This presentation proposes a pedagogical model that uses data to empower students by offering alternative pathways to project-based curriculum.
Ferrence Marton and Roger Saljo suggested in a seminal 1976 paper, "On Qualitative Differences in Learning" that learners embrace either "surface" or "deep" learning approaches depending on the topic or student interest level. Today, many learning analytics intervention strategies treat learners as homogenous agents who are "at risk" if they don't log in to the LMS by a certain date. Messages to these students are often canned and achieve mixed results. Inspired by the research of Vincent Tinto, it is proposed that "positive kudos," and encouragement about the process and effort put into learning may be more successful in terms of promoting high order and "deep" learning outcomes. Furthermore, a student dashboard of metrics and algorithms that transparently presents and explains the data and rationale behind intervention strategies places the student learner on equal footing to take control of his or her own learning goals and how to improve performance metrics. This is akin to a fitness app or exercise program, focusing on ongoing learning habits and learning processes, rather than just summative or behavioral metrics.
Another way that student data can be used to promote deep learning is to identify student interests or engagement in particular topics. Suppose, for example, that a student performs very well in a science course, but has virtually no engagements with the content or course site for an Art class in which she is also enrolled. An intervention in this case might be to propose that the student work on an alternative project that is more aligned with her interest to fulfill the art requirement (e.g., mapping constellations, or technical illustration). The language used when proposing this project work would be positive, reinforcing the student's natural interests and talents, not punitive or threatening. Providing pathways to project work would likely improve student persistence and retention in various delivery formats. It might also help an institution amass a collection of projects that are aligned to learning outcomes in various forms of transdisciplinarity. And, having access to one's own data, students may self-select into project-based alternatives rather than traditional for-credit courses.
There is rich potential to consider the workflows of using data to provide "off-ramps" to alternative project-based curricula. Participants in this session will be invited to collaboratively brainstorm:
ï What data points might be most germane in this model?
ï What kind of institutional governance should exist to create and review projects-based alternatives to credits in traditional disciplines?
ï What are effective assessment strategies for these authentic project-based activities?
ï Where are examples of these types of initiatives occurring already?