scholarly journals Models for early prediction of at-risk students in a course using standards-based grading

2016 ◽  
Vol 103 ◽  
pp. 1-15 ◽  
Author(s):  
Farshid Marbouti ◽  
Heidi A. Diefes-Dux ◽  
Krishna Madhavan
Keyword(s):  
At Risk ◽  
At Risk Students ◽  
Early Prediction ◽  
Standards Based Grading

scholarly journals Developing and Comparing Data Mining algorithms that Work Best for Predicting Student’s Performance

2022 ◽  
Vol 18 (1) ◽  
pp. 0-0
Keyword(s):  
At Risk ◽  
Student Performance ◽  
At Risk Students ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Standards Based Grading ◽  
Data Mining Algorithms ◽  
Student Failure ◽  
Student’S Performance ◽  
Mining Algorithms

Learning data analytics improves the learning field in higher education using educational data for extracting useful patterns and making better decision. Identifying potential at-risk students may help instructors and academic guidance to improve the students’ performance and the achievement of learning outcomes. The aim of this research study is to predict at early phases the student’s failure in a particular course using the standards-based grading. Several machines learning techniques were implemented to predict the student failure based on Support Vector Machine, Multilayer Perceptron, Naïve Bayes, and decision tree. The results on each technique shows the ability of machine learning algorithms to predict the student failure accurately after the third week and before the course dropout week. This study provides a strong knowledge for student performance in all courses. It also provides faculty members the ability to help student at-risk by focusing on them and providing necessary support to improve their performance and avoid failure.

scholarly journals Early Prediction for At-Risk Students in an Introductory Programming Course Based on Student Self-Efficacy

Informatica ◽  
2021 ◽  
Vol 45 (6) ◽  
Author(s):  
Mona Jamjoom ◽  
Eatedal Alabdulkreem ◽  
Myriam Hadjouni ◽  
Faten Karim ◽  
Maha Qarh
Keyword(s):  
At Risk ◽  
Self Efficacy ◽  
At Risk Students ◽  
Early Prediction ◽  
Introductory Programming

Phonological Awareness Screening to Identify At-Risk Readers

1998 ◽  
Vol 29 (2) ◽  
pp. 109-116 ◽  
Author(s):  
Margie Gilbertson ◽  
Ronald K. Bramlett
Keyword(s):  
At Risk ◽  
Phonological Awareness ◽  
Reading Ability ◽  
At Risk Students ◽  
First Grade ◽  
Receptive Vocabulary ◽  
Standardized Assessments ◽  
Cutoff Score ◽  
Invented Spelling ◽  
Reading Measures

The purpose of this study was to investigate informal phonological awareness measures as predictors of first-grade broad reading ability. Subjects were 91 former Head Start students who were administered standardized assessments of cognitive ability and receptive vocabulary, and informal phonological awareness measures during kindergarten and early first grade. Regression analyses indicated that three phonological awareness tasks, Invented Spelling, Categorization, and Blending, were the most predictive of standardized reading measures obtained at the end of first grade. Discriminant analyses indicated that these three phonological awareness tasks correctly identified at-risk students with 92% accuracy. Clinical use of a cutoff score for these measures is suggested, along with general intervention guidelines for practicing clinicians.

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