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Record Nr. |
UNINA9910865287503321 |
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Autore |
Sifaleras Angelo |
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Titolo |
Generative Intelligence and Intelligent Tutoring Systems : 20th International Conference, ITS 2024, Thessaloniki, Greece, June 10-13, 2024, Proceedings, Part II |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing AG, , 2024 |
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©2024 |
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ISBN |
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9783031630316 |
9783031630309 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (330 pages) |
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Collana |
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Lecture Notes in Computer Science Series ; ; v.14799 |
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Altri autori (Persone) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Intro -- Preface -- Organization -- Invited Talks -- Unleashing Potential: Harnessing the Power of Generative AI in Intelligent Tutoring Systems -- Sharing from Experience: Competencies for "Intelligent Dialogues" with Emerging Technologies -- Contents - Part II -- Contents - Part I -- Generative Intelligence and Healthcare Informatics -- Elevating Medical Efficiency and Personalized Care Through the Integration of Artificial Intelligence and Distributed Web Systems -- 1 Introduction -- 2 Literature Review -- 2.1 Significant Works in the Field of Study -- 2.2 Authors and Research Groups Analysis -- 3 Method -- 3.1 Distributed Web Systems Architecture -- 3.2 Personal Care Using Artificial Intelligence -- 4 Results -- 5 Conclusion -- References -- Human Interaction, Games and Virtual Reality -- Cognitive Engagement Detection of Online Learners Using GloVe Embedding and Hybrid LSTM -- 1 Introduction -- 2 Related Work -- 3 System for Cognitive Engagement Detection -- 3.1 Text Pre-processing and Feature Engineering -- 3.2 GloVe Embedding -- 3.3 Model Architecture -- 4 Experiment and Results -- 4.1 Dataset Preparation -- 4.2 Experiment Setup -- 4.3 Results -- 4.4 Explaining the Hybrid LSTM Prediction with LIME -- 5 Conclusion -- References -- Assessing Cognitive Workload of Aircraft Pilots Through Face Temperature -- 1 Introduction -- 2 Related Work -- 3 Experiments -- 4 Results |
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and Discussion -- 4.1 Face Detection (Step 1) -- 4.2 Extract Facial Landmark Points (Step 2) -- 4.3 Extract Temperature from the Points (Step 3) -- 5 Conclusion -- References -- Profiles of Performance: Game-Based Assessment of Reading Comprehension Skill -- 1 Introduction -- 1.1 Assessing Reading Comprehension via Games -- 2 Method -- 2.1 Participants -- 2.2 Materials -- 2.3 Procedure -- 3 Results -- 3.1 Preliminary Analyses. |
3.2 RQ1: Does Each Game Account for Unique Variance in GMRT Reading Comprehension Performance? -- 3.3 RQ2: Can Students' Performance on Three Reading Games Be Used to Form Student Profiles that Relate to Standardized Test Performance, but also Provide Greater Insights into More Specific Areas for Improvement? -- 4 Discussion -- References -- Towards Neuro-Enhanced Education: A Systematic Review of BCI-Assisted Development for Non-academic Skills and Abilities -- 1 Introduction -- 1.1 Research Rationales and Objectives -- 2 Methodology -- 2.1 Searching Strategy -- 2.2 Selection Process -- 2.3 Data Extraction -- 3 Results -- 3.1 RQ1: Which Non-academic Skills or Abilities (NaSAs) Can Be Facilitated or Enhanced Through the Application of Brain-Computer Interface (BCI) Technology? -- 3.2 RQ2: In What Ways Can BCI Technology Be Facilitated or Leveraged to Support Students' Development of Non-academic Skills or Abilities (NaSAs)? -- 4 Discussion -- 4.1 Brain Computer Interface (BCI) and Its Application in Education -- 4.2 Non-academic Skills and Abilities (NaSAs) in Education -- 4.3 Current Limitations and Future Avenues -- 5 Conclusion -- References -- From Novice to Expert: Unraveling the Impact of Experience on Cognitive Load and Physiological Responses in Aviation Pilots -- 1 Introduction -- 2 Related Work -- 3 Experiments -- 3.1 Experiment 1 -- 3.2 Experiment 2 -- 3.3 Flight Scenarios -- 3.4 Materials and Measures -- 3.5 Procedure -- 4 Results and Discussion -- 4.1 Results of Experiment 1 -- 4.2 Results of Experiment 2 -- 5 Conclusion -- References -- Kahoot! as a Tool to Maintain Students' Attention and Increase Retention Rates: An Experience Report with Computer Science Students -- 1 Introduction -- 2 Methodology -- 3 Results -- 4 Conclusions and Perspectives -- References. |
Adoption of Digital Games as Pedagogical Aids for Teachers and Pupils in Primary Schools to Overcome Learning Problems in Arithmetic -- 1 Introduction -- 2 Learning Problems for Primary School Children -- 2.1 Cognitive Development -- 2.2 Individual Differences (Learning Styles) -- 2.3 Motivation and Commitment -- 3 Primary School Children Are Faced with These Problems -- 4 Game-Based Learning vs Gamification -- 5 The General Architecture of the Proposed Digital Game: Game and Learn -- 6 Methodology -- 6.1 Case Study -- 6.2 Different Scenarios Offered by the "Substracto" Game -- 7 Conclusion -- References -- Educational Games for Computational Thinking: Evaluation of the Scaffolded aMazeD Game -- 1 Introduction -- 2 Literature Review on CT Educational Games -- 3 aMazeD CT Educational Game -- 4 Methodology -- 4.1 Research Questions -- 4.2 Research Design and Participants -- 4.3 Instrument -- 5 Findings -- 5.1 Evaluation of the aMazeD Game -- 6 Discussion and Conclusions -- 7 Limitations -- References -- Neural Networks and Data Mining -- MonaCoBERT: Monotonic Attention Based ConvBERT for Knowledge Tracing -- 1 Introduction -- 2 Related Work -- 2.1 Knowledge Tracing -- 2.2 Locality Issues of Transformer -- 3 Methodology -- 3.1 Problem Statement -- 3.2 Proposed Model Architecture -- 3.3 Experiment Setting -- 4 Result and Discussion -- 4.1 Overall Performance -- 4.2 Ablation Studies -- 4.3 In-Depth Analysis of Attention and Embedding -- 4.4 Discovery of Relationships Between Concepts -- 5 Conclusion -- |
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References -- Detection of Pre-error States in Aircraft Pilots Through Machine Learning -- 1 Introduction -- 2 Related Works -- 3 Methods -- 3.1 Flanker Dataset -- 3.2 EEG Preprocessing and Dataset Creation -- 3.3 Adjusting Imbalanced Datasets -- 3.4 Models -- 4 Experiments -- 5 Results -- 6 Conclusion -- References. |
Mining Discriminative Sequential Patterns of Self-regulated Learners -- 1 Introduction -- 2 Principle and Notations -- 2.1 Assumptions -- 2.2 Preliminaries -- 3 Related Works -- 3.1 Sequence Mining for SRL Measurement -- 3.2 Discriminative Sequential Pattern Mining -- 4 Mining Successful Behavioral Sequences -- 5 Implementation and Data Collection -- 6 Results and Discussion -- 6.1 SRL Behaviors that Impact the Learner Performance -- 6.2 Discriminative Sequential Patterns at the Platform, Task and Learner Level -- 7 Conclusion and Implications -- References -- Analysis of Machine Learning Models for Academic Performance Prediction -- 1 Introduction -- 2 Related Work -- 3 Research Methodology -- 3.1 Dataset -- 3.2 Preprocessing -- 3.3 Representations -- 3.4 Models -- 3.5 Prediction Objectives -- 4 Results -- 5 Conclusions -- References -- Simplifying Decision Tree Classification Through the AutoDTrees Web Application and Service -- 1 Introduction -- 2 Decision Tree Classification -- 3 The AutoDTrees Application -- 3.1 Description -- 3.2 The Web Interface -- 3.3 The Web Service -- 4 Usability Testing -- 5 Conclusions and Future Work -- References -- LBKT: A LSTM BERT-Based Knowledge Tracing Model for Long-Sequence Data -- 1 Introduction -- 2 Related Work -- 2.1 Knowledge Tracing -- 2.2 Transformer-Based Model and Application -- 3 Methodology -- 3.1 Proposed Model Architecture -- 3.2 Experiment Setting -- 4 Results and Discussion -- 4.1 Overall Performance -- 4.2 Analysis of Embedding Strategy -- 5 Conclusion -- References -- Educational Support for Automated Classification of UML Diagrams Using Machine Learning -- 1 Introduction -- 2 Problem Description -- 3 Related Work -- 4 Feature Extraction for UML Diagrams Classification -- 4.1 Steps to Follow -- 4.2 Dataset -- 4.3 Neural Network Architecture -- 4.4 Examples of Results -- 5 Conclusion. |
References -- Model Decomposition of Robustness Diagram with Loop and Time Controls to Petri Net with Considerations on Resets -- 1 Introduction -- 2 Petri Net -- 2.1 Definition ch17malinaoPhD2017 -- 2.2 Classical Soundness in PN ch17vanderaalstFAC2011 -- 2.3 Components and Variants of PN -- 3 Robustness Diagram with Loop and Time Controls -- 3.1 Definition ch17malinaoPhD2017 -- 3.2 Vertex-Simplified RDLT ch17malinaoPhD2017 -- 3.3 Extended RDLT ch17malinaoPhD2017 -- 3.4 Activity Extraction in RDLT ch17malinaoPhD2017 -- 3.5 Expanded Reusability in RDLT ch17malinaoWCTP2023 -- 3.6 Processes in RDLT ch17rocaITS2024 -- 3.7 Classical Soundness in RDLT ch17malinaoPhD2017 -- 3.8 Existing Mapping of RDLT to PN -- 4 Improving RDLT to PN Mapping -- 4.1 Mapping of RDLT Components into PN -- 4.2 Proposed Mapping Algorithm of RDLT to PN -- 4.3 Validation and Analysis of Proposed Mapping Algorithm -- 5 Conclusions and Future Work -- References -- Well-Handledness in Robustness Diagram with Loop and Time Controls -- 1 Introduction -- 1.1 Robustness Diagram with Loop and Time Controls -- 1.2 Control Flow and Parallelism in RDLT -- 1.3 Soundness in RDLT -- 1.4 L-Safeness in RDLT -- 1.5 Expanded Vertex Simplifications Algorithm -- 1.6 Antecedent and Consequent Sets -- 1.7 Well-Handledness in Petri Net -- 2 Methodology -- 2.1 Well-Handledness in RDLT -- 2.2 Proposed Algorithm for Balanced RDLT Verification -- 2.3 Proposed Algorithm for Well-Handledness Verification in RDLT -- 2.4 Profiles of Well-Handled RDLTs -- 3 |
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Conclusions and Future Work -- References -- Generative Intelligence and Metaverse -- Enhancing Reinforcement Learning Finetuned Text-to-Image Generative Model Using Reward Ensemble -- 1 Introduction -- 2 Background -- 2.1 Diffusion Models -- 2.2 Reinforcement Learning Training of Diffusion Models -- 2.3 Reward Models -- 3 Proposed Approach. |
3.1 Reward Model Ensemble. |
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