Development of an Approach to Measuring Learnability Based on NGOMSL from Perspectives of Extended Learnability

ABSTRACT

The present study proposes an approach to measuring learnability, focusing on the learning process from a perspective of extended learnability. In developing a predictive model for time performance without any interruption or error based on NGOMSL (Natural GOMS Language), the concepts of “expertise time,” “actual time,” “learning deficit,” and “learning level” are considered with a few assumptions, and a learning deficit curve for the relationship between learning deficit and learning level is presented. Experimental data on repetitive use in sample users’ first use session of a system demonstrate the application of our approach herein. For exploratory tasks on four simulated websites with different levels of usability and aesthetics, responses from 64 users were obtained in terms of time performance, perceived usability, and user satisfaction. Through this application, we were able to discern some information about the dynamic properties of learning in relation to users’ subjective responses: (1) learning level according to number of uses, (2) the number of uses required to reach a competency level, (3) effects of usability and aesthetics factors on learning level, and (4) learning level in connection with perceived usability and user satisfaction. The model is expected to have potentials as an effective approach to measuring learnability insofar as it provides useful information in ways that differ from existing methods for learnability.

Additional information

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP; Ministry of Science, ICT & Future Planning) (NRF-2017R1C 1B 1003650). This research was supported by the MIST (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW supervised by the IITP(Institute for Information & communications Technology Promotion) (2015-0-00914).

Notes on contributors

Sangwon Lee

Sangwon Lee is an associate professor in the department of Interaction Science, Sungkyunkwan University. He has obtained his PhD degree in Industrial Engineering at the Pennsylvania State University in 2010. His research interests include human-computer interaction, user experience, affective computing, and user modeling.

Young June Sah

Young June Sah (PhD, Michigan State University) is an adjunct professor in the department of Interaction Science, Sungkyunkwan University. His research interests include psychological and behavioral effects of media technologies and their cognitive mechanisms.