The unbearable lightness of knowledge working – E-Learning Curve Blog

“In my view, organisational performance is highly dependent on worker ability and motivation”


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The unbearable lightness of knowledge working

Here’s an interesting article that’s been rattling around the web for a while now, but worth revisiting: according to Jared Sandberg at the WSJ –

In the Information Age, so much is worked on in a day at the office but so little gets done. In the past, people could see the fruits of their labor immediately: a chair made or a ball bearing produced. But it can be hard to find gratification from work that is largely invisible, or from delivering goods that are often metaphorical. You can’t even leave your mark on a document in increasingly paperless offices. It can be even harder trying to measure it all.

Doing a job well provides tangible benefits

Doing a job well is gratifying and provides tangible benefits

In Ireland recently, a survey by the recruitment company Executive Connections suggested that most people in the job market are on the move not, as you might imagine for greater remuneration, but because of a perceived lack of career progression and satisfaction with their current role; people really do have an inherent need for, and are motivated by responsibilities in their job.

As learning practitioners – and particularly in e-learning – we’re aware of the importance of motivating and rewarding learners. Many of us have first-hand experience of the numbingly dull exercise of proceeding through linear old skool CBT-style courseware. We try to design, develop, and deliver engaging and interactive  experiences that will reward our learners, or at least evoke a degree of satisfaction when they accomplish a task or successfully complete an activity, and we also know about the statistics of the high drop-out rates for e-learning programmes.

I’m wondering if there’s a relationship between these two phenomena? In my view, organisational performance is highly dependent on worker ability and motivation.

Ability depends on educational development, experience, and training; developing this facility is an ongoing process that doesn’t reap immediate rewards. However, motivation can be improved quickly. As a guideline, there are broadly seven strategies for motivation.

  • Positive reinforcement / high expectations
  • Effective discipline and punishment
  • Treating people fairly
  • Satisfying employees needs
  • Setting work related goals
  • Restructuring jobs
  • Base rewards on job performance

These are the basic strategies, though the mix varies from situation to situation/workplace to workplace. In the under- or unmotivated worker (including those who drop out of e-learning courses for example) we can say that there is a disconnect between an individuals actual state and some desired state: so how do we resolve this disconnect, and ultimately support knowledge workers find increased psychological satisfaction in their professional endeavours?

Answers on a postcard please.

References:

Sandberg, J. (2008) A Modern Conundrum: When Work’s Invisible, So Are Its Satisfactions. The Wall Street Journal [Internet]. Available from: http://ift.tt/RXNABi
SB120338000214975633.html?mod=psp_editors_picks
[Accessed 12th July 2017]

Related

Social networks, long tails, social tiesJuly 19, 2017In “E-Learning”

The Problem of KnowledgeSeptember 30, 2010In “E-Learning”

Irish Learning Showcase 2010 in DublinJuly 16, 2010In “E-Learning”

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Using Learning Curves

How it takes more time to master the Bloom top levels.


1

Using Learning Curves

In the introduction to my previous E-Learning Curve Blog post, focussing on the topic of learning curves, I referenced the axiom that ‘practice makes perfect’ – the concept that the acquisition and improvement of new skills, knowledge or expertise are broadly predicated upon the learner’s facility and willingness to rehearse and become more proficient in the tasks or activities being practised.

This is not news. We have all experienced this process, and Behaviorists would venture to assert that perhaps we know it intuitively. What may be more surprising is that the rate and shape of improvement of learning can be described geometrically on a curve.

The concept of the learning curve illustrates a simplified model of learning in which knowledge of a given subject is acquired through a progression of steps. Figure 1 shows a model of an idealized (and simplified, and in no way scientifically nor pedagogically accurate) learning curve applied to Bloom’s Taxonomy. At the lowest levels of the curve, the learner is a novice who then progresses through the various stages of cognitive development, where at each stage they increase in competency until (perhaps up to a decade and / or 10,000 practice hours later) they become an expert, with an overarching competency in the domian being studied.

Figure 1. Bloom’s Taxonomy charted on a learning curve

The learning curve has substantial implications for e-learning: it suggests that practice always helps improve performance, but that the most dramatic improvements happen first, with smaller and smaller incremental improvements being accrued over time. Another implication is that with sufficient practice, learners can achieve comparable levels of performance. For example, extensive practice on mental arithmetic (Staszewski, reported in Delaney et al., 1998) and on digit memorization have turned average individuals into high performers in the discipline.

The learning curve was devised from the historical observation that individuals who perform repetitive tasks demonstrate an improvement in performance as the task is repeated over time. It was first studied empirically in the 1930’s by T. P. Wright. In his text Factors Affecting the Cost of Airplanes, Wright drew three conclusions upon which the current theory and practice surrounding learning curves are based:

  1. The time required to perform a task decreases as the task is repeated
  2. The amount of improvement decreases as more units are produced
  3. The rate of improvement has sufficient consistency to allow its use as a prediction tool

In this study, Wright concluded

that consistency in improvement has been found to exist in the form of a constant percentage reduction in time required over successively doubled quantities of units produced. The constant percentage by which the costs of doubled quantities decrease is called the Rate of Learning.

More…

_________

References:

Delaney, P. F., Reder, L. M., Staszewski, J. J., & Ritter, F. E. (1998). The strategy specific nature of improvement: The power law applies by strategy within task. Psychological Science, 9(1), 1-8.

Wright, T.P. (1936). Factors Affecting the Cost of Airplanes. Journal of Aeronautical Sciences, 3.4 : 122 -128. [Internet] Available from: http://ift.tt/2usEFmK Accessed August 1 2017

Related

The Long Tail, the 80:20 Rule and the role of learning professionalsJuly 24, 2017In “E-Learning”

Experiential Workplace LearningApril 6, 2010In “E-Learning”

A Holistic Approach to Workplace CompetenciesMarch 3, 2010In “E-Learning”

.

.


Using Learning Curves

How it takes more time to master the Bloom top levels.


1

Using Learning Curves

In the introduction to my previous E-Learning Curve Blog post, focussing on the topic of learning curves, I referenced the axiom that ‘practice makes perfect’ – the concept that the acquisition and improvement of new skills, knowledge or expertise are broadly predicated upon the learner’s facility and willingness to rehearse and become more proficient in the tasks or activities being practised.

This is not news. We have all experienced this process, and Behaviorists would venture to assert that perhaps we know it intuitively. What may be more surprising is that the rate and shape of improvement of learning can be described geometrically on a curve.

The concept of the learning curve illustrates a simplified model of learning in which knowledge of a given subject is acquired through a progression of steps. Figure 1 shows a model of an idealized (and simplified, and in no way scientifically nor pedagogically accurate) learning curve applied to Bloom’s Taxonomy. At the lowest levels of the curve, the learner is a novice who then progresses through the various stages of cognitive development, where at each stage they increase in competency until (perhaps up to a decade and / or 10,000 practice hours later) they become an expert, with an overarching competency in the domian being studied.

Figure 1. Bloom’s Taxonomy charted on a learning curve

The learning curve has substantial implications for e-learning: it suggests that practice always helps improve performance, but that the most dramatic improvements happen first, with smaller and smaller incremental improvements being accrued over time. Another implication is that with sufficient practice, learners can achieve comparable levels of performance. For example, extensive practice on mental arithmetic (Staszewski, reported in Delaney et al., 1998) and on digit memorization have turned average individuals into high performers in the discipline.

The learning curve was devised from the historical observation that individuals who perform repetitive tasks demonstrate an improvement in performance as the task is repeated over time. It was first studied empirically in the 1930’s by T. P. Wright. In his text Factors Affecting the Cost of Airplanes, Wright drew three conclusions upon which the current theory and practice surrounding learning curves are based:

  1. The time required to perform a task decreases as the task is repeated
  2. The amount of improvement decreases as more units are produced
  3. The rate of improvement has sufficient consistency to allow its use as a prediction tool

In this study, Wright concluded

that consistency in improvement has been found to exist in the form of a constant percentage reduction in time required over successively doubled quantities of units produced. The constant percentage by which the costs of doubled quantities decrease is called the Rate of Learning.

More…

_________

References:

Delaney, P. F., Reder, L. M., Staszewski, J. J., & Ritter, F. E. (1998). The strategy specific nature of improvement: The power law applies by strategy within task. Psychological Science, 9(1), 1-8.

Wright, T.P. (1936). Factors Affecting the Cost of Airplanes. Journal of Aeronautical Sciences, 3.4 : 122 -128. [Internet] Available from: http://ift.tt/2usEFmK Accessed August 1 2017

Related

The Long Tail, the 80:20 Rule and the role of learning professionalsJuly 24, 2017In “E-Learning”

Experiential Workplace LearningApril 6, 2010In “E-Learning”

A Holistic Approach to Workplace CompetenciesMarch 3, 2010In “E-Learning”

.

.


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