Another learning analytics academic organization
In the last few weeks I have shared several academic organizations that sponsor conferences, journals and other forums for discussion about learning analytics. This acadenic organization also offers some excellent resources for scholars in the area of learning analytics. I hope that it is helpful for you and your research.
Comments
You have presented a question that has no easy answer: "How can I help this person shed biases?"
Learning to write without biases for students and professors begins with self-awareness. As we read and interpret data, we bring our experiences with us into the learning process. What is important is to understand that as we read and conduct research, we must have an awareness of our biases so that we may read and interpret despite personal biases and experiences. Self-awareness and exploration of beliefs would be a solid place to begin as students read and begin interpretation of research. I believe professors can assist students in examining beliefs through a series of questions regarding upbringing, family values, and other cultural beliefs that may play a role in research. Sometimes students are busy looking forward. Looking inward may assist students in understanding biases and the role biases play in research and data interpretation. What do you think about self-awareness?
Margaret, you are making a great point about how often students forge ahead in an anxious attempt to begin writing their dissertation proposals. When they do not begin with a slate of self-awareness of who they are, they simply waste a lot of time treading water. Thank you for your insightful response. Liz
Liz, thank you for your response. What are your thoughts on how to assist students to interpret their study data without biases? From writing the research instrument to interview techniques, students must be cautious while they interpret data. Member checking during interpretation of participants' responses may assist student researchers in reducing biases. What do you think about CQR (Consensual Qualitative Research) methodology, wherein multiple researchers interpret the data and discuss the emerging themes to come to a consensus? Do you believe this would help researchers increase their self-awareness and address biases during data interpretation in qualitative research? What are your thoughts on coming to a group consensus on emerging themes?