TY  - JOUR
AB  - Assistance systems should be able to adapt to individual task-related skills and knowledge. Structural-dimensional analysis of mental representations (SDA-M) is an established method for retrieving human memory structures related to specific activities. For this purpose, SDA-M involves a semi-automatized survey of users (the “split procedure”), which yields data about users’ associations between action representations in long-term memory. Up to now this data about associations has commonly been clustered and visualized by SDA-M software in the form of dendrograms that can be used by human experts as a tool to (manually) assess users’ individual expertise and identify potential issues with respect to predefined action sequences. This article presents new algorithmic approaches for automatizing the process of assessing task-related memory structures based on SDA-M data to predict probable errors in action sequences. This automation enables direct integration into technical systems, e.g. user-adaptive assistance systems. An evaluation study has compared the automatized computational assessments to predictions made by human scholars based on visualizations of SDA-M data. The different algorithms’ outputs matched human experts’ manual assessments in 84% to 86% of the test cases.
DA  - 2019
DO  - 10.1371/journal.pone.0212414
LA  - eng
IS  - 2
PY  - 2019
SN  - 1932-6203
T2  - PLOS ONE
TI  - Computational assessment of long-term memory structures from SDA-M related to action sequences
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-29196040
Y2  - 2024-11-22T02:24:50
ER  -