Digital learning experience design and research of a self-paced online course for risk-based inspection of food imports

Li, Shangman; Singh, Kanupriya; Riedel, Nathan; Yang, Fan; Jahnke, Isa

doi:10.1016/j.foodcont.2021.108698

Cited by 6 publications

(5 citation statements)

References 31 publications

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“…Currently, the focus is on deep empathy techniques for mixed method research. This analysis is in line with many scholars such as Li et al (2022), Schmidt and Huang (2021), Schmidt and Tawfik (2022), and Thurber (2021), all stated that LXD practice informed by UXD methods and design thinking for learning contexts. Such concepts are related to human-centered design that considers the mental and emotional quality of learner mentality or emotional well-being in digital learning to create a good experience and continually profound learning in the long run.…”

Section: Discussionsupporting

confidence: 86%

A systematic review of changing conceptual to practice in learning experience design: Text mining and bibliometric analysis

Phommanee,

Plangsorn,

Siripipattanakul

2023

CONT ED TECHNOLOGY

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Learning experience design (LXD) is a new wave in educational technology and learning design. This study was conducted to clarify conceptual change to practice by applying a systematic literature review to a combination text mining and bibliometric analysis technique to visualization network. Based on the study selection articles from SCOPUS. Our research questions focused on the changing concept, the elements of dimensionality, and the process or practice of LXD. The findings showed that 152 articles were finally selected to be analyzed. Conceptualizing LXD is currently underway in design thinking and user research methods with emphasis on the empathize process. Moreover, three dimensions to consider including (1) design dimension focus on user experience design in a technology context, (2) learning dimension focus on instructional design and learning theory, and (3) standard dimension focus on assessment and evaluation in learning goal and project management. In addition, five steps cycle for practice follows: research learners as users and learning goals, design with ideate, develop prototyping, validity testing, and launch and follow-up. These factors enhance learning engagement and aesthetics for a great learner experience and learning efficacy.

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Section: Discussionsupporting

confidence: 86%

A systematic review of changing conceptual to practice in learning experience design: Text mining and bibliometric analysis

Phommanee,

Plangsorn,

Siripipattanakul

2023

CONT ED TECHNOLOGY

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“…However, if one looks at the specifics of individual learning growth (relative learning growth), a different picture emerges. A comparison between the results before and after the test was carried out using the "Setting Growth Targets for Student Learning Objectives" (Li et al, 2021), which is a framework that places learners in different levels from 1 to 5, (from beginners to advanced learners) based on their baseline knowledge level. Using this framework allows researchers to see learners' growth relative to their previous knowledge rather than just their end knowledge level.…”

Section: User Experience (Study 2) Resultsmentioning

confidence: 99%

“…While Study 2 focused on the feasibility and usability of RIFI's early prototype, Study 3 focused on learners' experience with the finished design and whether the design is effective for learning. Table 1 provides an overview of the specific data collection instruments (Li et al. , 2021).…”

Section: Case Study: Illustrating Learning Experience Design and Eval...mentioning

confidence: 99%

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Quality of digital learning experiences – effective, efficient, and appealing designs?

Jahnke¹

2022

IJILT

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PurposeFischer et al. (2022) present a framework for rethinking education, including broad design components such as learning-on-demand or learning takes place in the context of authentic problems. How can we bring those design components into practice? I argue that the design of innovative learning approaches for the digital age requires rigor in design and evaluation methods to understand what works and how and why it works.Design/methodology/approachThis case study applies the research to improve approach with iterative steps of design, development, and formative evaluation. Methods to be used are borrowed from user and learning experience fields, such as usability studies and pre/posttests for learning growth.FindingsThe design of digital learning experiences rests on the three dimensions of how learners interact with (a) the digital tool, space, or service; (b) the pedagogical elements of goals, activities, and assessments (e.g. sense-making); and (c) other peers or instructors.Originality/valueThis study provides useful insights on how to conduct research to improve versus research to prove and indicates the importance of data analysis related to the effectiveness, efficiency, and attractiveness of digital learning experiences. Learning experience design and research provides sufficient rigor and could play an important role in new ways of learning in the digital age with the goal “making learning a part of life” as pointed out by Fischer et al. (2022).

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“…Additionally, Sun et al, offers insights into efficient image data handling, applicable to both telecommunications and large-scale image datasets [23]. Moreover, the transition challenges highlighted by Li et al in their study on the sudden shift of engineering courses to remote learning shed light on adapting educational strategies in computer vision, a crucial consideration during unforeseen disruptions like public health crises [24,25]. These studies collectively advance the field of computer vision by integrating cross-disciplinary research findings into practical applications and education.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Survey of State-of-the-Art Convolutional Neural Network Architectures and Their Applications in Image Classification

Pan,

Luo,

Zhou

2022

IAET

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Image classification is a vital research direction in computer vision all over the world. Before the advent of deep learning, image classification relied on manual feature extraction and conventional machine learning algorithms. However, Convolutional Neural Networks (CNNs) revolutionized this field by automatically learning features from data. The article discusses the fundamental principles of convolutional neural networks and compares various CNN architectures. Key layers such as convolutional, pooling, activation, fully connected, and dropout layers are explained in detail, along with techniques like backpropagation and optimization algorithms. Additionally, common CNN models like LeNet, AlexNet, VGGNet, GoogLeNet, ResNet, SENet, and EfficientNet are introduced, highlighting their characteristics and applications.

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Digital learning experience design and research of a self-paced online course for risk-based inspection of food imports

Cited by 6 publications

References 31 publications

A systematic review of changing conceptual to practice in learning experience design: Text mining and bibliometric analysis

A systematic review of changing conceptual to practice in learning experience design: Text mining and bibliometric analysis

Quality of digital learning experiences – effective, efficient, and appealing designs?

Comprehensive Survey of State-of-the-Art Convolutional Neural Network Architectures and Their Applications in Image Classification

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