The Evaluation of Cognitive Load Significance for Mobile Learning Application via User Interface Design Violations
2022 IEEE International Conference on Computing, ICOCO 2022
; : 392-397, 2022.
Article
in English
| Scopus | ID: covidwho-2258842
ABSTRACT
COVID-19 has changed how the world operates, and education is one of the sectors that are highly affected by these changes. Previously, mobile learning is just an optional or a supplementary learning method. However, with the increased in the number of COVID-19 cases around the world, education system has switched from the traditional face-to-face mode in a classroom setting, to an online learning environment. Learning using a mobile device or mobile learning is a concept that is new to most learners, especially those who have never before experienced an online learning setting. One of the prevalent factors that leads to ineffective mobile learning process is badly designed user interfaces that will disengage learners from learning materials presented, and increase the cognitive load of the learners. Among the factors that results in bad user interface is the violation of a user interface guideline/framework. Therefore, the main objective of this research-work is to evaluate the learners' cognitive load significance for mobile learning application by identifying Nielsen's Heuristics' violation. By implementing this study, important user interface design (UID) attributes that increase learner's cognitive load can be identified. Understanding how UID can affect the learners' cognitive load can assist designers in deciding which user interface designs that can improve or minimize learners' cognitive load. The outcome of this research will enable mobile learning application designers, developers, educators, teachers and people who are interested in developing a mobile learning application to deliver an effective mobile learning experience to learners. © 2022 IEEE.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
Language:
English
Journal:
2022 IEEE International Conference on Computing, ICOCO 2022
Year:
2022
Document Type:
Article
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