Evaluating Alternative Training Interventions Using Personalized Computational Models of Learning

• URL: http://arxiv.org/abs/2408.13684v1
• Date: Sat, 24 Aug 2024 22:51:57 GMT
• Title: Evaluating Alternative Training Interventions Using Personalized Computational Models of Learning
• Authors: Christopher James MacLellan, Kimberly Stowers, Lisa Brady,
• Abstract summary: evaluating different training interventions to determine which produce the best learning outcomes is one of the main challenges faced by instructional designers. We present an approach for automatically tuning models to specific individuals and show that personalized models make better predictions of students' behavior than generic ones. Our approach makes predictions that align with previous human findings, as well as testable predictions that might be evaluated with future human experiments.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evaluating different training interventions to determine which produce the best learning outcomes is one of the main challenges faced by instructional designers. Typically, these designers use A/B experiments to evaluate each intervention; however, it is costly and time consuming to run such studies. To address this issue, we explore how computational models of learning might support designers in reasoning causally about alternative interventions within a fractions tutor. We present an approach for automatically tuning models to specific individuals and show that personalized models make better predictions of students' behavior than generic ones. Next, we conduct simulations to generate counterfactual predictions of performance and learning for two students (high and low performing) in different versions of the fractions tutor. Our approach makes predictions that align with previous human findings, as well as testable predictions that might be evaluated with future human experiments.

Related papers

• Is Your Model "MADD"? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Models [0.0]
  We propose a novel metric, the Model Absolute Density Distance (MADD), to analyze models' discriminatory behaviors. We evaluate our approach on the common task of predicting student success in online courses, using several common predictive classification models.
  arXiv  Detail & Related papers  (2023-05-24T16:55:49Z)
• Designing Optimal Behavioral Experiments Using Machine Learning [8.759299724881219]
  We provide a tutorial on leveraging recent advances in BOED and machine learning to find optimal experiments for any kind of model. We consider theories of how people balance exploration and exploitation in multi-armed bandit decision-making tasks. As compared to experimental designs commonly used in the literature, we show that our optimal designs more efficiently determine which of a set of models best account for individual human behavior.
  arXiv  Detail & Related papers  (2023-05-12T18:24:30Z)
• Predicting student performance using sequence classification with time-based windows [1.5836913530330787]
  We show that accurate predictive models can be built based on sequential patterns derived from students' behavioral data. We present a methodology for capturing temporal aspects in behavioral data and analyze its influence on the predictive performance of the models. The results of our improved sequence classification technique are capable of predicting student performance with high levels of accuracy, reaching 90 percent for course-specific models.
  arXiv  Detail & Related papers  (2022-08-16T13:46:39Z)
• Prediction of Dilatory Behavior in eLearning: A Comparison of Multiple Machine Learning Models [0.2963240482383777]
  Procrastination, the irrational delay of tasks, is a common occurrence in online learning. Research focusing on such predictions is scarce. Studies involving different types of predictors and comparisons between the predictive performance of various methods are virtually non-existent.
  arXiv  Detail & Related papers  (2022-06-30T07:24:08Z)
• Zero-shot meta-learning for small-scale data from human subjects [10.320654885121346]
  We develop a framework to rapidly adapt to a new prediction task with limited training data for out-of-sample test data. Our model learns the latent treatment effects of each intervention and, by design, can naturally handle multi-task predictions. Our model has implications for improved generalization of small-size human studies to the wider population.
  arXiv  Detail & Related papers  (2022-03-29T17:42:04Z)
• Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations [97.91630330328815]
  We conduct a crowdsourcing study, where participants interact with deception detection models that have been trained to distinguish between genuine and fake hotel reviews. We observe that for a linear bag-of-words model, participants with access to the feature coefficients during training are able to cause a larger reduction in model confidence in the testing phase when compared to the no-explanation control.
  arXiv  Detail & Related papers  (2021-12-17T18:29:56Z)
• Predictive machine learning for prescriptive applications: a coupled training-validating approach [77.34726150561087]
  We propose a new method for training predictive machine learning models for prescriptive applications. This approach is based on tweaking the validation step in the standard training-validating-testing scheme. Several experiments with synthetic data demonstrate promising results in reducing the prescription costs in both deterministic and real models.
  arXiv  Detail & Related papers  (2021-10-22T15:03:20Z)
• Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning [109.74041512359476]
  We study a number of design decisions for the predictive model in visual MBRL algorithms. We find that a range of design decisions that are often considered crucial, such as the use of latent spaces, have little effect on task performance. We show how this phenomenon is related to exploration and how some of the lower-scoring models on standard benchmarks will perform the same as the best-performing models when trained on the same training data.
  arXiv  Detail & Related papers  (2020-12-08T18:03:21Z)
• Counterfactual Predictions under Runtime Confounding [74.90756694584839]
  We study the counterfactual prediction task in the setting where all relevant factors are captured in the historical data. We propose a doubly-robust procedure for learning counterfactual prediction models in this setting.
  arXiv  Detail & Related papers  (2020-06-30T15:49:05Z)
• Predicting Performance for Natural Language Processing Tasks [128.34208911925424]
  We build regression models to predict the evaluation score of an NLP experiment given the experimental settings as input. Experimenting on 9 different NLP tasks, we find that our predictors can produce meaningful predictions over unseen languages and different modeling architectures.
  arXiv  Detail & Related papers  (2020-05-02T16:02:18Z)
• Facial Feedback for Reinforcement Learning: A Case Study and Offline Analysis Using the TAMER Framework [51.237191651923666]
  We investigate the potential of agent learning from trainers' facial expressions via interpreting them as evaluative feedback. With designed CNN-RNN model, our analysis shows that telling trainers to use facial expressions and competition can improve the accuracies for estimating positive and negative feedback. Our results with a simulation experiment show that learning solely from predicted feedback based on facial expressions is possible.
  arXiv  Detail & Related papers  (2020-01-23T17:50:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.