RESEARCH PORTFOLIO
Research Agenda
Introduction
I plan to research, broadly speaking, the role of learner motivation and engagement in instructional design. As this term takes on many different meanings in various research communities (e.g., Maslow, 1943; Weiner, 1974; Dweck, 1986; Bandura, 1997; Keller, 1983, 2009; Ryan & Deci, 2000), more specifically, I will explore more narrative-based constructs of motivation such as that of intentionality (see Gantt & Williams, 2014; Martin & Sugarman, 1999) or mattering/concernful involvement (Yanchar, 2011). I argue that learning requires more than mere sustained attention, but a reason to care – the content and learning activities need to matter to the learners. Therefore, I will explore and articulate the role that such mattering plays in the learning process, and formalize how this might influence instructional design and design theory.
Borrowing insights from situated learning (e.g., Brown, Collins, Duguid, 1989; Lave & Wenger, 1991), embodied cognition (e.g., Lakoff & Johnson, 1980), and participational agency and learning as embodied familiarization – an approach that borrows insights from Heideggerian thought and uses disclosure as the test case for learning (Yanchar, Spackman, & Faulconer, 2013) – I will research ways to design in-person and online learning experiences that are grounded in the assumption that what matters to learners as they encounter and engage with learning activities plays a consequential role in how and what they learn.[1] I believe that instructors and instructional designers can leverage this connection in useful ways when designing and implementing instruction. [1] An example borrowed from Nemirovsky, et al. (2011), might be that of learning to wield an axe; the axe (and the process of learning to use it) might disclose itself entirely differently to someone who is preparing to defend his family from attack than it would to someone who is preparing for the oncoming winter. In an academic context, a correlation coefficient might disclose itself entirely differently to a student trying to complete a dissertation study (and hoping for a significant results to report) than a student who simply needs an A on a test.
Below are a sampling of the kinds of questions that I find engaging:
Theoretical Research
What is the role of psychological egoism in motivation theory? I suspect that many (if not most) motivation theories implicitly assume psychological egoism – an assumption that human action is motivated by some form of self-interest (conscious or otherwise), most explicitly illustrated in needs-based motivation theories. Are there accounts of learner motivation that do not assume psychological egoism? If not, what might such accounts look like, and how would they differ from existing theories? How might such theories influence instructional design and research? These are vital theoretical questions, because there are strong philosophical (and religious) reasons to question the assumptions of psychological egoism.
What insights might embodied cognition offer in how we understand learner motivation? How might motivation theory take human embodiment seriously when conceptualizing learner motivation and engagement in statistics learning, and what would that look like on a theoretical level? How might the work of embodied cognition theorists be brought to bear on the subject of learner motivation in statistics?
What insights might situated learning have about learner motivation? For example, if learning is conceptualized as an inherently social phenomenon, how does this impact how we think of motivation? How might learners’ participation in various communities of practice influence what matters to them in a learning context?
Is there a theoretically justifiable difference between motivational incentives and mattering? Schools have developed a fairly robust system of norms and incentives that motivate learners — despite the learning activities being seen by learners as inconsequential (evidenced by the age-old question, “How are we going to use this?”). How can we theoretically differentiate mattering, relevance, and motivation, and should we?
Empirical Research
How do we detect and document mattering in a research context? Many approaches to studying learner motivation depend on operationalizing learner motivation as a series of endogenous and exogenous variables, measured by survey-based instruments. If, however,
we conceptualize statistics learning as a form of concernful engagement (Yanchar, Spackman, & Faulconer, 2013), or if we think of human action as holistic, immediate, and situated – “in-the-world” (a Heideggerian term) rather than merely in relation with it – we may need to rethink how we get at and investigate learner motivation. What forms of research data and analysis best allow us to “get at” mattering in a practical research context? How could such metrics and measures be validated?
How is mattering (qualitatively) experienced by learners, in contrast with mere interest or motivation? What does mattering really look like in the activities and experiences of learners in a math or science classroom, for example? How do the life-narratives of individual statistics learners influence the way(s) in which instruction and learning activities are disclosed to them as mattering (as more than activities required to pass a class or get a degree)? For example, what life-narratives, career aspirations, life experiences, extracurricular activities, etc., help statistics learners to view learning activities as mattering (for more reasons than pleasing instructors and getting a good grade)? How can statistics instruction be made consequential to learners?
What role does mattering play in the design of instruction? When designers and instructors design and implement statistics instruction, at what stage(s) in the process do they take mattering and learner motivation into consideration? What assumptions do various instructional design models and theories make about learner motivation? When elementary, high school, or university instructors design and plan statistics instruction, for example, at what point do they consider how to frame the learning activities in ways that are more immediately consequential to learners?
How do institutional, structural, and social factors inform what matters to individual learners? How do the norms and expectations of the learning community influence the motivations of individual learners, and what matters to them? Are there detectable differences in what matters to learners in online and in-person contexts in statistics instruction? What social and institutional factors influence the motivation and engagement of statistics learners over time?
How can instruction be designed that highlights our responsibility for our fellow man? How can instructors better cultivate learning communities where learners feel called upon to act in ways that advance not just their own learning, but the learning of those around them?
Dissertation Study
Summary
For my dissertation study, for example, I am exploring how instructors can use self-data in statistics instruction in order to connect to existing concerns and interests of learners. A recent movement known as Quantified Self has lead many consumers to look for ways to quantify their daily activities and habits, and in the process engage in some practices of statistical inquiry (Lee, 2014) — and it may be possible to leverage those practices in statistics instruction. The purpose of this study is to explore whether the use of self-data collected by learners in an undergraduate introductory statistics course offers the learners opportunities for engagement that connect with their concerns and is therefore more meaningful to them. It is hoped that the use of self-data will help ground statistical concepts by providing learners with an opportunity to practice them in the context of an inquiry that is personally meaningful and relevant to them.
The genesis of this project was a (successful) attempt to connect my particular research interests with an ongoing grant-funded project of my advisor, Dr. Victor Lee (see, e.g., Lee & Drake, 2013; Lee, et al., 2015). I was interested in exploring the phenomenon of mattering in a learning context, and he was studying the way self-data might provide points of contact between conceptual ideas in statistics and lived, embodied experience. The resulting project thus explores a new but relevant aspect of the instructional intervention he has been exploring using other conceptual frameworks. This particular study focuses less on how the intervention helps learners to understand the statistical concepts, and more on how the intervention increases opportunities for learners to care about what they are learning (although I take as a founding assumption of my theoretical orientation that the two are deeply connected; see, e.g., Yanchar, Spackman, & Faulconer, 2013).
We think there are ways in which using self-data when teaching statistics might influence the way statistical concepts and data collection matter to undergraduate statistics learners – for example, when learners have little or no concrete expectations for how they would use statistical inquiry in their future professional endeavors, it may be fruitful to demonstrate how statistical inquiry can reveal something useful about their lives in the present. More specifically, the question I am asking is, “What forms of self-data matter most to learners, and under what conditions does this influence the way learning about statistical inquiry matters to the learners?” I have adopted participational agency and learning as embodied familiarization (Yanchar, Spackman, and Faulconer, 2013) as the primary theoretical orientation for this study.
Theoretical Orientation
The primary focus of this study is to investigate the ways in which the use of self-data in statistics learning can support personal, concernful engagement with topics in statistics — that is, whether and how collecting and exploring self-data matters to learners, and how this influences the way statistics learning matters to learners. This means that the particular interests of learners on an individual level — and more particularly, the way in which statistical practices are (or are not) disclosed to individual learners’ as personally relevant — are vital phenomena of interest. Further, the use of self-data is an intrinsically embodied experience, as learners will be tracking different elements of their physical activities, daily habits, and physical health.
In these ways, the primary phenomenon of interest in this study can be thought of as concernful engagement or mattering, and the language and constructs of participational agency and learning as embodied familiarization (PAEF), as described by Yanchar, Spackman, and Faulconer (2013), provide useful tools of analysis for getting at this phenomenon. Learning as embodied familiarization extends the philosophical and theoretical projects of situated learning and embodied cognition by emphasizing the experiential (and social) nature of human learning, and draws on hermeneutic and phenomenological traditions to explicate such experiences. In addition to the themes of situated learning, PAEF introduces a narrative approach that also emphasizes disclosure as a test case for learning (Yanchar, 2014), and how learning interfaces with the concerns of the learner (more on that shortly).
Research Questions
What matters to the learners — that is, the form of their concernful involvement — may in fact be crucial to how the subjects they are learning disclose themselves to the learners; in other words, how learners come to understand statistical concepts depends greatly on the way those concepts matter (or do not matter) to the them. I hope to explore whether and how the use of self-data connects the learning experiences to the concerns to the participants. The precise nature of this mattering (as related to the use of self-data) — what it looks like in context, how it is manifest in activity, etc. — is not precisely clear, which is why I have chosen to take a more qualitative, exploratory approach in this study. The specific research questions of this study are:
- What new possibilities for concernful involvement are disclosed to learners who collect and explore self-data when learning statistics? In what way do learners step into those possibilities and adopt those concerns in their learning activities? (In other words, how does collecting and exploring self-data matter to the learners, or does it?)
- How are the backgrounds, personal interests, career interests, and other elements of the learners’ antecedent familiarity brought to bear on their exploration of self-data, and their concernful involvement in statistical practices? (That is, how do their past experiences and anticipated futures influence how and in what way the collection and exploration of self-data matters to them?)
Methods
Because “mattering” is inherently individual (and in some ways deeply subjective), to answer this question, I am using qualitative, case study methods. In the study, I explore the narrative experiences of 6-8 undergraduate statistics learners who have collected data about their physical and daily activities (using devices such as Fitbit activity trackers, smart scales, and smart phones), and have then explored their data using statistical concepts they learn in their undergraduate statistics course. Prior to the study, participants were given an opportunity to choose 2-3 aspects of their life and activities to track (from an initial list of 10), and provided the tools they needed to do so.
I met with each participant on 5 separate occasions — an initial interview, a final interview, with three "data exploration" meetings in between (for a total of 5 meetings with each participant). During each data exploration meetings, I met with the participant and together we asked questions about the data he or she had collected about themselves. The questions we asked were answerable using statistical measures and constructs they had learned the previous two weeks in their statistics course. Depending on the tool favored by their instructor, we used R or Excel to assist in the various analyses.
The study, as designed, will be best conceptualized as a multiple-case study that focuses on the experiences of participants whose stories seem particularly revealing in terms of the research questions: perhaps they are outliers (e.g., the use of self-data did not matter at all, or became a passion of theirs), or particularly representative or illustrative of the other participants (e.g., their experiences seem similar to the experiences of others, but they were more articulate about their experiences, and thus provide more interesting data). The study will also draw insights and tools from narrative inquiry and other qualitative approaches.
about Jeffrey
Jeffrey is a passionate about finding new ways to teach the big ideas that inform our worldviews and life activities. He lives in Cache Valley, Utah, and currently is a doctoral candidate at Utah State University.
