Adaptive Learning for Faculty Training: Considerations of Technologies
One challenge of faculty training for online teaching is to satisfy instructors with different levels of knowledge and skills. Adaptive learning can be a solution. Three adaptive learning tools are considered, compared, and discussed based on a set of criteria that will be shared in this session.
Preparing instructors to design, develop, and teach online courses is not only necessary, but also impactful to the university’s missions. The experience of more than 10 years offering faculty training revealed that one of the challenges is to fulfill the needs of instructors with varied levels of knowledge and skills. For example, some instructors have taught online and therefore, are more familiar with learning management systems; some instructors have never used online learning technologies but are knowledgeable about learning theories. The faculty are busy individuals and when encountering ineffective training often feel their time is not well-spent or the training is not sufficient to help them develop their online courses.
This learning challenge can be overcome by employing an adaptive learning strategy. Adaptive learning places individual learners in the levels best suit to them. The learners are assessed at the outset and can skip content and topics that they have already mastered. Studies have shown that adaptive learning increases learners’ satisfaction and knowledge retention.
At the University of Central Florida’s Center for Distributed Learning, a team of instructional designers is working to design an adaptive learning faculty training program that is expected to serve more than 40 instructors per semester. One of the important first steps is to select the most appropriate adaptive learning technology. Three technologies are under consideration: RealizeIt, Canvas’s MasteryPaths, and ObojoboNext, an in-house application. These technologies are assessed based on a set of criteria listed below:
- Adaptive capabilities
- Content authoring/editing
- Learner usability
- Integration and maintenance
- Data analytics
The assessment and comparison of these technologies is conducted based on available information of the technologies and on the experience of the instructional designers who have used these technologies. A rating scale has also been used to evaluate each criterion objectively and systematically.
In this session, we hope to share the process of selecting adaptive learning technology. We will discuss the criteria and why they are significant. Participants can add to the discussion by sharing adaptive learning platforms that they use at their institution, evaluation strategies, relevant implementation methods, and additional considerations.