Reflective Memo - Learning, Design and Technology

Neha Kumar Educ 151 03.18.2008 Reflective Memo Coming from a heavily quantitative background, I had long nurtured the narrow purview that qualitative essentially included everything that was not quantitative. My free‐write for qualitative research, at the beginning of class, said, “Qualitative research methods are used where quantitative methods may not apply.” As I reflect upon the ten lectures of the class today, I find myself indoctrinated into a new way of thinking. Qualitative research is no longer just “not quantitative”; it involves a different skill set, a different approach to research, a different mode of critical thinking and, in Merriam’s words – a “search for meaning and understanding” (Merriam, ’02). I consider myself fortunate to be equipped with this unique skill set, and – as I embark upon a Ph.D. in an area that will require significant expertise in qualitative research methods – I am excited to view my research using the lens of Educ 151. One of my primary take‐away ideas from this class is that human behavior is highly nuanced. Starting with the exercise of creating a portrait‐sketch without taking one’s eyes off the subject’s face, all the way to the point of interviewing and transcribing interviews, I was continually in awe of the things I was learning about human behavior. Many of these lessons are succinctly presented by Taylor & Bogdan in their “Introduction to Qualitative Research Methods”, where they comprehensively address the various elements of qualitative research – from entering the field to the consideration of ethics (pp. 45‐86). The art of interviewing also addresses human behavior to a large degree, and Glesne & Peshkin (1992) describe the interview process in detail. In addition to directing my approach to field observations and interviews, these readings were most informative in their descriptions of human relationships and behavioral tendencies. Overall, I found the process of conducting qualitative research deeply fascinating. The idea of hypothesizing and then sifting through large quantities of qualitative data to prove hypotheses is intimidating, to say the least. I am in awe of Ray McDermott’s article titled “Acquisition of a Child by a Learning Disability” (1993) for the light that it sheds on the weakness of the biased human mind. This article further highlights the necessity to be cognizant of one’s biases and lends greater meaning to the Peshkin reading from “The Color of Strangers The Colors of Friends” titled “In Search of Subjectivity” (I have since become more careful to notice the subjectivities in my opinions and beliefs). I was intrigued to discover the deeply scientific nature of qualitative research. This became especially apparent after our data collection phase, when we dealt with analysis, where transcribed data needed to be categorized and coded. I found interesting parallels with the disciplines of data mining and information retrieval in computer science, which manage large chunks of unparsed data and try to assign categories and classifications. I enjoyed our class discussion on visual representation of data, and realized what a difference it made to view data in pictures instead of words. The concentric circles diagram denoting “the ebb and flow of relations in the Dukes’ network” from Taylor & Bogdan (pp. 150, 1998) serves as a good example. The process of coding was equally scientific and even more challenging, for not only did it involve reading all collected data, we needed to understand it as well in order to derive meaning from it. In the remainder of this memo, I would like to discuss the lessons learned from the application of the lessons learned in the classroom and the readings. To start with, we encountered stumbling blocks in our project. For our research project, we had decided to observe a class at the Design School, to understand and assess its teaching methods and class design. We started out in complete confidence that our previous association with the Design School would allow us access to the class but we were mistaken. It took much persistence and several iterations of emails, back and forth, before we were given access. It was an important lesson for us to not take prior acquaintances for granted. The field observations and interviews we conducted also connected lessons learned in class to the real world. Most importantly, the exercises we did in class helped us remember key points. In both these activities, what I grappled with most was the realization that everything happened outside of my control. Not only was I a mere observer/listener, my presence could – in fact – only serve to dilute the validity of the data I collected. This was a humbling experience of a unique kind. We had thought that interviewing an acquaintance would make our task simpler in some way. In retrospect, perhaps not, because that made our conversation flow along more informally, making it harder to follow a previously determined protocol. Had we not known our interview subject personally, we may have found it easier to keep our interview focused. That – in turn – may have had its own cons though. The fact remains that there is no foolproof interview method that can make every interview a successful one, but the guidelines outlined by Glesne & Peshkin, and in class, definitely improve the chances. As for the coding process, it forced us to think deeper about the data we had collected and transcribed from our interviews. We found that not one or two, but multiple passes were necessary to be convinced that we had subjected the data to adequate scrutiny. In the first pass of coding, we had more general codes such as “positive feedback” or “negative feedback” about the class we were studying. We realized as we coded, however, that these categories were too general and we needed to make them more specific to make greater sense of the data. Hence, we decided to explore different types of negative feedback from the users, since there was more negative feedback than positive. Then, we came up with codes such as “class design” and “teaching expertise” which were most commonly brought up in the interviews. Breaking down the qualitative research process into the above‐mentioned steps helped us address our research question in a satisfactory manner. Would this always be the case though? I look forward to applying this process to other real‐
world problems that will surface in my research. The focus of my research is on the deployment of technologies for developing regions and how they change (or do not change) the lives of the socially and economically impoverished. Quantitative data, in this area, can barely address most questions that arise, as in any human‐centered research area. To truly address the ways in which technologies can change the lives of the poor, one would need a qualitative assessment of their well‐being, of technology inclusion, of their familiarity with existing and new technologies, etc. I am confident that the skills I have acquired in this class will help me greatly in the pursuit of my doctoral research and equip me better to answer some of these questions above.