Predicting Career Decisions Among Graduates of Tafseer and Hadith

Jamal, Khairunnas; Kurniawan, Rahmad; Husti, Ilyas; Zailani,; Nazri, Mohd Zakree Ahmad; Arifin, Johar

doi:10.1109/iccis49240.2020.9257663

Cited by 8 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One study (Jamal et al, 2020) used decision tree models such as C5.0 and CHAID to predict the profession choices among the graduates of a university's department in the span of 25 years (from 1995 to 2019), which yielded a prediction accuracy of approximately 92.6 percent. Moreover, along with the accuracy, they also gained more insight into the factors influencing those graduates' career choices, namely CGPA, additional expertise, and gender.…”

Section: Student Career Prediction Using Decision Tree Classificationmentioning

confidence: 99%

Career path prediction using XGBoost Model and students’ academic results

Nguyen,

et al. 2023

CTU J. of Inn. & Sus. Dev.

View full text Add to dashboard Cite

This paper proposes an approach for constructing a system for career prediction by applying the eXtreme Gradient Boosting (XGBoost) Decision Tree model to the academic results of Ho Chi Minh International University’s School of Computer Science and Engineering graduates in the past 5 years. Initially, the dataset is cleaned up and normalized to be usable for the prediction algorithm with the help of Python 3 programming language. It is then split into 2 subsets: one for training (80 percent) and the other for testing (20 percent). After that, the algorithm uses the training subset to build the classification model. Finally, the testing subset is loaded into the model to predict each student’s career path based on the respective inputs and hyper-parameters tuning is employed to boost the model’s accuracy. By utilizing this solution, the problem related to predicting students’ future career paths based on their performance throughout their years studying at the university can be adequately addressed and handled.

show abstract

Section: Student Career Prediction Using Decision Tree Classificationmentioning

confidence: 99%

Career path prediction using XGBoost Model and students’ academic results

Nguyen,

et al. 2023

CTU J. of Inn. & Sus. Dev.

View full text Add to dashboard Cite

show abstract

“…Various methods of artificial intelligence and machine learning have also been investigated to provide decision support for and predict individual career choice [22][23][24][25][26][27][28]. These career choice prediction and decision support studies have achieved some success at being able to predict career choice, with performance varying from 68% to 85% area under the receiver operating characteristic curve.…”

Section: Introductionmentioning

confidence: 99%

Exploring Medical Career Choice to Better Inform Swiss Physician Workforce Planning: Protocol for a National Cohort Study

Abbiati,

Nendaz,

Cerutti

et al. 2024

JMIR Res Protoc

View full text Add to dashboard Cite

Background A medical student’s career choice directly influences the physician workforce shortage and the misdistribution of resources. First, individual and contextual factors related to career choice have been evaluated separately, but their interaction over time is unclear. Second, actual career choice, reasons for this choice, and the influence of national political strategies are currently unknown in Switzerland. Objective The overall objective of this study is to better understand the process of Swiss medical students’ career choice and to predict this choice. Our specific aims will be to examine the predominately static (ie, sociodemographic and personality traits) and predominately dynamic (ie, learning context perceptions, anxiety state, motivation, and motives for career choice) variables that predict the career choice of Swiss medical school students, as well as their interaction, and to examine the evolution of Swiss medical students’ career choice and their ultimate career path, including an international comparison with French medical students. Methods The Swiss Medical Career Choice study is a national, multi-institution, and longitudinal study in which all medical students at all medical schools in Switzerland are eligible to participate. Data will be collected over 4 years for 4 cohorts of medical students using questionnaires in years 4 and 6. We will perform a follow-up during postgraduate training year 2 for medical graduates between 2018 and 2022. We will compare the different Swiss medical schools and a French medical school (the University of Strasbourg Faculty of Medicine). We will also examine the effect of new medical master’s programs in terms of career choice and location of practice. For aim 2, in collaboration with the Swiss Institute for Medical Education, we will implement a national career choice tracking system and identify the final career choice of 2 cohorts of medical students who graduated from 4 Swiss medical schools from 2010 to 2012. We will also develop a model to predict their final career choice. Data analysis will be conducted using inferential statistics, and machine learning approaches will be used to refine the predictive model. Results This study was funded by the Swiss National Science Foundation in January 2023. Recruitment began in May 2023. Data analysis will begin after the completion of the first cohort data collection. Conclusions Our research will inform national stakeholders and medical schools on the prediction of students’ future career choice and on key aspects of physician workforce planning. We will identify targeted actions that may be implemented during medical school and may ultimately influence career choice and encourage the correct number of physicians in the right specialties to fulfill the needs of currently underserved regions. International Registered Report Identifier (IRRID) DERR1-10.2196/53138

show abstract