Non-Linear Learning Paths
This week I was fortunate to enjoy several conversations on how students learn, specifically do they prefer a step-by-step linear (objectivist) format, or a more open, non-linear (constructivist) approach. So, I would like to share the recent article, “Non-linear learning path: A systematic review” by Rahayu et. al (October 2024).
The authors found that students benefit from a nonlinear learning path, also known as a nonsequential learning path, because it allows them to control the pace and sequence of their learning. Their study aimed to map various nonlinear learning paths and explore the potential for personalisation within these environments. They conducted a comprehensive review of 3,418 articles from three databases, focusing on 30 that were relevant to nonlinear learning paths in open learning environments. They discovered that a nonlinear learning path involves path selection, cyclical paths, or skipped paths, all influenced by the design of the learning materials. A nonlinear learning path is facilitated by activities such as discussions, self-study materials, student-created content, project mentoring or coaching, peer feedback, and co-learning activities. Additionally, personalisation, tailored by educators is key to preventing students from becoming disoriented within these open learning environments. The findings highlight the importance of promoting nonlinear learning paths, developing learning path recommender systems, and creating supportive learning materials.
Nonlinearity in learning does not eliminate the need for structured materials (Rootzen, 2015). These environments empower students to determine what they consider important in their learning (Land & Oliver, 2012) and to navigate their own learning paths. The following are the research questions for this study:
How do Open Learning Environments (OLEs) support nonlinear learning paths?
To what extent do OLEs enable personalised nonlinear learning paths?
Personalisation is essential to mitigate disorientation in nonlinear learning and assist students in achieving students’ autonomy. Based on the interaction type, personalisation can be adaptable or adaptive. Adaptable personalisation tailor’s recommendations based on student’s input, while adaptive approaches use inferences data drawn from student-content interactions (Van Velsen, et al., 2008). Examples of adaptability are metacognitive maps (Correa, 2017), whereas adaptive could use a recommender system informed by the feedback from a social network (Khaled, et al., 2019).
Nonlinear learning aligns with the natural human ability to think non-linearly. With the expansion of the internet, nonlinear learning involves jumping non sequentially between different pieces of information using hyperlinks -or links- among learning materials. Hargis (2001) found similar results in the article, “Can students learn science using the Internet?” A constructivist and an objectivist online instructional format containing the same concepts was used to explore non/linear learning effects on age, gender, racial identity, attitude, aptitude, self-regulated learning, and self-efficacy.
References
Rahayu, N. W., Adi, A. N., Ferdiana, R., Kusumawardani, S. S. (2024). Nonlinear learning path: A systematic review. Turkish Online Journal of Distance Education, 25(4), 143-161. https://doi.org/10.17718/tojde.1349356
Hargis, J. (2001). Can students learn science using the Internet? ISTE Journal of Research on Computing in Education, 33(4), 475-487.
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