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1. Logistic Knowledge Tracing Tutorial: Practical Educational Applications

   https://doi.org/10.5281/zenodo.12730033  

   Philip_I_Pavlik_Jr; Eglington, Luke G; Cao, Meng; Chu, Wei (July 2024, International Educational Data Mining Society)

   Benjamin, Paaßen; Carrie, Demmans Epp (Ed.)

   Logistic Knowledge Tracing (LKT) is a framework for combining various predictive features into student models that are adaptive, interpretable, explainable, and accurate. While the name logistic knowledge tracing was coined for our R package that implements this methodology for making student models, logistic knowledge tracing originates with much older models such as Item Response Theory (IRT), the Additive Factors Model (AFM), and Perfor-mance Factors Analysis (PFA), which exemplify a type of model where student performance is represented by the sum of multiple components each with some sort of feature computed for the component. Features may range from the simple presence or ab-sence of the component to complex functions of the prior history of the component. The LKT package provides a simple interface to this methodology, allowing old models to be specified or new models to be created by mixing and matching components with features. We will provide concrete examples of how the LKT framework can provide interpretable results on real-world datasets while being highly accurate. 

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2. Automatic Domain Model Creation and Improvement

   Pavlik Jr, P. I. (January 2021, Proceedings of The 14th International Conference on Educational Data Mining)

   null (Ed.)

   We describe a data mining pipeline to convert data from educational systems into knowledge component (KC) models. In contrast to other approaches, our approach employs and compares multiple model search methodologies (e.g., sparse factor analysis, covariance clustering) within a single pipeline. In this preliminary work, we describe our approach's results on two datasets when using 2 model search methodologies for inferring item or KCs relations (i.e., implied transfer). The first method uses item covariances which are clustered to determine related KCs, and the second method uses sparse factor analysis to derive the relationship matrix for clustering. We evaluate these methods on data from experimentally controlled practice of statistics items as well as data from the Andes physics system. We explain our plans to upgrade our pipeline to include additional methods of finding item relationships and creating domain models. We discuss advantages of improving the domain model that go beyond model fit, including the fact that models with clustered item KCs result in performance predictions transferring between KCs, enabling the learning system to be more adaptive and better able to track student knowledge. 

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3. Personalized Deep Bi-LSTM RNN Based Model for Pain Intensity Classification Using EDA Signal

   https://doi.org/10.3390/s22218087  

   Pouromran, Fatemeh; Lin, Yingzi; Kamarthi, Sagar (November 2022, Sensors)

   Automatic pain intensity assessment from physiological signals has become an appealing approach, but it remains a largely unexplored research topic. Most studies have used machine learning approaches built on carefully designed features based on the domain knowledge available in the literature on the time series of physiological signals. However, a deep learning framework can automate the feature engineering step, enabling the model to directly deal with the raw input signals for real-time pain monitoring. We investigated a personalized Bidirectional Long short-term memory Recurrent Neural Networks (BiLSTM RNN), and an ensemble of BiLSTM RNN and Extreme Gradient Boosting Decision Trees (XGB) for four-category pain intensity classification. We recorded Electrodermal Activity (EDA) signals from 29 subjects during the cold pressor test. We decomposed EDA signals into tonic and phasic components and augmented them to original signals. The BiLSTM-XGB model outperformed the BiLSTM classification performance and achieved an average F1-score of 0.81 and an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.93 over four pain states: no pain, low pain, medium pain, and high pain. We also explored a concatenation of the deep-learning feature representations and a set of fourteen knowledge-based features extracted from EDA signals. The XGB model trained on this fused feature set showed better performance than when it was trained on component feature sets individually. This study showed that deep learning could let us go beyond expert knowledge and benefit from the generated deep representations of physiological signals for pain assessment. 

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4. A Big Data Approach for the Regional-Scale Spatial Pattern Analysis of Amazonian Palm Locations

   https://doi.org/10.3390/rs17050784  

   Drouillard, Matthew J; Cummings, Anthony R (March 2025, Remote Sensing)

   Arecaceae (palms) are an important resource for indigenous communities as well as fauna populations across Amazonia. Understanding the spatial patterns and the environmental factors that determine the habitats of palms is of considerable interest to rainforest ecologists. Here, we utilize remotely sensed imagery in conjunction with topography and soil attribute data and employ a generalized cluster identification algorithm, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), to study the underlying patterns of palms in two areas of Guyana, South America. The results of the HDBSCAN assessment were cross-validated with several point pattern analysis methods commonly used by ecologists (the quadrat test for complete spatial randomness, Morista Index, Ripley’s L-function, and the pair correlation function). A spatial logistic regression model was generated to understand the multivariate environmental influences driving the placement of cluster and outlier palms. Our results showed that palms are strongly clustered in the areas of interest and that the HDBSCAN’s clustering output correlates well with traditional analytical methods. The environmental factors influencing palm clusters or outliers, as determined by logistic regression, exhibit qualitative similarities to those identified in conventional ground-based palm surveys. These findings are promising for prospective research aiming to integrate remote flora identification techniques with traditional data collection studies. 

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5. Towards Interpretable Automated Machine Learning for STEM Career Prediction

   https://doi.org/10.5281/zenodo.4008073  

   Liu, R.; Tan, A. (August 2020, Journal of educational data mining)

   In this paper, we describe our solution to predict student STEM career choices during the 2017 ASSISTments Datamining Competition. We built a machine learning system that automatically reformats the data set, generates new features and prunes redundant ones, and performs model and feature selection. We designed the system to automatically find a model that optimizes prediction performance, yet the final model is a simple logistic regression that allows researchers to discover important features and study their effects on STEM career choices. We also compared our method to other methods, which revealed that the key to good prediction is proper feature enrichment in the beginning stage of the data analysis, while feature selection in a later stage allows a simpler final model. 

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