Bridging the fields of cognitive science and educational technology to provide a better understanding of effective educational approaches

With the continuous evolution of the technology that dictates much of our modern society, the field of education has undergone critical transformation throughout the last several decades. Technology proponents argue that technology is the key step to enhancing learning outcomes, but the effectiveness of modern educational technologies must undergo a critical analysis from the lens of a cognitive science perspective. This paper aims to review the literature in cognitive science regarding how educational technology supports learning cognitive processes and analyze how internal mental models influence perception and learning. Specifically, the paper aims to bridge the fields of cognitive science and educational technology to provide a better understanding of effective educational approaches to promoting key learning outcomes for diverse learners. Additionally, this paper will consider the application of this knowledge to educational technology design.

Learning is highly dependent on the prior experiences of the learners and internal processes that support the synthesis of new knowledge. This essay will focus on the following:

Understanding the role of internal mental models in learning is essential for designing effective educational technologies that can support diverse learners.
Identify pain points within the process of knowledge transfer between experts and amateurs, followed by a consideration of the factors involved in creating an effective learning environment such as problem-based learning
Highlight the need for educational technologies that align with cognitive processes.

By designing educational technologies with a cognitive science lens, educators can promote more effective and efficient learning outcomes.

Mental Models and their Role in Facilitating Learning Processes

To facilitate effective learning processes, it is incredibly important to understand the cognitive processes behind the acquisition of new knowledge. Mental models are cognitive structures that enable individuals to represent and understand the surrounding world, providing an internal representation of the surrounding and supporting one’s understanding of how things work within a specific domain⁵. These frameworks form the the foundations for one’s perception of the world, undergoing iterative development over time through a process of connecting experiences and knowledge with their existing mental models to improve their understanding of the world around them. These fundamental models play a crucial role in helping learners organize and integrate new information back into their existing knowledge structures⁶. This is most evident through childhood development as children constantly override and improve on their internal mental models. As a result, these mental models can reflect interactions in the real world. For example, a child who has developed a mental model for gravity would be able to predict that a ball thrown into the air will fall back to the ground. The mental model creates an internal construct simulating the cause-and-effect relationships between objects that are necessary for such predictions. Mental models can help learners to identify potential solutions and to evaluate the effectiveness of different approaches. The process of generating new hypotheses and testing them through new learning experiences captures the iterative nature of mental models⁶.

As shown, to facilitate learning processes, it is essential to consider learners’ mental models. Well-developed mental models enable individuals to identify patterns and relationships in new knowledge and incorporate this new understanding into the existing mental models. When considering learning processes, this further emphasizes the need for educational technologies that align with the cognitive processes involved in the construction of mental models⁶. However, capturing and understanding learner mental models remains a challenge, deeming it necessary to understand the fundamental expert/amateur dynamic problem that effective educational technology design seeks to overcome.

The Transfer of Knowledge from Experts to Amateurs

The transfer of knowledge from experts to amateurs is a complex process that traditional teaching methods — such as those relying on lectures and textbooks — struggle to facilitate long-term learning and understanding². One example of this is in the field of medicine where medical students often struggle to apply knowledge drawn from textbooks and lectures to real-life scenarios with patients. This is due to the complexity and problem-solving techniques needed to apply this knowledge to real patients, which traditional learning methods fail to teach. Alternatively, Bransford advocates for a more constructivist “learner-centered” approach which emphasizes the importance of active engagement alongside the integration of new knowledge and prior knowledge to their own understanding¹. This approach requires a design shift in focus from the teacher to the learner, alongside a recognition of the importance of learner motivation — both intrinsic and extrinsic. However, the expert-amateur dynamic presents a significant challenge in the transfer of knowledge. Experts often present and teach knowledge in a “finished form”, while paradoxically, learners must navigate their own process to reach a solution³. This results in a significant disconnect between the expert’s understanding and the learner’s experience, making it incredibly difficult for learners to integrate new knowledge into their existing mental models. To address this, Cohen suggests a more conscious effort by educators to reflect on their own foundational learning experiences. By doing so, they are able to scaffold their instruction to draw similarities to the same pain points experienced by current learners³.

Understanding how experts and amateurs differently handle real-world problems provides helpful insight into the understanding the expert/learner dynamic. Experts can notice key features of a problem space in order to extrapolate an approach or solution, while amateurs struggle to do so and often capture irrelevant insights². This is because experts have developed a rich mental network of knowledge and problem-solving skills through years of experience. One key proposal to address this difference is the integration of perceptual contrasts into educational technologies. This approach places real-world scenarios side-by-side and allows learners to self-identify previously hidden features through verbal and/or perceptual contrasts. This stands in sharp contrast to heavily guided learning where educators advise students exactly what to look for and students consequently fail to reapply this information in new but similar problem spaces. Instead, the use of perceptual contrasts promotes spontaneous application of previously acquired knowledge, whereby learners are able to “notice” the same features after noticing these features through comparison and self-identification². This, in turn, allows learners to build the connections in their internal models to identify these key features and apply them in future scenarios.

While perceptual contrasts are one of many potential applications of cognitive science research, analyzing educational technology frameworks can help in designing comprehensive learning environments that holistically tackle the challenges of the expert/learner dynamic.

Designing Effective Learning Environments

Designing effective learning environments requires an understanding of the entire learning experiences required to facilitate acquisition of new knowledge. As demonstrated by the previous review, the context of knowledge transfer is critical, and flawed approaches to learning can prevent the reapplication of acquired knowledge. Learners may be able to “think about the model”, but not “in terms of the model”². This presents a flaw whereby the context in which learners acquire information is not applicable, and knowledge can only be accessed in restricted contexts. Simply put, they are not able to apply this information in the desired contexts.

One approach to designing effective learning environments that promote the application of acquired knowledge is problem-based learning (PBL). This approach breaks the learning process down into distinct phases, including the identification of facts in the problem scenario, generation of hypothesis, realization of knowledge deficiencies, and application of new knowledge, followed by the abstraction and evaluation of initially defined facts an hypothesis⁴. Hmelo-Silver discusses a study conducted by Schmid et al. (1997) to compare the effectiveness of problem-based learning to traditional lecture-based instruction in the area of medical education⁴. More specifically, the study sought to measure the accuracy and clinical reasoning skills of medical students using the two instructional methods. Both groups took two tests, one asking for a diagnosis based on a case study, and the other asking for a reasoning behind another diagnosis. As a whole, the group who used problem-based learning had higher scores on both tests, suggesting that problem-based learning can be effective for teaching clinical reasoning skills in medical education. More generally, problem-based learners may have better reapplication of acquired knowledge, and has potential to be effective in designing more comprehensive learning environments. This further aligns to the concept of situated cognition, where learning is most effective when it occurs in authentic contexts relatable to learners’ lives and experiences.

Discussion

The intersection of cognitive science and educational technologies is a rapidly growing area which has potential to transform the way we learn and teach the next generation. The goal of this paper is to discuss the cognitive learning processes behind effective educational technology development and how mental models serve a fundamental basis in facilitating this process. The critical problem that educational technology seeks to tackle is overcoming the challenges of transferring knowledge from experts to amateurs. However, educational technologies that align with cognitive processes can directly lead to improved learning outcomes.

The review of literature includes articles grounded in cognitive science and educational technology supplementary papers. New Approaches to Instruction: Because Wisdom Can’t Be Told (Bransford, 1999) and How people learn: Brain, mind, experience, and school (Bransford et al, 2000) were foundational to this analysis, such as the emphasis on the importance of mental models in facilitating learning processes, the expert-amateur dynamic, and the need for educational technologies that align with cognitive processes. However, there are also limitations to the research. For example, the research behind problem-based learning environments may not be as applicable to all fields of learning. It is important to recognize these limitations to ensure that future research addresses these gaps.

Both Teaching and Its Predicaments (Cohen, 2011) and Problem-Based Learning: What and How Do Students Learn? (Hmelo-Silver, 2004) provide a good analysis of the flaws and complexities of teaching without offering as many concrete solutions or strategies for addressing these issues. Specifically, with problem-based learning, both papers fail to recognize the additional load on educators required to develop effective problems — a benefit of traditional teaching methods. As a whole, the application of theoretical research is critical for effectiveness. While there are limitations to the research, future studies can build on this foundation to design more comprehensive learning environments that promote the application of acquired knowledge in authentic contexts.

However, all the papers reviewed allude to the need for individualized education. Educational technologies need to adapt to the individual needs of unique backgrounds of learners in order to be more effective in promoting learning outcomes, compared to traditional textbook and lecture-style teaching. Furthermore, a greater emphasis on applied learning and problem-based learning can promote greater retrieval of acquired knowledge and overcome the pitfalls of developing inert knowledge.

There is a lot of potential in evaluating virtual environments and the design of effective educational technologies. An emergence of educational solutions emerged during the COVID-19 pandemic — both good and bad solutions — as well as new technologies in the virtual space. Further research into these areas could help to identify best practices and approaches to more meaningful education. Overall, by designing educational technologies that align with cognitive processes and promote effective learning outcomes through a deep understanding of the internal mental models involved in the learning process, educators can better support diverse learners and their quality of education.

References

Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn: Brain, mind, experience, and school. Washington, DC: National Academy Press.
Bransford, J. D., Franks, J. J., Vye, N. J., & Sherwood, R. D. (1989). New approaches to instruction: Because wisdom can’t be told. In S. Vosniadou & A. Ortony (Eds.), Similarity and analogical reasoning (pp. 470–497). New York: Cambridge University Press.
Cohen, D. K. (2011). Teaching and Its Predicaments. Harvard University Press. http://www.jstor.org/stable/j.ctt2jbtnr
Hmelo-Silver, C.E. (2004) Problem-Based Learning: What and How Do Students Learn? Educational Psychology Review, 16, 235–266.
http://dx.doi.org/10.1023/B:EDPR.0000034022.16470.f3
Markman, A. B. (1999) Knowledge representation. Psychology Press.
Vosniadou, S. (2002). Mental models in conceptual development.