Random Forests for Evaluating Pedagogy and Informing Personalized Learning

Spoon, Kelly M.; Beemer, Joshua; Whitmer, John C.; Fan, Juanjuan; Frazee, James P.; Stronach, Jeanne; Bohonak, Andrew J.; Levine, Richard A.

doi:10.5281/zenodo.3554595

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…Prediction models, such as the model described in this manuscript, are one of the primary uses of ML in higher education as the goal of predictive analytics is to identify students at-risk in mastering knowledge and successful course completion—enabling timely interventions [ 21 , 64 ]. Other potential uses of ML surrounding personalizing learning experiences for veterinary students include adaptive learning platforms.…”

Section: Discussionmentioning

confidence: 99%

“…We also kept pre-admission GPA and GRE scores continuous because random forests handle high non-linearity between independent variables [ 34 ]. Non-linear parameters typically do not affect the performance of the decision tree models because the splitting of the decision nodes is based upon absolute values, “yes” or “no” ( Figure 4 ), and the branches are not based upon a numerical value of the feature [ 20 , 21 , 23 , 34 , 35 ].…”

Section: Simulation Of Dataset and Creation Of A Random Forest Machin...mentioning

confidence: 99%

“…We opted to use random forest as the example ML algorithm to help illustrate the steps for creating and evaluating a ML model. This model is one of the most utilized supervised learning algorithms and offers numerous benefits such as handling non-parametric data and being robust to outliers [ 6 , 19 , 20 , 21 , 22 , 23 ], both of which are commonly observed in veterinary educational data.…”

Section: Introductionmentioning

confidence: 99%

“…We also kept preadmission GPA and GRE scores continuous because random forests handle high nonlinearity between independent variables[34]. Non-linear parameters typically do not affect the performance of the decision tree models because the splitting of the decision nodes is based upon absolute values, "yes" or "no" (Figure4), and the branches are not based upon a numerical value of the feature[20,21,23,34,35].GRE is a continuous variable, with two of the simulated datasets containing missing GRE scores. It is important to determine if the data are missing not at random (MNAR) due to a specific reason (i.e., a student does poorly on the GRE and so does not report the result), or if the missing data are missing at random (MAR) or missing completely at random (MCAR).…”

mentioning

confidence: 99%

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Using Machine Learning in Veterinary Medical Education: An Introduction for Veterinary Medicine Educators

Hooper,

Hecker,

Artemiou

2023

Veterinary Sciences

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Machine learning (ML) offers potential opportunities to enhance the learning, teaching, and assessments within veterinary medical education including but not limited to assisting with admissions processes as well as student progress evaluations. The purpose of this primer is to assist veterinary educators in appraising and potentially adopting these rapid upcoming advances in data science and technology. In the first section, we introduce ML concepts and highlight similarities/differences between ML and classical statistics. In the second section, we provide a step-by-step worked example using simulated veterinary student data to answer a hypothesis-driven question. Python syntax with explanations is provided within the text to create a random forest ML prediction model, a model composed of decision trees with each decision tree being composed of nodes and leaves. Within each step of the model creation, specific considerations such as how to manage incomplete student records are highlighted when applying ML algorithms within the veterinary education field. The results from the simulated data demonstrate how decisions by the veterinary educator during ML model creation may impact the most important features contributing to the model. These results highlight the need for the veterinary educator to be fully transparent during the creation of ML models and future research is needed to establish guidelines for handling data not missing at random in medical education, and preferred methods for model evaluation.

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Section: Discussionmentioning

confidence: 99%

Section: Simulation Of Dataset and Creation Of A Random Forest Machin...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Using Machine Learning in Veterinary Medical Education: An Introduction for Veterinary Medicine Educators

Hooper,

Hecker,

Artemiou

2023

Veterinary Sciences

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Section: Data Preprocessingmentioning

confidence: 99%

Using Machine Learning in Veterinary Medical Education: An Introduction for Veterinary Medicine Educators

Hooper¹,

Hecker²,

Artemiou³

2023

Preprint

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Machine learning (ML) offers potential opportunities to enhance the learning, teaching and assessments within veterinary medical education including but not limited to assisting with admissions processes as well as student progress evaluations. The purpose of this primer is to assist veterinary educators in appraising and potentially adopting these rapid upcoming advances in data science and technology. In the first section, we introduce ML concepts and highlight similarities/differences between ML and classical statistics. In the second section, we provide a step-by-step worked example using simulated veterinary student data to answer a hypothesis driven question. Python syntax with explanations is provided within the text to create a random forest ML prediction model and within each step, specific considerations such as how to manage incomplete student records are highlighted when applying ML algorithms within the veterinary education field. The results from the simulated data demonstrate how decisions by the veterinary educator during ML model creation may impact the most important features contributing to the model. These results highlight the need for the veterinary educator to be fully transparent during the creation of ML models and future research is needed to establish guidelines for handling data not missing at random in medical education, and preferred methods for model evaluation.

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Trends of Educational Technology in Korea and the U.S.: A Report on the AECT-Korean Society for Educational Technology (KSET) Panel Discussion

Cho

Grant

et al. 2020

TechTrends

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Random Forests for Evaluating Pedagogy and Informing Personalized Learning

Cited by 4 publications

References 18 publications

Using Machine Learning in Veterinary Medical Education: An Introduction for Veterinary Medicine Educators

Using Machine Learning in Veterinary Medical Education: An Introduction for Veterinary Medicine Educators

Using Machine Learning in Veterinary Medical Education: An Introduction for Veterinary Medicine Educators

Trends of Educational Technology in Korea and the U.S.: A Report on the AECT-Korean Society for Educational Technology (KSET) Panel Discussion

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