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Towards a Work Task Simulation Supporting Training of Work Design Skills during Qualification-based learning 14

Towards a Work Task Simulation Supporting Training of Work D...

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14th international conference on Computer Supported Education (CSEDU)

作者： Srbecky, Ramona Winterhagen, Michael Wallenborn, Benjamin then, Matthias Vu, Binh Fraas, Wieland Dettmers, Jan Hemmje, Matthias Fernuniv Dept Multimedia & Internet Applicat Hagen Germany Fernuniv Dept Work & Org Psychol Hagen Germany Fernuniv Zentrum Digitalisierung & IT ZDI Hagen Germany

ISBN: (纸本)9789897585623

this paper describes a novel approach towards integrating work task simulation-based training of skills related to configuring relevant features for work design with the Qualifications-Based learning Model (QBLM) approach. To achieve this, nine psychologically relevant work design characteristics from work content, workflow/organization, and social relations can be manipulated in the simulated work training tasks and their training context. the concretization of these work design characteristics requires extensive psychological testing and fine-tuning of the parameters for simulating the respective working conditions. For this purpose, Kirkpatrick's evaluation model from 1998 will be used. therefore, the existing approach of QBLM will be used to develop an Applied Game for a simulation of work tasks. the existing tools and systems for QBLM will be extended by a QBLM-oriented gaming and learning analytics framework and the approach of QBLM-based Structural Didactical Templates. Besides the relevant state of the art, the conceptual modelling for the approach as well as a first set of initial visual prototypes of the system image will be presented following a user centered design methodology. Furthermore, a cognitive walkthrough of the visual prototype will be performed to support a first formative evaluation. the paper concludes with a summary and the remaining challenges of the approach.

关键词： QBLM Qualifications-based learning learning Analytics Work Task Simulation Work Design Serious/Applied Gaming ACT-R Applied Games Game-based learning Simulation-based learning Competence-based Knowledge Space theory

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Shapley Values for Explaining the Black Box Nature of Machine learning Model Clustering 14

Shapley Values for Explaining the Black Box Nature of Machin...

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14th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and the 6th international conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Louhichi, Mouad Nesmaoui, Redwane Marwan, Mbarek Lazaar, Mohamed ENSIAS Mohamed v University in Rabat Morocco

Machine learning (ML) models are becoming increasingly complex. In fact, a sophisticated model (XGBoost boosting or deep learning) generally leads to more accurate predictions than a simple model (linear regression or decision tree). there is therefore a trade-off between the performance of a model and its interpretability: what a model gains in performance, it loses in interpretability (and vice versa), where interpretability is the ability for a human to understand the reasons for a model's decision. However, explaining the predictions made by machine learning models aims at computing and interpreting the importance of features. To achieve this, game theory has recently gained attention for better understanding the similarity between group members. In this paper, we use SHAP (SHapley Additive exPlanations), which is a method based on cooperative game theory, to analyze and evaluate the properties of each group. More importantly, we rely k-means PCA and Light gbm classifier to improve the data preparation before grouping the features into multiple clusters. the simulation results prove the importance of shapley value in creating an accurate and meaningful representation of each cluster. © 2023 Elsevier B.V.. All rights reserved.

关键词： Game theory

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Digital learning Space in Universities from the Perspective of Agglomeration Advantage theory 14th

Digital Learning Space in Universities from the Perspective ...

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14th international conference on Frontier Computing, FC 2024

作者： Jiang, Chi School of Automation Wuhan University of Technology Hubei Wuhan China

ISBN: (纸本)9789819627974

With the rapid development of computers and information technology, e-learning has become one of the main ways for people to live, work, and learn. In this situation, traditional classroom teaching can no longer meet people's demand for knowledge. the rise of electronic learning has greatly improved the efficiency of students’ extracurricular learning, made up for the shortcomings of traditional classrooms, achieved lifelong learning, and promoted education for all. Electronic learning (E-learning), due to its abundant resources, diverse forms, and unconstrained time and space, creates fertile soil and growth space for learners to engage in self-directed learning. Simply put, E-learning is a new mode of online teaching, which involves building an internet platform within the scope of teaching, allowing students to learn through the internet. Its architecture includes a learning management system, a content management system, a learning objective library, and a virtual learning community. learning usually occurs in specific time and space, and learning temporal dynamics is an important direction of learning research. However, with the arrival and development of the digital age, the connotation and form of educational space have also changed, and have been pushed to a new level. Research shows that 75% of college students tend to use digital learning environments more, indicating a higher acceptance of digital learning. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Prediction of Speeding Intention Based on the theory of Planned Behavior 14

Prediction of Speeding Intention Based on the Theory of Plan...

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14th international conference on Intelligent Systems: theories and Applications, SITA 2023

作者： Abderrahim, El Hafidy Taoufik, Rachad Ensias Mohammed V University Software Project Management Research Team Rabat Morocco

ISBN: (纸本)9798350308211

Speeding remains a critical factor contributing to road crashes. therefore, understanding drivers' intentions to speed is crucial for designing effective interventions helping drivers to respect speed limit on road. Demographic and psychological factors are widely used to explain several human behaviors. However, their predictive power change in relation to the kind of targeted behavior. Moreover, many studies consider psychological factors as primary contributors to behaviors and intentions and consider demographics variables as background factors. this paper investigates the predictive power of demographic and psychological factors using machine learning algorithms to predict drivers speeding intentions. A survey based on theory of Planned Behavior (TPB) was conducted to collect data about the speeding intention of 254 drivers in Morocco. the survey includes questions about demographic and psychological factors. Five machine learning algorithms were used to assess the accuracy of speeding prediction with the combination background and psychological factors. Also, the correlation between all variables was assessed. Finally, a Structural Equation Modeling (SEM) is used to have a deep understanding of speeding intention. the results of this study highlight the importance of considering demographic features as background factors when predicting the intention to speed, using psychological factors. Moreover, the study underlines the potential of the TPB in the understanding of speeding intention. Overall, the findings contribute to the ongoing efforts to develop interventions aimed at combating speeding behavior and promoting safer driving practices. © 2023 IEEE.

关键词： Forecasting

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14th international Symposium on Intelligence Computation and Applications, ISICA 2023

14th International Symposium on Intelligence Computation and...

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14th international Symposium on Intelligence Computation and Applications, ISICA 2023

ISBN: (纸本)9789819743957

the proceedings contain 44 papers. the special focus in this conference is on Intelligence Computation and Applications. the topics include: Research on Smart Agriculture Big Data System Based on Spark and Blockchain;a Multiparty Reversible Data Hiding Scheme in Encrypted Domain Based on Hybrid Encryption;packet Performance Predictor Based on Graph Isomorphism Network for Neural Architecture Search;visualization Analysis of Convolutional Neural Network Processes;multimedia Information Retrieval Method Based on Semantic Similarity;Research on Intelligent Clustering Scoring of English Text Based on XGBOOST Algorithm;multi-recipient Public-Key Authenticated Encryption with Keyword Search;machine learning-Assisted Optimization of Direction-Finding Antenna Arrays;research on the Innovative Application of Computer Aided Design in Environmental Design;key Technology and Application Research Based on Computer Internet of things;mutate Suspicious Statements to Locate Faults;petrochemical Commodity Price Prediction Model Based on Wavelet Decomposition and Bayesian Optimization;construction and Quality Evaluation of learning Motivation Model from the Perspective of Course Ideology and Politics;Study on TNM Classification Diagnosis of Colorectal Cancer Based on Improved Self-supervised Contrast learning;design and Application of a Teaching Evaluation Model Based on the theory of Multiple Intelligences;edible Oil Price Forecasting: A Novel Approach with Group Temporal Convolutional Network and BetaAdaptiveAdam;artificial Intelligence in Intelligent Clothing: Design and Implementation;research on the Routing Protocol Algorithm Driven by the Dedicated Frequency Points of the Internet of things to Build a Network;research on Communication Power of Cross-Cultural Short Video Based on Qualitative Comparative Analysis;research on Bayberry Traceability Platform Based on Blockchain;preface.

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Trust Forge: Harnessing Machine learning to Build Trust on Social Networks 14th

Trust Forge: Harnessing Machine Learning to Build Trust on S...

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14th EAI international conference on Broadband Communications, Networks, and Systems, BROADNETS 2024

作者： Chitrala, Kavitha Makka, Shanthi Sowjanya, S. Vardhaman College of Engineering Telangana Kacharam Shamshabad Hyderabad501218 India Computer Science and Engineering Vardhaman College of Engineering Telangana Kacharam Shamshabad Hyderabad501218 India

ISBN: (纸本)9783031811708

A social media platform is a form of service offered by an online platform that facilitates easy communication between individuals, as well as the establishment of interpersonal connections and social exchanges. Additionally, it supplies users with a webpage where they may create an open persona and engage adding additional users. Trust is a significant concern in social networking sites, and to address this issue, we have employed the Naive Bayes algorithm to establish trust in online networks. this algorithm is implemented through direct and indirect communication, and trust values are calculated using Dempster-Shafer theory and Bayesian conditional. the effectiveness of our proposed approach is demonstrated through the reenactment results obtained with various parameter arrangements. "In summary, our comparison demonstrates that Multi-faceted trust modeling is statistically and significantly superior to Naive Bayes Model in addressing based on accuracy." © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Adversarial machine learning

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Digital Game-based learning in Primary School: What Issues Does/Does Not Recent Research Focus on? 14

Digital Game-based Learning in Primary School: What Issues D...

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14th international conference on Computer Supported Education (CSEDU)

作者： Koutantou, Maria Rangoussi, Maria Natl & Kapodistrian Univ Athens Dept Early Childhood Educ 13A Navarinou Str Athens Greece Univ West Attica Dept Elect & Elect Engn 250 Thivon Str Athens Greece

ISBN: (纸本)9789897585623

Recent research on Digital Game-Based learning (DGBL) applied specifically in Primary Education is reviewed in a systematic way. 87 journal papers published in the last 4 years (2017 - 2020) are selected and analyzed in order to draw conclusions regarding the effectiveness of DGBL (i) in producing cognitive domain learning outcomes, (ii) in developing social skills, (iii) in producing affective / metacognitive outcomes, and (iv) in offering an enjoyable experience to Primary Education students. Apart from the classic questions of literature reviews, aimed at describing the methodology, aims and outcomes of the reviewed works, the current review is also interested (a) in the ways of integrating the digital game in the educational practice, in relation to the adoption of a specific learning theory and educational method and (b) in the measurement tools employed to evaluate the effectiveness of DGBL interventions, in relation to the aims set and the results obtained. the goal of this review is to reveal those aspects of DGBL that recent research focuses on and at the same time those aspects that are not adequately researched and require more attention and effort. the latter result is especially useful in the planning of future studies. the results record a steadily increasing research interest in DGBL and a strongly positive effect of DGBL in all the examined axes. On the other hand, they reveal an almost general lack of a solid foundation of DGBL interventions in learning theories and consequent educational methods - an alerting situation that deserves careful examination.

关键词： Digital Game-based learning Elementary School Systematic Literature Review learning Outcomes Social Skills Metacognitive Outcomes Research Tools learning theories Constructivism Social Constructivism

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A DEVELOPED PLAN-DO-CHECK-ACT AND CONTEXTUAL TEACHING AND learning MODEL 14

A DEVELOPED PLAN-DO-CHECK-ACT AND CONTEXTUAL TEACHING AND LE...

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14th international conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2024

作者： Hu, Jun Tang, Chaoju Department of Foreign Languages University of Electronic Science and Technology Chengdu China

ISBN: (纸本)9781837242672

Despite some application of Contextual Teaching and learning (CTL) theories in practice, their effectiveness has not been prominent, necessitating comprehensive optimization. To enhance the quality and the success of typical processes like education activities, this study integrates CTL with Deming's PDCA (Plan-Do-Check-Act) cycle theory. through the stages of planning, implementation, and evaluation, the CTL model has been significantly improved. Methodologically, the study employs an inductive approach and experimental methods for data analysis. Results show significant improvements in the mentioned activities and enhances of performance and satisfaction of stakeholders like the population involved with the optimized PDCA & CTL model. the results of this paper also confirmed the efficacy of the proposed method in process quality improvement. © the Institution of Engineering & Technology 2024.

关键词： CTL MODEL PDCA PROCESSES QUALITY IMPROVEMENT TRAINING

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Privacy Preserving Federated learning: A Novel Approach for Combining Differential Privacy and Homomorphic Encryption 1

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14th IFIP WG 11.2 international conference on Information Security theory and Practice (WISTP)

作者： Aziz, Rezak Banerjee, Soumya Bouzefrane, Samia Conservatoire Natl Arts & Metiers CEDRIC 292 Rue St Martin F-75003 Paris France

ISBN: (数字)9783031603914

ISBN: (纸本)9783031603907;9783031603914

Ensuring the data security and privacy stands as a prominent concern in the landscape of machine learning. the conventional approach of centralizing training data raises privacy concerns. Federated learning addresses this by avoiding the need to transfer local data when training a global model, opting to share only local model updates. Despite this, the challenge of information leakage persists. Various attempts tried to tackle this issue, but existing solutions lead to a tradeoff between accuracy, privacy and computation time. this is an unevitable challenge. In this paper, we address that challenge by combining differential privacy and homomorphic encryption. this approach allow to add less noise to the data by shuffling to anonymize the data, not only at the client level but also at the parameter level. Hence, it improves the accuracy of the output models while offering strong privacy guarantees. Importantly, our method avoids complex homomorphic operation, thereby mitigating the computational overhead of HE. In this manner, the data remains protected from all participants in the learning process. Our findings demonstrate that, for an equivalent level of privacy, our method introduces less noise compared to the local DP method, resulting in increased accuracy after aggregation. However, the privacy amplification requires a substantial number of clients, which make our approach more suitable for crossdevice Federated learning.

关键词： Federated learning Differential Privacy Homomorphic Encryption Shuffling Anonymization

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Clean Code Tutoring: Makings of a Foundation 14

Clean Code Tutoring: Makings of a Foundation

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14th international conference on Computer Supported Education (CSEDU)

作者： Luburic, Nikola Vidakovic, Dragan Slivka, Jelena Prokic, Simona Grujic, Katarina-Glorija Kovacevic, Aleksandar Sladic, Goran Univ Novi Sad Fac Tech Sci Trg Dositeja Obradovica 6 Novi Sad Serbia

ISBN: (纸本)9789897585623

High-quality code enables sustainable software development, which is a prerequisite of a healthy digital society. To train software engineers to write higher-quality code, we developed an intelligent tutoring system (ITS) grounded in recent advances in ITS design. Its hallmark feature is the refactoring challenge subsystem, which enables engineers to develop procedural knowledge for analyzing code quality and improving it through refactoring. We conducted a focus group discussion with five working software engineers to get feedback for our system. We further conducted a controlled experiment with 51 software engineering learners, where we compared learning outcomes from using our ITS with educational pages offered by a learning management system. We examined the correctness of knowledge, level of knowledge retention after one week, and the learners' perceived engagement. We found no statistically significant difference between the two groups, establishing that our system does not lead to worse learning outcomes. Additionally, instructors can analyze challenge submissions to identify common incorrect coding patterns and unexpected correct solutions to improve the challenges and related hints. We discuss how our instructors benefited from the challenge subsystem, shed light on the need for a specialized ITS design grounded in contemporary theory, and examine the broader educational potential.

关键词： Intelligent Tutoring Systems e-learning Clean Code Refactoring Code Readability Software Engineering

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