Automated Feedback = Adequate Feedback?

By December 15, 2016English

Recently, I had to call the bank over some credit card matters. Albeit I am no stranger to automated response, still the experience left me feeling demotivated to pursue the matter any further. I had expected to find at least some form of advice amid the rich list of standardized replies the buttons would generate … I supposed the bank didn’t or couldn’t pre-empt a situation as atypical as mine. I believe my frustration with automated replies is not unique. I began to recall my conversation earlier this year with a renowned professor in an interview on learning in the 21st century borderless classroom. He remarked that for any learning to take place, feedback is instrumental, and he emphasized adequate feedback.

Discourse on the quality of online education often revolves around instructional design strategies and technological affordances to enhance the quality of the learning experience. Whilst I do not discount nor dismiss the importance of these elements in designing quality online courses, the appropriation of feedback remains a critical area to be addressed. In the review on “The Power of Feedback”, Hattie & Timperley (2007) contend that feedback is one of the most powerful influences on learning and achievement and they define feedback as information provided by an agent on one’s performance or understanding. And for feedback to be effective, the ‘teacher’ or the agent rendering the feedback need to assess the situation rightly to determine when, how, and at what level feedback should be meted. This mirrors Bereiter and Scardamalia’s (1987) notion of procedural facilitation. One of the main drawbacks of mass online education lies in automated feedback. It is impossible for online education to cater to learners’ need for differentiated scaffolds and differentiated feedback. In other words, it cannot personalise learning, it can only offer prescribed alternative routes just like any intelligent auto-reply. Effective feedback also encompasses feedback at two other equally significant levels: the process level and the self-regulation level. Feedback at the process level informs a learner of the processes necessary to understand and/ or perform a task and feedback at the self-regulation level enhances self-monitoring and self-regulation of actions. What essentially automated feedback does, or can only do, is to provide feedback at the task level, i.e., informs one to distinguish between correct from incorrect answers. Automated feedback is unable to inform on the meta-cognitive attributes of a task. It is not surprising that the rapid growth of web-based instruction has raised many questions about the quality of online courses. Only when effective feedback is combined with effective instructional design, perhaps can we ensure meaningful learning will occur. Automated feedback is not adequate feedback.

Author Esther Tan

Assistant professor at the Welten Institute, Research Center for Learning, Teaching and Technology. She was an education officer with the Educational Technology Division, Ministry of Education, Singapore and was also part of the research team at the Learning Sciences Lab (LSL), National Institute of Education (NIE) for the Singapore Future School project on mobile learning activities to foster critical thinking and collaborative knowledge building. Her current research projects are MOOQ (Quality of MOOCs), Let’s Learn to Learn (LELLE) and Inspiring Science Education (ISE).

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