When we analyze learning and measure learning outcomes, we usually want to determine its effectiveness. However, learning relies on more than the content provided. In the brain, other factors influence learning. Mindset, instructor trust, context, value and design also impact learning, and these factors may operate entirely outside of awareness. Learning analytics must take these dynamic and abstract factors into account.
In this brief reflection, you will gain insights on the nature of these factors, as well as how to analyze and evaluate them in learning environments.
A growth mindset reflects the belief that one can successfully develop their own intelligence. When people hold this belief, it increases their intrinsic motivation to learn. Motivation is also increased when people internalize external values. As a result, people with growth mindsets are more likely to respond promptly to errors. In order to foster growth mindsets in students, trust must be established between the learner, the instructor and the learning environment.
Students’ trust in their instructor helps facilitate learning. Both growth mindset and instructor trust can increase the student’s commitment to learning. However, trust must be established early, as opinions developed within six seconds may influence student evaluation of the instructor.
It is important to note that the instructor’s mindset matters as well. Teachers who have a growth mindset appreciate successive improvements more than those who do not. Consequently, students are more motivated by teachers with growth mindsets. Additionally, teacher feedback is least effective when focus is placed on final results rather than the learning process.
When learning is delivered, the importance of context matters but may vary depending on the student . Some studies show that learners’ application of training depends on their prior level of knowledge and their ability to transfer knowledge across domains. In the brain, context matters and can either improve or worsen learning. A brain circuit, involving short-term memory, long-term memory, self and emotional processing, participates in establishing learning context. Therefore, all content must adequately address the material, contain vehicles to enhance long-term memory (e.g. diagrams or mnemonics), and remain relevant and inviting.
Value also guides behavior in the brain. Students are more likely to learn when they perceive the learning as valuable, as this is rewarding to the brain. Reward-based learning enhances students’ ability to attend to the information at hand. Additionally, learning feels more relevant when students are actively involved in the learning process. Learning is enhanced by content’s relevancy, because the brain feels rewarded
Finally, design is critical to learning. When learning is sublime rather than simply visually pleasing, it isarousing to the brain In effect, students are most engaged when they are swept off their feet by content design. Instructors should evaluate their content based on this.
Implementing Brain-based Evaluation Practices
Analyzing and evaluating learning processes and design goes far beyond the content and level of application. Other factors determine brain-based learning success and should be included in the evaluation process. The following screening questions may form the basis for additional learning analytics and evaluation practices:
• Does the learner have a growth mindset?
• Does the instructor have a growth mindset?
• Does the student trust the teacher?
• Was trust established early on? How?
• Is the learning motivating?
• Has the context been optimized? (vehicles to enhance long-term memory, relatability, emotional relevance, etc.)
• Does the student value the learning?
• Has the student been included in the learning?
• Does the student find the learning relevant?
• Does the learning have sublime elements?
When these factors are addressed, brain-based learning can be optimized.