48,818 research outputs found
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
A hybrid information approach to predict corporate credit risk
This article proposes a hybrid information approach to predict corporate credit risk. In contrast to the previous literature that debates which credit risk model is the best, we pool information from a diverse set of structural and reduced-form models to produce a model combination based credit risk prediction. Compared with each single model, the pooled strategies yield consistently lower average risk prediction errors over time. We also find that while the reduced-form models contribute more in the pooled strategies for speculative grade names and longer maturities, the structural models have higher weights for shorter maturities and investment grade names
Predicting time to graduation at a large enrollment American university
The time it takes a student to graduate with a university degree is mitigated
by a variety of factors such as their background, the academic performance at
university, and their integration into the social communities of the university
they attend. Different universities have different populations, student
services, instruction styles, and degree programs, however, they all collect
institutional data. This study presents data for 160,933 students attending a
large American research university. The data includes performance, enrollment,
demographics, and preparation features. Discrete time hazard models for the
time-to-graduation are presented in the context of Tinto's Theory of Drop Out.
Additionally, a novel machine learning method: gradient boosted trees, is
applied and compared to the typical maximum likelihood method. We demonstrate
that enrollment factors (such as changing a major) lead to greater increases in
model predictive performance of when a student graduates than performance
factors (such as grades) or preparation (such as high school GPA).Comment: 28 pages, 11 figure
Credit ratings and credit risk
This paper investigates the information in corporate credit ratings. We examine the extent to which firms' credit ratings measure raw probability of default as opposed to systematic risk of default, a firm's tendency to default in bad times. We find that credit ratings are dominated as predictors of corporate failure by a simple model based on publicly available financial information (`failure score'), indicating that ratings are poor measures of raw default probability. However, ratings are strongly related to a straightforward measure of systematic default risk: the sensitivity of firm default probability to its common component (`failure beta'). Furthermore, this systematic risk measure is strongly related to credit default swap risk premia. Our findings can explain otherwise puzzling qualities of ratings.Credit Rating, Credit Risk, Default Probability, Forecast Accuracy, Systematic Default Risk
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