84,920 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
Modeling student pathways in a physics bachelor's degree program
Physics education research has used quantitative modeling techniques to
explore learning, affect, and other aspects of physics education. However,
these studies have rarely examined the predictive output of the models, instead
focusing on the inferences or causal relationships observed in various data
sets. This research introduces a modern predictive modeling approach to the PER
community using transcript data for students declaring physics majors at
Michigan State University (MSU). Using a machine learning model, this analysis
demonstrates that students who switch from a physics degree program to an
engineering degree program do not take the third semester course in
thermodynamics and modern physics, and may take engineering courses while
registered as a physics major. Performance in introductory physics and calculus
courses, measured by grade as well as a students' declared gender and ethnicity
play a much smaller role relative to the other features included the model.
These results are used to compare traditional statistical analysis to a more
modern modeling approach.Comment: submitted to Physical Review Physics Education Researc
A Multi-Gene Genetic Programming Application for Predicting Students Failure at School
Several efforts to predict student failure rate (SFR) at school accurately
still remains a core problem area faced by many in the educational sector. The
procedure for forecasting SFR are rigid and most often times require data
scaling or conversion into binary form such as is the case of the logistic
model which may lead to lose of information and effect size attenuation. Also,
the high number of factors, incomplete and unbalanced dataset, and black boxing
issues as in Artificial Neural Networks and Fuzzy logic systems exposes the
need for more efficient tools. Currently the application of Genetic Programming
(GP) holds great promises and has produced tremendous positive results in
different sectors. In this regard, this study developed GPSFARPS, a software
application to provide a robust solution to the prediction of SFR using an
evolutionary algorithm known as multi-gene genetic programming. The approach is
validated by feeding a testing data set to the evolved GP models. Result
obtained from GPSFARPS simulations show its unique ability to evolve a suitable
failure rate expression with a fast convergence at 30 generations from a
maximum specified generation of 500. The multi-gene system was also able to
minimize the evolved model expression and accurately predict student failure
rate using a subset of the original expressionComment: 14 pages, 9 figures, Journal paper. arXiv admin note: text overlap
with arXiv:1403.0623 by other author
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
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