Subjectivity Statement

The purpose of this page to to consider how my background and experiences may shape (or bias) my research. This page will be updated as I continue to reflect on who I am as a researcher and as I reflect on what interactions I have had and how these may challenge my research assumptions and frameworks.

What is learning and teaching?

I am a proponent of social constructivism, which proposes that successful learning and teaching is dependent on social, cultural, and interpersonal interactions and discussion (Prawat, 1992). Individuals are active participants in the creation of their own knowledge by means of these interactions. In particular, interactions that include experience, supported by ideas, create knowledge through discovery and exploration of problems. In the constructivist learning process, human beings do not find or discover knowledge so much as construct or make it. We invent concepts, models and schemes to make sense of experience and further, we continually test and modify these constructions in the light of new experience. (Schwandt, 1994, pp. 125–6).

What does this mean for my reflection activities and design?

Fundamentally, this means that I need to have experiences that challenge my existing theoretical perspective in order to learn for myself. I view myself as a lifelong learner and I believe I am motivated to discover new knowledge. However, I sometimes feel overwhelmed with the process of reflection, which is critical to understanding experiences and creating new knowledge, is difficult. Being overwhelmed, I frequently find myself becoming distracted and I miss out on opportunities to learn.

It also means that as an instructional designer, I need to think more about what a learner needs to build new knowledge rather than simply presenting new ideas and expecting them to soak them up like a sponge. That is to say, my design work requires more reflection (as an instructional designer) about the experiences learners bring with them as well as those experiences I create for the learning event itself. My designs need to support the learners’ processes of comparing past experiences with new experiences and discovering the differences. Then they can make connections and construct new knowledge. This may or may not happen because a lot of the conditions for learning depend on the motivation of the learners. That is something I can’t control, and I think I tend to assume that all learners are like me, always looking to learn something new; motivated to understand new things.

As a researcher, I think my theoretical position on teaching and learning means that I tend to focus on research events and believe the findings that I see are self-evident. I think this indicates that I have a bias towards my own world-view. While I have had a lot of experience in my lifetime, I cannot say that I’m particularly diverse in my viewpoints. However, I am always open to consider new viewpoints. My study topic is entrenched in information science, and my purpose is to expose those IS methodologies to learning theory and application. Having a social constructivist world-view will likely motivate me to question how technology can be used to facilitate interactions that drive new experiences, and I may, perhaps, miss alternative applications of technology in learning.

What else do I expect to see?

In regards to my topic for dissertation research, the use of AI for automated assessment in simulation-based learning, I expect to see variations on the use of machine learning to assess learner performance. I expect to see, primarily, examples of studies that use AI for the purpose of classification of performance results, especially classifying performance as either expert or novice. Because of the cost of implementing AI assessment at scale, I expect to see studies of prototypes and small sample groups.

Can AI be used to effectively provide formative as well as summative assessment results? I believe that most studies will find that it can. I expect that broad studies of this question are not yet available and may not be conducted until researchers understand more about how to make machine learning results explainable.

I expect that most of the studies I find will be related to medical training. I also expect that most of the research will focus on technical methodologies that include descriptions of the algorithms used and the models used to implement them. In other words, I believe I’ll find that most studies will focus on the technical nature of the AI under study and will not address the potential for human learning as much.

What do I already know about this topic?

I already know a fair amount about this topic as I conducted a literature review last semester on the topic of using AI for real-time performance assessment in simulation-based training. Having said that, I believe I may have cast my net too wide in my search for sources. I do know that the medical industry is leading the way in the application of AI for training purposes. I also know that artificial neural networks are more common in the literature since approximately 2017. I do not know what it is about 2017… I know that the field of research in the use of AI for performance assessment in simulation-based training is wide open. It is practically unstudied outside of the medical field. I know that the studies that have been conducted have resulted in positive reviews of the technology as it allows for more flexible scheduling of instructors and training resources and it provides more consistent (i.e. objective) performance assessment results in the fields where AI is being used to assess learner performance.

Challenge of what I think I already know

Because I find that the medical field is dominant in this domain of research, I may be moved to try and find examples of the use of AI in learner performance assessment in other fields where studies may not be available in the usual databases of academic journals. Because I know that most studies since 2017 have focused on the use of ANNs, I may find that different searches will result in the expanded use of other AI technologies, such as intelligent tutoring systems. Perhaps my literature review has biased me toward ANNs for the purpose of real-time performance assessment, and that may limit what I could accomplish for my own dissertation research.