Adaptively-branching video tutorials: examples from the geosciences
Concurrent Session 6
Adaptively-branching video tutorials (ABVTs) branch in response to how students answer questions, allowing for a fine-grained view of how students think and for instructors to capture data on learning progressions. We will demonstrate examples from the geosciences, and host a discussion on how these could be developed for other disciplines.
Students struggle to master content for different reasons, many of which an instructor can determine during a one-on-one tutorial. The instructor asks probing questions to identify misconceptions, then provides appropriate instruction. However, tutorials are time intensive, and instructors cannot reach all students who would benefit. Adaptively-branching video tutorials (ABVTs) are an innovative alternative to deliver targeted instruction in response to misconceptions. In an ABVT, a student starts with a question. If the student already has sufficient knowledge to answer that initial question, they move to subsequently more challenging material. If they answer incorrectly, students can be re-directed to instructional material specific to their answer choice.
Because ABVTs branch in response to student input, they provide an opportunity for large-scale, individualized instruction. They also provide a uniquely fine-grained view of how students think about concepts and where they struggle, allowing us to identify common learning progressions. A learning progression is the path a student takes from an initial point of understanding to the end goal of mastery (Simon, 1995). We will demonstrate some ABVTs developed on key geoscience topics, each of which includes multiple assessment questions and 30-60 second videos. The ABVTs have been developed in Adobe Captivate, which allows the instructor to identify common learning progressions, variations based on demographic factors, and where students struggle within tutorials. Learner interactions data from Captivate ABVTs are collected on a secure database using Adobe Quiz Results Analyzer. As students progress through each tutorial, data revealing their path through the material, time spent receiving instruction, and the number of attempts at each built-in assessment is collected. Student performance influences how the lesson branches, directing students to additional instruction on concepts they are struggling with.
Each ABVT incorporates multiple working hypotheses about how students’ understanding of a concept becomes more sophisticated over time, with instruction. This approach utilizes Gagne’s hierarchies of programmed learning as a theoretical framework to explore what students need to know to be able to apply advanced knowledge. Gagne (1962) proposed beginning with the end task in mind and asking the question, “What kind of capability would an individual have to possess if he were able to perform this task successfully, were we to give him only instructions?” (p. 356). This question is repeated to break a concept down into its component parts, and establish a hierarchy of subordinate ideas.
We believe the software package created for geoscience ABVTs can be readily used to identify learning progressions in other disciplines, and would like to discuss this potential with others interested in teaching with video-based resources, or in developing ABVTs for other disciplines. Understanding learning progressions informs curriculum and assessment development, and allows us to evaluate the effectiveness of different instructional approaches.