The move toward personalized learning with options for remediation and acceleration requires identification of ‘hooks’ to collect data informing recommended resources and direction for students needing remediation or options for acceleration. The session shares the foundational process and discusses how it can be applied to course development at other institutions.
Audience and Objectives This presentation was developed for instructional designers, content developers, data analysts and faculty working in Higher Education to design and develop learning experiences that collect and use data for analytics, predictive analysis, and machine-directed recommendations for remediation and acceleration.
Through participation in this session, those in attendance will be able to:
- Describe the challenges and the opportunities of collaborative teams focused on the identification of data points in the design of courses and learning experiences
- Relate the experience shared with that of their own institution as it pertains to the move toward adaptive, personalized and machine-directed learning
- Prepare a draft of a similar process for consideration in the design and development activity at their institution
- Justify the investment of time and effort into the changes needed to implement data point identification in the instructional design process
Context The advancing conversations and consideration of adaptive, personalized, predictive, and machine-directed learning makes it essential that we consider how these initiatives impact the instructional design process as well as the skills, knowledge and experience required to enact that revised process. In addition, we need to consider the collaboration that is essential to making the process work.
Recently, Virginia Tech embarked on the investigation of developing data-informed learning experiences into adaptive and personalized courses. Learning Experience Design (LED) a unit of Technology-enhanced Learning and Online Strategies (TLOS) became involved to help shape and move forward the investigation and resulting implementation. LED houses instructional designers, instructional technologist, graphic and web developer, studio and field media production and scanning of 3D models, slides, art, fabric, photos and documents. When VT embarked upon this investigation, graduate students with data analysis skills and familiarity with data-informed programming were added to the team.
While consideration of third-party service and platform providers was initiated, it was determined that the University also had the essential skills and potential to investigate ways to start the process of shifting to a collaborative approach to incorporation of data collection into the design and development process. This session will present an overview of the evolution of that work to prepare for personalized, adaptive and machine directed learning experiences as well as a worksheet developed for use by instructional designers in consultation with faculty developers.
Research and Experience At the start, there was a great deal of confusion over just what University expectations were for courses, faculty and instructional designers. To help ease that confusion, research was conducted on the work being done at other institutions and on resources to be used for advancing the knowledge of the current staff. Key people were asked to join conversations and inform the foundational resources being developed for this endeavor. Various teams were formed to conduct reviews of research, collect resources and share with other teams and larger groups.
Members of the instructional design team looked at the overall course design process and mapped out the project into components, steps and stages in a segmented project plan. Then, they worked with data analysts to consider the plan from the perspective of data-driven programming. The project plan became complex but both relevant and understandable for everyone on the collaborative team.
From there, the role of faculty was considered with and how the subject matter experts and content developers would interact with this plan. Adding faculty to the collaborative team would not be difficult if the description of the plan was clear in terms of the instructional design process and visible in a logical, linear fashion. A template was created in a spreadsheet to show the process and allow for collaborative identification of data points.
After determining a process for identifying data points with recommendations for some common points for most courses, work expanded to consider how the data collected from the points could be used for both learning analytics and predictive analysis. Predictive analysis would allow for recommendations for targeted remediation and options for acceleration. As understanding and excitement grew, the conversation and exploration incorporated resources, elements of past conversations, etc. and knowledge grew.
Engagement Participants will be asked to consider the current state of design and development of data-driven learning experiences at their institutions and share the concerns, challenges, opportunities and potential for identification of commonalities. This will be done in small groups to share with the larger group with the potential for establishing connections for future conversations and collaborations.
Presenters will share and demonstrate the spreadsheet providing visual representation of the process as developed by LED and will be asked to share if and, if so, how the template might benefit the advancement of the design and development of learning experiences incorporating data-informed options for adaptive and personalized learning.