PREREQ learns concept prerequisites from online educational resources.
problem Inferring prerequisite relations between educational concepts.
method PREREQ uses latent representations of concepts from Pairwise Latent Dirichlet Allocation and a Siamese neural network to learn from course prerequisites and labeled data.
result PREREQ outperforms state-of-the-art approaches and can learn from less data.
Paper aims to improve education online in South Africa using NMT for Setswana.
problem Inclusion of machine translation for low-resourced local languages in online education.
method Demonstrates state-of-the-art performance on English-to-Setswana translation using the Transformer architecture.
result Current NMT techniques show promise for African languages, beating previous methods by 5.33 BLEU points.
Study analyzes online student behavior patterns using log data.
problem Understanding and optimizing student learning in online educational systems.
method Non-negative matrix factorization techniques for soft clustering.
result Behavioral changes of individual students and the system over time.
Educational game on crypto investment helps students grasp macroeconomics.
problem Weak connections between microeconomic decision-making and macroeconomic concepts in classroom games.
method Design and study of an educational game on cryptocurrency investment.
result Engages students in understanding macroeconomics through incentivized individual investment decisions.
The Open University studies student online behavior in virtual learning environments.
problem Improving retention rates in online modules.
method GUHA and Markov chain-based analysis of student activity.
result Both methods are valid for modeling student activities.
Interactive tools help teach economics online.
problem Teaching macroeconomic models online.
method Browser-based simulation toolkits.
result Intuitive approach to comparative statics.
Bias and heterogeneity in peer assessment can lead to the issue of unfair scoring in the educational field. To deal with this problem, we propose a reference ranking method for an online peer assessment system using HodgeRank. Such a scheme provides instructors with an objective scoring reference based on mathematics.
TrueLearn Python library for personalized educational recommendations.
problem Building educational recommendation systems with humanly-intuitive user representations.
method Online learning Bayesian models and open learner concept.
result Library includes models and representations for user control and interpretability.
TrueLearn uses Bayesian algorithms to match learners with educational resources.
problem Matching lifelong learners to open educational resources efficiently and effectively.
method TrueLearn uses a text ontology and Bayesian strategies to match learners with educational resources based on their background knowledge and material novelty.
result TrueLearn algorithms show promise in building an effective educational recommendation system.
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.
EduGym simplifies reinforcement learning education for students.
problem Students struggle to translate between equations and code in reinforcement learning.
method EduGym provides educational environments and interactive notebooks.
result 86% of students and researchers find EduGym useful for education.
In modern computer science education, massive open online courses (MOOCs) log thousands of hours of data about how students solve coding challenges. Being so rich in data, these platforms have garnered the interest of the machine learning community, with many new algorithms attempting to autonomously provide feedback t…
Massive Open Online Courses (MOOCs) bring together thousands of people from different geographies and demographic backgrounds -- but to date, little is known about how they learn or communicate. We introduce a new content-analysed MOOC dataset and use Bayesian Non-negative Matrix Factorization (BNMF) to extract communi…
New metric MADD assesses fairness of predictive student models.
problem Predictive student models can be biased and unfair, leading to discrimination.
method Proposes MADD metric to analyze model's discriminatory behaviors.
result Fair predictive performance does not guarantee fair behaviors or outcomes.
Develops a fair post-processing method for student success predictions.
problem Ensuring fairness in predictive student models for educational applications.
method Uses the MADD metric to improve model fairness while maintaining accuracy.
result Successfully improved fairness of predictive models for student success.
The paper presents a method for personalized exercise recommendations that improves learner skill gain.
problem Adapting to individual needs in large, diverse groups of learners in digital environments.
method Contextual Thompson Sampling to select exercises that advance learner skill.
result The method recommends exercises associated with greater skill improvement and adapts to learner differences.
Study predicts online procrastination using machine learning.
problem Predicting procrastination in eLearning to prevent drop-outs.
method Comparison of multiple machine learning models with subjective and objective predictors.
result Models with objective predictors outperform those with subjective predictors.
LectureBank helps students find the right NLP course sequence.
problem Finding the right NLP course sequence for students with no background.
method Embedding-based method and neural graph-based networks to learn prerequisite relations.
result LectureBank dataset aids in educational and application purposes.
Boosts A/B test precision using auxiliary data from historical users.
problem Small sample sizes and imprecise estimates in A/B tests.
method Coupling design-based causal estimation with machine-learning models of historical user data.
result Effect estimates using auxiliary data are roughly equivalent to increasing sample size by 20%, or up to 50-80% in some cases.
EduQG generates better educational questions by pre-training on scientific text.
problem Improving the quality of educational questions for scalable self-assessment.
method Adapting a large language model for educational question generation, pre-trained on scientific text.
result EduQG produces superior educational questions compared to baseline models.
AI could democratize education but risks inequality.
problem AI risks creating educational inequality.
method Speculation and posing questions on AI in education.
result AI could exacerbate educational inequality.
KT models struggle with student concept drift, but BKT remains the most stable.
problem Impact of student concept drift on KT models.
method Applied four KT models to five academic years of data.
result KT models generally degrade in performance with concept drift, BKT remains stable.
We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA), that jointly (i) traces learner concept knowledge over time, (ii) an…
Quizlet is the most popular online learning tool in the United States, and is used by over 2/3 of high school students, and 1/2 of college students. With more than 95% of Quizlet users reporting improved grades as a result, the platform has become the de-facto tool used in millions of classrooms. In this paper, we expl…
In this paper the correlation between education, research and macroeconomic strength of countries at a global scale is analyzed on the basis of statistical data published by the UNIDO and OECD. It uses sets of composite indicators describing the economical performance and competitiveness as well as those relevant for h…
Neural pedagogical agent updates user models in real-time for mobile education apps.
problem Real-time user modeling for dynamic mobile education platforms.
method Bidirectional recurrent neural networks with attention mechanism over embedded question-response pairs.
result Model outperforms existing approaches in predicting user response correctness.
Tailoring the presentation of information to the needs of individual students leads to massive gains in student outcomes~\cite{bloom19842}. This finding is likely due to the fact that different students learn differently, perhaps as a result of variation in ability, interest or other factors~\cite{schiefele1992interest…
Improves content allocation in educational platforms with sparse data.
problem Imbalanced content allocation and delayed convergence in adaptive strategies.
method Introduces WAPTS, an algorithm that refines Thompson Sampling for data-sparse environments.
result Demonstrates earlier and more reliable identification of promising treatments.
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.
The Web has enabled one of the most visible recent developments in education---the deployment of massive open online courses. With their global reach and often staggering enrollments, MOOCs have the potential to become a major new mechanism for learning. Despite this early promise, however, MOOCs are still relatively u…
Builds a novel educational recommender for lifelong learners.
problem Challenges in creating scalable and transparent models for lifelong learning.
method Integrative approach combining content novelty, background knowledge, and learner engagement.
result TrueLearn achieves promising performance while retaining a human interpretable learner model.
Hidden Markov Model predicts student performance in educational games.
problem Estimating student knowledge progression in educational games.
method Time series analysis using hidden Markov model with game features.
result State trajectories accurately predict student performance.
We present three case studies of organizations using a data science competition to answer a pressing question. The first is in education where a nonprofit that creates smart school budgets wanted to automatically tag budget line items. The second is in public health, where a low-cost, nonprofit women's health care prov…
Paper classifies brain signals using eigenvalues for 2D and 3D educational content questions.
problem Classifying brain signals for 2D and 3D educational content questions.
method Eigenvalues of covariance matrix used as features; KNN and SVM classifiers applied.
result No significant difference in learning, memory retention, and recall between 2D and 3D educational content.
Student performance prediction - where a machine forecasts the future performance of students as they interact with online coursework - is a challenging problem. Reliable early-stage predictions of a student's future performance could be critical to facilitate timely educational interventions during a course. However, …
Model predicts higher education dropout risk with interpretable parameters.
problem Predicting and understanding student dropout risk in higher education.
method Sparse interpretable post-clustering logistic regression.
result Model identifies distinct dropout risk subgroups within the student population.
Semantic TrueLearn uses semantic graphs to improve educational recommendation systems.
problem Challenges in handling semantic and hierarchical structure in knowledge areas.
method Introduces a novel learner model that exploits semantic relatedness between knowledge components using a Wikipedia link graph.
result Achieves statistically significant improvements in predictive performance for educational engagement.
This paper ranks Latin American countries based on AI potential.
problem Identifying emerging AI powers in Latin America.
method Ranking based on infrastructure, education, and finance.
result Argentina, Colombia, Uruguay, Costa Rica, and Ecuador are new emerging powers in AI.
A new framework for playing and learning board games.
problem Tackling the tedious and repetitive aspects of coding for board game AI.
method Developed a generic TD(λ)-n-tuple agent for arbitrary board games. result TD(λ)-n-tuple outperforms other generic agents on various games. AR app visualizes Quranic Surah al-Fil for Islamic education.
problem Lack of interactive and context-rich learning materials for Quranic studies.
method Research and development approach, including data collection, user requirement analysis, interface design, 3D asset creation, and integration of AR technology.
result AR application achieved high accuracy and user satisfaction, enhancing learner engagement and understanding.
SKVMN improves KT models by tracing student knowledge states and dependencies.
problem Tackling limitations of existing KT models in deep learning.
method Proposes SKVMN, a deep learning model unifying recurrent and memory capacities.
result Significantly outperforms state-of-the-art KT models on multiple datasets.
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…
We revisit a recently introduced agent model[ACS {\bf 11}, 99 (2008)], where economic growth is a consequence of education (human capital formation) and innovation, and investigate the influence of the agents' social network, both on an agent's decision to pursue education and on the output of new ideas. Regular and ra…
Co-adaptation is a special form of on-line learning where an algorithm A must assist an unknown algorithm B to perform some task. This is a general framework and has applications in recommendation systems, search, education, and much more. Today, the most common use of co-adaptive algorithms is …
Study reveals hierarchical structure of global geo-industrial clusters using labor flow network.
problem Identifying and analyzing the hierarchical structure of geo-industrial clusters globally.
method Constructed a labor flow network from LinkedIn data and applied recursive network community detection.
result Geo-industrial clusters defined by labor flow show stronger association with financial performance.
Unified QuesNet learns comprehensive representations for diverse test questions.
problem Lack of labeled data for test questions in online learning systems.
method Unified framework and two-level hierarchical pre-training algorithm for unsupervised learning of heterogeneous question representations.
result QuesNet effectively learns comprehensive question representations and outperforms existing methods.
Causal models help ensure fairness in systems with changing environments.
problem Ensuring fairness in systems with dynamic, long-term effects.
method Causal directed acyclic graphs (DAGs) to model fairness and manipulate causal assumptions.
result Causal assumptions enable simulation and off-policy estimation of interventions.
The gender wage gap varies significantly among women based on socio-economic characteristics.
problem Understanding the extent of gender inequality in earnings among U.S. women.
method High-dimensional wage regression and double lasso analysis of 2016 American Community Survey data.
result The gender wage gap varied substantially across women and was driven by marital status, having children, race, occupation, industry, and educational attainment.