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检索条件"任意字段=14th International Conference on Algorithmic Learning Theory"

共 820 条记录，以下是1-10 订阅

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Hierarchical Dependencies in Classroom Settings Influence algorithmic Bias Metrics 24

Hierarchical Dependencies in Classroom Settings Influence Al...

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14th Annual international conference on learning Analytics and Knowledge (LAK) - learning Analytics in the Age of Artificial Intelligence

作者： Belitz, Clara Lee, HaeJin Nasiar, Nidhi Fancsali, Stephen E. Ritter, Steve Almoubayyed, Husni Baker, Ryan S. Ocumpaugh, Jaclyn Bosch, Nigel Univ Illinois Sch Informat Sci Champaign IL 61820 USA Univ Penn Grad Sch Educ Philadelphia PA USA Carnegie Learning Inc Pittsburgh PA USA Univ Illinois Dept Educ Psychol Champaign IL USA

ISBN: (纸本)9798400716188

Measuring algorithmic bias in machine learning has historically focused on statistical inequalities pertaining to specific groups. However, the most common metrics (i.e., those focused on individualor group-conditioned error rates) are not currently well-suited to educational settings because they assume that each individual observation is independent from the others. this is not statistically appropriate when studying certain common educational outcomes, because such metrics cannot account for the relationship between students in classrooms or multiple observations per student across an academic year. In this paper, we present novel adaptations of algorithmic bias measurements for regression for both independent and nested data structures. Using hierarchical linear models, we rigorously measure algorithmic bias in a machine learning model of the relationship between student engagement in an intelligent tutoring system and year-end standardized test scores. We conclude that classroom-level influences had a small but significant effect on models. Examining significance with hierarchical linear models helps determine which inequalities in educational settings might be explained by small sample sizes rather than systematic differences.

关键词： Interactive learning environments algorithmic bias Intelligent tutoring systems Predictive analytics

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Places to Intervene in Complex learning Systems 24

Places to Intervene in Complex Learning Systems

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14th Annual international conference on learning Analytics and Knowledge (LAK) - learning Analytics in the Age of Artificial Intelligence

作者： Kitto, Kirsty Gibson, Andrew Univ Technol Sydney Ultimo Australia Queensland Univ Technol Brisbane Qld Australia

ISBN: (纸本)9798400716188

Responding to recent questioning of learning Analytics (LA) as a field that is achieving its aim of understanding and optimising learning and the environments in which it occurs, this paper argues that there is a need to genuinely embrace the complexity of learning when considering the impact of LA. Rather than focusing upon ' optimisation ', we propose that LA should seek to understand and improve the complex socio-technical system in which it operates. We adopt a framework from systems theory to propose 12 different intervention points for learning systems, and apply it to two case studies. We conclude with an invitation to the community to critique and extend this proposed framework.

关键词： learning Complex Systems Intervention theory

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the Fading of F(AI)th: Tracing the Technological Promises of aWellbeing App in K-12 Education 14th

The Fading of F(AI)th: Tracing the Technological Promises of...

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14th international conference on Methodologies and Intelligent Systems for Technology Enhanced learning

作者： Sperling, Katarina Linkoping Univ Dept Behav Sci & Learning S-60174 Norrkoping Sweden

ISBN: (纸本)9783031735370;9783031735387

this paper explores the implementation of a wellbeing app in a Swedish Upper Secondary School. the aim is to understand how ideas of data driven school improvement underpinned by promises of artificial intelligence (AI) and learning analytics (LA) change the work of teachers. the study draws on video-ethnography from 17 meetings between five teachers/form tutors. the produced data is analysed using actor-network theory to focus on the various stages of the implementation process and the interactions between the learning analytics dashboard (LAD) and the teachers. To capture the complexity of the data, the empirical material is presented through cartoon-inspired illustrations grounded in a thinking through Cartoons methodology. Findings show how teachers took on new roles and responsibilities in relation to the wellbeing app, most notably the role of collecting data from students. Teachers came to act as data analysts which imposed constant negotiations and uncertainties. To address the declining engagement of students over time, a student-facing LAD was introduced. the teachers shifted their focus to motivate students to engage with their own data in different ways. Despite no improvements in students' response rates teachers remained committed to the app, trusting that new AI and LA functionalities would compensate unsatisfactory outcomes. In conclusion, instead of improving teachers' capacity to identify at-risk students, the wellbeing app increased teachers' workload and led to different dilemmas related to teacher-student relations and teachers' professional judgement.

关键词： Artificial Intelligence K12 data-driven schooling actor-network theory (ANT) learning analytics EdTech socio-materiality PERMA-model

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Sentiment Driven Reinforcement learning Trading Strategies to Enhance Market Performance 14

Sentiment Driven Reinforcement Learning Trading Strategies t...

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14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Rohilla, Rajesh Raj Maiya, Raaghav Bharti, Ritvik Delhi Technological University Department of Electronics & Communication Delhi114002 India

ISBN: (纸本)9798350335095

this research paper seeks to present a comprehensive examination of algorithmic trading algorithms, encompassing a range of techniques that extend from supervised learning to sentiment-aware reinforcement-based trading algorithms. the study further investigates the transformative potential of artificial intelligence (AI) in the field of stock trading, emphasizing the advantages of leveraging AI within this domain. Furthermore, the paper explores the incorporation of natural language processing (NLP) algorithms to account for the influence of news on algorithmic predictions. Subsequently, a comparative analysis of the algorithms' performance is carried out based on the profits generated by each. Lastly, the paper outlines the future implications of the subject matter and concludes by summarizing the obtained results. © 2023 IEEE.

关键词： Reinforcement learning

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algorithmic Trading using Machine learning 14

Algorithmic Trading using Machine Learning

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14th international conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Shayank, Satvik Saroj, Sujit Singh, Aditya Pinjarkar, Vijaya Department of Information Technology K J Somaiya Institute of Technology Mumbai400022 India

Predicting the value of a company's stocks is the motive of stock market predictions. Today’s market use Machine learning to forecast current market values by training on their prior values. As part of the machine learning process, various models are used to facilitate and authenticate forecasting. In the paper, we present results from machine learning and regression based on LSTMs for predicting stock value. A number of factors come into play, such as the open, the close, the low, the high, and the volume. Data quality, overfitting, and interpretability are examined as challenges and limitations of machine learning in algorithmic trading. Our final section discusses the future directions for algorithmic trading and machine learning research. Fusion can be viewed as a method of combining data or qualities in general in order to enhance prediction using a combinational method that can support one another. Financial market machine learning models are also examined for interpretability. the final part of our discussion discusses the future of algorithmic trading and machine learning research. Fusion can be viewed as a method of combining data or qualities in general in order to enhance prediction using a combinational method that can support one another. Ultimately, the integration of machine learning into algorithmic trading has the potential to revolutionize financial markets and improve trading decision-making efficiency. © Grenze Scientific Society, 2023.

关键词： Forecasting

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Synthetic Dataset Generation for Fairer Unfairness Research 24

Synthetic Dataset Generation for Fairer Unfairness Research

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14th Annual international conference on learning Analytics and Knowledge (LAK) - learning Analytics in the Age of Artificial Intelligence

作者： Jiang, Lan Belitz, Clara Bosch, Nigel Univ Illinois Sch Informat Sci Champaign IL 61820 USA Univ Illinois Dept Educ Psychol Champaign IL USA

ISBN: (纸本)9798400716188

Recent research has made strides toward fair machine learning. Relatively few datasets, however, are commonly examined to evaluate these fairness-aware algorithms, and even fewer in education domains, which can lead to a narrow focus on particular types of fairness issues. In this paper, we describe a novel dataset modification method that utilizes a genetic algorithm to induce many types of unfairness into datasets. Additionally, our method can generate an unfairness benchmark dataset from scratch (thus avoiding data collection in situations that might exploit marginalized populations), or modify an existing dataset used as a reference point. Our method can increase the unfairness by 156.3% on average across datasets and unfairness definitions while preserving AUC scores for models trained on the original dataset ( just 0.3% change, on average). We investigate the generalization of our method across educational datasets with different characteristics and evaluate three common unfairness mitigation algorithms. the results show that our method can generate datasets with different types of unfairness, large and small datasets, different types of features, and which affect models trained with different classifiers. Datasets generated with this method can be used for benchmarking and testing for future research on the measurement and mitigation of algorithmic unfairness.

关键词： datasets student data fair machine learning data generation

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learning Multiple Multicriteria Additive Models from Heterogeneous Preferences 8th

Learning Multiple Multicriteria Additive Models from Heterog...

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8th international conference on algorithmic Decision theory

作者： Auriau, Vincent Belahcene, Khaled Malherbe, Emmanuel Mousseau, Vincent Univ Paris Saclay CentraleSupelec MICS Gif Sur Yvette France

ISBN: (纸本)9783031739026;9783031739033

Additive preference representation is standard in Multiple Criteria Decision Analysis, and learning such a preference model dates back from the UTA method [11]. In this seminal work, an additive piece-wise linear model is inferred from a learning set composed of pairwise comparisons. In this setting, the learning set is provided by a single Decision-Maker (DM), and an additive model is inferred to match the learning set. We extend this framework to the case where (i) multiple DMs with heterogeneous preferences provide part of the learning set, and (ii) the learning set is provided as a whole without knowing which DM expressed each pairwise comparison. Hence, the problem amounts to inferring a preference model for each DM and simultaneously "discovering" the segmentation of the learning set. In this paper, we show that this problem is computationally difficult. We propose a mathematical programming based resolution approach to solve this Preference learning and Segmentation problem (PLS). We also propose a heuristic to deal with large datasets. We study the performance of both algorithms through experiments using synthetic and real data.

关键词： Additive preference model preference learning heterogeneous preferences

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2024 14th international conference on Pattern Recognition Systems, ICPRS 2024

2024 14th International Conference on Pattern Recognition Sy...

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14th international conference on Pattern Recognition Systems, ICPRS 2024

ISBN: (纸本)9798350375657

the proceedings contain 42 papers. the topics discussed include: LLM-aided knowledge graph construction for zero-shot visual object state classification;enhancing Apple’s defect classification: insights from visible spectrum and narrow spectral band imaging;analyzing emotional and topical patterns in conspiracy theory narratives: a discourse comparative study on the 2023 Hawaii wildfires;non-invasive estimation of moisture content in mushrooms using hyperspectral imaging and machine learning-based stacking regressor model;a concept drift based approach to evaluating model performance and theoretical lifespan;autism spectrum disorder prediction using machine learning classifiers;adversarial contrastive representation learning for passive Wi-Fi fingerprinting of individuals;and SAI-ChileanDiet: a multi-label food dataset with self-acquired images of the Chilean diet.

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ICCNS 2024 - Proceedings of the 2024 14th international conference on Communication and Network Security

ICCNS 2024 - Proceedings of the 2024 14th International Conf...

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14th international conference on Communication and Network Security, ICCNS 2024

ISBN: (纸本)9798400717994

the proceedings contain 29 papers. the topics discussed include: meta GPT-based agent for enhanced phishing email detection;detecting CDN domain names based on resolution graph with PU learning;a new lattice-based strong designated verifier signature scheme in the standard model;parallel compensation method for nonlinear impairments of optical fiber based on regular perturbation theory;an efficient and lightweight authenticated key agreement scheme cloud-assisted for smart agricultural monitoring systems;privacy protection in mobile crowdsensing based on local differential privacy;Argos: a detection-based method for log fusion and provenance graph compression;and a lightweight and trustworthy authentication protocol for UAV networks based on PUF.

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Bridging theory and Practice in Outcome-Based Assessment: Developing a Web Application for learning Outcome Alignment 14

Bridging Theory and Practice in Outcome-Based Assessment: De...

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14th international conference on Educational and Information Technology, ICEIT 2025

作者： Alhazmi, Abdulsalam K. University of Science and Technology Faculty of Engineering & Computing Aden Yemen

ISBN: (纸本)9798331540883

Assessment remains a cornerstone of the educational process, with standardized testing often serving as a primary method for evaluating learning. However, as pedagogical approaches continue to evolve, Outcome-Based Assessment (OBA) has gained prominence as a more comprehensive model that aligns learning outcomes with teaching strategies. this paper explores the significance of OBA, grounded in frameworks of domain learning, and highlights its global adoption as a measure of institutional quality. By focusing on measurable learning outcomes across cognitive domains, OBA enhances student engagement, critical thinking, and skill development. Extending this theoretical foundation, the paper presents an Outcome Based Assessment System (OBAS) developed to facilitate the practical implementation of OBA. OBAS provides a structured framework that links program learning outcomes to course learning outcomes, integrating considerations of learning domains and difficulty levels. By supporting the creation of standardized and authentic OBA assessments, this application empowers educators and institutions to uphold educational quality, foster accountability, and align assessments with targeted learning objectives. © 2025 IEEE.

关键词： Teaching

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