Using Learning Analytics to Augment Feedback Processing and Transfer of Learning (Poster 4)

• In Event: Critically Examining Space and Time in Learning and Design for More Just and Equitable Futures

Abstract

Objectives
Experiential learning theory proposes that “learning results from synergetic transactions between the person and the environment” (Kolb & Kolb, 2009, p. 44). Experience is transferred into knowledge by stepping through the experiential learning cycle; with feedback from others used to deepen the transfer of experience to knowledge that can be stored, and used in the future. This study examines how real-time learning analytics can augment the process of leveraging feedback from the classroom and the real-world to deepen the knowledge extracted from the present for use in the future.

Theoretical perspectives
Transfer is a cognitive process of taking knowledge acquired in one context and applying it to another context (Jackson et al. 2018). Transferring knowledge acquired in the classroom to a real-world context is considered far transfer (National Research Council, 2015). To leverage feedback from multiple others to deepen the knowledge gained from present experience in a way that it can be stored, and used in a future experience is a more cognitively complex process than far transfer. I theorize that real-time learning analytics holds the potential to augment this complex cognitive process and support a students ability to link past and current learning from diverse others to future spaces and places.

Modes of inquiry & Data
This study is a reflexive examination of two design-based research studies focused on broadening participation in experiential learning using emerging technologies (Authors, 2022b; 2021c). The reflexive examination uses the lens of space time and specifically Bakhtin’s notion of chronotopes (1981) to understand how real-time learning analytics built into the experiential learning platform can augment the cognitively complex process of transferring experience into knowledge that is transferable into future experiences.

Warrants for arguments
A salient learning that emerged from the design-based research studies is that learning insights generated from real-time learning analytics analysis are utilized to a greater extent than raw data points. For example, in a team-based capstone project, students received a spider graph comparing their own perception of their teamwork skills to their peers perception of their teamwork skills (learning insight) and written qualitative feedback from each of their peers (raw data). In reflection essays completed after receiving the feedback students reflected on insights derived from the spider graphs with more frequency that the qualitative feedback from their peers.

Scholarly significance
This study illuminates how learning insights generated from real-time learning analytics analysis can decrease the cognitive complexity of transferring knowledge from present experiences to future spaces and places by processing raw data into digestible learning insights. The process of leveraging multiple others feedback on present experience to knowledge that is stored and transferred to future experience is a hallmark of lifelong learning. Using real-time learning analytics to augment how we process, transfer and utilize knowledge gained through experience in different spaces and times could increase the velocity at which students learn from experience.

Author

• Nikki James, Northeastern University
  Presenting Author