Bayesian model identifies skill difficulties and student subgroups in engineering education.
problem Identifying and supporting diverse student needs in entry-level university engineering modules.
method Hierarchical Bayesian modeling of student response data.
result Clear patterns of skill mastery and distinct student subgroups identified.
Proposes RaT to mitigate bias in student-teacher estimation.
problem Systematic bias in teacher's predictions propagates to student model.
method Uses teacher to estimate residuals in student's predictions.
result RaT method reduces teacher bias effect and achieves optimal rate.
OMERF extends random forest for hierarchical data and ordinal responses.
problem Analyzing hierarchical data and ordinal responses using tree-based methods.
method Ordinal Mixed-Effects Random Forest (OMERF) that preserves flexibility and hierarchical structure.
result OMERF identifies discriminating student characteristics and estimates school effects.
Student performance modelling (SPM) is a critical step to assessing and improving students performances in their learning discourse. However, most existing SPM are based on statistical approaches, which on one hand are based on probability, depicting that results are based on estimation; and on the other hand, actual i…
Teaches reproducible research to medical students and postgrads.
problem Lack of reproducibility in medical research practices.
method Designed and delivered a lecture series on reproducible research.
result Encountered practical obstacles in reproducing a published analysis.
Paper presents deep learning and ML for automated student performance estimation.
problem Evaluation of students' performance during the pandemic.
method In-depth analysis of deep learning and machine learning approaches.
result Better performance across different prediction tasks with fully data-driven approach.
KT models improved slightly with synthetic student data.
problem Limited access to real student data and lack of diversity in public datasets.
method Simulated student data using three statistical strategies and tested on KT baselines.
result Synthetic data can lead to similar performance as real data.
The paper characterizes the efficiency of transferring knowledge from a teacher to a student classifier over finite domains.
problem Characterizing the statistical efficiency of knowledge transfer over finite domains.
method Three progressive levels of privileged information: hard labels, teacher probabilities, and soft labels. Novel empirical loss functions used to achieve the fundamental limits.
result Achieving the fundamental limits of knowledge transfer through specific levels of privileged information and novel loss functions.
Teaches deep learning to statisticians.
problem Statisticians lack expertise in deep learning.
method Developed a program and taught DL to statistics graduate students.
result Provided tips and resources for teaching DL.
The paper proposes count echo state networks for forecasting graduate student enrollments.
problem Forecasting graduate student enrollments from historical data.
method Developed hierarchical count echo state networks and compared them to Poisson autoregressions and negative binomial models.
result Hierarchical negative binomial based echo state network is the superior model.
In this article, a large data set containing every course taken by every undergraduate student in a major university in Canada over 10 years is analysed. Modern machine learning algorithms can use large data sets to build useful tools for the data provider, in this case, the university. In this article, two classifiers…
Paper uses econometrics time series model with T-student Distribution for short-term load forecasting.
problem Accurate short-term load forecasting for optimizing electrical sources and protecting energy.
method Uses SARIMA-GARCH model with T-student Distribution to forecast electric load.
result The proposed model outperforms the ARIMA model with Normal Distribution.
This paper explores the suitability of using automatically discovered topics from MOOC discussion forums for modelling students' academic abilities. The Rasch model from psychometrics is a popular generative probabilistic model that relates latent student skill, latent item difficulty, and observed student-item respons…
Improved normalising flows using Student's t-distribution for robust training.
problem Training deep probabilistic models with robust statistics.
method Propose Student's t-distribution as a robust alternative to Gaussian in normalising flows.
result Improved robustness and reduced generalization gap with Student's t-distribution.
We seek to utilize the nonextensive statistics to the microscopic modeling of the interacting many-investor dynamics that drive the price changes in a market. The statistics of price changes are known to be fit well by the Students-T and power-law distributions of the nonextensive statistics. We therefore derive models…
Distillation affects some classes more than others, impacting fairness and bias.
problem Distillation affects some classes more than others, impacting fairness and bias.
method Examined class-wise accuracy and fairness metrics (DPD, EOD) on models trained with different datasets.
result Increasing the distillation temperature improves the distilled student model's fairness and individual fairness.
Study shows human advisors use context to improve student outcomes in algorithm-assisted advising.
problem How human advisors use context to guide interventions in algorithm-assisted advising.
method Mixed-methods approach combining quantitative and qualitative data from a randomized controlled trial.
result 2 out of 3 interventions by advisors were plausibly 'expertly targeted' to students using non-algorithmic context.
New model shows hierarchical proof structure helps theorem provers.
problem How to efficiently learn proofs from teacher traces.
method Model proof search as MDP, analyze imitation learning from traces.
result Hierarchical proof structure leads to more efficient learning.
Distillation improves simple models by approximating complex labels.
problem Why does distillation improve simple models?
method Statistical perspective on distillation, connecting to extreme multiclass retrieval.
result Distillation helps by approximating underlying class-probabilities, reducing bias and variance.
New results show neural networks generalize well due to polynomial regression, not just overparametrization.
problem Generalization of neural networks despite overparametrization.
method Teacher/Student model with polynomial regression.
result Student networks interpolating teacher-generated data generalize well with a minimal sample size.
Study analyzes catastrophic forgetting in continual learning using teacher-student networks.
problem Catastrophic forgetting in continuously learning systems.
method Teacher-student learning framework, similarity of input distributions and target functions.
result Network can avoid catastrophic forgetting with small input distribution similarity and large target function similarity.
A new test statistic speeds up MMD while maintaining power.
problem Efficiently testing two distributions without permutations.
method Cross-MMD statistic based on sample-splitting and studentization.
result Cross-MMD has a limiting standard Gaussian distribution under the null.
Deep neural networks learn spatially heterogeneous patterns from input data.
problem Understanding the hidden layers of deep neural networks.
method Statistical mechanics approach with a teacher-student setting.
result Learning by deep neural networks is spatially heterogeneous, with central regions less correlated.
Superposition accelerates training to a universal power-law exponent.
problem Training dynamics in neural networks.
method Teacher-student framework and analytic theory.
result Superposition leads to a universal power-law exponent of ~1, independent of data and channel statistics.
Improved VAE for heavy-tailed data using Student's t-distributions.
problem Over-regularization in VAEs with Gaussian priors.
method Proposed t3VAE framework with Student's t-distributions for prior, encoder, and decoder. result Significantly outperforms other models on heavy-tailed datasets.
The study uses Hidden Markov Models to analyze student enrollment patterns and academic performance.
problem Limited understanding of how enrollment patterns affect academic performance.
method Applied Hidden Markov Models to categorize enrollment strategies and compare academic outcomes.
result Mixed enrollment strategies lead to better academic performance, especially during part-time semesters.
Sharpe ratio is widely used in asset management to compare and benchmark funds and asset managers. It computes the ratio of the excess return over the strategy standard deviation. However, the elements to compute the Sharpe ratio, namely, the expected returns and the volatilities are unknown numbers and need to be esti…
Predicts academic risk in college students using interpretable machine learning.
problem Predicting academic risk from high-dimensional, unbalanced student data.
method Binary classification task using LightGBM model and Shapley value.
result 8 predictors for academic risk identified, including quality of academic partners and dormitory study atmosphere.
Predicts student performance in interactive online question pools using GNNs.
problem Predicting student performance in interactive online question pools with evolving knowledge.
method Proposes R^2GCN, a GNN model for heterogeneous networks to predict student performance.
result Achieves higher accuracy in student performance prediction than traditional methods.
A new family of nonparametric statistics, the r-statistics, is introduced. It consists of counting the number of records of the cumulative sum of the sample. The single-sample r-statistic is almost as powerful as Student's t-statistic for Gaussian and uniformly distributed variables, and more powerful than the sign and…
Enhances student diversity in collaborative learning.
problem Student homogenization in large groups.
method Random routing, diverse feature sets, and random subgroup imitation.
result Significantly outperforms state-of-the-art approaches.
Each year, roughly 30% of first-year students at US baccalaureate institutions do not return for their second year and over $9 billion is spent educating these students. Yet, little quantitative research has analyzed the causes and possible remedies for student attrition. Here, we describe initial efforts to model stud…
Deep neural networks bring in impressive accuracy in various applications, but the success often relies on the heavy network architecture. Taking well-trained heavy networks as teachers, classical teacher-student learning paradigm aims to learn a student network that is lightweight yet accurate. In this way, a portable…
We introduce a toy probabilistic model to analyze job-matching processes in recent Japanese labor markets for university graduates by means of statistical physics. We show that the aggregation probability of each company is rewritten by means of non-linear map under several conditions. Mathematical treatment of the map…
Student-teacher learning improves generalization with noisy inputs.
problem Transfer knowledge from clean inputs to noisy inputs.
method Analyzes student-teacher learning using deep linear networks and experiments with nonlinear networks.
result Three factors are vital for success: zero training loss, teacher knowledge, and feature decomposition.
Machine learning methods tend to outperform traditional statistical models at prediction. In the prediction of academic achievement, ML models have not shown substantial improvement over logistic regression. So far, these results have almost entirely focused on college achievement, due to the availability of administra…
A new teacher-class network method compresses DNNs by distributing knowledge to multiple student networks.
problem Overwhelming size of Deep Neural Networks (DNNs).
method Single teacher with multiple student networks, transferring knowledge to each student.
result The combined knowledge of the class of students achieves better performance and reduces parameters.
We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Noisy Student Training achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On robustness test sets, i…
Contributions: Prior studies on education have mostly followed the model of the cross sectional study, namely, examining the pretest and the posttest scores. This paper shows that students' knowledge throughout the intervention can be estimated by time series analysis using a hidden Markov model. Background: Analyzing …
Lecture notes on advanced linear regression methods.
problem Understanding the properties of linear regression estimators in high dimensions.
method Proposition-proof exploration of least squares, ridgeless, ridge, and lasso estimators.
result Detailed analysis of the existence, uniqueness, relations, computation, and non-asymptotic properties of these estimators.
In this paper we do the first large scale analysis of writing style development among Danish high school students. More than 10K students with more than 100K essays are analyzed. Writing style itself is often studied in the natural language processing community, but usually with the goal of verifying authorship, assess…
An important, yet largely unstudied, problem in student data analysis is to detect misconceptions from students' responses to open-response questions. Misconception detection enables instructors to deliver more targeted feedback on the misconceptions exhibited by many students in their class, thus improving the quality…
We propose gradient adversarial training, an auxiliary deep learning framework applicable to different machine learning problems. In gradient adversarial training, we leverage a prior belief that in many contexts, simultaneous gradient updates should be statistically indistinguishable from each other. We enforce this c…
Study connects covariance cleaning theory to information theory for heavy-tailed distributions.
problem Optimizing covariance matrices for heavy-tailed distributions using information theory.
method Minimizing Frobenius norm and information loss between true and estimated covariance matrices.
result Asymptotic regime of large matrices minimizes information loss for Student's t distributions.
Deep neural networks outperform traditional methods in high-dimensional classification.
problem Understanding the empirical success of deep neural networks in high-dimensional classification.
method Proposed a teacher-student framework with Bayes classifier as ReLU neural networks, derived convergence rates for 0-1 and hinge losses.
result Sharp rate of convergence for classifiers trained using 0-1 or hinge loss, with Od(n−2/3) or Od(n−1) under separable data distribution. Modeling student behaviors and multiple predictions for early intervention.
problem Predicting student outcomes and interactions among multiple tasks.
method Proposes a variant of LSTM and soft-attention mechanism for heterogeneous behaviors, and co-attention mechanism for task interactions.
result Demonstrated effectiveness in predicting student outcomes and interactions.
New method for constructing confidence intervals for time series data.
problem Constructing confidence intervals for statistical functionals from time series data.
method Proposes a general purpose confidence interval procedure based on overlapping batches of time series data.
result Large overlapping batches yield confidence intervals of higher quality than generic methods.
Paper proposes a generalized precision matrix for t-Student distributions to improve portfolio optimization.
problem Limitations of inverse covariance matrix in non-Gaussian settings.
method Exploits local dependence function to define generalized precision matrix (GPM) for multivariate t-Student distribution.
result GPM leads to statistically significant lower out-of-sample variances in minimum-variance portfolios.