Predicting Grades

To increase efficacy in traditional classroom courses as well as in Massive Open Online Courses (MOOCs), automated systems supporting the instructor are needed. One important problem is to automatically detect students that are going to do poorly in a course early enough to be able to take remedial actions. Existing grade prediction systems focus on maximizing the accuracy of the prediction while overseeing the importance of issuing timely and personalized predictions. This paper proposes an algorithm that predicts the final grade of each student in a class. It issues a prediction for each student individually, when the expected accuracy of the prediction is sufficient. The algorithm learns online what is the optimal prediction and time to issue a prediction based on past history of students' performance in a course. We derive a confidence estimate for the prediction accuracy and demonstrate the performance of our algorithm on a dataset obtained based on the performance of approximately 700 UCLA undergraduate students who have taken an introductory digital signal processing over the past 7 years. We demonstrate that for 85% of the students we can predict with 76% accuracy whether they are going do well or poorly in the class after the 4th course week. Using data obtained from a pilot course, our methodology suggests that it is effective to perform early in-class assessments such as quizzes, which result in timely performance prediction for each student, thereby enabling timely interventions by the instructor (at the student or class level) when necessary.Comment: 15 pages, 15 figure

The Relationship Between Prior Experiences in Mathematics and Pharmacy School Success

Objective. To assess students’ pre-pharmacy math experiences, confidence in math ability, and relationship between experiences, confidence, and grades in math-based pharmacy courses. Methods. A cross-sectional survey of first year to third year pharmacy students was conducted. Students reported type of pre-pharmacy math courses taken, when they were taken [high school (HS) vs. college] and year of HS and college graduation. Students rated their confidence in math ability using the previously validated 11-item Fogerty Math Confidence Scale (Cronbach alpha=0.92). Math grade point average (GPA), Pharmacy College Admission Test quantitative (PCAT quant) scores, and grades (calculations and kinetics) were obtained from transcripts and school records. Spearman correlation and multivariate linear regression were used to compare math experiences, confidence, and grades. Results. There were 198 students who reported taking math courses 7.1 years since HS graduation and 2.9 years since their last schooling prior to pharmacy school. Students who took math courses with more time since HS/last schooling had lower calculations and kinetics grades. Students reporting having taken more HS math courses had better calculations grades. Students with higher math GPA, and PCAT quant scores also had higher calculations and kinetics grades. Greater confidence in math ability was associated with higher calculations grades. In multivariate regressions, PCAT quant scores and years since HS independently predicted calculations grades, and PCAT quant scores independently predicted kinetics grades. Conclusion. The number of pre-pharmacy math courses and time elapsed since they were taken are important factors to consider when predicting a pharmacy student’s success in math-based pharmacy school courses

EVALUATING FORECASTS OF DISCRETE VARIABLES: PREDICTING CATTLE QUALITY GRADES

Little research has been conducted on evaluating out-of-sample forecasts of limited dependent variables. This study describes the large and small sample properties of two forecast evaluation techniques for limited dependent variables: receiver-operator curves and out-of-sample-log-likelihood functions. The methods are shown to provide identical model rankings in large samples and similar rankings in small samples. The likelihood function method is slightly better at detecting forecast accuracy in small samples, while receiver-operator curves are better at comparing forecasts across different data. By improving forecasts of fed-cattle quality grades, the forecast evaluation methods are shown to increase cattle marketing revenues by $2.59/head.Marketing,

Machine learning predicts histologic type and grade of canine gliomas based on MRI texture analysis.

Conventional MRI features of canine gliomas subtypes and grades significantly overlap. Texture analysis (TA) quantifies image texture based on spatial arrangement of pixel intensities. Machine learning (ML) models based on MRI-TA demonstrate high accuracy in predicting brain tumor types and grades in human medicine. The aim of this retrospective, diagnostic accuracy study was to investigate the accuracy of ML-based MRI-TA in predicting canine gliomas histologic types and grades. Dogs with histopathological diagnosis of intracranial glioma and available brain MRI were included. Tumors were manually segmented across their entire volume in enhancing part, non-enhancing part, and peri-tumoral vasogenic edema in T2-weighted (T2w), T1-weighted (T1w), FLAIR, and T1w postcontrast sequences. Texture features were extracted and fed into three ML classifiers. Classifiers' performance was assessed using a leave-one-out cross-validation approach. Multiclass and binary models were built to predict histologic types (oligodendroglioma vs. astrocytoma vs. oligoastrocytoma) and grades (high vs. low), respectively. Thirty-eight dogs with a total of 40 masses were included. Machine learning classifiers had an average accuracy of 77% for discriminating tumor types and of 75.6% for predicting high-grade gliomas. The support vector machine classifier had an accuracy of up to 94% for predicting tumor types and up to 87% for predicting high-grade gliomas. The most discriminative texture features of tumor types and grades appeared related to the peri-tumoral edema in T1w images and to the non-enhancing part of the tumor in T2w images, respectively. In conclusion, ML-based MRI-TA has the potential to discriminate intracranial canine gliomas types and grades

Integrative Motivation as a Predictor of Achievement in the Foreign Language Classroom

This study examines the relationship among five independent variables—integrative motivation, instrumental motivation, the need to fulfill a foreign language requirement, grade point average (GPA), and previous years studying Spanish—as predictors of five dependent variables: scores on a simulated oral proficiency interview (SOPI), final exam grades, final grades, the desire to enroll in Spanish courses after completing the language requirement, and intention to major in Spanish. Data from a questionnaire and a SOPI administered to 130 students enrolled in fourth-semester Spanish identified integrative motivation as a significant predictor of SOPI scores and final exam grades. Furthermore, integrative motivation was a significant predictor of students’ desire to enroll in additional coursework after completing the four-semester foreign language requirement. It also had an important role in students’ intention to major in the language. A negative relationship was found between the need to fulfill the language requirement and intent to continue with further studies in Spanish. The findings demonstrate that integrative motivation is important in predicting student achievement in the foreign language classroom

Longitudinal associations of cognitive ability, personality traits and school grades with antisocial behaviour

This study investigated the role of adolescents’ cognitive ability, personality traits and school success in predicting later criminal behaviour. Cognitive ability, the five–factor model personality traits and the school grades of a large sample of Estonian schoolboys ( N = 1919) were measured between 2001 and 2005. In 2009, judicial databases were searched to identify participants who had been convicted of misdemeanours or criminal offences. Consistent with previous findings, having a judicial record was associated with lower cognitive ability, grade point average, agreeableness, and conscientiousness and higher neuroticism. In multivariate path models, however, the contributions of cognitive ability and conscientiousness were accounted for by school grades and the effect of neuroticism was also accounted for by other variables, leaving grade point average and agreeableness the only independent predictors of judicial record status. Copyright © 2011 John Wiley &amp; Sons, Ltd. </jats:p

Prediction of First Year Mathematic Grades at Central Washington College of Education with the ACE Psychological Examination

The purposes of this study are: (1) to acquaint the reader with some of the studies already published on the relationship between the ACE and mathematic grades and other devices for predicting success in mathematics; (2) to present information on the relationship between the ACE and the first year mathematics grades at Central Washington college of Education; and (3) to present information on the relationship between grades in the different mathematics classes taught primarily during the freshman year at CWCE

Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning

Diabetic eye disease is one of the fastest growing causes of preventable blindness. With the advent of anti-VEGF (vascular endothelial growth factor) therapies, it has become increasingly important to detect center-involved diabetic macular edema (ci-DME). However, center-involved diabetic macular edema is diagnosed using optical coherence tomography (OCT), which is not generally available at screening sites because of cost and workflow constraints. Instead, screening programs rely on the detection of hard exudates in color fundus photographs as a proxy for DME, often resulting in high false positive or false negative calls. To improve the accuracy of DME screening, we trained a deep learning model to use color fundus photographs to predict ci-DME. Our model had an ROC-AUC of 0.89 (95% CI: 0.87-0.91), which corresponds to a sensitivity of 85% at a specificity of 80%. In comparison, three retinal specialists had similar sensitivities (82-85%), but only half the specificity (45-50%, p<0.001 for each comparison with model). The positive predictive value (PPV) of the model was 61% (95% CI: 56-66%), approximately double the 36-38% by the retinal specialists. In addition to predicting ci-DME, our model was able to detect the presence of intraretinal fluid with an AUC of 0.81 (95% CI: 0.81-0.86) and subretinal fluid with an AUC of 0.88 (95% CI: 0.85-0.91). The ability of deep learning algorithms to make clinically relevant predictions that generally require sophisticated 3D-imaging equipment from simple 2D images has broad relevance to many other applications in medical imaging