Instructional designers (IDs) have an ongoing challenge to create learning experiences that are motivating, engaging, and educational, and authoring tools like Evolve allow IDs to explore the limits of their creativity. The e-learning packages created by these authoring tools, however, often do not follow established, evidence-based web usability guidelines (Mehlenbacher et al., 2005). This lack of standardization in the interface can cause a number of problems for the user, including confusion, dissatisfaction with the platform, a lack of engagement, and cognitive overload (Rouet & Potelle, 2005; Sung & Mayer, 2012; Wang et al., 2014).
Inline Evolve content, which inherits the navigation elements of the Intellum platform, solves this problem by enhancing the overall content usability, which studies have shown can lead to a greater learner satisfaction and increased knowledge comprehension and retention (Sung, 2009). With Inline Evolve, users are no longer searching for navigational aids that can move based on IDs’ design preferences; the Intellum platform’s interface is consistent and was designed with established website usability heuristics in mind (see Mehlenbacher et al., 2005).
A consistent navigation can also lead to an increased user flow, characterized by Csikszentmihalyi (1997) as the state in which people become so involved in an activity that they lose sense of time and place. This state of flow can also be tied to intrinsic motivation within learners (Liao, 2006; Mahfouz, Joonas, & Opara, 2020), leading to greater completion rates of e-learning content (Rodriguez-Ardura & Meseguer-Artola, 2017).
To empirically test learners’ flow state and the ease of platform navigation, we compared the enrollment and completion rates of traditional Evolve and Inline Evolve content. In a study examining over 3 million enrollment records, our analysis indicated that the completion of Inline Evolve content was significantly higher than the completion of traditional Evolve content. Users completed Inline Evolve content 90.3% of the time; traditional Evolve content for the same timeframe was completed at a rate of only 70.3%. This 20% increase was calculated as statistically significant using Pearon’s chi-square test with one degree of freedom with a p value less than .001.
These findings are consistent with previous empirical studies, which have shown that usage of e-learning systems with usable navigation structures is correlated with significantly better learner performance, satisfaction, and an overall increase in learner flow (Sung, 2009; Sung & Mayer, 2012; Porta et al., 2015).
Csikszentmihalyi, M. (1997). Finding flow: the psychology of engagement with everyday life. New York, NY: Basic Books.
Liao, L.F. (2006). A flow theory perspective on learner motivation and behavior in distance education. Distance Education, 27(1), 45-62.
Mahfouz, A.Y., Joonas, K., & Opara, E.U. (2020). An overview of and factor analytic approach to flow theory in online contexts. Technology in Society, 61.
Mehlenbacher, B., Bennett, L., Bird, T., Ivey, M., Lucas, J., Morton, J., & Whitman, L. (2005). Usable e-learning: A conceptual model for evaluation and design. Appeared in Proceedings of the HCI International 2005: 11th International Conference on Human-Computer Interaction, Volume 4 -- Theories, Models, and Processes in HCI. Las Vegas, NV: Mira Digital Publishing, 1-10.
Porta, L., Beneito, R., Melenchon, J., & Marina, A. (2015). Effects of applying the site map principle in an online learning environment in higher education. International Journal of Educational Technology, 10(7), 31-38.
Rodriguez-Ardura, I. & Meseguer-Artola, A. (2017). Flow in e-learning: What drives it and why it matters. British Journal of Educational Technology, 48(4), 899-915.
Rouet, J.F. & Potelle, H. (2005). Navigational principles in multimedia learning. In R.E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 297-312). Cambridge, MA: Cambridge University Press.
Sung, E. (2009). A development and effectiveness of the visual design strategy on text information structure for e-learning contents design. The Journal of Educational Information and Media, 15(2), 133-158.
Sung, E. & Mayer, R.E. (2012). Affective impact of navigational and signaling aids to e-learning. Computers in Human Behavior, 28, 473-483.
Wang, Q., Yang, S., Liu, M., Cao, Z., & Ma, Q. (2014). An eye-tracking study of website complexity from cognitive load perspective. Decision Support Systems, 62.