Page 18 - International Perspectives on Effective Teaching and Learning in Digital Education

Maša Černelič-Bizjak and Sabina Ličen

tion (Deci & Ryan, ). Integrating social components, such as collabora-
tive learning, discussion forums, and virtual study groups, which promote a
sense of belonging and social connection. This social presence can positively
influence motivation and long-term engagement in digital learning. Timely
and personalized feedback is also essential when students receive immedi-
ate, specific feedback tailored to their progress, they are more likely to stay
motivated and persist in their learning efforts. Additionally, incorporating re-
al-world applications and goal-setting techniques helps learners see the rele-
vance of their studies, increasing their persistence and overall engagement in
digital environments (Pintrich, 3). By incorporating these strategies, dig-
ital learning environments can better support student motivation, ensuring
sustained interest and improved learning outcomes.

Cognitive Load and Information Processing
Cognitive load and information processing are important psychological fac-
tors in digital learning. Given the rapid development of digital learning, it is
important to advance the understanding of cognitive load theory (CLT) in
line with this growing body of research (Skulmowski & Xu, ).
In cognitive psychology, cognitive load refers to the amount of working
memory resources used. Cognitive load can be understood as the mental ef-
fort required to process and retain information, which is limited by the con-
straints of working memory. Effective instructional design aims to manage
these cognitive demands to optimize learning and prevent cognitive over-
load, and CLT describes the different categories of load that can occupy their
memory capacity (Sweller et al., 1998). CLT suggests that excessive information
can overwhelm a learner's working memory, thereby reducing their ability to
learn effectively. The central focus of CLT categorizes the demands on working
memory into three types: intrinsic cognitive load (ICL), extraneous cognitive
load (ECL), and germane cognitive load (GCL) (Sweller et al., 1998). Intrinsic
cognitive load is the effort associated with understanding a specific topic,
extraneous cognitive load relates to how information or tasks are presented,
and germane cognitive load involves the effort put into creating a permanent
store of knowledge (Sweller et al., 19). However, over the years, the additive
nature of these types of cognitive load has been examined and questioned. It
is now believed that they influence each other in a more circular manner.
Recent research (Skulmowski & Xu, ; Orru & Longo, 2019; Januchta et al.,
) emphasizes that cognitive load and information processing are crucial
factors in digital learning environments. The latest studies expand on CLT by
refining its model to better fit the complexities of digital learning settings,

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