While centrally maintained learning environments, such as Open Learning Initiative (OLI) and Lumen Learning courseware, have been successful, they represent, by definition, a generalized solution that is not always easy to adapt to specific local contexts or needs. Though the materials are designed and improved by teams of experts, even a large and diverse team cannot effectively represent all perspectives, and often those missing perspectives are ones that would be most useful in identifying and addressing challenges facing specific learner populations. The challenges of a limited authoring team can be especially apparent during data-driven, iterative improvement, when addressing identified course deficiencies can require new insights and creativity. As one OU author noted, “The data is showing me that there’s a problem here, but I’m not sure how to address it. .. I’ve already used my best stuff [in the course].” STEM learners benefit when course materials are adapted to local contexts and needs, but too often such local adaptations lack a strong evidence base and are driven purely by faculty intuition. Moreover, extensive evidence has demonstrated the importance of recognizing and connecting to the novice perspective, particularly for early STEM learners (Kelley & Knowles, 2016). But by definition, experts are removed from this novice perspective; this “expert blind spot” has proven to be a major impediment in developing learning activities that that can engage with learners’ perspectives and that identify and address learner misconceptions [Koedinger et al., 2001].
“Community Sourced, Data-Driven Improvements to Open, Adaptive Courseware” will improve outcomes for STEM learners in targeted courses by deploying and improving open, adaptive courseware. This project builds on Open Learning Initiative (OLI) and Lumen Learning courseware that has been demonstrably effective in closing gaps and improving performance for underrepresented learners in STEM.
The project has two main thrusts: effectiveness and barriers. Effectiveness research will investigate the impact of multi-sourced data driven improvement on outcomes for targeted STEM learners, and barriers research will investigate the impact of this approach on faculty attitudes and culture. Improvements will be guided by analytic tools developed for this project that provide faculty, student, and crowdsourced feedback and participation. This approach ensures that student voices will play a central role in identifying areas of difficulty, evaluating materials and improvements, and recognizing student experience. Barriers research expands upon established protocols from Carnegie Mellon University, including embedding a cultural anthropologist who will use a mixed-methods approach to better understand barriers and facilitators for effective adoption of technology enhanced learning (TEL) innovations. This research complements and informs effectiveness research, employing a research-based approach to integrate these new tools into existing educational contexts.
The project will produce:
- Open, adaptive STEM courseware that has been improved using data to target underrepresented learners.
- Open tools to support the iterative, data-driven improvement of open courseware, via contributions from students, instructors, and broader crowdsourced mechanisms.
- A clearer understanding of the ways that these data-driven improvement approaches can support or hinder learning, particularly for vulnerable learners.
- Insights into the barriers and facilitators for sustained adoption and effective use of these TEL innovations