and training activities, to aid assessment improvement.

Dataset Information

Expert judgement of collaborative cloud classroom quality and its criteria using the many-facets rasch model.

Publications

Expert judgement of collaborative cloud classroom quality and its criteria using the many-facets rasch model.

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