Investigating Gender Differences in Digital Learning Games with Educational Data Mining
Effective Years: 2022-2026
Given the potential motivational benefits of digital learning games, games might provide a pathway for reducing students' math anxiety and increasing their self-efficacy and interest in math. This project will explore whether and how digital learning games can lead to less math anxiety and better learning in students. It will study learning with two existing digital learning games: Decimal Point, which teaches foundational math concepts (decimal numbers and operations) to 5th and 6th grade students; and Angle Jungle, which targets a similar age range (4th and 5th graders) and has a similar thematic design (i.e., a game map, cartoon characters), but with different game mechanics, content (angles), and instructional approach. The study will explore how and why Decimal Point has, over the course of several experiments spanning multiple years, consistently produced a learning advantage for players. In doing so, investigators will identify principles regarding the relationship between student learning and game features that can be shared with game developers and used in other games, starting with Angle Jungle. The study will investigate two pathways hypothesized to lead to learning differences among students: first, that the playful features of the games reduce the saliency of the math content, making it less likely to prompt math anxiety; and second, that the games' thematic details are more appealing and engaging to some learners based on their interests and videogame preferences. In Year 1, educational data mining will be used to infer students' cognitive and affective processes while playing Decimal Point and compare data to the distinct processes predicted by these two pathways. In Year 2, investigators will assess whether the hypothesized pathways and learner differences replicate in the context of Angle Jungle. In Year 3, hypotheses will be further tested by manipulating Decimal Point's emphasis on math content in one version of the game and enjoyment and playful features in another. The project will compare learning outcomes between the two versions to more deeply explore the competing hypotheses. The ultimate aim of this work is to provide insights into how different students learn from digital games, providing principles and guidance for other researchers and game designers in developing and revising digital learning games. Thus, the project has the potential to transfer Decimal Point's success with learning outcomes to other digital learning games and advance knowledge on the game features that best support students' learning outcomes. Furthermore, findings will allow investigators to revise both games and make them available to thousands of late elementary and middle school students across the country. Even during this project, approximately 1,950 students--including many from districts with low math proficiency--will benefit from learning with Decimal Point and Angle Jungle. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
45 Publications Related to This Project:
- Investigating Racial and Ethnic Differences in Learning with a Digital Game and Tutor for Decimal Numbers
- Understanding Gender Effects in Game-Based Learning: The Role of Self-Explanation
- Evaluating Chatgpt's Decimal Skills and Feedback Generation in a Digital Learning Game
- Exploring Machine Learning Algorithms And Numerical Representations Strategies To Develop Sequence-Based Predictive Models For Protein Networks
- Automated Hand-Raising Detection In Classroom Videos: A View-Invariant And Occlusion-Robust Machine Learning Approach
- Training Language Models For Programming Feedback Using Automated Repair Tools
- Examining the Impact of Flipped Learning for Developing Young Job Seekers' AI Literacy
- Measuring The Quality Of Domain Models Extracted From Textbooks With Learning Curves Analysis
- Towards Enriched Controllability For Educational Question Generation
- Eliciting Proactive And Reactive Control During Use Of An Interactive Learning Environment
- How To Repeat Hints: Improving AI-Driven Help In Open-Ended Learning Environments
- Automatic Detection Of Collaborative States In Small Groups Using Multimodal Features
- Reducing The Cost: Cross-Prompt Pre-Finetuning For Short Answer Scoring
- Real-Time Hybrid Language Model For Virtual Patient Conversations
- A Computational Model For The ICAP Framework: Exploring Agent-Based Modeling As An AIED Methodology
- Automated Program Repair Using Generative Models For Code Infilling
- Development And Experiment Of Classroom Engagement Evaluation Mechanism During Real-Time Online Courses
- A Machine-Learning Approach To Recognizing Teaching Beliefs In Narrative Stories Of Outstanding Professors
- A Personalized Learning Path Recommendation Method For Learning Objects With Diverse Coverage Levels
- Investigating The Utility Of Self-Explanation Through Translation Activities With A Code-Tracing Tutor
- Unsupervised Concept Tagging Of Mathematical Questions From Student Explanations
- Confusion, Conflict, Consensus: Modeling Dialogue Processes During Collaborative Learning With Hidden Markov Models
- Content Matters: A Computational Investigation Into The Effectiveness Of Retrieval Practice And Worked Examples
- A Multi-Theoretic Analysis Of Collaborative Discourse: A Step Towards AI-Facilitated Student Collaborations
- Can You Solve This On The First Try? – Understanding Exercise Field Performance In An Intelligent Tutoring System
- Gender Differences in Learning Game Preferences: Results Using a Multi-Dimensional Gender Framework
- Designing For Student Understanding Of Learning Analytics Algorithms
- Efficient Feedback And Partial Credit Grading For Proof Blocks Problems
- Automatic Educational Question Generation With Difficulty Level Controls
- Trustworthy Academic Risk Prediction With Explainable Boosting Machines
- Contrastive Learning For Reading Behavior Embedding In E-Book System
- Learning When To Defer To Humans For Short Answer Grading
- Impact Of Learning A Subgoal-Directed Problem-Solving Strategy Within An Intelligent Logic Tutor
- "Why My Essay Received a 4?": A Natural Language Processing Based Argumentative Essay Structure Analysis
- Leveraging Deep Reinforcement Learning for Metacognitive Interventions Across Intelligent Tutoring Systems
- Involving Teachers In The Data-Driven Improvement Of Intelligent Tutors: A Prototyping Study
- Algebra Error Classification With Large Language Models
- Exploration Of Annotation Strategies For Automatic Short Answer Grading
- Matching Exemplar As Next Sentence Prediction (Mensp): Zero-Shot Prompt Learning For Automatic Scoring In Science Education
- Does Informativeness Matter? Active Learning For Educational Dialogue Act Classification
- Smartphone: Exploring Keyword Mnemonic With Auto-Generated Verbal And Visual Cues
- The Automated Model Of Comprehension Version 3.0: Paying Attention To Context
- Improving Automated Evaluation Of Student Text Responses Using Gpt-3.5 For Text Data Augmentation
- Real-Time AI-Driven Assessment and Scaffolding that Improves Students' Mathematical Modeling During Science Investigations
- Can Virtual Agents Scale Up Mentoring?: Insights From College Students' Experiences Using The Careerfair.AI Platform At An American Hispanic-Serving Institution
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