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Analytics-enabled authentic assessment design approach for digital education

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Abstract

There are known issues in authentic assessment design practices in digital education, which include the lack of freedom-of-choice, lack of focus on the multimodal nature of the digital process, and shortage of effective feedbacks. This study looks to identify an assessment design construct that overcomes these issues. Specifically, this study introduces an authentic assessment that combines gamification (G) with heutagogy (H) and multimodality (M) of assessments, building upon rich pool of multimodal data and learning analytics (A), known as GHMA. This is a skills-oriented assessment approach, where learners determine their own goals and create individualized multimodal artefacts, receive cognitive challenge through cognitively complex tasks structured in gamified non-linear learning paths, while reflecting on personal growth through personalized feedback derived from learning analytics. This pilot research looks to: (i) establish validity of all elements within the assessment design, and (ii) identify if application of assessment design leads to improved learner satisfaction. Results showed positive validations of all key elements of the GHMA assessment model, as beneficial factors tied to positive learner satisfaction on assessment delivery. There existed statistically significant post- and pre-treatment differences between experimental and control group satisfaction levels, indicating positive receptivity of GHMA authentic assessment design in digital education.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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  1. School of Business Management, Nanyang Polytechnic, 180 Ang Mo Kio Ave 8, 569830, Singapore, Singapore

    Tristan Lim, Jun Wei Ng & Christopher Pang

  2. School of Computing and Information Systems, Singapore Management University, 80 Stamford Road, 178902, Singapore, Singapore

    Tristan Lim, Swapna Gottipati & Michelle Cheong

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  1. Tristan Lim
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Lim, T., Gottipati, S., Cheong, M. et al. Analytics-enabled authentic assessment design approach for digital education. Educ Inf Technol 28, 9025–9048 (2023). https://doi.org/10.1007/s10639-022-11525-3

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