Moving Forward in Empathic Learner-Led Design through Statistics

Introduction:

Xing and Manning (2005) describe air traffic control as a “dynamic environment where controllers constantly receive a large volume of information from multiple sources to monitor changes in the environment, make decisions, and perform effective actions in a timely manner” (p. 1). Due to COVID-19, pre-course material of general aviation knowledge within this complex environment now must be taught solely online. Prior, training was done via classroom instruction with subject matter experts. Currently, Chain of Command (CoC) has dictated that students will self-study via online notes and textbook styled resources. This is no different than simply giving them a textbook and telling them to go teach themselves. Although the material is basics of aviation, complex topics still exist and confirmation of understanding is important to foster confidence in the material prior to the more stressful training environments students will see via simulation. My initial idea was to implement learning videos, that could be made by the subject matter experts to aid in student learning and engagement of the pre-course material. However, in order to know if this was a good direction to go in, I completed a short independent samples t-test to determine if the videos would be relevant and helpful to students. This leant well to my empathic design implementation using a student led process as encouraged by Worsham and Roux (2019). I haven’t tried empathic design principles and methods with an emphasis of student-led designers at the forefront, but the idea intrigued me to give it a try.

Method:

Two independent groups of 30 students each were selected to complete a mini-lecture on altimeters. For those who do not know: An altimeter is “an aneroid barometer designed to register changes in atmospheric pressure accompanying changes in altitude” (“Altimeter”, n.d., para. 1). One group was to complete the lecture via lecture notes only and then asked to rate the learning tool as helpful or not: a 1 representing unhelpful and a 5 representing very helpful. The other group was given both the lecture notes and a learning video on altimeters and asked the same question and given the same rating scale.

Results

Independent Samples T-Test

t = (M1 – M2)/√(s2M1 + s2M2) = -1.12/√0.04 = -5.92

The t-value is -5.92122. The p-value is < .00001. The result is significant at p < .05.

Conclusion:

The test indicated the rejection of the null hypothesis, thus concluding that the results were statistically significant. This now gives me more substantial evidence to pursue a learning video as a course of action as a prototype for my learning technology to develop. It also shows the inclusion of the learner to directly have say in the developing of a potential learning technology and resources to be used – lending to learner-led design. It is important to note that the t-test and p-value calculation do not reveal that learning videos are the best choice of learning technologies. The tests simply show that lecture notes combined with a learning video were statistically significant to determine they were possibly more effective than only having lecture notes. More to follow in my design note and video presentation into the nuances of exactly how I will design the prototype. For now, the t-test puts me on a good footing to spring into my empathic design process and justification for my prototype.

Reference:

Altimeter. (n.d.). In Merriam-Websters Dictionary. Retrieved from https://www.merriam-webster.com/dictionary/altimeter

Worsham, D., & Roux, S. (2019). Foundations in Learner-Centered Design. Retrieved from https://uclalibrary.github.io/foundations

Xing, J., & Manning, C.A. (2005). Complexity and automation displays of air traffic control: Literature review and analysis (Report No. DOT/FAA/AM-05/4). Washington, DC: US Department of Transportation Federal Aviation Administration.