Career Change Skills for Experienced Workers

Post COVID-19: Distance Learning to Kick-Start the Economy – Reflections on Career Change Skills for Workers Using the Analytical Lens of Behavioural Science

Abstract

Many Governments are facing the major problem of getting large numbers of people back to work, into different jobs and careers over the next 6-12 months during 2020-21, as a result of the COVID-19 pandemic. The standard reskilling programmes with in-classroom, in-person delivery and assessment methods cannot be simply transposed into an online environment with little modification, as this approach does not always work.

Likewise technical training alone for these experienced workers is not enough. Many of them are in their thirties and forties and have had this career change imposed upon them by these extraordinary circumstances and the economic decisions that were taken by Governments to control it. These experienced workers are unprepared for how to go about finding a new job and building a new career in a new industry at this stage of their working lives.

This paper examines the experiences of a private training provider in implementing an online career coaching programme in addition to a technical one, for experienced workers looking to make a transition into a new industry. Not surprisingly, the addition of career change skills to new technical ones enhanced the direct benefits of securing a new job more quickly for these workers. However when given the option of completing both the technical module and the career coaching module, 80% of experienced workers choose to do only the technical module.

Given the benefits of adding the career coaching, it was decided to make it mandatory for everyone to complete it, however the question remained as to why did these otherwise capable experienced workers not want to do it.

Post completion surveys of these workers found that the career coaching programme also has the indirect benefit of increasing their confidence in the future, even if they didn’t secure a job from the reskilling initiative, which in turn made them more open to engaging with future Government initiatives in this area. This indirect benefit is of great value to Governments who will be making significant investments in reskilling initiatives, as this will ensure that every reskilling programme with combined technical and career coaching elements will have broader benefits to more workers than simply rolling out a technical one.

The combination of direct and indirect benefits from making the career coaching programme mandatory led to the key research question, “Why the mandatory inclusion of this career coaching skills module (which the experienced workers would not have chosen to do by themselves) increased their confidence in the future, and their attitudes to lifelong learning?”.

When looking to answer this key research question, the analytical lens and theoretical framework of Behavioural Science was applied, in particular Bounded Rationality and Dual-System Planner-Doer Models. These suggest some explanations for the behaviour of these experienced workers and the positive outcomes from having completed a career coaching module that they did not want to do in the first place.

The key outcome from this research will have great benefits for Governments in kick-starting their economies, identifying best practices for incorporating online career change skills programmes alongside technical training ones. These can be implemented in major training investments by Governments through education providers, so as to get more effective outcomes when engaging with retrenched workers through back-to-work initiatives.

Keywords: Online Learning, Behavioural Science, Adult Learners, Career Coaching, Experienced Workers, Reskilling

Introduction

Career change has been imposed on many experienced workers all around the world as a result of the economic fallout following the Government decisions that were made to control the COVID pandemic. In a world where increasingly there are no longer “jobs for life”, experienced workers need to have the ability to upskill themselves so as to be able to make multiple job changes over their working lives.

Following the COVID pandemic and the subsequent social distancing measures implemented by Governments, research into online learning has never been more relevant, with the upcoming back-to-work programmes Governments will be implementing to kick start the economy.

This paper takes the experiences of a private training provider in reskilling experienced workers for new jobs in new industries. It uses the data and insights generated to provide a unique perspective on the key elements of a reskilling framework, combining online career change skills with relevant technical programmes to transition experienced workers into new jobs and careers.

The data set has been gathered over a three-year period (2017/18/19) from over 500 adult learners, coming from a variety of educational and employment backgrounds, with 5 to 25 years of work experience. All were exposed to the same technical and career coaching programme.
This paper is broadly practitioner research using case studies as illustrative of real-world phenomena. The methodology for comparison draws heavily on Bereday’s model of comparative styles and their predispositions (Bereday, 1964).

Conceptual Framework

This paper is broadly practitioner research using case studies as illustrative of real-world phenomena. The methodology for comparison draws heavily on Bereday’s model of comparative styles and their predispositions (Bereday, 1964).

In Bereday’s model, ‘everyday’ comparability is distinguished from socially-scientific or laboratory methods. The everyday comparability approach fits with individualistic practitioner research in that it favours establishing relations between observable facts, noting similarities and graded differences, drawing out universal observations and criteria, and ranking them in terms of similarities and differences.

In everyday comparability, the view is subjectively from within and deliberately without perspectives detachment. It focuses on group interests, social tensions, impact factors and collective beliefs, patterns, and behaviours as experienced by the authors.

In terms of analytical steps, this paper uses Bereday’s four stages as illustrated by Jones (Jones, 1971), as follows:

  • Stage 1: Description of each case using a common approach to present fact
  • Stage 2: Interpretation of the facts in each case using knowledge other than the authors
  • Stage 3: Juxtaposition for preliminary comparison using a set of relevant criteria
  • Stage 4: Simultaneous comparison, emergence of conclusions and hypotheses

The perspective in this paper is the authors’ own as the private training provider of vocational education programmes, mindful of the particular risks of insider research (Rooney, 2005).

Current Practice

Over the last 3-years, over 500 experienced workers have taken this career change skills programme, and with each group, there has been a refinement of how the career change programme has been implemented, based on the feedback that has been received in the annual post-programme survey.

The career coaching programme covers the basics of how to find a new job, in a new industry including where to look, how to write a CV, interview skills, and the key transferable skills than an experienced worker is bringing to the new industry.

Most importantly, as these experienced workers are going through a career change, some are overconfident about their job hunting capabilities, and others are unduly worried. At the start of the programme, the experienced worker’s job huntings capabilities are measured using a short 10-question survey (called the Job Hunting Baseline quiz). This questionnaire is used to normalise both groups. After the completion of the 5-week module, the same quiz is retaken, to demonstrate to the learner how far they have come in only a month. The results of these short quizzes will be discussed in the next section.

The key changes made to the career coaching programme across the group of 500 experienced workers is as follows:

  • 2017 – the career coaching programme was offered to everyone. However, only 19% of students availed of this option. In the post-programme survey however, there was very positive feedback from them about the programme.
  • 2018 – the career coaching programme was offered to 50%, and was mandatory to complete for the other 50%. There was a subsequent rise in the percentage of experienced workers who secured employment – and there was a significant increase in the student’s positive outlooks on lifelong learning and job security.
  • 2019 – the career coaching programme was made mandatory for everyone. There was another rise in the percentage of experienced workers who found a job, as well as another increase in the positive outlooks on job security and lifelong learning.

The data for each of the years will be discussed in more detail in the next section – Research Findings.

Research Findings

The data gathered for this paper is quantitative. The limitations of quantitative studies – as potentially statistically relevant due to large data sets while being humanly irrelevant, missing the contextual details surrounding the results – are acknowledged. However, in this case, in the straddling between insider-actor mode and outsider-observer mode (Robson, 2011), and due to the research question in hand, the research generated provides a large enough basis on which to build observations.

For the purposes of this paper, a demonstration of the movement of grade distribution on the job hunting baseline quiz from before they started the course to after they finished it. The same quiz was given in both circumstances to demonstrate a difference in grade before and after the course of study. This data representation is the clearest way of demonstrating the effectiveness of the career coaching programme on these experienced workers, as visually this difference is very apparant, moving from low-to-medium grades for all the workers at the start to medium-to-high grades at the end.

The data for this paper was gathered directly by the training provider using tools including online quizzes through the Moodle platform. The data has been processed for ease of reading using Moodle’s graphing tool as this was the purest way to graphically represent the data without excluding any outliers. There is a change between the number of students on the course at the beginning and at the end, this is due to the student drop off rate on the course as other factors prevented them from studying and completing the course.

2017 started documenting data on the students who completed the course, specifically about how many of the workers who actually took the course recognised the help that it gave and whether this was demonstrable. Based on this data, it was possible to gain a preliminary idea of how the students saw our course and the benefits that it provided to them in their life.

Then in 2019, it was decided to revamp this method of surveying the students by expanding the areas investigated. This has resulted in a much more in detail understanding of specifically how and where the career coaching helps the experienced workers taking these programmes. This has also allowed the provider to edit the course further, putting more emphasis on the areas that were marked as more relevant to the students career.

Figures 1&2: Bar charts of number of people and their associated grade achieved in the same Job Hunting Quiz before and after taking the course

(Class 1 Before Taking Course – 44 Students)
(Figure 1)

Figures 1&2: Bar charts of number of people and their associated grade achieved in the same Job Hunting Quiz before and after taking the course

(Class 1 After Taking Course – 35 Students)
(Figure 2)

Figures 1&2: Bar charts of number of people and their associated grade achieved in the same Job Hunting Quiz before and after taking the course.
The 2 key points to be considered from this are:

  • As can be seen from the graphs there is a significant shift to the Right across all the grades (i.e. higher grades) between start and finish of the career coaching course.
  • In the post-module survey, 22 of the initial 35 students found a job using the methods that they learned from the course (63% out of all students that finished the course).
Bar charts of number of people and their associated grade achieved in the same Job Hunting Quiz before and after taking the course in Class 2 for comparison of the performance of other classes

(Class 2 Before Taking Course – 20 Students)
(Figure 3)

Bar charts of number of people and their associated grade achieved in the same Job Hunting Quiz before and after taking the course in Class 2 for comparison of the performance of other classes

(Class 2 After Taking Course – 31 Students)
(Figure 4)

Figures 3&4: Bar charts of number of people and their associated grade achieved in the same Job Hunting Quiz before and after taking the course in Class 2 for comparison of the performance of other classes.
The key point to be considered from this is:

  • Once again the graphs show a significant shift to the Right across all the grades (i.e. higher grades) between start and finish of the career coaching course.
Bar charts of number of people and their associated grade achieved in the same Job Hunting Quiz before and after taking the course in Class 3

(Class 3 Before Taking Course – 20 Students)
(Figure 5)

Bar charts of number of people and their associated grade achieved in the same Job Hunting Quiz before and after taking the course in Class 3

(Class 3 After Taking Course – 17 Students)
(Figure 6)

Figures 5&6: Bar charts of number of people and their associated grade achieved in the same Job Hunting Quiz before and after taking the course in Class 3.
These graphs were put in for more points of comparison between the classes as the wider data set the better we are able to see trends
The key point to be considered from this is:

  • The distribution of students once again shifts to the Right (i.e. higher grades) after the end of the course, to reflect the knowledge that they gained through the course.

The original post-programme survey (2017 and 2018 students) looked to measure the percentage who gained employment, the percentage who took the course, and the student’s overall outlook on lifelong learning, job security and confidence in the future. Table 1 below details these results: (Please note that Δ means “increased”)

post-programme survey

(Data Gathered in 2017 & 2018)
(Table 1)

The key points of this table are:

  • The proportional increase in the percentage of people who gained a relevant job after taking the career coaching course.
  • In addition to this the addition of this course contributed to the mental wellbeing of our students as their confidence in the future increased
  • Due to the increased number of people who have gained employment due to this course, there has been a correlated increase in their confidence in their future job security, further demonstrating that this course is both beneficial for the workers as it aids to their gaining of employment.

Table 2 below, details in much more depth the post-programme 2019 survey, where the response the students gave on each of the questions regarding their outlook on life, was categorised with respect to Job Success and Course Completion.

post-programme 2019 survey

(Data Gathered in 2019)
(Table 2)

The key points of this table are:

  • The further breakdown of the courses results has allowed us to see that the student’s engagement with lifelong learning irrespective of gaining employment or even finishing the course is high, meaning that these students are likely to either take another course or suggest that others take a course.
  • In addition to this, courses like this improve the confidence and motivation of the student irregardless of the outcome as they feel as if they as a person have progressed and gained skills that make them more employable, in turn making them more likely to take one of these courses again and engage with Government back-to-work programmes.

All of these points together led to the key research question of this paper:
“Why the mandatory inclusion of this career coaching skills module (which the experienced workers would not have chosen to do by themselves) increased their confidence in the future, and their attitudes to lifelong learning?

Theoretical Framework for Analysis

Previous research from this private training provider was reported at the Research Work Learning Conference 2015 in Singapore, the ICDE World Conference on Online Learning 2019 and the IMSCI Conference 2020. This work found the lens of Behavioural Science to be particularly useful to interpret the decisions of learners in an online environment. This current analysis further builds on those ideas.

Behavioural Science is the study of human motivation, decision making, and actions. It tries to understand how people interpret information; why they make the decisions they do when faced with multiple options; and, ultimately, why people behave the way they do.

The analytical lens of behavioural science suggests some explanations in answer to the key research question.

Overview of the field of Behavioural Science:

As a species, humans have evolved a strategy of decision making that uses mental shortcuts – or heuristics – to allow subconscious decision making.

Colloquially – these heuristics are called “rules of thumb”. Daniel Kahneman describes this process as basically trading a difficult question, for an easier one within our brains. While there is an evolutionary advantage for using these rules of thumb in the increasingly complex world in which we live, they can lead to cognitive biases.

A cognitive bias is a systematic error in thinking. In other words, the decision that arose was due to an error in how information was processed due to an inherent reliance on our innate heuristic.

Herbert A. Simon is seen as the founder of modern behavioral science, after winning the Noble Prize for Economics in 1978 for his theory of Bounded Rationality. He demonstrated that humans make decisions to achieve a satisfactory outcome, rather than an optimal one because our decisions are made on the knowledge we have, our ability to process this knowledge, and the amount of time we have to make the decision (Simon, 1955). This suggests that learner’s abilities to implement the knowledge they receive on career coaching is dependent on these three factors.

Key Behavioural Science Concepts for this Paper:

There are 12 key behavioural science concepts when examining this research question:

  1. Ambiguity (uncertainty) aversion – this is the tendency to favour the unknown over the known due to an innate fear of unknown risks. (Ellsberg, 1961)
  2. Availability Bias – this is when judgements are made about the likelihood of an event based on how easy it is to recall a case that comes to mind. (Tversky & Kahneman, 1974)
  3. Choice Architecture – is when changing how a choice is laid out, changes the context in which a person makes a decision (Thaler & Sunstein, 2008).
  4. Confirmation Bias – is the tendency to use hindsight to confirm that our choices are the right one’s (even if this wasn’t the case) (Wason, 1960)
  5. Default (opt