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Generative Intelligence and Intelligent Tutoring Systems : 20th International Conference, ITS 2024, Thessaloniki, Greece, June 10-13, 2024, Proceedings, Part II
| Generative Intelligence and Intelligent Tutoring Systems : 20th International Conference, ITS 2024, Thessaloniki, Greece, June 10-13, 2024, Proceedings, Part II |
| Autore | Sifaleras Angelo |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2024 |
| Descrizione fisica | 1 online resource (330 pages) |
| Altri autori (Persone) | LinFuhua |
| Collana | Lecture Notes in Computer Science Series |
| ISBN |
9783031630316
9783031630309 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 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 -- 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 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. |
| Record Nr. | UNINA-9910865287503321 |
Sifaleras Angelo
|
||
| Cham : , : Springer International Publishing AG, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Generative Intelligence and Intelligent Tutoring Systems : 20th International Conference, ITS 2024, Thessaloniki, Greece, June 10-13, 2024, Proceedings, Part I
| Generative Intelligence and Intelligent Tutoring Systems : 20th International Conference, ITS 2024, Thessaloniki, Greece, June 10-13, 2024, Proceedings, Part I |
| Autore | Sifaleras Angelo |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2024 |
| Descrizione fisica | 1 online resource (450 pages) |
| Altri autori (Persone) | LinFuhua |
| Collana | Lecture Notes in Computer Science Series |
| ISBN |
9783031630286
9783031630279 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 I -- Contents - Part II -- Generative Intelligence and Tutoring Systems -- Using Large Language Models to Support Teaching and Learning of Word Problem Solving in Tutoring Systems -- 1 Introduction -- 2 Skill-Based Categorization -- 3 Methodology -- 3.1 Dataset -- 3.2 LLMs Considered -- 3.3 Experimental Setting -- 4 Results -- 5 Conclusions -- References -- A Generative Approach for Proactive Assistance Forecasting in Intelligent Tutoring Environments -- 1 Introduction -- 2 Related Work -- 2.1 Assistance Dilemma and Proactive Hints -- 2.2 Sequence Modelling -- 3 Proposed Method -- 4 Experimental Setting -- 4.1 Dataset -- 4.2 Model Training -- 4.3 Evaluation Metric -- 5 Results and Discussion -- 5.1 Comparison to Other Methods -- 5.2 Ablation Study -- 6 Conclusion -- References -- Combined Maps as a Tool of Concentration and Visualization of Knowledge in the Logic of Operation of the Intelligent Tutoring Systems -- 1 Introduction -- 2 Existing Solutions -- 3 Method -- 3.1 Methodological Approach to Data Generalization -- 3.2 CMKD Method -- 3.3 Combined Map as an Element of ITS Operation Logic -- 4 Experiment -- 5 Results and their Analysis -- 6 Conclusion -- References -- Fast Weakness Identification for Adaptive Feedback -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 3.1 Domain Model -- 3.2 Student Model -- 3.3 Transition Model -- 3.4 Bandit Model -- 3.5 Weak KC Identification Algorithms -- 4 Simulations -- 4.1 Simulations with Real-Student Dataset Using IRT -- 4.2 Simulations with Random Domain Graphs and Simulated Students -- 4.3 Experiments and Results -- 4.4 Discussion.
5 Conclusion and Future Work -- References -- QuizMaster: An Adaptive Formative Assessment System -- 1 Introduction -- 2 Background and Related Work -- 3 System Overview -- 3.1 System Architecture -- 3.2 Content Library -- 3.3 Course Model -- 3.4 Question Sequencing and Student Model -- 3.5 Formative Feedback Generation and Presentation -- 4 Evaluation of Methods and Future Testing -- 5 Conclusions and Future Work -- References -- Preliminary Systematic Review of Open-Source Large Language Models in Education -- 1 Introduction and Literature -- 2 Methods -- 2.1 Research Questions -- 2.2 Search Strategy -- 2.3 Screening and Selection -- 3 Results -- 3.1 Analysis of Current Open-Source LLMs in Educational and Industry Sectors -- 3.2 Suitability and Open-Source LLMs for Educational Integration -- 4 Discussion and Implications -- 5 Conclusion and Future Directions -- References -- Jill Watson: Scaling and Deploying an AI Conversational Agent in Online Classrooms -- 1 Introduction -- 2 Related Work -- 3 Overview of Jill Watson's Architecture -- 3.1 Knowledge Base -- 3.2 Agent Memory -- 3.3 Question Answering -- 4 Results and Discussion -- 5 Conclusion -- References -- Improving LLM Classification of Logical Errors by Integrating Error Relationship into Prompts -- 1 Introduction -- 2 Related Works -- 2.1 Automated Program Repair -- 2.2 Teaching Programming to Beginners: An Instructor's Perspective on Educational Environment -- 3 Definition of Logical Error Types -- 4 Classification and Augmentation Using LLMs -- 4.1 Logical Error Classification Prompt -- 4.2 Logical Error Augmentation Prompt -- 4.3 Experimental Results -- 5 Conclusion -- References -- Enhancement of Knowledge Concept Maps Using Deductive Reasoning with Educational Data -- 1 Introduction -- 2 Related Work -- 2.1 Bregman Iterative Approach -- 2.2 Deduction and Syllogism. 2.3 Expansion of Causality -- 2.4 Learning Path and Knowledge Concept Maps -- 3 Proposed Method -- 3.1 Data -- 3.2 Research Procedure -- 4 Results and Analysis -- 5 Conclusion and Further Study -- References -- Individualised Mathematical Task Recommendations Through Intended Learning Outcomes and Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Design and Implementation -- 3.1 Reinforcement Learning Environment -- 3.2 Assessing Student Progress -- 4 Experiments -- 4.1 Recommendation Pool -- 4.2 Experimental Setup -- 4.3 Results -- 5 Discussion -- 6 Conclusion -- References -- Developing Conversational Intelligent Tutoring for Speaking Skills in Second Language Learning -- 1 Introduction -- 2 Related Work -- 2.1 Conversational Intelligent Tutoring Systems -- 2.2 LLM-Integrated Second Language Learning -- 2.3 Dialogue Datasets for Deep Learning-Based Second Language Tutoring -- 3 KORLINGS Dataset Construction -- 3.1 Tutoring Dialogues with Annotations -- 3.2 Dataset Statistics -- 3.3 Dataset for Generation -- 4 Development -- 4.1 System Design -- 4.2 Implementation -- 5 Experiment -- 5.1 Evaluation Criteria -- 5.2 Evaluation Results -- 5.3 Human Evaluation -- 6 Conclusion -- Appendix -- References -- SAMI: An AI Actor for Fostering Social Interactions in Online Classrooms -- 1 Introduction -- 2 Related Work -- 3 SAMI System Design -- 3.1 Natural Language Processing Module -- 3.2 Knowledge Base Module -- 3.3 Matchmaking Module -- 3.4 Response Generation Module -- 3.5 SAMI on Slack -- 4 Results and Discussion -- 4.1 Early Results -- 4.2 Intermediate Results -- 4.3 Results from SAMI on Slack -- 4.4 Recent Deployments -- 5 Conclusion -- References -- Exploring the Methodological Contexts and Constraints of Research in Artificial Intelligence in Education -- 1 Introduction -- 2 Methodology -- 3 Results. 3.1 Educational Contexts and AI (RQ1) -- 3.2 Methodological and Study Designs in AIED (RQ2) -- 3.3 AI Algorithms and AI Technologies Used in Education (RQ3) -- 4 Discussion -- 4.1 In Which Educational Contexts is AI Used? (RQ1) -- 4.2 What are the Methodological and Study Designs Employed in AIED Research? (RQ2) -- 4.3 What AI Algorithms and Technologies are Used in Education? (RQ3) -- 4.4 Theoretical and Practical Implications -- 5 Conclusion -- References -- A Constructivist Framing of Wheel Spinning: Identifying Unproductive Behaviors with Sequence Analysis -- 1 Introduction -- 2 VERA: A Platform for Inquiry-Based Learning -- 3 Background -- 3.1 Inquiry-Based Learning -- 3.2 Wheel Spinning -- 3.3 End-of-Session Activities -- 3.4 Productive Persistence and Quitting Behavior -- 3.5 Sequential Pattern Mining -- 4 Methodology -- 4.1 Cohorts -- 4.2 Data Recording and Behavior Classification -- 5 Results -- 5.1 Actions Before Coach Request -- 5.2 Sequences of Note -- 5.3 Total User History and Patterns Across Sessions -- 5.4 Markov Diagram -- 6 Discussion -- 6.1 When to Offer Proactive Tutoring -- 7 Conclusion -- References -- Evaluating the Ability of Large Language Models to Generate Motivational Feedback -- 1 Introduction -- 2 Related Work -- 3 Overall Picture -- 4 Evaluation Framework -- 4.1 Component View -- 4.2 Quantitative Evaluation -- 4.3 Qualitative Evaluation -- 5 Experimentation Activities and Results -- 5.1 Settings and Execution -- 5.2 Results -- 6 Conclusions and Future Works -- References -- Towards Cognitive Coaching in Aircraft Piloting Tasks: Building an ACT-R Synthetic Pilot Integrating an Ontological Reference Model to Assist the Pilot and Manage Deviations -- 1 Introduction -- 2 Related Work -- 2.1 The Reference Model -- 2.2 Automatic Execution of Reference Model -- 2.3 Automatic Flight Deviation Detection. 2.4 Execution of a Piloting Task by an ACT-R Agent -- 3 The Cognitive Synthetic Pilot -- 4 Methodology and Results -- 4.1 Methodology -- 4.2 Results -- 5 Conclusion and Future Works -- References -- Impact of Conversational Agent Language and Text Structure on Student Language -- 1 Introduction -- 1.1 Theoretical Framework and Coh-Metrix -- 1.2 Summarization and Text Structures -- 2 Method -- 2.1 Participants and Procedures -- 2.2 Manipulation for Agents Language -- 3 Analyses, Findings, and Discussions -- 3.1 The Impact of Agent Language Within Comparison Texts -- 3.2 The Impact of Agent Language Within Causation Texts -- 4 Conclusions, Future Directions, and Implications -- References -- Analyzing the Role of Generative AI in Fostering Self-directed Learning Through Structured Prompt Engineering -- 1 Introduction -- 2 Backgrounds and Literature Review -- 2.1 Generative AI in Education -- 2.2 Prompt Engineering -- 2.3 Identification of Gaps in Existing Literature -- 3 Study Design -- 3.1 Selection of Concept of Data Analysis for the Task -- 3.2 Dataset for the Task and Problem Statement for the Task -- 3.3 Instrument Designed: Pre-post Test and Sus Survey -- 3.4 Design of Structured Prompt Training -- 3.5 Participant -- 3.6 Study Procedure -- 4 Results and Discussion -- 4.1 Data Analysis -- 4.2 Discussion -- 5 Conclusion and Future Work -- References -- Detecting Function Inputs and Outputs for Learning-Problem Generation in Intelligent Tutoring Systems -- 1 Introduction -- 2 Related Works -- 3 Method of Identifying Data Items in Function Description -- 4 Results -- 4.1 Test Sample -- 4.2 Evaluation -- 5 Discussion -- 6 Conclusion -- References -- Automated Analysis of Algorithm Descriptions Quality, Through Large Language Models*-1.0pc -- 1 Introduction -- 1.1 Research Questions -- 2 Related Work -- 3 Methods -- 4 Results. 5 Conclusions and Future Work. |
| Record Nr. | UNINA-9910865282703321 |
|
Sifaleras Angelo
|
||
| Cham : , : Springer International Publishing AG, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||