The Little and Often Principle
Timely and Iterative Feedback in Management Learning:
An Empirical Analysis

Bryan JonesMammed Bagher,  Dearne Valley Business School,  Doncaster,  UK.

This paper considers/proposes a learning framework that might apply to both study and to work.  The framework focuses on the nature and timeliness of feedback mechanisms.  Key to the efficacy of this approach is understanding whether reflexive learning, e.g. like learning how to ride a bike, is transferable to study and to management contexts.  If we consider that we learn how to ride a bike by continuously avoiding falling off, then the key mechanism operating is continuous feedback: i.e. reflexiveness (instantaneous assessment); and iterativeness (an increased frequency of assessment). Thus the individual framework, once established, is continuously revised and updated through practice and evaluation.

In asking managers whether they can explain how they learn, there are some common answers: “by experience”, “by questioning” and “No”.  Very few managers articulate a conscious definition of how they, as an individual, think that they learn.  More significantly, whilst Business Schools and learning organisations encourage more use of management learning frameworks, there are limitations in predicting the value of such frameworks for a manager over time.  It is not clear whether an individual’s ability to articulate learning, as defined by their work as a student, is a precursor to success as a manager.

In order to achieve the above consideration, the paper utilises both qualitative and quantitative data in a form of semi-structured interviews and questionnaires from Business schools management students, both under and post graduate. Management consultancy clients of the business school, together with the academic staff at Dearne Valley Business School.

A particular contribution of this paper is the consideration of parallel between the theory and practice.

Key Words: Learning, Learning styles, Retention, Management, Continuous, Learning Methods, learning environment

Return to Abstracts Index