CPSX: Using AI-Machine Learning for Mapping Human-Human Interaction and Measurement of CPS Teamwork Skills

Chopade, Pravin; Edwards, David; Khan, Saad; Andrade, Alejandro; Pu, Shi

doi:10.1109/hst47167.2019.9032906

Cited by 10 publications

(3 citation statements)

References 21 publications

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“…[1], [30], [31], [19], [34], [23], [43], [46], [48], [53], [57], [61], [75], [78], [81], [82], [83], [90], [97], [111], [115], [116] [58], [59], [60], [66], [67], [79], [102], [107], [108], [109], [117] Table 1 shows a summary of research articles analyzed in this research. The table portrays that machine learning techniques are being are applied to address problems in diverse fields.…”

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confidence: 99%

Research Methods in Machine Learning: A Content Analysis

Kamiri

Mariga

2021

IJCIT

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Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research. To achieve this the researchers analyzed a total of 100 articles published since 2019 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research.

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confidence: 99%

Research Methods in Machine Learning: A Content Analysis

Kamiri

Mariga

2021

IJCIT

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“…This technique offers a more precise and impartial assessment of the dynamics of collaboration, thus enhancing the reliability of the evaluation process in collaborative settings. Teamwork skills and team dynamics appear through verbal and nonverbal behaviors; these behaviors can be captured and analyzed with machine learning and natural language processing (NLP) techniques ( Chopade et al, 2019 ). Although large language models (LLMs) are not free from concerns regarding biases, recognizing these issues and continuing to improve upon these models still allow them to offer valuable insights and substantially increase the efficiency of collaborative assessments.…”

Section: Leveraging Generative Ai For Inclusion In Stem Teamsmentioning

confidence: 99%

Catalyzing Equity in STEM Teams: Harnessing Generative AI for Inclusion and Diversity

Nixon,

Lin,

Snow

2024

Policy Insights from the Behavioral and Brain Sciences

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Collaboration is key to STEM, where multidisciplinary team research can solve complex problems. However, inequality in STEM fields hinders their full potential, due to persistent psychological barriers in underrepresented students’ experience. This paper documents teamwork in STEM and explores the transformative potential of computational modeling and generative AI in promoting STEM-team diversity and inclusion. Leveraging generative AI, this paper outlines two primary areas for advancing diversity, equity, and inclusion. First, formalizing collaboration assessment with inclusive analytics can capture fine-grained learner behavior. Second, adaptive, personalized AI systems can support diversity and inclusion in STEM teams. Four policy recommendations highlight AI's capacity: formalized collaborative skill assessment, inclusive analytics, funding for socio-cognitive research, human-AI teaming for inclusion training. Researchers, educators, and policymakers can build an equitable STEM ecosystem. This roadmap advances AI-enhanced collaboration, offering a vision for the future of STEM where diverse voices are actively encouraged and heard within collaborative scientific endeavors.

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“…[119] 6 Solutions Health 24 [1], [30], [31], [19], [34], [23], [43], [46], [48], [53], [57], [61], [75], [78], [81], [82], [83], [90], [97], [111], [115], [116] Solutions in Analytics and Artificial 13 [52], [3], [58], [59], [60], [66], [67], [79], [102], Intelligence [107], [108], [109], [117] Table 1 shows a summary of research articles analyzed in this research. The table portrays that machine learning techniques are being are applied to address problems in diverse fields.…”

Section: IIImentioning

confidence: 99%

Research Methods in Machine Learning: A Content Analysis

Shukla¹

2022

IJSREM

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Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research. To achieve this the researchers analyzed a total of 100 articles published since 2021 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research. Keywords: Research methods in machine learning, machine learning algorithms, machine learning techniques.

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CPSX: Using AI-Machine Learning for Mapping Human-Human Interaction and Measurement of CPS Teamwork Skills

Cited by 10 publications

References 21 publications

Research Methods in Machine Learning: A Content Analysis

Research Methods in Machine Learning: A Content Analysis

Catalyzing Equity in STEM Teams: Harnessing Generative AI for Inclusion and Diversity

Research Methods in Machine Learning: A Content Analysis

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