TP8.2.8 Medical School Selection scores correlate with MRCS Part A performance

Aims Selection into UK medical school involves a combination of three measures: prior academic attainment, selection tests (e.g. the University Clinical Aptitude Test (UCAT), Biomedical Admissions Test (BMAT), or Graduate Medical School Admissions Test (GAMSAT)) followed by interview. We investigated the predictive power of current UK medical selection tests and measures of prior attainment on success in the Membership of the Royal College of Surgeons (MRCS) examination. Methods The UKMED database was used to analyse A-Levels and medical school selection data for all UK graduates who attempted the MRCS Part A written examination (n = 9729) and Part B clinical examination (n = 4644) between 2007 and 2017. Univariate analysis and Pearson correlation coefficients examined the relationship between selection scores and first attempt MRCS success. Results Successful MRCS Part A candidates scored higher in A-Levels, UCAT, BMAT and GAMSAT examinations (p < 0.05) than their unsuccessful peers, but no differences were observed for MRCS Part B. Statistically significant positive correlation was found between MRCS Part A, BMAT (r = 0.32, p < 0.001) and GAMSAT scores (r = 0.35, p = <0.001). While a weaker statistically significant correlation was found between Part A, A-Level (r = 0.14, p < 0.001) and UCAT scores (r = 0.25, p < 0.001). Conclusions This, the first study to investigate the relationship between all UK medical school selection tests and success in a postgraduate examination found statistically significant correlations between selection test scores and performance on Part A of the MRCS. The strength of correlations found in this study are similar to those of other validated selection tests used in the United States.

221 UK Medical School Selection Criteria Predicts Success in Part A of The Intercollegiate Membership of The Royal College of Surgeons (MRCS) Examination

Background Selection into UK medical school typically involves a combination of three measures: prior academic attainment, selection tests (e.g., the University Clinical Aptitude Test (UCAT), Biomedical Admissions Test (BMAT), Graduate Medical School Admissions Test (GAMSAT)), and an interview. We investigated whether prior attainment and selection test scores can predict MRCS success. Method We used the UKMED database to analyse selection data for all UK graduates who attempted MRCS Part A (n = 9729) and Part B (n = 4644) between 2007-2017. Univariate analysis and Pearson correlation coefficients were used to examine the relationship between selection scores and first attempt MRCS success. Results Successful MRCS Part A candidates had better A-Levels and higher scores in UCAT, BMAT and GAMSAT examinations (p < 0.001) than their unsuccessful peers. No statistically significant difference was observed for MRCS Part B. A moderate positive correlation was found between Part A, BMAT (r = 0.315, p < 0.001) and GAMSAT scores (r = 0.346, p < 0.001). A weak positive correlation was found between Part A, A-Level (r = 0.144, p < 0.001) and UCAT scores (r = 0.246, p < 0.001). Conclusions A-level results and medical school selection tests predict success in the knowledge-based (Part A) MRCS examination.

PRELIMINARY STUDY OF KUNTOWIJOYO STUDENT VALUE ASSESSMENT INSTRUMENTS (HIGH SCHOOL CASE STUDY)

<em>The purpose of education is to develop the potential that exists in humans. Making humans as human beings. Educating forms humans to have real character. Many studies on the application of character education begin when a human is in the bench of study. Not only about science, but also about the science of life. So it takes a character to make humans better. Kuntowijoyo in his social theory explains that human character should be brought to the values of humanization, liberation, and transcendence. Where the three of them are related and support each other to present a better human figure. The planting of prophetic values proclaimed by Kuntowijoyo will truly be felt by the community where during their education they are able to continue to be evaluated. So that a student is able to continue to develop and get character development. Therefore we need an initial study to see whether the development of an instrument to measure the prophetic value of Kuntowijoyo in students is needed or not. In order to see this, a study was conducted to see the need for assessment in several senior high schools in Central Java Province. There are 6 SMA schools representing 6 different districts or cities in Central Java. Data obtained by using simple interviews to 6 respondents who represent the 6 existing SMA. School selection was carried out by looking at the majority population in Central Java who were Muslim, therefore 6 Islamic-style high schools were taken with a populist reputation regarding the cultivation of good character values. Based on the data obtained, it is necessary to have an instrument that can be used and standardized to support character education. The instrument that will show the evaluation value of character education is seen from the prophetic value of Kuntowijoyo. An instrument that can be used by various types of educational institutions in Indonesia</em>

Performance at medical school selection correlates with success in Part A of the intercollegiate Membership of the Royal College of Surgeons (MRCS) examination

Medical schools in the UK typically use prior academic attainment and an admissions test (University Clinical Aptitude Test (UCAT), Biomedical Admissions Test (BMAT) or the Graduate Medical School Admissions Test (GAMSAT)) to help select applicants for interview. To justify their use, more information is needed about the predictive validity of these tests. Thus, we investigated the relationship between performance in admissions tests and the Membership of the Royal College of Surgeons (MRCS) examination.The UKMED database (https://www.ukmed.ac.uk) was used to access medical school selection data for all UK graduates who attempted MRCS Part A (n=11 570) and Part B (n=5690) between 2007 and 2019. Univariate and multivariate logistic regression models identified independent predictors of MRCS success. Pearson correlation coefficients examined the linear relationship between test scores and MRCS performance.Successful MRCS Part A candidates scored higher in A-Levels, UCAT, BMAT and GAMSAT (p<0.05). No significant differences were observed for MRCS Part B. All admissions tests were found to independently predict MRCS Part A performance after adjusting for prior academic attainment (A-Level performance) (p<0.05). Admission test scores demonstrated statistically significant correlations with MRCS Part A performance (p<0.001).The utility of admissions tests is clear with respect to helping medical schools select from large numbers of applicants for a limited number of places. Additionally, these tests appear to offer incremental value above A-Level performance alone. We expect this data to guide medical schools’ use of admissions test scores in their selection process.

Clustering Students' Interest Determination in School Selection Using the K-Means Clustering Algorithm Method

Pendidikan merupakan satu diantara fokus utama program kerja Pemerintah Kabupaten Indragiri Hilir. Berdasarkan data Badan Pusat Statistik Daerah kabupaten Indragiri tahun 2019 memaparkan, tingginya tingkat minat siswa dalam mengenyam bangku sekolah adalah pada jenjang SD dan SMP. K-means Clustering merupakan salah satu Teknik pengeolompokan data dengan cara membagi data yang ada ke dalam bentuk satu atau lebih cluster. Pengelompokan sekolah berdasarkan minat siswa merupakan hal penting dikarenakan pada tingkat SMA minat siswa dalam mengenyam pendidikan sudah berkurang sehingga di perlukan informasi sekolah mana yang sangat diminati, cukup diminati dan kurang diminati oleh siswa pada tingkat SMP ketika setelah selesai dari pendidikan SD. Penelitian ini bertujuan membantu pihak Dinas Pendidikan dalam proses pengambilan keputusan untuk menentukan sekolah mana yang paling banyak diminati oleh siswa guna sebagai acuan dalam pembangunan baik dari segi kualitas maupun kuantitas. Data yang digunakan dalam penelitian ini adalah data Dapodikdasmen tahun 2019. pengolahan data dalam penelitian ini menggunakan metode K-means Clustering dengan jumlah 3 cluster yaitu cluster 0 (C0) kurang diminati, Cluster 1 (C1) cukup diminati, cluster 2 (c2) sangat diminati siswa dalam memilih sekolah. Hasil dari proses clustering dengan 2 kali iterasi menyatakan bahwa untuk cluster 0 berjumlah 6 data sekolah, untuk cluster 1 berjumlah 3 data sekolah cluster 2 berjumlah 1 data sekolah.

Clustering Students' Interest Determination in School Selection Using the K-Means Clustering Algorithm Method

Pendidikan merupakan satu diantara fokus utama program kerja Pemerintah Kabupaten Indragiri Hilir. Berdasarkan data Badan Pusat Statistik Daerah kabupaten Indragiri tahun 2019 memaparkan, tingginya tingkat minat siswa dalam mengenyam bangku sekolah adalah pada jenjang SD dan SMP. K-means Clustering merupakan salah satu Teknik pengeolompokan data dengan cara membagi data yang ada ke dalam bentuk satu atau lebih cluster. Pengelompokan sekolah berdasarkan minat siswa merupakan hal penting dikarenakan pada tingkat SMA minat siswa dalam mengenyam pendidikan sudah berkurang sehingga di perlukan informasi sekolah mana yang sangat diminati, cukup diminati dan kurang diminati oleh siswa pada tingkat SMP ketika setelah selesai dari pendidikan SD. Penelitian ini bertujuan membantu pihak Dinas Pendidikan dalam proses pengambilan keputusan untuk menentukan sekolah mana yang paling banyak diminati oleh siswa guna sebagai acuan dalam pembangunan baik dari segi kualitas maupun kuantitas. Data yang digunakan dalam penelitian ini adalah data Dapodikdasmen tahun 2019. pengolahan data dalam penelitian ini menggunakan metode K-means Clustering dengan jumlah 3 cluster yaitu cluster 0 (C0) kurang diminati, Cluster 1 (C1) cukup diminati, cluster 2 (c2) sangat diminati siswa dalam memilih sekolah. Hasil dari proses clustering dengan 2 kali iterasi menyatakan bahwa untuk cluster 0 berjumlah 6 data sekolah, untuk cluster 1 berjumlah 3 data sekolah cluster 2 berjumlah 1 data sekolah.