Feedback: Educational Environment #UKEdchat #Edchat

Educational Feedback

While working at a higher educational institution based in Lancashire I got involved in teaching mathematics to a group of adults (N = 45) with an age range of 18 to 50. These students required GCSE Mathematics in order to progress within their chosen careers. I became aware that other tutors were not providing as much feedback to their students compared to myself and this interested me. I was providing as much feedback as possible to all my students in order for them to ascertain their own strengths and areas for improvement. The vision was to provide a basis for self-reflection and study. Therefore, this essay will focus on the ‘use of feedback within an educational environment’.

Aim of this study:

The aim of this short study will be to define what is meant by feedback and consider its primary function, elaborate on the ingredients to better facilitate application, consider psychological theories such as the informative tutorial feedback model (Narciss, 2006), cognitive load theory (Plass, Moreno, & Brunken, 2010), and expertise reversal effect (Kalyuga, Ayres, Chandler, & Sweller, 2003), and provide the author’s overall analysis of the above in relation to the direct application of feedback as a discussion and its potential usage.

There are decades of research dedicated to the powerful effects of feedback in relation to student achievement within the classroom (Hattie & Timperley, 2007). It’s a tool used in many professions and the information gathered from the performance and understanding that a learner uses to either confirm, modify or even reject prior knowledge can be considered feedback (Spector, Merrill, Merrienboer, & Driscoll, 2008). Fyfe (2012) stated the primary function of feedback is to promote the continual application of correct methods while identifying and eradicating errors. However, Davis & McGowen (2007) highlights that although teachers are able to analyse results to establish strengths and improvements of a learner, teachers don’t always incorporate their findings to promote curricula changes, therefore not catering for both the individual and multiple needs of their current and future cohorts. Goguadze & Melis (2008) considers feedback to form an important and instructional process to facilitate effective learning and concludes it requires two ingredients:

Correct / incorrect (‘flag’) feedback: Students require some form of feedback highlighting the total amount of incorrect and correct answers given. It can provide the students with a broad basis for understanding their overall levels of ability. This type of feedback can also benefit a teacher to highlight which students require additional support, and also gather average scores in order to better project formative and summative assessments prior to delivery (i.e., achievement forecasts) (Goguadze & Melis, 2008).

Topic, task, errors and solutions feedback: By providing additional feedback about the topic covered, the objectives of the task, highlighting errors made by learners at different stages of their calculations, and also providing potential solutions or directional instructions to modify a current understanding would allow the learner to adapt to the demands of a much wider curriculum (Goguadze & Melis, 2008).

These two ingredients were derived using the psychological framework of Narciss (2006): Informative Tutorial Feedback Model (ITF). The ITF aims to contribute to a learning process that allows one to master tasks, and acquire the skills and knowledge of a multi-dimensional instructional activity. The framework conceptualises between meta-cognitive controlled variables, motivational levels and cognitive abilities (Narciss, 2006; Goguadze & Melis, 2008). It is argued that two feedback-loops interact to form the instructional context:

Internal loop: A loop that processes the internal feedback of the learner’s intrinsic state (e.g., confidence, perceived effort) and the physical state (e.g., actual effort) (Narciss, 2006).

External loop: A loop that processes the values determined by the instructor (e.g., teacher, software packages) i.e., learning medium (Narciss, 2006).

Goguadze & Melis (2008) states external feedback can be determined using three facets involving: instructional goals and objectives (Fyfe, 2012), content relationship (Goguadze & Melis, 2008), and feedback frequency and timing (Alfieri, Aldrich, & Tenenbaum, 2011). Internal feedback is derived from a learner’s existing procedural and conceptual knowledge (Fyfe, 2012; Goguadze & Melis, 2008), ability to identify corrective actions (Spector, Merrill, Merrienboer, & Driscoll, 2008), and the learner’s level of skill and motivation (Piaget, 1963; Goguadze & Melis, 2008).

Piagetian theory demonstrates those with instructional power (e.g., teachers) have an adverse effect on developing higher aspects of intelligence (e.g., in the learner). This is achieved through the continual use of positive feedback which in turn allows the learner to develop higher conceptual and procedural knowledge (Davis & McGowen, 2007; Piaget, 1963; Fyfe, 2012). However, Fyfe (2012) demonstrates that not all feedback has a positive outcome.

Feedback given at different stages of a learner’s knowledge has a different outcome according to Fyfe (2012). As one learns a new concept they construct a schema in order to achieve the desired outcome (e.g., correct answer) (Kalyuga, Ayres, Chandler, & Sweller, 2003). As feedback is provided, one re-constructs the meaning and a new schema is developed. However, as these schemas become more advanced in their ability to execute action sequences to solve higher problems (e.g., procedural knowledge), the benefits of feedback decrease (Salden, Aleven, Schwonke, & Renki, 2010). If a learner was considered a novice, it was found that they had increased benefits from worked examples rather than having to problem solve unaided. As one’s skill set increased their procedural knowledge became the superior learning activity; in turn promoting self-study decreasing the benefits of feedback given (Kalyuga, Ayres, Chandler, & Sweller, 2003). This supports the expertise reversal effect demonstrating the more advanced one gets, less feedback is required (Salden, Aleven, Schwonke, & Renki, 2010; Kalyuga, Ayres, Chandler, & Sweller, 2003). Due to novice learners lacking the required domain specific schemas, noval tasks could overload working memory and reduce the effects of external guidance. However, cognitive load theory demonstrates giving instructional feedback when domain specific schemas are already stored in long-term memory could also overload working memory due to processing unnecessary information (Plass, Moreno, & Brunken, 2010).

It would appear that if students are given simplistic feedback such as correct / incorrect, their ability to learn domain specific schemas could be reduced. However, by providing topic, task, error and solution based feedback, one could establish their domain specific strengths and also areas of improvement to either provide mindful revision of the curriculum, or a domain specific guided discovery that helps facilitate deeper learning than simply instruction alone. Not having the domain specific schemas to calculate solutions could impact on working memory causing cognitive overload. However, the perceptive levels of students’ abilities by their instructors could have an adverse effect. Recognising how students respond to specific types of feedback could allow teachers to become aware of their (learners) actual representative knowledge and not merely what has been perceived. It would also appear that feedback is highly dependable upon the internal and external processing loops. By applying the numerous facets involved in external feedback an instructor could create a large variety of feedback strategies. One could allow a student to choose which type of feedback they wanted, monitor its usage and any potential impact on learning to help generate more effective tutorial strategies; catering for their current and future cohorts. Research demonstrates that feedback can stimulate self-regulation and study, positively contributing to the transformation process critical to developing intelligence. However, as the transformation takes place one must recognise the cognitive restraints providing unnecessary feedback has on working memory. Considering the plethora of studies, projects, and many publications, the assessment of mathematical learning would seem to remain elusive and without careful application, a struggle to deliver improvements will remain (Stiggins, 2007). The technical aspects of assessment should not be the main focus but the consideration of what helps the students to learn, ‘dialogue’ and ‘feedback’ should be (Callingham, 2008). This needs to be tailored to individual students and be context specific. It would appear that a simple set of instructions although followed, does not ensure a positive result (Callingham, 2008).

Additional Reading:

Should students respond to feedback? – Learning Spy


Alfieri, L., Aldrich, P. J., & Tenenbaum, H. R. (2011). Does discovery-based instruction enhance learning? Journal of Educational Psychology, 103(1), 1-18. doi:10.1037/a0021017

Callingham, R. (2008). The practice of assessing mathematics learning. The Australian Association of Mathematics Teachers’, 13(3), 18-21.

Davis, G. E., & McGowen, M. A. (2007). Formative feedback and mindful teaching of mathematics. Australian Senior Mathematics Journal, 21(11), 19-29.

Fyfe, E. R. (2012, May). The effects of feedback during exploratory mathematics problem solving: prior knowledge matters. Nashville, Tennessee, USA: Graduate School of Vanderbilt University. Retrieved November 15, 2015, from

Goguadze, G., & Melis, E. (2008). Feedback in ActiveMath Exercises. Saarbrucken, Germany: German Research Institute for Artificial Intelligence (DFKI). Retrieved November 14, 2015, from

Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81-112. doi:10.3102/003465430298487

Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The Expertise Reversal Effect. Educational Psychologist, 38(1), 23-31.

Narciss, s. (2006). Informatives Tutorielles Feedback. Muenster: Waxmann.

Piaget, J. (1963). The psychology of Intelligence. New York: Routledge.

Plass, J. L., Moreno, R., & Brunken, R. (2010). Cognitive load theory. Cambridge: University Press.

Salden, R., Aleven, V., Schwonke, R., & Renki, A. (2010). The expertise reversal effect and worked examples in tutored problem solving. Instructional Science, 38(3), 289-307.

Spector, J., Merrill, M., Merrienboer, J. V., & Driscoll, M. P. (Eds.). (2008). Handbook of research on educational communications and technology: A project for the Association for Educational Communications and Technology (3 ed.). New Jersey, USA: Routledge.

Stiggins, R. (2007). Assessment through the student’s eyes. Educational Leadership, 64(8), 22-26.

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