Community Colleges, as well as most colleges, often have students, notably non-traditional students, who are unfamiliar with successfully engaging in discussions in online learning environments. This inhibits their success and decreases the student retention rate. To address this, we developed an Adaptive Feedback Assistant to help faculty offer student support for students in need of guidance in succeeding in online discussions. While the assistant will not replace mentoring or faculty interaction, it does provide a tool to create individualized support for students. The tool was also developed without significant cost for adoption and provides open resources for any college wishing to either adopt the Adaptive Feedback Assistant or just the training.

Adaptive learning offers a method for creating a learning environment that is personalized for each learner and has been linked to increases student retention (Nakic, Granic & Glavinic, 2015) and student success (Thompson & Fuchs, 2016). The Adaptive Feedback Assistant is a resource that creates personalized learning experiences and assists students in need of improvement in online discussions. Using our learning management system (Blackboard), tools available at the college, animation skills of the Teaching and Learning Specialist, and free resources, we developed a system of adaptive release commands that delivers an open resource to effectively support students in need of guidance with online discussion participation. At the first sign of a student requiring assistance in succeeding in a discussion section, the Feedback Assistant is deployed and provides student support with targeted trainings that employ principles of Universal Design for Learning. The threshold for triggering can be modified by the instructor to better reflect the course, or kept at the schools standard. By utilizing this technique, students who are not in need of the training are not burdened with unnecessary training or extraneous cognitive load. Students needing support have access to the training with no extra software needed other than the learning management system. The advantage of this approach is it offers targeted feedback to those who need it with a method that will scale with the growth of online curricula.

The training is chunked into three short sessions: overall tips for online discussions, writing a post, and writing a reply. Each are short lessons (one page) organizing and presented following principles of Cognitive Load Theory and Universal Design for Leaning. Learners are offered options of textual format or short video that offers audio and visual components to accommodate various learning styles.

The videos followed a strict instructional rubric to maximize their effectiveness. Some of the best practices the rubric ensured included: keeping videos brief (Guo, Kim, and Robin, 2014), following universal design and including closed captions (Rose and Meyer, 2002), relying on cognitive load theory when ‘chunking’ then information into readily available packets (Sweller, 1988), and using context focused scripts (Hibbert, 2014). Among the videos was a whiteboard animation style because they encourage learner engagement and increase information retention by up to 20% (Turkay, 2016). The videos also contained a fair representation of learners to best represent our diverse student population. Naturally the videos had a closed caption option for accessibility. These were housed on a YouTube channel to take advantage of mobile accessibility and to offer playlists that would allow students to select the content the suited their needs.

We first piloted the Adaptive Feedback Assistant with one course, LIT 233 Detective Fiction and Film, to test its effect. The response was positive and it was later added to a course model designed to ensure that the college online courses meet or exceed the OSCQR standards. In the four months since we added it to the course model and applied the assistant to at least four new courses, we have tracked over 300 views of the videos. Over 100 of these have occurred within the first two weeks of the semester when offering student support is critical for student retention and success. Moreover, students have reported liking the assistant and prefer the videos. The positive response has encouraged us to work on adding assistance for writing skills as well.

The Adaptive Feedback Assistant was developed with the intent of it being an OER. It is embedded within a course model that is also made available as Blackboard package that can be imported into any course using the Blackboard LMS. Any college can adapt this model that has a structure that meets over 40% of the OSCQR standards before content is added. Alternative formats of the training are also available for faculty. The training is available in PDF format with hyperlinks to associated videos embedded within the document as well as an ePub version that can be embedded within a web page. Moreover, the mobile friendly videos can be used individually or as a playlist independent of the training or adaptive assistant. All of these alternative formats of the training lack the adaptive nature of the assistant, however, the adaptive aspects can be built within other LMSs, such as Moodle.

References
ELI (2017) 7 things you should know about adaptive learning, Educause, January 2017

Guo PJ, Kim J, and Robin R (2014). How video production affects student engagement: An empirical study of MOOC videos. ACM Conference on Learning at Scale.

Millar, L (2010) Video in the C-Suite: Executives Embrace the Non-Text Web. Forbes: Insight.

Nakic, J., Granic, A., & Glavinic, V. (2015). Anatomy of student models in adaptive learning systems: A systematic literature review of individual differences from 2001 to 2013. Journal of Educational Computing Research, 51(4), 459-489

Rose, D and Meyer, A (2002) Teaching Every Student in the Digital Age: Universal Design for Learning. Alexandria, VA: ASCD.

Sweller, J (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science 12 (2): 257–285

Thompson, J., and S. Fuchs (2016) Improved student success and retention with adaptive courseware: An Arizona State University case study. ASU Pilot Study

Turkay S. (2016) The Effects of Whiteboard Animations on Retention and Subjective Experiences when Learning Advanced Physics Topics, Computers & Education 98: 102-114. http://www.sciencedirect.com/science/article/pii/S0360131516300550

TRAINING: https://instructionaldesignsccc.blogspot.com/2019/04/guiding-students-with-online-discussion.html

Playlist of embedded videos in Training: https://www.youtube.com/embed/watch?v=_Q2yCD0H6SU&feature=youtu.be&list=PLLOZT-hm8RLG7iTr56_z1XZKgYCso8tRL

Course Model with Adaptive Feedback Assistant: https://instructionaldesignsccc.blogspot.com/2019/03/a-boilerplate-course-model-to-promote.html (the link to the Blackboard Package can be found in this post. For a direct link to the Blackboard package: https://app.box.com/s/z37r5z9yzec4qrjy864s9ecxo04xvcls )

https://drive.google.com/open?id=1y3fLKnt10212_9xhv77EN0pRBV9JB6CW