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Memory-Based Dynamic Bayesian Networks for Learner Modeling: Towards Early Prediction of Learners’ Performance in Computational Thinking

https://doi.org/10.3390/educsci14080917
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Effects of technology‐enhanced learning approaches on learners with different prior learning attitudes and knowledge in computational thinking
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A Mathematical Theory of Communication
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AutoThinking: An Adaptive Computational Thinking Game
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Beyond Knowledge Tracing: Modeling Skill Topologies with Bayesian Networks
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Adapting Feedback Types According to Students’ Affective States
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Modeling Dynamic Processes with Memory by Higher Order Temporal Models
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Modeling Learner Affect with Theoretically Grounded Dynamic Bayesian Networks
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Adaptive and Personalized Learning Based on Students’ Cognitive Characteristics
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On-Line Student Modeling for Coached Problem Solving Using Bayesian Networks
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Dynamic Bayesian Networks
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Temporal learner modelling through integration of neural and symbolic architectures
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Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques
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Sequential and temporal characteristics of self and socially regulated learning
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Student mental state inference from unintentional body gestures using dynamic Bayesian networks
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Prediction of students’ procrastination behaviour through their submission behavioural pattern in online learning
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Predictive Student Modeling in Game-Based Learning Environments with Word Embedding Representations of Reflection
inferredcites →https://doi.org/10.1007/s40692-021-00199-4openalex-snapshot
A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020
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The EM algorithm for graphical association models with missing data
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BAYESIAN INFERENCE
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Explanation in artificial intelligence: Insights from the social sciences
inferredcites →https://doi.org/10.1016/j.compedu.2012.06.012openalex-snapshot
Using Bayesian networks to improve knowledge assessment
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Locally averaged Bayesian Dirichlet metrics for learning the structure and the parameters of Bayesian networks
inferredcites →https://doi.org/10.1080/00273171.2019.1590794openalex-snapshot
Dynamic Bayesian Network Modeling of Game-Based Diagnostic Assessments
inferredcites →https://doi.org/10.1080/00273171.2021.1932403openalex-snapshot
A Sequential Response Model for Analyzing Process Data on Technology-Based Problem-Solving Tasks
inferredcites →https://doi.org/10.1080/08957347.2018.1495217openalex-snapshot
Dynamic Bayesian Networks in Educational Measurement: Reviewing and Advancing the State of the Field
inferredcites →https://doi.org/10.1109/access.2023.3249286openalex-snapshot
Modeling Learners to Early Predict Their Performance in Educational Computer Games
inferredcites →https://doi.org/10.1109/bigdata.2015.7363847openalex-snapshot
Next-term student grade prediction
inferredcites →https://doi.org/10.1109/icalt.2016.96openalex-snapshot
Modelling Students' Algebraic Knowledge with Dynamic Bayesian Networks
inferredcites →https://doi.org/10.1109/smc.2013.501openalex-snapshot
Student Engagement Modeling Using Bayesian Networks
inferredcites →https://doi.org/10.1109/tlt.2016.2541664openalex-snapshot
Early Prediction of Student Profiles Based on Performance and Gaming Preferences
inferredcites →https://doi.org/10.1109/tlt.2017.2689017openalex-snapshot
Dynamic Bayesian Networks for Student Modeling
inferredcites →https://doi.org/10.1109/tlt.2019.2922356openalex-snapshot
DeepStealth: Game-Based Learning Stealth Assessment With Deep Neural Networks
inferredcites →https://doi.org/10.1111/bjet.12858openalex-snapshot
Explanatory learner models: Why machine learning (alone) is not the answer
inferredcites →https://doi.org/10.1111/j.2517-6161.1977.tb01600.xopenalex-snapshot
Maximum Likelihood from Incomplete Data Via the EM Algorithm
inferredcites →https://doi.org/10.1111/jcal.12761openalex-snapshot
Analysing adaptive scaffolds that help students develop self‐regulated learning behaviours
inferredcites →https://doi.org/10.1145/1118178.1118215openalex-snapshot
Computational thinking
inferredcites →https://doi.org/10.1609/aiide.v6i1.12403openalex-snapshot
Modeling User Knowledge with Dynamic Bayesian Networks in Interactive Narrative Environments
inferredcites →https://doi.org/10.3389/fpsyg.2022.742956openalex-snapshot
Evidence centered design framework and dynamic bayesian network for modeling learning progression in online assessment system
inferredcites →https://doi.org/10.3390/make6010028openalex-snapshot
Augmenting Deep Neural Networks with Symbolic Educational Knowledge: Towards Trustworthy and Interpretable AI for Education
inferredcites →https://doi.org/10.3390/su141912663openalex-snapshot
Supporting Future Teachers to Promote Computational Thinking Skills in Teaching STEM—A Case Study
inferredcites →https://doi.org/10.48550/arxiv.1711.03902openalex-snapshot
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
inferredcites →https://doi.org/10.5220/0009790301880196openalex-snapshot
Investigating the Learning Impact of Autothinking Educational Game on Adults: A Case Study of France
inferredcites →https://doi.org/10.5281/zenodo.3554751openalex-snapshot
Analyzing Student Process Data in Game-Based Assessments with Bayesian Knowledge Tracing and Dynamic Bayesian Networks
inferredcites →https://doi.org/10.7334/psicothema2021.62openalex-snapshot
Early Prediction of Student Learning Performance Through Data Mining: A Systematic Review
inferredcites →https://doi.org/10.7551/mitpress/11740.001.0001openalex-snapshot
Computational Thinking

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Probabilistic Graphical Models for Evaluating Choral Conducting Effectiveness in Ethnic Colleges
inferred← citeshttps://doi.org/10.1145/3746175.3746187openalex-snapshot
Human-Centered AI for Predicting Critical Thinking: Modeling Learners' Application of Reasoning Standards with Bayesian Networks
inferred← citeshttps://doi.org/10.1145/3828183.3828455openalex-snapshot
Multi-Agent Collaborative System for Self-Reflective Learning Intervention: An Empirical Study from Reinforcement Learning Perspective
inferred← citeshttps://doi.org/10.3390/educsci15080977openalex-snapshot
Artificial Intelligence-Generated Content Empowers College Students’ Critical Thinking Skills: What, How, and Why
inferred← citeshttps://doi.org/10.3390/info16050409openalex-snapshot
Personalized Instructional Strategy Adaptation Using TOPSIS: A Multi-Criteria Decision-Making Approach for Adaptive Learning Systems
inferred← citeshttps://doi.org/10.54097/wznpy315openalex-snapshot
Learning Causal and Sequential Patterns in Student Knowledge Tracing via Transformer-Augmented Bayesian Networks
inferred← citeshttps://doi.org/10.5772/acrt.20250240openalex-snapshot
Artificial Intelligence for Pedagogical Decision-making: A Comparative Study of Probabilistic and Rule-based Learner Models
inferred← citeshttps://doi.org/10.65598/rps.6245openalex-snapshot
Enhancing student creative thinking: a new approach based on generative artificial intelligence a
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