Modeling Dynamic Processes with Memory by Higher Order Temporal Models

Lupinska-Dubicka, Anna; Drużdżel, Marek J.

doi:10.1007/978-3-319-28007-3_14

Cited by 2 publications

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“…The computer models of a woman's monthly cycle based on a modeling technique known as dynamic Bayesian networks, described in details in (Łupińska-Dubicka & Drużdżel, 2011, Łupińska-Dubicka & Drużdżel, 2015, used in the experiments, combine information retrieved from BBT charting with observations of cervical mucus secretions. They contain a variable Phase with four states: menstruation, follicular, ovulation, and luteal, standing for the phases of the menstrual cycle.…”

Section: Application Of Dynamic Bayesian Network For Monitoring Womamentioning

confidence: 99%

Handling Missing Entries in Monitoring a Woman’s Monthly Cycle and Controlling Fertility

Lupinska-Dubicka

2018

Studies in Logic, Grammar and Rhetoric

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Even a small percentage of missing data can cause serious problems with analysis, reducing the statistical power of a study and leading to wrong conclusions being drawn. In the case of monitoring a woman’s monthly cycle, missing entries can appear even in a woman experienced in fertility awareness methods. Due to the fact that in a system of controlling a woman’s fertility, it is the most important to predict the day of ovulation and, ultimately, to determine the fertile window as much precisely as possible, much attention should be paid to the quality of the used data. This paper presents the results of handling missing observations as far as predicting the time during the cycle when a woman can become pregnant is concerned. Data taken from a multinational European study of daily fecundability was used to learn the quantitative part of the variety of a higher-order dynamic Bayesian network modeling a woman’s monthly cycle. The main goal of this paper is to examine whether omitting observations has an influence on the model’s reliability. The accuracy of comparison was examined based on two measures: the average percentage length of the infertile time during the monthly cycle and average percentage of days inside the fertile window classified as infertile.

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Section: Application Of Dynamic Bayesian Network For Monitoring Womamentioning

confidence: 99%

Handling Missing Entries in Monitoring a Woman’s Monthly Cycle and Controlling Fertility

Lupinska-Dubicka

2018

Studies in Logic, Grammar and Rhetoric

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Memory-Based Dynamic Bayesian Networks for Learner Modeling: Towards Early Prediction of Learners’ Performance in Computational Thinking

Hooshyar,

Druzdzel

2024

Education Sciences

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Artificial intelligence (AI) has demonstrated significant potential in addressing educational challenges in digital learning. Despite this potential, there are still concerns about the interpretability and trustworthiness of AI methods. Dynamic Bayesian networks (DBNs) not only provide interpretability and the ability to integrate data-driven insights with expert judgment for enhanced trustworthiness but also effectively process temporal dynamics and relationships in data, crucial for early predictive modeling tasks. This research introduces an approach for the temporal modeling of learners’ computational thinking abilities that incorporates higher-order influences of latent variables (hereafter referred to as memory of the model) and accordingly predicts learners’ performance early. Our findings on educational data from the AutoThinking game indicate that when using only first-order influences, our proposed model can predict learners’ performance early, with an 86% overall accuracy (i.e., time stamps 0, 5, and 9) and a 94% AUC (at the last time stamp) during cross-validation and 91% accuracy and 98% AUC (at the last time stamp) in a holdout test. The introduction of higher-order influences improves model accuracy in both cross-validation and holdout tests by roughly 4% and improves the AUC at timestamp 0 by roughly 2%. This suggests that integrating higher-order influences into a DBN not only potentially improves the model’s predictive accuracy during the cross-validation phase but also enhances its overall and time stamp-specific generalizability. DBNs with higher-order influences offer a trustworthy and interpretable tool for educators to foresee and support learning progression.

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Modeling Dynamic Processes with Memory by Higher Order Temporal Models

Cited by 2 publications

References 0 publications

Handling Missing Entries in Monitoring a Woman’s Monthly Cycle and Controlling Fertility

Handling Missing Entries in Monitoring a Woman’s Monthly Cycle and Controlling Fertility

Memory-Based Dynamic Bayesian Networks for Learner Modeling: Towards Early Prediction of Learners’ Performance in Computational Thinking

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