Using machine learning to predict physics course outcomes

Zabriskie, Cabot; Yang, Jie; DeVore, Seth; Stewart, John

doi:10.1103/physrevphyseducres.15.020120

Cited by 48 publications

(31 citation statements)

References 40 publications

(52 reference statements)

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“…This study extends the results of Zabriskie et al [17] which will be referred to as study 1 in this work. Study 1 used institutional data such as ACT scores and college GPA (CGPA) as well as data collected within a physics class such as homework grades and test scores to predict whether a student would receive an A or B in the first and second semester of a calculus-based physics class at a large university.…”

Section: A Prior Study: Studysupporting

confidence: 90%

Using machine learning to identify the most at-risk students in physics classes

Yang

DeVore

Hewagallage

et al. 2020

Phys. Rev. Phys. Educ. Res.

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Machine learning algorithms have recently been used to predict students' performance in an introductory physics class. The prediction model classified students as those likely to receive an A or B or students likely to receive a grade of C, D, F or withdraw from the class. Early prediction could better allow the direction of educational interventions and the allocation of educational resources. However, the performance metrics used in that study become unreliable when used to classify whether a student would receive an A, B, or C (the ABC outcome) or if they would receive a D, F or withdraw (W) from the class (the DFW outcome) because the outcome is substantially unbalanced with between 10% to 20% of the students receiving a D, F, or W. This work presents techniques to adjust the prediction models and alternate model performance metrics more appropriate for unbalanced outcome variables. These techniques were applied to three samples drawn from introductory mechanics classes at two institutions (N ¼ 7184, 1683, and 926). Applying the same methods as the earlier study produced a classifier that was very inaccurate, classifying only 16% of the DFW cases correctly; tuning the model increased the DFW classification accuracy to 43%. Using a combination of institutional and in-class data improved DFW accuracy to 53% by the second week of class. As in the prior study, demographic variables such as gender, underrepresented minority status, first-generation college student status, and low socioeconomic status were not important variables in the final prediction models.

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Section: A Prior Study: Studysupporting

confidence: 90%

Using machine learning to identify the most at-risk students in physics classes

Yang

DeVore

Hewagallage

et al. 2020

Phys. Rev. Phys. Educ. Res.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Trees are constructed based on different samples and features selected randomly from the dataset to form the forest. It gets the result of the prediction from each created decision tree and selects the best prediction result based on the voting process [28,29]. Sequential Minimal Optimization (SMO) uses an optimization technique for training support vector machine (SVM) [6].…”

Section: Literature Reviewmentioning

confidence: 99%

“…This algorithm differs from linear regression by using "logistic function" instead of linear function for mapping the values of the prediction to probabilities. The probability of a dependent variable that has a binary value is predicted using a set of different independent values [30,29]. Multilayer Perceptron (MLP) is a multilayer network of interconnected neurons.…”

Section: Literature Reviewmentioning

confidence: 99%

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Predict Students’ Academic Performance based on their Assessment Grades and Online Activity Data

Alhassan¹,

Zafar²,

Mueen³

2020

IJACSA

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The ability to predict students' academic performance is critical for any educational institution that aims to improve their students' learning process and achievement. Although students' performance prediction problem is studied widely, it still represents a challenge and complex issue for educational institutions due to the different features that affect students learning process and achievement in courses. Moreover, the utilization of web-based learning systems in education provides opportunities to study how students learning and what learning behavior leading them to success. The main objective of this research was to investigate the impact of assessment grades and online activity data in the Learning Management System (LMS) on students' academic performance. Based on one of the commonly used data mining techniques for prediction, called classification. Five classification algorithms were applied that decision tree, random forest, sequential minimal optimization, multilayer perceptron, and logistic regression. Experimental results revealed that assessment grades are the most important features affecting students' academic performance. Moreover, prediction models that included assessment grades alone or in combination with activity data perform better than models based on activity data alone. Also, random forest algorithm performs well for predicting student a cademic performance, followed by decision tree.

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“…High school GPA and SAT scores typically account for some but not all of student success at university (GPA, etc.) [20][21][22]. Preparation for university often can be experienced differentially as well.…”

Section: Tinto's Theory Of Drop Outmentioning

confidence: 99%

Predicting time to graduation at a large enrollment American university

et al. 2020

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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).

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Using machine learning to predict physics course outcomes

Cited by 48 publications

References 40 publications

Using machine learning to identify the most at-risk students in physics classes

Using machine learning to identify the most at-risk students in physics classes

Predict Students’ Academic Performance based on their Assessment Grades and Online Activity Data

Predicting time to graduation at a large enrollment American university

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