Mira Suryani
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Mira Suryani has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Dana Indra Sensuse
Faculty of Computer Science, University of Indonesia
1 shared publication - Harry Budi Santoso
Universitas Indonesia
1 shared publication - Kautsarina Kautsarina
Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia
1 shared publication - Rizal Fathoni Aji
Universitas Indonesia
1 shared publication - Ryan Randy Suryono
Universitas Teknokrat Indonesia
1 shared publication - Setiawan Hadi
Universitas Padjadjaran
1 shared publication
Latest Publications
- Open Access
- 11 July 2024
- Published by Springer Nature in Cognition, Technology & Work
- Vol. 26 (4) , 653-672
An initial user model design for adaptive interface development in learning management system based on cognitive load
The cognitive aspect is crucial in developing interactive and adaptive systems, including learning management systems (LMS). By understanding human cognitive processes, developers can create adaptive systems that are sustainably used by targeted users. Cognitive traits need to be explored to become part of the user model, especially especially to develop an LMS with an adaptive interface based on cognitive load. However, there is limited research exploring the correlation between cognitive load and adaptive interfaces, as well as the visualization of information, within online learning settings, including those specifically within LMS platforms. Additionally, a lack of understanding of cognitive processes renders the use of LMS static and unable to adapt to learners’ abilities. Therefore, information regarding the specific characteristics or model of cognitive load that accurately represent LMS users as triggers for adaptive interfaces needs to be further examined. A total of four experts in the cognitive field were interviewed. The interview process was conducted in a semi-structured manner to obtain information about problems in the use of e-learning, the nature of cognitive, and the process of capturing cognitive load during learning using LMS. By using a soft system methodology, the results from the interviews are mapped into a cognitive load-based LMS user model design. The initial model design includes information on working memory capacity, task performance (time, true/false rate), learning behavior (learner log), physiological (in a lab scale), and self-reporting (Likert scale). This model design is a promising first step towards a more technical process in developing adaptive and interactive systems.
- 11 July 2024
- Published by Springer Nature in Cognition, Technology & Work
- Vol. 26 (4) , 653-672
Open AccessAn initial user model design for adaptive interface development in learning management system based on cognitive load
The cognitive aspect is crucial in developing interactive and adaptive systems, including learning management systems (LMS). By understanding human cognitive processes, developers can create adaptive systems that are sustainably used by targeted users. Cognitive traits need to be explored to become part of the user model, especially especially to develop an LMS with an adaptive interface based on cognitive load. However, there is limited research exploring the correlation between cognitive load and adaptive interfaces, as well as the visualization of information, within online learning settings, including those specifically within LMS platforms. Additionally, a lack of understanding of cognitive processes renders the use of LMS static and unable to adapt to learners’ abilities. Therefore, information regarding the specific characteristics or model of cognitive load that accurately represent LMS users as triggers for adaptive interfaces needs to be further examined. A total of four experts in the cognitive field were interviewed. The interview process was conducted in a semi-structured manner to obtain information about problems in the use of e-learning, the nature of cognitive, and the process of capturing cognitive load during learning using LMS. By using a soft system methodology, the results from the interviews are mapped into a cognitive load-based LMS user model design. The initial model design includes information on working memory capacity, task performance (time, true/false rate), learning behavior (learner log), physiological (in a lab scale), and self-reporting (Likert scale). This model design is a promising first step towards a more technical process in developing adaptive and interactive systems.