Publication Type
Journal Article
Version
publishedVersion
Publication Date
1-2023
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.
Keywords
Authentic Assessment Design, Digital Education, Pilot Study, Gamified Heutagogical Multi-modal AI-driven (“GHMA”) Approach, Learner Experience and Satisfaction
Discipline
Computer Sciences | Educational Assessment, Evaluation, and Research
Research Areas
Data Science and Engineering
Publication
Education and Information Technologies
Volume
28
First Page
9025
Last Page
9048
ISSN
1360-2357
Identifier
10.1007/s10639-022-11525-3
Publisher
Springer
Citation
LIM, Tristan; GOTTIPATI, Swapna; CHEONG, Michelle L. F.; NG, Jun Wei; and PANG, Christopher.
Analytics-enabled authentic assessment design approach for digital education. (2023). Education and Information Technologies. 28, 9025-9048.
Available at: https://ink.library.smu.edu.sg/sis_research/7764
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s10639-022-11525-3