| Enhancing Auto-scoring of Student Open Responses in the Presence of Mathematical Terms and Expressions | 0 | 0.34 | 2022 |
| Using Past Data to Warm Start Active Machine Learning - Does Context Matter? | 0 | 0.34 | 2021 |
| Effectiveness of Crowd-Sourcing On-Demand Assistance from Teachers in Online Learning Platforms | 0 | 0.34 | 2020 |
| Recent Advances in Multimodal Educational Data Mining in K-12 Education | 0 | 0.34 | 2020 |
| The automated grading of student open responses in mathematics. | 0 | 0.34 | 2020 |
| Understanding the Complexities of Chinese Word Acquisition within an Online Learning Platform | 0 | 0.34 | 2019 |
| Machine-Learned or Expert-Engineered Features? Exploring Feature Engineering Methods in Detectors of Student Behavior and Affect. | 0 | 0.34 | 2019 |
| Crowdsourcing and Education: Towards a Theory and Praxis of Learnersourcing | 0 | 0.34 | 2018 |
| Testing the Validity and Reliability of Intrinsic Motivation Inventory Subscales Within ASSISTments. | 0 | 0.34 | 2018 |
| Observing Personalizations in Learning: Identifying Heterogeneous Treatment Effects Using Causal Trees. | 0 | 0.34 | 2017 |
| Feedback Design Patterns for Math Online Learning Systems | 0 | 0.34 | 2017 |
| Guidance counselor reports of the ASSISTments college prediction model (ACPM). | 0 | 0.34 | 2017 |
| Incorporating Rich Features into Deep Knowledge Tracing. | 4 | 0.40 | 2017 |
| Modeling Wheel-spinning and Productive Persistence in Skill Builders. | 4 | 0.47 | 2017 |
| Using a Single Model Trained across Multiple Experiments to Improve the Detection of Treatment Effects. | 0 | 0.34 | 2017 |
| A Memory-Augmented Neural Model for Automated Grading. | 1 | 0.39 | 2017 |
| Sequencing content in an adaptive testing system: the role of choice. | 0 | 0.34 | 2017 |
| The Future of Adaptive Learning: Does the Crowd Hold the Key? | 1 | 0.36 | 2016 |
| Semantic Features of Math Problems: Relationships to Student Learning and Engagement. | 0 | 0.34 | 2016 |
| Enhancing the efficiency and reliability of group differentiation through partial credit. | 0 | 0.34 | 2016 |
| Predicting student performance on post-requisite skills using prerequisite skill data: an alternative method for refining prerequisite skill structures. | 4 | 0.40 | 2016 |
| AXIS: Generating Explanations at Scale with Learnersourcing and Machine Learning. | 23 | 1.12 | 2016 |
| Discovering 'Tough Love' Interventions Despite Dropout. | 0 | 0.34 | 2016 |
| The assessment of learning infrastructure (ALI): the theory, practice, and scalability of automated assessment. | 1 | 0.36 | 2016 |
| A Methodology for Discovering how to Adaptively Personalize to Users using Experimental Comparisons. | 1 | 0.37 | 2015 |
| The Utility of Clustering in Prediction Tasks | 0 | 0.34 | 2015 |
| The Effect of the Distribution of Predictions of User Models. | 0 | 0.34 | 2015 |
| Towards better affect detectors: effect of missing skills, class features and common wrong answers | 3 | 0.43 | 2015 |
| An analysis of the impact of action order on future performance: the fine-grain action model | 3 | 0.37 | 2015 |
| Improving Student Modeling Through Partial Credit and Problem Difficulty | 5 | 0.49 | 2015 |
| Predicting Student Aptitude Using Performance History. | 0 | 0.34 | 2015 |
| Defining Mastery: Knowledge Tracing Versus N- Consecutive Correct Responses. | 3 | 0.49 | 2015 |
| The Prediction of Student First Response Using Prerequisite Skills | 2 | 0.41 | 2015 |
| Connecting Collaborative & Crowd Work with Online Education | 4 | 0.47 | 2015 |
| Blocking Vs. Interleaving: Examining Single-Session Effects Within Middle School Math Homework | 2 | 0.43 | 2015 |
| The Impact of Incorporating Student Confidence Items into an Intelligent Tutor: A Randomized Controlled Trial. | 2 | 0.39 | 2015 |
| Grand Challenges for EDM and Related Research Areas. | 0 | 0.34 | 2015 |
| The Role Of Student Choice Within Adaptive Tutoring | 2 | 0.46 | 2015 |
| Developing Self-Regulated Learners Through An Intelligent Tutoring System | 0 | 0.34 | 2015 |
| Learning, Moment-By-Moment And Over The Long Term | 2 | 0.37 | 2015 |
| Refining Learning Maps with Data Fitting Techniques: Searching for Better Fitting Learning Maps. | 1 | 0.40 | 2014 |
| Predicting STEM and Non-STEM College Major Enrollment from Middle School Interaction with Mathematics Educational Software. | 0 | 0.34 | 2014 |
| Applying Clustering to the Problem of Predicting Retention within an ITS: Comparing Regularity Clustering with Traditional Methods. | 1 | 0.41 | 2013 |
| A Comparison of Two Different Methods to Individualize Students and Skills. | 1 | 0.35 | 2013 |
| Which Is More Responsible for Boredom in Intelligent Tutoring Systems: Students (Trait) or Problems (State)? | 4 | 0.49 | 2013 |
| Using ITS Generated Data to Predict Standardized Test Scores. | 0 | 0.34 | 2013 |
| Extending Knowledge Tracing to Allow Partial Credit: Using Continuous versus Binary Nodes. | 22 | 1.29 | 2013 |
| Towards an Understanding of Affect and Knowledge from Student Interaction with an Intelligent Tutoring System. | 16 | 1.33 | 2013 |
| Estimating the Effect of Web-Based Homework. | 10 | 1.02 | 2013 |
| Tutor Modeling Versus Student Modeling. | 0 | 0.34 | 2012 |
