Working Memory and Learning Underlying Website Structure

1. Working Memory and Learning Underlying Website Structure Steven Banas & Christopher A. Sanchez Cognitive Science & Engineering Arizona State University

2. Information Gathering on the Web • The World-Wide-Web is complex and organized in many different ways • Not all websites include navigational aids • Without navigational aids users must rely on mental models created from information from various sources to guide their searching • i.e., Previous experience with domain or web structure • Goal: match user’s mental model to actual structure

3. Matching mental models • During search, prior knowledge must be combined with incoming information to guide a users searching behavior • For example, previous experiences with Wikipedia and similarities with the current page • Effortful and conscious process • This process of combing incoming knowledge with previous knowledge has been shown to occur within the working memory system.

4. Working Memory • Working memory capacity (WMC) has emerged in the past 30+ years as a powerful theory that predicts performance and behavior across a wide array of tasks. • Reading performance, g, science learning, anti-saccade, etc. • Strongly tied to the notion of controlled attention • Ability to focus attention on relevant information and either suppress or otherwise ignore task irrelevant information. • More than just STM, as it includes aspects of both executive processing AND storage.

5. Working Memory and Web Learning • Remember: • WMC predicts how well individuals connect discrete concepts and make appropriate inferences • High WMC individuals have been shown to be better able to retain information that is relevant and useful for integrating textual information, even in the face of related processing demands • So… • Relative to the context of web search for understanding, WMC should also predict learning from multiple web documents • Integrating this information across discrete pages

6. Current study • Participants (N=62) read a Wikipedia-like page on Plant Taxonomy

7. Website • Hierarchical tree structure that contained 4 levels • 24 total pages • Each page ~ 500 words • Navigated only using links • Links mirrored hierarchical structure of content • Participants were not given a site map • Participants entered the website at the top

8. Screenshot of Website

9. Pre/Posttest Questions • Participants rated their knowledge of plants and biology on a 1-5 scale • Also completed • Hierarchical tree construction task. • Place terms in correct location in hierarchy • More global measure of hierarchy • Matching task • Choose item immediately connected in hierarchy • More local measure of hierarchy • Completed tasks again after reading

10. Search Questions • 18 short answer questions to be completed while searching the website • Simple factual questions, drawn evenly from the entire website. • i.e. “What is the scientific name of clubmosses? “

11. WMC Measure • Automated Operation Span task (AOSpan) • Equation-letter strings were presented in sets of between 2 and 7 strings. • Participants completed 3 trials of each set size, and the order of these sets was randomized. IS 8/4 +1 =2? C

12. Hypotheses • High WMC Individuals • Better able to construct a more accurate tree than lower WMC individuals due to a better more robust mental model of the material and inferencing • Better able to complete both the search questions and the tree construction due to the increased capability to handle both simultaneous tasks • Low WMC Individuals • More taxed by the secondary search task, less likely to develop an accurate mental model needed to complete the tree construction task

13. Results: Search Questions • Overall, participants were able to adequately complete the search task (M=9.93, SD=3.73). • Performance was not significantly correlated with • WMC (r(61)=.04, p>.05) • Knowledge of plants (r(61)=.07, p>.05) • Knowledge of biology (r(61)=.08, p>.05). • Search Questions were more or less difficult regardless of WMC of prior knowledge

14. Results: Matching • Significant improvement pre-post • F(1, 61)=34.99, ηp2=.37, p<.01

15. Change in Matching Task • Hierarchical regression on improvement • First block: WMC, knowledge of plants, and knowledge of biology • Second block: interaction terms between WMC and both prior knowledge variables • First block Results: • R2=.07, F(3, 61)=1.37, ns • No variables significant predictors • Second block Results: • Interaction Terms did not significantly improve the fit of the model • R2 change=.01, p>.05

16. Results: Tree Construction Task • Participants did significantly improve pre to post • F(1, 61)=36.15, ηp2=.37, p<.01).

17. Change in Tree Construction Task • Hierarchical regression on improvement • First block: WMC, knowledge of plants, and knowledge of biology • Second block: interaction terms between WMC and both prior knowledge variables • First block Results: • R2=.12, F(3, 61)=3.71, p<.05 • WMC only significant predictor of learning gains gain (β=.35, p<.05) • Second block Results: • Interaction Terms did not significantly improve the fit of the model

18. Discussion • Results show that WMC does influence how well individuals learn and remember the underlying, non-explicit, structure of complex material. • High WMC individuals improved their implicit understanding of the material on the website, lower did not • Effect was not mediated by prior knowledge • Results are important for online learning environment designers as it shows that individual differences do impact how learners grasp implicit information • Also shows that user control over what navigational tools are available to them would benefit the user experience and learning

19. Future Work • Extend to other domains • Other relevant individual differences • Test-bed for creation of better navigational tools and learning aids