Learners bring their unique perceptions, preferences, and abilities to a learning situation. The effect of individual differences on learning depends in part, on how the instructional system accommodates these differences. The current study examined the role of working memory capacity (WMC) and structured navigation in hypertext learning. One-hundred and seventy-four participants each participated in one of six groups of a 3 x 2 between-subject design, which focused on the interaction between the levels of the navigation structure (unconstrained index, expert index, or expert network) and the levels of the participants' WMC (high or low). The study aimed to (a) investigate the effect of three different types of navigational guides on learning outcomes and (b) examine how the navigational guides interacted with an individual's working memory capacity. It was expected that an expert-constrained navigation guide would improve learning, particularly for those with low-WMC. This study found support for such a relationship between working memory capacity and navigational structure. The unconstrained index guide produced the largest differences in performance; high-WMC learners performed significantly better in this environment than low-WMC learners. However, the low-WMC learners' performance improved in the expert index and expert network guides. The high-WMC learners did not perform as well in expert index navigation, but performance marginally improved in the expert network. The results show that working memory capacity does indeed moderate learning outcomes in a hypertext environment.
Level of Degree
Goldsmith, Timothy E.
First Committee Member (Chair)
Hodge, Gordon K.
Second Committee Member
Butler, Karin M.
Third Committee Member
Computer-assisted instruction, Conceptual structures (Information theory), Short-term memory, Hypertext systems.
Martinez-Papponi, Brenda L.. "Expert constrained navigation in hypertext learning and the effects of working memory capacity." (2014). https://digitalrepository.unm.edu/psy_etds/91
Available for download on Tuesday, December 17, 2024