Dialogic Feedback Process gets Easier with Technology and Learning Analytics

I’ll start this article by making one simple statement: Feedback loops work. Why? That’s the way we human beings learn, as feedback provides us with a sense of where we stand and an evaluation of our progress. After all, we cannot change what we don’t measure. With that said, I must confess that I am not a natural when it comes to feedback. I only developed the skills necessary to give and productively receive feedback after many years of trial and practice. And, I’m still learning to do so.

Why Feedback?

As early as 1950, Norbert Wiener, who helped create the interdisciplinary study of cybernetics, discussed the notion of feedback in learning:

Feedback is the control of a system by reinserting into the system the results of its performance. If these results are merely used as numerical data for criticism of the system and its regulation, we have the simple feedback of the control engineer. If, however, the information which proceeds backward from the performance is able to change the general method and pattern of the performance, we have a process which may very well be called learning.

 

We see further research into the feedback loop in the 1960s. Albert Bandura, a psychologist and pioneer in behaviorist learning theories, specifically explored the feedback loop and its correlation with behavioral changes. Since then, feedback loops have been extensively researched and validated in psychology, epidemiology, military strategy, engineering, and economics. You will also find feedback loops in athletic training plans and a multitude of other self-improvement programs (though some are less scientific than others).

Researchers like Dr. Faith Hill (2007) identified the essential role of feedback in the learning cycle. Black & William (1998) observed that a key element to formative assessment is feedback, in their seminal paper that identified the four components of a feedback system:

• Data on the actual level of some measurable attribute
• Data on the reference level of that attribute
• A mechanism for comparing the two levels, and generating information about the gap between the two levels
• A mechanism by which the information can be used to alter the gap

The feedback loop is an age-old strategy continuing to be revitalized by state-of-the-art technology.

So why Don’t we use Feedback Loops in Everyday Learning?

The importance of student feedback for improving the learning process is not a topic that is highly disputed. However, despite a vast volume of research and a proven capacity to affect human behavior, we don’t often use feedback loops in everyday learning.

This can be partly blamed on significant workload barriers for instructors that impede their capacity to collect learner feedback and provide timely and meaningful responses. Feedback during the learning process is typically facilitated through rather labor-intensive manual procedures. Although the basic idea of feedback tracking has been available to anyone willing to put in the effort, few instructors would stick with the routine of toting around another notebook and pen. It’s just too much on top of everything else. Technologists would argue – not erroneously – that keeping track of feedback data this way is not scalable.

Fortunately, this is all changing because of feedback technology like Blue and Bluepulse. Adding technology to the feedback equation helps solve this scalability problem. If feedback collection is automated with technology, instructors will have more time to transform that data into an emotional imperative that the learner can act on for improved learning.

Feedback as Dialogue gets Easier: Journey, Social Communication

The true value of feedback is not a destination, but rather the journey that leads to better understanding and ultimately insight. But what makes good feedback? How do we know that both the giver and receiver – namely learner and instructor – of feedback understand it? How do we ensure that feedback is working as intended? To answer these questions, let’s shift into process-oriented feedback, which can also be found in the assessment discipline as “feedback as dialogue.”

Sadler (1998) observes that it is the quality of feedback between instructors and students that is key to the success of the teaching and learning experience. Nicol et al. (2006) built on this further by defining what good quality external feedback as:

[I]nformation that helps students troubleshoot their own performance and self-correct; that is, it helps students take action to reduce the discrepancy between their intentions and the resulting effects

 

Nicol (2010) also proposes that feedback should be conceptualized as a dialogue between instructor-student, where meaning is constructed because dialogue is fundamental to successful learning and teaching.

In his paper “Feedback as Dialogue,” Carless (2016) summarizes feedback as a contextualized form of social communication, identifying the following key components of the dialogic feedback process.

• Activating the student role in seeking, generating, and using feedback
• The integration of feedback, assessment task design, and the development of student capacities to make academic judgments
• Timely discussion of student work, including in-class guidance, peer feedback, and technology-enhanced dialogues
• Creating course climates which encourage and facilitate the above

Professor David Carless is a featured keynote speaker at the Bluenotes GLOBAL 2019 Conference.

What’s common in the concept of feedback as dialogue is a learner-centered journey. Collins dictionary defines the journey as “the process of traveling from one place to another.” Not all processes are created equal. Let’s interject the concept of process maturity, which is defined as an indication of how close a process is to being complete and capable of continual improvement through qualitative measures and feedback. So, how do we guide this journey to take learners to their desired state? Start asking questions to gain insights on where the learners are in their learning process.

Ask Questions

The skill of the instructor in crafting useful feedback is an important element in the process. How do you go about crafting useful feedback? First, an instructor needs to understand where a learner is before offering feedback. Understanding is not a one-step process; it must be an integrated and ongoing element in the complete feedback journey.

What is the role of questions in all of this? Questions allow us to gain clarity both for ourselves and for the recipients of our feedback. They also defuse the tension of assuming a problem that isn’t there or giving feedback that might not fit a learner’s specific need. Have you ever received feedback that is completely unrelated to your experience? Not only is it unhelpful but also disheartening. You leave that conversation, assuming that the person did not understand nor appreciate your efforts, which is not conducive of good or bad feedback. So how do we breakdown these barriers to effective feedback?

• Start with understanding. Understanding the learner’s goal and recognition of their efforts is an essential part of the feedback process.
• Then ask specific, open-ended questions to understand where this person stands in terms of learning. That way, when you offer your feedback, it will be in service of their goals for action and not just what you think is important.
• Recognize what you need to know before providing or responding to feedback. This also helps you approach the conversation from a place of empathy and coaching — a key part of process-oriented feedback.
• Transform data collected into an emotional imperative that the learner can understand, make meaning of, as well as feel empowered and willing to act. This closely echoes the work of Nicol et al. (2006) who stated that effective feedback practice is one where “the student can understand the feedback (cognition), the student can attend to the feedback (motivation), and that they act on it (belief).”

Sounds like laborious manual procedures, right? Indeed, they are without technology. Feedback technology automates the capture of data and digitizes it so it can be readily crunched and transformed as necessary. This removes the labor-intensive manual procedures of feedback collection and subsequent analysis. Samoilova (2017) explored how to combine feedback with digital behavior data for more robust learning analytics, in particular, workload research. In their paper, Pardo et al. (2019) highlighted the potential of combining digital traces captured by technology in a digital learning environment with feedback for large student cohorts, exploring the use of learning analytics and feedback at scale.

We see more and more instructors employing feedback technology and analytics to guide their learner-centered formative feedback process, as demonstrated by Prof. Waters at the University of Mary Washington. As instructors add formative feedback technology like Bluepulse to their teaching process, they not only teach but also become inspiring, tech-savvy feedback-literate role models for the next generation.

References

• Sadler, D. R. (1998) ‘Formative assessment: Revisiting the territory.’ Assessment in Education: Principles, Policy & Practice, Vol. 5, pp. 77-84.
• Black, P. and Wiliam, D. (1998) ‘Assessment and Classroom learning.’ Assessment in Education, Volume 5, No. 1 pp7-74.
• Carless, D. (2006) ‘Differing perceptions in the feedback process.’ Studies in Higher Education, Vol. 31, No. 2, pp. 219 — 233.
• Nicol, David J. & Macfarlane-Dick, D. (2006) ‘Formative assessment and self-regulated learning: a model and seven principles of good feedback practice.’ Studies in Higher Education, Vol. 31, No. 2, pp. 199 — 218.
• Hill, F. (2007) ‘Feedback to enhance student learning: facilitating interactive feedback on clinical skills.’ International Journal of Clinical Skills, 1(1), 21-24.
• Nicol, D. J. (2010) ‘From monologue to dialogue: improving written feedback processes in mass higher education.’ Assessment & Evaluation in Higher Education, Vol. 35, Noo. 5, pp. 501 — 517.
• Samoilova, E., Keusch, F., & Wolbring, T. (2017) ‘Learning Analytics and Survey Data Integration in Workload Research.’ Zeitschrift für Hochschulentwicklung. Special Edition: Learning Analytics: Implications for Higher Education, 12(1), 65-78.
• Pardo, A., Jovanovic, J., Dawson, S., Gašević, D., & Mirriahi, N. (2019)’ Using learning analytics to scale the provision of personalised feedback.’ British Journal of Educational Technology, 50(1), 128-138.