scholarly journals Evaluation of faculty inter-variability OSCE grade scoring on overall student performance in a laboratory course

2022 ◽  
pp. 48-53
Author(s):  
Salome Bwayo Weaver ◽  
Monika Daftary ◽  
La'Marcus Wingate ◽  
Malaika Turner
Keyword(s):  
Linear Regression ◽  
Student Performance ◽  
Clinical Skills ◽  
Reliability And Validity ◽  
Pharmacy Students ◽  
Regression Analyses ◽  
Simple Linear Regression ◽  
Objective Structured Clinical Examinations ◽  
Overall Performance ◽  
Using Data

Introduction: Objective structured clinical examinations (OSCEs) are considered the gold standard for evaluating pharmacy students’ clinical skills due to their reliability and validity. Aim: The purpose of this study was to determine whether faculty inter-variability in OSCE grading had a significant impact on a student’s overall performance. Methods: A retrospective analysis was conducted using data from two cohorts of third-year pharmacy students. Descriptive statistics, simple linear regression, and multivariate linear regression analyses were conducted. Results: There were 120 students that participated in the OSCE with a mean score of 66.7%. Higher scores in the Integrated Therapeutics (IT) 2 lecture series and the IT 2 lab course corresponded to better OSCE scores. Out of 17 evaluators, six were found to rate students significantly lower and one was found to rate students significantly higher in comparison to a reference evaluator who evaluated students closest to the overall mean. Conclusion: It is likely that standardised grading, and possibly additional training, may be needed to ensure a fair and appropriate evaluation of OSCE performance.

scholarly journals Correlation Between Student Performances on Course Level Integrated Clinical Skills Examinations and Objective Structured Clinical Examinations in a Chiropractic College Program

2012 ◽  
Vol 26 (2) ◽  
pp. 138-145
Author(s):  
Brent S. Russell ◽  
Kathryn T. Hoiriis ◽  
Joseph Guagliardo
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Student Performance ◽  
Clinical Skills ◽  
Linear Regression Analysis ◽  
Correlation Coefficients ◽  
Multiple Linear Regression Analysis ◽  
Strong Positive Correlation ◽  
Objective Structured Clinical Examinations ◽  
Upper Level

Purpose: This retrospective study measured correlation of student performance between 2 objective structured clinical examinations (OSCEs) and an introductory integrated clinical skills course that preceded the OSCEs. The hypothesis was that there would be a strong, positive correlation between the earlier level examinations and the upper level OSCE, high enough that earlier examinations could be viewed as predictors of upper level OSCE performance. Methods: Using student scores for 5 academic terms of upper level OSCEs for 2008–2009 (n = 208) and respective earlier scores, correlation coefficients were calculated for the upper level OSCE and Clinical Skills course, and upper and lower level OSCEs. Multiple linear regression analysis was used to evaluate how well the lower level OSCE and clinical skills scores, both as lone and combined independent variables, predicted the upper level OSCE scores. Results: There was at least a moderate correlation between both sets of scores: r = .51 (p < .001) between upper level OSCE and clinical skills course, r = .54 (p < .001) between the upper and lower level OSCEs. A combination of clinical skills and lower level OSCE scores suggested a moderate prediction of upper level OSCE scores (R2 = .38.) Conclusions: Correlations were found to be of at least a moderate level. According to linear regression analysis, a combination of the earlier scores was moderately predictive for the upper level OSCE. More research could be done to determine additional components of student performance.

scholarly journals THE ANALYSIS OF OIL CONTENT IN SEDIMENT AND COMMUNITY STRUCTURE OF MACROZOOBENTHOS IN BELAWAN WATERS OF MEDAN CITY, NORTH SUMATERA PROVINCE

2020 ◽  
Vol 3 (2) ◽  
pp. 167-178
Author(s):  
Doli Juna Setia Tanjung ◽  
Bintal Amin ◽  
Syafruddin Nasution
Keyword(s):  
Community Structure ◽  
Linear Regression ◽  
Oil Content ◽  
Diversity Index ◽  
The Other ◽  
Regression Analyses ◽  
Simple Linear Regression ◽  
Dominance Index ◽  
Ocypode Quadrata ◽  
Evenness Index

This research was conducted in March 2019 to determine the oil content in sediment, it’s community structure of macrozoobenthos and it’s a relationship in Belawan Waters of Medan City, North Sumatera. Four sampling stations with five replications in each station were surveyed. The results showed that the average oil content in sediments exceeded the threshold had set by the National Academy of Science. Macrozoobenthos found consists of Ocypode quadrata, Scyla serrate, Rotun dicauda, Penaeus sp, Murex tribulus, and Nassarius olivaccus. The highest abundance was in Station 3 and the lowest was in Station 2. The diversity index in each station was generally very low. Dominance Index in Station 4 was medium, whilst the other stations were high. Evenness index showed in Station 3 and 4 were in high population, Station 1 was in medium population and Station 2 was in low population. Simple linear regression analyses between oil content in sediment with community structure of macrozoobenthos indicated negative correlation ( Y = 10,5-0,0001x , R2 = 0,0004 and r = 0,02 ) which indicated that the higher the oil content, the lower the macrozoobenthos abundance in sediment.

scholarly journals Evaluating Human Movement Coordination During Immersive Walking in a Virtual Crowd

2020 ◽  
Vol 10 (9) ◽  
pp. 130 ◽  
Author(s):  
Alexandros Koilias ◽  
Michael Nelson ◽  
Sahana Gubbi ◽  
Christos Mousas ◽  
Christos-Nikolaos Anagnostopoulos
Keyword(s):  
Linear Regression ◽  
Virtual Environment ◽  
Human Movement ◽  
Movement Behavior ◽  
Regression Analyses ◽  
Simple Linear Regression ◽  
Movement Coordination ◽  
The Road ◽  
Metropolitan City

This paper describes our investigation on how participants coordinate movement behavior in relation to a virtual crowd that surrounds them while immersed in a virtual environment. The participants were immersed in a virtual metropolitan city and were instructed to cross the road and reach the opposite sidewalk. The participants performed the task ten times. The virtual crowd that surrounded them was scripted to move in the same direction. During the experiment, several measurements were obtained to evaluate human movement coordination. Moreover, the time and direction in which the participants started moving toward the opposite sidewalk were also captured. These data were later used to initialize the parameters of simulated characters that were scripted to become part of the virtual crowd. Measurements were extracted from the simulated characters and used as a baseline to evaluate the movement coordination of the participants. By analyzing the data, significant differences between the movement behaviors of the participants and the simulated characters were found. However, simple linear regression analyses indicated that the movement behavior of participants was moderately associated with the simulated characters’ movements when performing a locomotive task within a virtual crowd population. This study can be considered as a baseline for further research that evaluates the movement coordination of participants during human–virtual-crowd interactions using measurements obtained by the simulated characters.

NATIVE FIXED AMMONIUM AND FIXATION OF ADDED AMMONIUM IN RELATION TO CLAY MINERALOGY IN SOME ONTARIO SOILS

1983 ◽  
Vol 63 (3) ◽  
pp. 631-639 ◽  
Author(s):  
D. R. DORAM ◽  
L. J. EVANS
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Key Words ◽  
Total Nitrogen ◽  
Clay Mineralogy ◽  
Soil Profiles ◽  
Regression Analyses ◽  
Simple Linear Regression ◽  
Southern Ontario

Six soil profiles belonging to the Brookston, Lockport, Guelph, Chinguacousy and Haldimand series were sampled in southern Ontario. Illite contents of the clay fractions ranged from 25 to 56% and vermiculite contents ranged from 13 to 53%. The native fixed ammonium content varied from 57 to 367 μg/g and accounted for between 3 and 44% of the total nitrogen, being proportionally less in Ap horizons. Results of simple linear regression analyses demonstrated significant correlations of native fixed ammonium with percentages of clay, illite, illite plus vermiculite and K2O. The amount of ammonium fixed increased with increasing concentration of added ammonium up to the highest rate of application (2000μg NH4+/g soil). Regression analysis indicated that vermiculite was more important in the fixation of added NH4+ than illite. Studies on Ap horizons comparing the amount of NH4+ fixed with the amount of K− fixed for equivalent amounts of added cation showed that K+ was fixed in greater quantities than NH4+. Key words: Native fixed NH4+, NH4+ fixation, clay mineralogy

scholarly journals IMPACT OF MACROECONOMIC INDICATORS ON PUBLIC DEBT OF SLOVAK REPUBLIC

2019 ◽  
Vol 20 (4) ◽  
pp. 734-753 ◽  
Author(s):  
Miroslava Knapková ◽  
Martin Kiaba ◽  
Samuel Hudec
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Public Debt ◽  
Slovak Republic ◽  
Simple Linear Regression ◽  
Bond Yields ◽  
Scientific Methods ◽  
Gdp Growth Rate ◽  
Macroeconomic Indicators ◽  
Using Data

The paper focuses on impact of macroeconomic indicators on the development of public debt in Slovakia. The aim of the paper was to identify those macroeconomic indicators which influence the most significantly public debt in Slovakia and to elaborate and verify simple model for public debt prediction. Research was based on the analysis of chosen macroeconomic indicators. Selection of macroeconomic indicators resulted from theoretical knowledge and study of various research papers. Authors used several scientific methods, such as content-causal analysis, comparison, mathematical and statistical methods, including simple linear regression. Macroeconomic indicators, which authors proved to be statistically significant, are GDP growth rate, openness of economy, size of public sector, government bond yields, and unemployment rate. Authors elaborated model of the public debt development in Slovakia by using a simple linear regression model. Regression model was calculated using the data for 1995-2016. Authors confirmed correctness of the model by using data for 2017. Research was limited by the fact, that there are limited data available for analysis (time series of 22 years) because of short existence of independent Slovakia. It will be necessary to continue with the research and to verify correctness of chosen indicators in longer period.

scholarly journals A Student Performance Prediction Model Using Data Mining Technique

2018 ◽  
Vol 7 (2.15) ◽  
pp. 61
Author(s):  
Rohaila Abdul Razak ◽  
Mazni Omar ◽  
Mazida Ahmad
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Decision Tree ◽  
Student Performance ◽  
Prediction Accuracy ◽  
Significant Variable ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Predicting Performance ◽  
Using Data

Predicting performance is very significant in the education world nowadays. This paper will describe the process of doing a prediction of student performance by using data mining technique. 257 data sets were taken from the student of semester 6 KPTM that involved four (4) academic programs which are Diploma in Computer System and Networking, Diploma in Information Technology, Diploma in Business Management and Diploma in Accountancy. Knowledge Discovery in Database (KDD) was used as a guide to the process of finding and extracting a knowledge from the dataset. A decision tree and linear regression were used to analyze the dataset based on variables selected. The variables used are Gender, Financing, SPM, GPASem1, GPASem2, GPASem3, GPASem4, GPASem5 and CGPA as a dependent variable. The result from this indicate the significant variable that contribute most to the students’ performance. Based on the analysis, the decision tree shows that GPASem1 has a strong significant to the CGPA final semester of the student and the prediction accuracy is 82%. The linear regression shows that the GPA for each semester has a highly significant with the dependent variable with 96.2% prediction accuracy. By having this information, the management of KPTM can make a plan to ensure that the student can maintain a good result and at the same time to make a strategic plans for those without a good result.  

scholarly journals Employee performance in Bappeda Palembang as perceived through interpersonal communication and work environment

2021 ◽  
Vol 7 (1) ◽  
pp. 147
Author(s):  
Yasir Arafat ◽  
Tri Darmawati ◽  
Harridlil Mukminin
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Work Environment ◽  
Interpersonal Communication ◽  
Employee Performance ◽  
Linear Regression Analysis ◽  
Regression Analyses ◽  
Simple Linear Regression ◽  
Simple Linear Regression Analysis

The purpose of this study was to determine the effect of interpersonal communication and the work environment on employee performance in Bappeda Palembang. The sample was 40 employees in Bappeda  Palembang. The survey for this study is based on 40 workers as respondents. Data were gathered and organized into a list of questions. The spss for Windows version 22 was used to analyze the outcomes using simple linear regression analysis, multiple linear regression analyses, t-tests, and f-tests. The interpersonal communication has a partially negative and insignificant effect on employee performance, work environment has a positive and significant effect on employee performance. Furthermore, obtained interpersonal communication as well as the workplace environment have a significant impact on employee performance.

scholarly journals Pengaruh Motivasi Terhadap Prestasi Kerja Karyawan Di Daima Hotel padang

2020 ◽  
Vol 11 (02) ◽  
pp. 174
Author(s):  
Redamasari Redamasari ◽  
Yuliana Yuliana
Keyword(s):  
Linear Regression ◽  
Work Performance ◽  
Work Motivation ◽  
Sampling Technique ◽  
Total Sample ◽  
Performance Assessments ◽  
Test Results ◽  
Simple Linear Regression ◽  
Regression Test ◽  
Using Data

1This1research originated from the Industrial Field Experience at Daima Hotel Padang. The author found problems, related to employee work performance and lack of work motivation. This study aims to analyze the effect of work motivation on work performance in Daima Hotel Padang. This1type1of1research1is1quantitative1with a1causal assosiative approach. The population in this study were employees of Daima Hotel Padang. The sample in this study used a purposive sampling technique with a total sample of 46 people, namely employees who already had work performance assessments. Data analysis techniques using data description and simple linear regression test using the SPSS Computer1Program1version116.00. Research results show that: Work motivation is in the good category (52.17%), Job Performance is in the quite good category (58.69%) and the simple linear regression test results obtained R Square value of 0.118. Meaning that motivation significantly influences work performance employees at 11.8% and 88.2% are influenced by other factors.

Time to flowering in narrow-leafed lupin

1995 ◽  
Vol 46 (5) ◽  
pp. 1063 ◽  
Author(s):  
MA Reader ◽  
M Dracup ◽  
EJM Kirby
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Flowering Time ◽  
Regression Analyses ◽  
Average Temperature ◽  
Interaction Term ◽  
Using Data ◽  
Leaf Appearance ◽  
The Relationship ◽  
Rate Of Leaf Appearance

The relationship between flowering time and daylength and temperature is described for L. angustifolius using multiple linear regression. The main cultivar was Gungurru but cvv. Danja, Yorrel and the L. albus cv. Kiev Mutant were also studied. Regression analyses were performed on time to flowering observations for lupins grown with serial sowings at up to 12 sites over up to 5 years in Western Australia (there were 102 separate observations of time to flowering for Gungurru). Time to flowering in the L. angustifolius cultivars was best explained by a model incorporating terms for average temperature and daylength between sowing and flowering. Models of this form were not satisfactory for L. albus, probably because of vernalization requirements which the L. angustifolius cultivars do not have. Using data from the experiment with the widest range of sowings, 94.6% of the variation in time to flowering was explained by the above model and an additional 3.5% was explained by including an interaction term. The rate of progress through all stages of development to flowering, except for the period between appearance of the last leaf and flowering, was sensitive to temperature. The rate of leaf appearance responded to both temperature and daylength and the rate of progress through the period between appearance of the last leaf and flowering was sensitive only to daylength.

scholarly journals Predicting Student Performance and Deficiency in Mastering Knowledge Points in MOOCs Using Multi-Task Learning

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1216 ◽  
Author(s):  
Shaojie Qu ◽  
Kan Li ◽  
Bo Wu ◽  
Xuri Zhang ◽  
Kaihao Zhu
Keyword(s):  
Student Performance ◽  
Short Term Memory ◽  
Online Courses ◽  
Recall Rate ◽  
Learning Material ◽  
Online Behavior ◽  
Overall Performance ◽  
Predicting Student Performance ◽  
And Performance ◽  
Using Data

Massive open online courses (MOOCs), which have been deemed a revolutionary teaching mode, are increasingly being used in higher education. However, there remain deficiencies in understanding the relationship between online behavior of students and their performance, and in verifying how well a student comprehends learning material. Therefore, we propose a method for predicting student performance and mastery of knowledge points in MOOCs based on assignment-related online behavior; this allows for those providing academic support to intervene and improve learning outcomes of students facing difficulties. The proposed method was developed while using data from 1528 participants in a C Programming course, from which we extracted assignment-related features. We first applied a multi-task multi-layer long short-term memory-based student performance predicting method with cross-entropy as the loss function to predict students’ overall performance and mastery of each knowledge point. Our method incorporates the attention mechanism, which might better reflect students’ learning behavior and performance. Our method achieves an accuracy of 92.52% for predicting students’ performance and a recall rate of 94.68%. Students’ actions, such as submission times and plagiarism, were related to their performance in the MOOC, and the results demonstrate that our method predicts the overall performance and knowledge points that students cannot master well.

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