Logistic Regression Model for the Academic Performance of First-Year Medical Students in the Biomedical Area

Urrutia-Aguilar, María Esther; Fuentes–García, Ruth; Martínez, Vargas Danae Mirel; Beck, Edward C.; León, Silvia Ortíz; Guevara‐Guzmán, Rosalinda

doi:10.4236/ce.2016.715217

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…Logistic Regression [41]: This is another popular classification technique that predicts a certain probability task. The logistic regression model aims to describe the relationship between one or more independent variables that may be continuous, categorical, or binary.…”

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confidence: 99%

Student Performance Prediction with Short-Term Sequential Campus Behaviors

Wang

Guo

et al. 2020

Information

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As students’ behaviors are important factors that can reflect their learning styles and living habits on campus, extracting useful features of them plays a helpful role in understanding the students’ learning process, which is an important step towards personalized education. Recently, the task of predicting students’ performance from their campus behaviors has aroused the researchers’ attention. However, existing studies mainly focus on extracting statistical features manually from the pre-stored data, resulting in hysteresis in predicting students’ achievement and finding out their problems. Furthermore, due to the limited representation capability of these manually extracted features, they can only understand the students’ behaviors shallowly. To make the prediction process timely and automatically, we treat the performance prediction task as a short-term sequence prediction problem, and propose a two-stage classification framework, i.e., Sequence-based Performance Classifier (SPC), which consists of a sequence encoder and a classic data mining classifier. More specifically, to deeply discover the sequential features from students’ campus behaviors, we first introduce an attention-based Hybrid Recurrent Neural Network (HRNN) to encode their recent behaviors by giving a higher weight to the ones that are related to the students’ last action. Then, to conduct student performance prediction, we further involve these learned features to the classic Support Vector Machine (SVM) algorithm and finally achieve our SPC model. We conduct extensive experiments in the real-world student card dataset. The experimental results demonstrate the superiority of our proposed method in terms of Accuracy and Recall.

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confidence: 99%

Student Performance Prediction with Short-Term Sequential Campus Behaviors

Wang

Guo

et al. 2020

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“…In terms of predictive analytics, Urrutia-Aguilar et al (2016) employed also a logistic regression model for the prediction of variables that have impact on the academic performance of first year biomedical students. Soule (2017) also employed multiple logistic regressions to improve prediction techniques regarding the future performance of students in selected university courses and his study showed that in all cases, logistic prediction models matched or exceeded the performance of current prediction methods while using an equal or lesser number of explanatory variables.…”

Section: Related Workmentioning

confidence: 99%

Role of absence in academic success: an analysis using visualization tools

Etemadpour¹,

Zhu²,

Zhao³

et al. 2020

Smart Learn. Environ.

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Understanding the academic performance of students in colleges is an essential topic in Education research field. Educators, program coordinators and professors are interested in understanding how students are learning specific topics, how specific topics may influence the learning of other topics, how students' grades/attendances in each course may represent important indicators to measure their performance, among other tasks. The use of data visualization and analytics is expanding in education institutions to perform a variety of tasks related to data processing and gaining into data-informed insights. In this paper, we present a visual analytic tool that combines data visualization and machine learning techniques to perform some visual analysis of students' data from program courses. Two educational data collections were used to guide the creation of i) predictive models employing a variety of well known machine learning strategies, attempting to predict students' future grade based on grade and attendance previous semesters and ii) a set interactive layouts that highlight the relationship between grades and attendance, also including additional variables such as gender, parents education level, among others. We performed several experiments, also using these data collections, to evaluate the layouts ability of highlighting interesting patterns, and we obtained promising results, demonstrating that such analysis may help the education experts to understand deficiencies on course structures.

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“…Algoritma ini menggunakan teknik regresi. Logistic Regression juga telah digunakan pada penelitian sebelumnya untuk meneliti prakiraan hujan es [11], customer churn [12], [13], dan performa akademik [14].…”

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Prediksi Employee Churn Dengan Uplift Modeling Menggunakan Algoritma Logistic Regression

Kinoto,

Damanik,

Situmorang

et al. 2020

JUTIKOMP

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Pada sebuah perusahaan, karyawan merupakan aset yang berharga dan dapat menunjang kesuksesan perusahaan tersebut. Namun, hilangnya tenaga kerja dapat merugikan perusahaan. Kondisi ini disebut dengan Employee Churn. Salah satu solusi untuk mengatasi Employee Churn adalah dengan menerapkan model Uplift Modeling. Dalam penelitian ini, penulis menganalisa penerapan Logistic Regression terhadap Uplift Modeling dalam permasalahan Employee Churn. Data yang diteliti adalah data karyawan dari IBM HR Analytics. Hasil prediksi pada penelitian ini mendapat akurasi sebesar 64,40%, sedangkan hasil preskripsi menghasilkan hasil yang cukup baik apabila menerapkan waktu kerja tambahan pada karyawan. Berdasarkan hasil yang didapat, diketahui bahwa para karyawan justru cenderung bertahan di perusahaan apabila diberikan waktu kerja tambahan.

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Logistic Regression Model for the Academic Performance of First-Year Medical Students in the Biomedical Area

Cited by 12 publications

References 17 publications

Student Performance Prediction with Short-Term Sequential Campus Behaviors

Student Performance Prediction with Short-Term Sequential Campus Behaviors

Role of absence in academic success: an analysis using visualization tools

Prediksi Employee Churn Dengan Uplift Modeling Menggunakan Algoritma Logistic Regression

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