Assessment design for digital education: An analytics-based authentic assessment approach

Publication Type

Conference Proceeding Article

Publication Date

12-2022

Abstract

This study looks to identify an assessment design construct that overcomes known issues in authentic assessment design practices in digital education. These include lack of "freedom-of-choice", lack of focus on multimodal nature of the digital process, and shortage of effective feedbacks. This study proposes an authentic assessment, that combines gamification (G) with heutagogy (H) and multimodality (M) of assessments, building upon learning analytics (A), known as GHMA. Proposed assessment design is a skills-oriented game-based assessment approach. Learners can 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 looked to: (i) establish validity of all key elements within the assessment design through structural equation modelling, and (ii) identify if application of assessment design leads to improved learner satisfaction based on differences-in-differences estimation using hierarchical linear regression. Results validated all key elements of GHMA assessment model, as beneficial factors tied to positive learner satisfaction on assessment delivery. There existed statistically significant positive post- and pre-treatment differences between the experimental and control group satisfaction survey scores, which indicated positive receptivity of proposed assessment design.

Keywords

Authentic Assessment Design, Digital Education, Pilot Study, Gamified Heutagogical Multi-Modal AI-driven (“GHMA”) Approach, Learner Experience and Satisfaction

Discipline

Databases and Information Systems | Educational Assessment, Evaluation, and Research

Research Areas

Data Science and Engineering

Publication

Proceedings of 2022 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), Hung Hom, Hong Kong, December 4-7

ISBN

9781665491181

Identifier

10.1109/TALE54877.2022.00063

Publisher

IEEE

City or Country

Piscataway, NJ

Comments

https://ieeexplore.ieee.org/document/10148539

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