Blended Phenomenography: An Alternative to Investigate Learning

Phenomenography is a research approach that has been widely used in Engineering Education to study student’s experiences with various phenomena such as Human-Centered Design, interdisciplinary learning, multiple solutions, conditional and repetition structures, among others. Still, researchers face different issues when working with phenomenography. One of the key ones is the unawareness of the existence of two or more different approaches. The Swedish educational researcher Ference Marton has developed two approaches (first and second phenomenography). John Bowden, an Australian researcher, developed another that is called Developmental phenomenography, and other Nordic researchers have developed other variations. In this paper, we want to contribute to the differentiation of two relevant approaches: Marton’s second and Bowden’s developmental. Each of these phenomenographic approaches has different implications that researchers should follow for valid data collection, data analysis, and outcome spaces. One of these differences can be seen in data collection. Using Marton’s second phenomenography approach implies the collection of data with participants addressing the same task. This is because, according to Marton, that makes visible what the participant knows about an object of learning. On the contrary, a researcher using the developmental phenomenography approach will collect data on interviews with participant’s describing their experiences with an object of learning. Similarly, in data analysis, there are key differences. For example, in the developmental approach, the unit of analysis is the whole transcript, while in the Marton’s first and second, the unit of analysis are quotes from the interviews. Similarly, the outcome spaces are different. Beyond such differentiation, in this paper, we will share our experience on blending these two approaches for studying Engineering Problem-Solving. We used Marton’s second Phenomenography for data collection, and a first data analysis phase to find, according to Variation Theory, the critical aspects and critical features of the object of learning. Accordingly, we will share our process of finding the right tasks, the interview process, and our data analysis process. In addition, we will describe how we used Bowden’s developmental approach for the second phase of our data analysis to discover a developmental path for our object of learning. We believe that blending phenomenography has advantages of the two phenomenographic methods, and it can be a valuable approach for researchers and practitioners when trying to identify and understand the learning trajectory of an engineering object of learning.