Lifelong learning means continuously updating our knowledge and skills, especially as technology keeps changing around us. For educators, it’s not enough to just be exposed to new tools. Real integration happens when time is taken to evaluate, model, and actively engage with them. Adopting new technologies into lessons or workflows isn’t always easy, but when educators see tools in action and learn alongside others, the process becomes more meaningful. By modeling technology use and participating in professional learning networks (PLNs), educators create space for shared learning, peer support, and real growth.
Cognitive Processes in Technology Adoption
To understand how modeling influences digital tool adoption, it is crucial to explore the cognitive processes involved in learning new technologies. Learning to adopt new digital tools, or really learning anything, involves internal cognitive processing. Cognitive theories focus on how learners receive, organize, store, and retrieve information, and how educators can optimize these processes to support effective learning and smooth integration of new tools (West et al., 2018).
The Role of Cognitivism
Cognitive theories explain that learning occurs through internal mental activities like encoding, storing, and retrieving information (West et al., 2018). When adopting new technologies, individuals must engage in these processes to integrate and retain new information.
Memory operates in three stages: sensory memory, short-term memory, and long-term memory (Spielman et al., 2014). Each of these stages plays a key role in how information about new tools is processed and retained:
- Encoding is the first step, where sensory information is converted into a form that the brain can store. This can be done automatically or with effort. There are different types of encoding, including semantic (meaning), visual (images), and acoustic (sounds) (Spielman et al., 2014).
- Storage refers to the retention of encoded information. While sensory memory only holds information briefly, short-term memory stores it for a longer period (up to 20 seconds), and long-term memory holds information indefinitely (West et al., 2018). Long-term memory can be explicit, meaning we can consciously recall it, or implicit, which is learned unconsciously through practice.
- Retrieval is the process of accessing information from long-term memory. We can retrieve information through recall (getting the information without cues), recognition (identifying something we’ve learned before), or relearning (refreshing knowledge that has been forgotten) (West et al., 2018).
Based off this structure, learning happens when information is encoded and stored in an organized, meaningful way, making it easier to retrieve and apply when needed.
Active Learning as a Cognitive Necessity
Adopting new technologies is most effective when learners actively engage with the content. Cognitive theories suggest that environmental cues and instructional components alone aren’t enough for successful learning (West et al., 2018). Instead, learners need to use intentional, active learning strategies to promote deeper cognitive processing.
For example, a study by Freeman et al. (2014) demonstrated that active learning significantly enhances course grades, particularly in smaller class sizes. Students who participated in active learning were 1.5 times less likely to fail compared to those who experienced traditional teaching methods.
To strengthen memory, learners can use strategies like elaborative rehearsal (connecting new information to existing knowledge), chunking (grouping information into manageable parts), and mnemonic devices (creating memory aids like acronyms or visual cues) (Spielman et al., 2014). Additionally, expressive writing—like taking notes or journaling—has been shown to enhance short-term memory capacity.
For effective technology adoption, learning these tools needs to be integrated in a way that helps move learned information from short-term to long-term memory. Constructivist approaches support this process, which requires learners to engage, reflect, and apply new knowledge (West et al., 2018).
Example: OneNote and Hands-On Rehearsal
Consider a professional learning session where educators are introduced to Microsoft OneNote. If the session is simply a passive demonstration of its features—like showing how to create digital notebooks or embed multimedia—learners might walk away with some basic knowledge (short-term memory), but they won’t necessarily understand how to integrate it into their own workflow or retain the information for very long.
To promote deeper cognitive processing and to ensure meaning adoption, it’s important to involve participants actively in the learning process. In this case, educators should create their own digital notebooks, organize sections, add tags, and integrate multimedia elements like images or audio. This hands-on experience taps into multiple forms of encoding: visual encoding (through the design and structure of the notebook), semantic encoding (by connecting the tool’s features to their own teaching practices), and procedural encoding (through repeated use and practice).
Reflecting on their experiences, discussing how OneNote could fit into their teaching, and revisiting the tool later for further practice helps reinforce memory consolidation and retrieval. By engaging with the tool in meaningful ways, educators aren’t just passively learning about new technology; they are applying it directly, making the experience more personal and ensuring that OneNote becomes a lasting part of their digital toolkit.
Constructing Knowledge: Modeling and Social Learning
When reflecting on how educators best learn and adapt to new learning experiences, such as mastering new digital tools, it’s crucial to understand how knowledge adapts and builds over time. Constructivism emphasizes that learning is not a passive process of absorbing facts, but an active one where learners construct meaning by integrating new information with prior knowledge (West et al., 2018). For technology integration to be effective, learners must connect new tools to existing knowledge, allowing concepts to evolve with each use.
This process of knowledge construction rarely happens in isolation. Learning with technology is most impactful when it is hands-on, social, and rooted in real-world situations (West et al., 2018). As mentioned earlier, simply demonstrating how a tool works is often not enough. Learners gain the most from opportunities to see the tool in action, experiment with it directly, adapt it to their own contexts, and reflect on the results. Each time a tool or concept is revisited, new experiences and collaborative efforts deepen understanding. Memory becomes a dynamic record, constantly reshaped through ongoing interaction (West et al., 2018). This approach fosters deep understanding and supports meaningful application.
From Constructivism to Social Learning
Building upon the constructivist approach, learners can also construct new ideas from social interactions with others. Although retaining new information is most effective when learners actively engage and participate, modeling can be a powerful complement to active learning when used intentionally. In Social Learning Theory, Albert Bandura emphasizes how learning can best take place through observational learning.
Observational learning refers to the process of learning by watching others and then imitating, or modeling, their behavior (Spielman et al., 2014). This type of learning can occur in several ways: through live models, where someone demonstrates a behavior or tool in real time; verbal models, where the process is described rather than shown; and symbolic models, which involve learning from others’ experiences, such as stories or testimonies (Spielman et al., 2014). Each of these approaches helps learners visualize a process or behavior before applying it themselves, creating a bridge between observation and action.
Bandura outlines four key steps in the modeling process (Spielman et al., 2014):
- Attention – Focus must be directed toward the model and the behavior being demonstrated. This initiates active learning and helps learners begin forming connections.
- Retention – The learner must retain what they observed. This often involves note-taking or reflecting on key points, which supports encoding the information into short-term memory.
- Reproduction – The learner attempts to replicate the observed behavior. This step reinforces the process and helps transfer the knowledge from short-term memory to long-term memory.
- Motivation – The learner must feel motivated to engage with the behavior. Motivation plays a critical role in sustaining effort and applying new skills whether driven by intrinsic interest, like personal satisfaction or curiosity, or from external rewards, like praise or recognition.
To further touch up on motivation, Bandura argued that motivation is tied closely to self-efficacy, which is our belief in our ability to succeed (Spielman et al., 2014). When learners feel capable, they are more likely to take risks, persist through challenges, and set higher goals for themselves. For example, someone who believes they can master a complex tool is far more likely to engage deeply and continue exploring its features, even when they hit a roadblock.
Overall, Bandura’s perspective on modeling and observational learning doesn’t replace active learning; it strengthens it. When learners see what is possible and believe that success is within reach, they’re more likely to engage meaningfully and apply new tools in ways that work for them.
Social Constructivism and Collaborative Learning
In addition to Bandura’s Social Learning Theory, Lev Vygotsky offers another valuable perspective by extending constructivist thinking into social interactions with others. His concept of the Zone of Proximal Development (ZPD) suggests that learners make the most progress when working in community, particularly alongside individuals who are slightly more advanced and can offer appropriate guidance through the learning process (West et al., 2018). This form of guided participation allows learners to stretch their thinking, reflect on their experiences, and apply new ideas with greater confidence.
In collaborative learning experiences, it is important that there’s a shared sense of common understanding. When learners work together to co-construct meaning, they’re not just participating, but they’re actively shaping the experience together (West et al., 2018). This kind of partnership works best when there is scaffolding in place: support that’s given right when it’s needed and gradually pulled back as confidence builds (West et al., 2018). That support might look like modeling a strategy, offering feedback, or breaking a task into smaller, more manageable steps. This process is especially relevant when learning digital tools, as digital tools often need to be adapted to meet the specific needs of the educator using them.
PLNs as Social Learning Environments
Professional Learning Networks (PLNs) create an ideal space for meaningful social learning. A PLN is a group of connected educators or professionals who come together to share ideas, ask questions, and learn from one another. These spaces foster growth through collaboration, where participants exchange experiences and resources that deepen understanding and appreciation for the work being done (Mohammed & Kinyo, 2020). Within these communities, educators are actively observing, experimenting, reflecting, and adapting —drawing from both modeling and social constructivist approaches. Learning happens through exposure to tools, strategies, and real classroom examples shared by peers who are engaging with similar challenges and goals. For example, a teacher in a PLN might see a peer’s lesson plan that utilizes a new AI tool, adapt it for their own lesson plan, and then share their experience with the group. This contributes to the ongoing cycle of collaborative and continuous learning.
Conclusion
Modeling the evaluation and adoption of digital tools, paired with cognitive and constructivist strategies, helps educators engage with technology in ways that are both meaningful and effective. Lifelong learning is an essential skill for educators to develop, especially as new digital tools emerge at a rapid pace. Being part of PLNs gives educators continuous opportunities for social learning, which not only supports motivation but also reinforces knowledge retention. These networks encourage collaboration, reflection, and adaptability, which are key components in staying current and responsive to the ever-evolving tech landscape. By committing to ongoing learning, educators can ensure their use of technology remains intentional, impactful, and aligned with their professional growth.
References
Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415. https://doi.org/10.1073/pnas.1319030111
Mohammed, S., & Kinyo, L. (2020). CONSTRUCTIVIST THEORY AS A FOUNDATION FOR THE UTILIZATION OF DIGITAL TECHNOLOGY IN THE LIFELONG LEARNING PROCESS. Turkish Online Journal of Distance Education, 90–109. https://doi.org/10.17718/tojde.803364
Spielman, R. M., Dumper, K., Jenkins, W., Lacombe, A., Lovett, M., & Perlmutter, M. (2014, December 8). 8.1 How Memory Functions—Psychology | OpenStax. OpenStax. https://openstax.org/books/psychology-2e/pages/8-1-how-memory-functions
West, R. E. et al. (2018). Foundations of Learning and Instructional Design Technology. https://edtechbooks.s3.us-west-2.amazonaws.com/pdfs/3/_3.pdf