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  • Publication
    Metadata only
    Crossing valley: Development of a serious game to measure cognitive flexibility in a problem-solving context
    (Springer, 2024)
    Fu, Wei Ling
    ;
    Fischer, Nastassja Lopes
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    Kalaivanan, Kastoori
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    Ong, G. S. T.
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    Oh, A. J.
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    Tripathi, S.
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    Ellefson, M. R.
    ;
    ; ;
    Cognitive flexibility (CF), the ability to swiftly shift between and adapt mental strategies to navigate novel situations, has been increasingly recognized as pivotal in classroom learning. Traditional behavioral measures tend to oversimplify the CF construct, mainly reducing it to set-shifting (i.e., attention switching within a task) or task-switching (i.e., alternating response between tasks) skills. However, recent literature has suggested that CF may encompass a wider range of abilities (e.g., adaptability to changes in the environment). To address this gap, we are adopting a unified framework that embraces a broader perspective and employs a serious game (SG) to assess CF within an educationally relevant, problem-solving context. By designing a serious game, we aim to provide a platform for an ecological assessment of CF skills within a problem-solving context. Our goal is to use game elements to enhance participant motivation, and to infuse educational relevance into assessments, thereby bridging the gap between psychological testing and real-world application.
  • Publication
    Metadata only
    Smartphone lidar reliably estimates leaf nitrogen concentration and shoot biomass on leafy vegetable crops
    (IEEE, 2024)
    Harikumar, Aravind
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    Shenhar, Itamar
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    Pebes-Trujillo, Miguel R.
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    Lin, Qin
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    Moshelion, Menachem
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    ;
    Ng, Kee Woei
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    Gavish, Matan
    ;
    Herrmann, Ittai
    Leafy vegetables have a huge demand in subtropical global markets for its high nutritional value and low cost. Accurate estimation of traits like leaf nitrogen concentration and shoot biomass is critical for optimal fertilizer dosing, nutrient content assessment as well as phenotyping studies. Hand-held smartphone with high-density Light Detection and Ranging (LiDAR) systems capture huge amounts of three-dimensional (3D) plant structural and intensity information that can be used to estimate plant traits. Thus, we propose a semiautomatic proximal sensing approach to model plant nitrogen content and shoot biomass using the structural and intensity information acquired by a smartphone based LiDAR sensor. The performance of the models in estimating leaf nitrogen concentration and shoot dry-weight biomass was quantified on Chinese broccoli (Brassica oleracea) plants with prior knowledge of nitrogen dossing concentrations, and produced a minimal root mean squared error of 8.75 mg nitrogen per gram of dry weight, and 2.38 g, respectively. The potential of the proposed modelling approach to accurately estimate leaf nitrogen concentration and shoot dry-weight biomass, from smartphone LiDAR data is proven.
  • Publication
    Open Access
    Improving code readability for novice coders: A tool for actionable feedback
    (2023)
    Alviento, Adrian Nicolas Belleza
    ;
    ;
    Ahmed Hazyl Hilmy
  • Publication
    Open Access
  • Publication
    Open Access
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