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Journal of Further and Higher Education
ISSN: 0309-877X (Print) 1469-9486 (Online) Journal homepage:
Increasing student engagement and reducing
exhaustion through the provision of demanding
but well-resourced training
Brad Hodge, Brad Wright & Pauleen Bennett
To cite this article: Brad Hodge, Brad Wright & Pauleen Bennett (2017): Increasing student
engagement and reducing exhaustion through the provision of demanding but well-resourced
training, Journal of Further and Higher Education, DOI: 10.1080/0309877X.2017.1363385
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Published online: 06 Sep 2017.
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Date: 27 October 2017, At: 07:30
Journal of Further and Higher Education, 2017
Increasing student engagement and reducing exhaustion
through the provision of demanding but well-resourced training
Brad Hodge, Brad Wright and Pauleen Bennett
School of Psychological Science and Public Health, La Trobe University, Bundoora, Australia
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Despite the fact that both workplace and training environments can be
inherently demanding, these environments sometimes manage to elicit a
level of engagement and enthusiasm that is surprising. The Job DemandsResources (JD-R) model has been used extensively within the workplace to
predict both engagement and burnout. It suggests that high demands lead
to burnout, but that appropriately targeted resources mitigate the impact of
these demands, and increase engagement. In order to test this model within
the university context a survey was developed to assess participants of a
short academic skills training programme. The survey measured students’
perception of demands, resources, engagement and burnout immediately
following the programme. Evidence suggests that resources were positively
related to engagement, and that demands had a positive relationship with
exhaustion, but not the other components of burnout. The relationship
between the actual demands of the training and exhaustion was mediated
by the individual’s emotional or stress related experience of that demand.
This research suggests that the JD-R model has value in predicting both
engagement and exhaustion for participants in short training programmes.
Received 9 June 2016
Accepted 19 February 2017
Job Demands-Resources;
training; engagement;
university; student
Experience suggests that workplace and training environments can be inherently demanding, requiring
staff and students to expend a great deal of effort in order to achieve goals, often set by someone else.
Sometimes these environments manage to elicit a level of engagement and even enthusiasm that is
surprising considering the immediate costs to the individual. Understanding how this occurs is central to
the development of better training. The Job Demands-Resources model (JD-R) has been used to explore
components of the workplace that lead to engagement and burnout, defined as a ‘non-productive relationship with work’ which is characterised by low energy, low involvement and low effectiveness (Leiter
and Maslach 2001). This has led to an improved understanding of workplace components that lead to
engagement and burnout (Bakker 2014b; Bakker and Demerouti 2007; Demerouti et al. 2001). Despite
obvious parallels between workplace and training environments, the role of demands and resources
in predicting burnout and engagement within training environments remains poorly understood. One
important difference is that training can be either a short- or long-term undertaking. The present investigation was designed to assess the generalisability of the JD-R model to a short-term training programme.
The Job Demands-Resources model within the workplace
Two models played an important part in development of the JD-R model; the effort-reward imbalance
model (ERI) and the demand-control model (D-CM; Karasek 1979). The ERI model suggests that ill-health
CONTACT Brad Hodge © 2017 UCU
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is more probable when work efforts are not perceived to be matched by work rewards (Siegrist 1996).
The ERI model also proposes that the disposition/cognitive style of ‘overcommitment’ moderates the
link between effort-reward imbalance and manifest ill-health (Siegrist 1996). The demand-control model
(D-CM) suggests that workplace stress is a function of both the degree of demand present within the
workplace and how much control the person perceives they have over their responsibilities (Karasek
1979). These two models have garnered much empirical support and provided the foundation for
development of the JD-R model. The JD-R model was initially proposed to explain burnout within the
workplace (Demerouti et al. 2001). The theoretical model, as shown in Figure 1, suggests that, if there are
high demands and low resources within a workplace, such that an employee perceives a requirement
for high output but little support, facilitation or instruction, there is an increased likelihood of burnout
(Bakker 2014b). Burnout is proposed to have three components, comprised of exhaustion, cynicism
and ineffectiveness (Schaufeli et al. 2002), and is a slow process, whereby the individual experiences a
progressive loss of energy and enthusiasm (Kant et al. 2004). The JD-R theory posits that poorly designed
jobs, with high demands and low resources, exhaust the employees’ internal resources, leading to a
depletion of energy (Bakker and Demerouti 2007). The central components of this model speak to the
importance of an energy driven process, whereby energy is depleted via demands, and whereby this
depletion can be at least partially mitigated through provision of appropriate resources. In addition
to this, resources appear to act as a catalyst for deeper engagement, more specifically the investment
of effort.
Whilst there are marked differences between an extended period of employment in the workplace
and participation in a short-term training course, it seems logical that the demands of training may
mirror the demands of the workplace, at least in their effect upon exhaustion. Thus, despite the fact
that the JD-R model was clearly designed with the workplace in mind, it may have relevance to training settings. Teachers place demands upon students, which often lead to increased stress. They also
provide varied levels of resourcing to enable students to cope with those demands. It may be the case
that a short course with high demands and low resources also leads to low engagement and, perhaps,
even to the experience of some components of burnout. Figure 1 illustrates how the JD-R model may
generalise to the training environment.
Traditionally, job demands have been characterised as those physical, social or organisational aspects
of a person’s job that require sustained effort, and come at a physical or psychological cost (Demerouti
et al. 2001). Demands within the workplace have an inherent cost to the individual, regardless of whether
Figure 1. Job Demands-Resources Model. From Bakker, Demerouti, and Sanz-Vergel (2014).
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those demands are perceived as desirable or not (Bakker, Albrecht, and Leiter 2011). Studying at a
university places certain demands upon students, in a way that seems to mirror that of the workplace.
These demands are often first encountered during short-term training programmes designed to prepare
students for the university experience. Demands within the training environment would include any
elements of the training which require effort and come at a psychological cost. These might include
things such as workload, task intensity, time constraints or even perceived difficulty.
Whilst demands have the strongest relationship with increased emotional exhaustion, research
­suggests that the lack of an appropriate level of resources also leads to an increased likelihood of
burnout and decreased engagement (Bakker and Demerouti 2007). Job resources generally refer to
the physical, psychological, social or organisational aspects of the job that enable an individual to cope
with the negative implications of the demands of the workplace (Demerouti et al. 2001). For example,
these might include support from supervisors or colleagues or even financial remuneration (Bakker et al.
2007; Sawang 2012). Resources within the training environment might include things such as feedback
from staff, support from colleagues and staff, or any other support provided for students to better meet
the demands of the course. Considering the fact that the university experience is, or perhaps, should
be, inherently demanding, our understanding of the way in which demands and resources can lead
to burnout at work may shed light on the most effective ways to reduce the exhaustion which results
from intense training environments. In addition, the JD-R model hints at ways to increase engagement,
which is recognised as being a core challenge in modern universities (Krause 2005).
The importance of increasing engagement
A key measure of a training environment is the degree to which students are engaged. Engagement
can be understood as being a fulfilling, positive state of mind characterised by a combination of vigour,
dedication and absorption (Schaufeli and Bakker 2004). In contrast with burnout, which is related to
negative health outcomes, engagement is more strongly related to positive motivational outcomes
(Bakker, Demerouti, and Sanz-Vergel 2014). The JD-R model suggests that high levels of engagement
are the product of both high levels of resources and high levels of challenge type demands (Bakker,
Demerouti, and Sanz-Vergel 2014). Challenge demands are those demands which promote personal
growth or gains and trigger positive emotions (Crawford, Lepine, and Rich 2010). Crawford, Lepine, and
Rich (2010) conducted a meta-analysis of 55 manuscripts, and reported that, whilst some demands
were negatively associated with engagement, other types of demands were positively associated. This
suggests that, whilst all demands are inherently costly to the individual, some demands, when coupled
with appropriate resources, may actually be predictive of increased engagement.
This has important implications for training, in that it may be possible to develop training that is
highly demanding and engaging, without exhausting, or burning out, participants. Whilst burnout is
unlikely to be present as a direct result of a single training course, it may be the case that some of the
components of burnout are evident and can be explained by the JD-R model. In contrast, it may also be
the case that short-term demands initially facilitate engagement, and that it is only when these same
demands persist over a much longer period, perhaps coupled with diminished coping resources, that
they lead to symptoms of burnout. This will require investigation in future. More immediately important, however, is whether the JD-R model provides an explanation of the components of training that
lead to engagement.
The Job Demands-Resources model in the academic training environment
A great deal of research has tested the JD-R model within the workplace, but only limited research
has been conducted to assess its validity within post-compulsory education or training environments.
Preliminary attempts have been made to apply the JD-R model to a high school setting. Salmela-Aro
and Upadyaya (2014) established some support for the model, demonstrating that increased academic study demands were related to burnout a year later and increased study resources related to
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schoolwork engagement in 1709 adolescent participants. This study was longitudinal in nature, with
students completing the survey at the end of ninth grade, during both the first and second year of
post-comprehensive school (age 16 and 17 years respectively), and then again two years after the start
of post-compulsory education (age 19 years). Students were asked to state a personal goal related
to their education. Demands were assessed with questions that specifically focussed upon students’
study-related goals, such as ‘How challenging is this goal?’ Resources were assessed with questions
such as ‘To what extent have you progressed towards achieving this goal?’ This operationalisation of
demands and resources is quite different from the standard JD-R approach, which generally focuses
upon specific types of demands, but did provide some support for the idea that academic demands
relate to burnout.
Salanova et al. (2010), investigated whether obstacles and facilitators had an indirect effect upon
student academic performance through an effect on wellbeing, and also found support for the JD-R
model. Obstacles were defined as the characteristics of a given situation which impeded performance.
Facilitators were those things that promoted performance. Participants were given a list of potential
obstacles and facilitators and chose those which were present within their academic environment.
Engagement mediated the impact of obstacles and facilitators upon academic performance but not
burnout, however burnout did not detract from academic performance. Their conclusion was that,
when students perceive many facilitators and few obstacles, they are more likely to be engaged,
which is likely to boost performance. They also found a positive relationship between the number of
obstacles and facilitators. This was understood to reflect the fact that perhaps students who perceive
many obstacles are more likely to actively look for facilitators in order to cope. It should be noted,
however, that the measure of demands focussed solely upon hindrance demands, which are characterised as demands perceived as being peripheral to the goal or main task, and viewed as undesirable
and frustrating as they can impede progress towards the main goal. Robins, Roberts, and Sarris (2015)
used the JD-R model as a means of better understanding the predictors of engagement and burnout
amongst health profession university students. Students were involved in a large placement component comparable to a work situation. The study found support for the motivational processes of the
JD-R model, in that demands had a positive relationship with burnout, and resources had a positive
relationship with engagement. It is unfortunate that none of these examples assessed short-term
post-compulsory training, however they do give an indication of the potential for the JD-R model to
enable better understanding of the classroom experience, within a short preparatory course, taken
at the commencement of university.
In order to ensure that the training experience elicits maximum effort and engagement without
increasing burnout, it may be beneficial to better understand ways in which the JD-R model might
predict engagement and burnout within the context of training. Rates of burnout amongst students
tend to vary from course to course and between studies. Almeida, Souza, Almeida, Almeida, and
Almeida et al. (2016) found that medical students had burnout rates of 14.9% and Mafla et al. (2015)
found rates of 7% amongst dental students. Amongst students, burnout has been associated with a
reduction in academic achievement (McCarthy, Pretty, and Catano 1990) and reduced engagement
(Uludag and Yaratan 2010). More research is required to better understand the nature of the demands
experienced by students, however, and how they relate to engagement and burnout. In addition, it will
be important to better understand those things that mitigate the ill-effects of those demands, thus
fulfilling the role of resources. Based upon similarities between the training setting and the workplace,
the JD-R model might be expected to accurately explain the way in which the demands and resources
of the training experience relate to the level of engagement and burnout for the student. The aim of
this study was to determine if the JD-R model could be generalised to the short-term training environment. Based upon workplace research, two JD-R hypotheses were tested. The first was that demands
would be associated with burnout, and that this relationship would be mediated by resources. The
second was that resources would be associated with engagement, and that this relationship would
be mediated by demands.
Newly enrolled university students (N = 150) voluntarily enrolled in an academic skills training programme at a regional Australian university. Most (n = 79, females = 57) accepted the opportunity to
be involved in the research component of the training upon registration. Students paid a nominal fee
to be involved in the training. Institutional ethics approval for the project was granted (FHEC14/R92).
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The JD-R questionnaire (Bakker 2014a) was modified for use in the present investigation. The demands
and resources scales were adapted for applicability to a short training course (Appendix 1). Some questions were omitted and others adjusted to apply directly to a time-limited training environment, e.g.
‘does your work require a lot of concentration’ was changed to ‘with regard to the smart skills training
programme, did the training require a lot of concentration’. All questions were rated on a five point
Likert scale from ‘never’ to ‘very often’. The questionnaire was comprised of the following four sections.
Demands and resources
The demands of training were assessed with 10 statements which assessed the student’s perceptions
of the level of demand entailed by the training. The demands scale included questions such as ‘Did you
have to work quickly?’ and ‘Was it mentally straining?’ Resources were assessed with 13 statements which
assessed the student’s perception of whether they had access to appropriate resources such as staff
feedback, autonomy, classmate support etc. This included questions such as ‘If necessary could you ask
your classmates for help?’ and ‘Did you find out how well you were doing in class?’ The questionnaire
is included in the appendix (Appendix 1).
A modified student version of the Utrecht Work Engagement Scale (UWES-9) (Schaufeli and Bakker
2003) was used to measure the time-limited training environment, with nine statements including ‘I felt
bursting with energy’ and ‘I felt happy when I worked intensely in this training’ (Appendix 2). Questions
were rated on a seven point Likert scale from ‘never’ to ‘always’.
Burnout was measured with a student version of the Maslach Burnout Inventory (Schaufeli et al. 2002).
The scale includes three factors which are exhaustion, cynicism and ineffectiveness. This scale was also
adapted for use with a short-duration academic skills programme using 15 Likert style statements
including ‘I felt emotionally drained during this training’ and ‘I doubt the significance of this training’
(Appendix 3). Questions were rated on a seven point Likert scale from ‘never’ to ‘always’.
Perceived benefits
In addition to the demands-resources questionnaire, participants were asked to rate how beneficial
they found each component of the training, which included presentations regarding essay writing
structure (essay structure), the process of writing (improved writing), how to format appropriately
(formatting) and getting organised (organisation). Each was rated on a six point Likert scale, from ‘not
at all’ to ‘very beneficial’.
The academic training was conducted over a period of three days and involved short lectures and tutorials on topics including referencing, organisational skills and how to structure an essay. Each training
day included four hours of training. All participants were in the one room but often broke into smaller
groups to do tasks. The training required active participation, and has previously been shown to be
effective at changing intentions to use these academic skills in the future (Hodge, Wright, & Bennett,
2016). The training was designed to be highly demanding of students, asking that they trial, and begin
to master the skills being taught. Participants were given a range of feedback both generally to the
whole cohort and more specifically to individual students. At the conclusion of the training, participants
were given 15 minutes to complete the survey. They were offered a 1 in 15 chance to win a gift voucher
as an incentive to participate.
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Data analysis
IBM SPSS statistics (Version 22.0) computer software was used for all of the analyses conducted. A
series of principle components analyses were performed to determine what factors were present for
demands, resources, engagement and burnout. A series of hierarchical multiple regressions were conducted to investigate whether perceived demands and resources predicted burnout, engagement or
perceived benefit. Mediation analyses were conducted using the SPSS extension PROCESS (Hayes 2013).
A mediation analysis determines whether the relationship between a predictor variable and outcome
can be explained by the presence of a third variable (Field 2013). Mediation analyses were conducted
using a bootstrap resampling technique (N = 5000) whereby the model was tested on a large number
of generated datasets in order to obtain a distribution of the statistic. The utility of this approach is
described by (Hayes 2013). All statistical tests used a criterion value of p < .05.
Factor analysis
Principal components analyses were conducted to identify the factor structure of the demands, resources
and burnout subscales. As a result, the resources scale was treated as a single factor. The demands scale
was treated as two factors, which were identified as effort required and subjective difficulty. A mean
demands total score was also calculated. In line with Maslach, Schaufeli, and Leiter (2001) recommendations, the three components of burnout (exhaustion, cynicism, ineffectiveness) were held to be individual
factors. Engagement was treated as a single factor. Assumptions of normality, linearity, homoscedasticity
and independence of residuals were assessed, and met, based on recommendations of Tabachnick
& Fidell (2000). The positively skewed variables, effort required and cynicism were transformed with a
reflected transformation, exhaustion was corrected with a square root transformation. Effectiveness was
negatively skewed and was transformed using a natural logarithm adjustment. Participants generally
rated resources reasonably high and demands quite low, as shown in Figure 2.
Do demands predict burnout?
A correlation analysis was conducted to identify relationships between the identified factors. The correlation analysis indicated that, whilst there was no relationship between the demands components (effort
required & subjective difficulty) and either cynicism or effectiveness, there was a relationship between
demands and exhaustion (Table 1).
A mediation analysis using the SPSS extension PROCESS (Hayes 2013) was conducted to determine
whether resources mediated the relationship between demands (effort required & subjective difficulty
combined) and exhaustion. There was no indirect effect of demands on exhaustion through resources
(b = −.14, BCa CI [–.35, .07]). There was, however, a significant medium sized direct effect of demands
upon exhaustion (R2 = .17, b = .44, t = 3.69, p < .01). This suggests that, whilst training resources do not
reduce the effect of training demands upon exhaustion, as suggested by our first hypothesis, there is
a moderate effect of training demands on exhaustion; as training demands increase, perceptions of
exhaustion also increase.
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Figure 2. Histogram showing the distribution of scores for both demands and resources, rated from never (1) to very often (5).
Table 1. Correlation matrix including demands, burnout, engagement and rating.
1. Effort required
2. Subjective difficulty
3. Effectiveness
4. Exhaustion
5. Cynicism
6. Rating
7. Engagement
p < .05.
Figure 3. Mediation analysis with effort required as the independent variable, subjective difficulty as the mediator and exhaustion
as the dependant variable.
Notes: Path coefficients are the standardised beta coefficients from the regression analysis. The number in brackets is the direct effect of effort required
upon exhaustion prior to including the mediator in the model. **p < .01, *p < .05.
Moderate correlations between exhaustion and both effort required in training (r = .28) and subjective
difficulty of training (r = .40) were noted and linear multiple regression revealed a relationship of effort
required in training and subjective difficulty of training with exhaustion (R2 = .16, F(2, 73) = 7.05, p < .01).
However, only subjective difficulty was a significant explanatory variable (b = .36, t = 2.66, p = .01). As
such, a mediation analysis using the SPSS extension PROCESS (Hayes 2013) was conducted to determine whether subjective difficulty of training was mediating the relationship between effort required in
training and exhaustion (Figure 3). The model was significant using N = 5000 bootstrap resamples and
indicates that there was a significant indirect effect of effort required upon exhaustion through subjective
difficulty of training (i.e. the confidence interval did not include zero, b = .21, CI [.06, .41]). This indicates
that the increase in exhaustion experienced as a result of the effort required at training was mediated
through the individual’s subjective experience of that requirement.
A second mediation analysis was conducted to determine whether demands mediated the relationship between resources and engagement. There was no indirect effect of demands upon engagement
(b = .01, BCa CI [−.01, .08]). There was a medium sized direct relationship between resources and
engagement (R2 = .18, b = .41, t = 3.84, p < .01). This reveals that individuals with more perceived training
resources felt more engagement, however, contrary to our second hypothesis, demands did not mediate
the relationship between resources and engagement.
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The Job Demands-Resources model has generally been applied to employees and, more recently, to
students engaged in long-term educational experiences. The present research suggests that, in addition,
the model may provide useful explanations of why students experience engagement and exhaustion
within a short training course. In support of hypothesis one, which held that demands would be associated with burnout, and that this relationship would be mediated by resources, the demands associated
with the training had a positive relationship with the exhaustion component of burnout. In addition,
the effect of these demands was mediated by the individual’s subjective experience of those demands.
In support of hypothesis two, which held that resources would be associated with engagement, and
that this relationship would be mediated by demands, resources such as staff feedback, autonomy and
classmate support had a positive relationship with engagement, with more resources increasing the
likelihood of engagement. However, demands did not mediate the relationship between resources
and engagement.
Despite the fact that demands did not relate to either cynicism or effectiveness, the demands variables did relate to exhaustion. This is in line with research from Alarcon (2011), which indicated that
demands have the strongest association with emotional exhaustion. Alarcon also found a relationship
between demands and both cynicism and effectiveness, however the meta-analysis was assessing studies that looked at workplaces rather than short-term training courses. Bakker, Demerouti, and Verbeke
(2004) hypothesised that demands would lead to an increase in the exhaustion component of burnout,
suggesting that the performance element of burnout was predicted by an increase of exhaustion. The
evidence from the academic skills training programme in the current study supports the idea that only
longer term exposure to demands will lead to an increase in cynicism and reduction in performance.
It could be expected, therefore, that, if research were assessing the full university experience, it would
be more likely to find relationships between demands, cynicism and performance.
The subjective experience of demands within our training programme played an interesting role
in mediating the effect of demands upon exhaustion. Whilst the demands of the programme, which
were assessed with questions such as ‘did you work under time pressure?’ and ‘did you have to work
quickly’, had a moderate effect upon exhaustion, this effect was mediated by the participants’ subjective experience of the demands. Subjective experience of demands was measured using items that
captured the stressor/demand, such as, ‘was the training stressful?’, and ‘was it mentally straining?’. The
fact that the subjective experience of strain within the training mediated the effect of demand upon
exhaustion, speaks volumes to the role of individual differences in determining training outcomes.
More specifically, the appraisal of the stressor by the individual is key to whether or not the demand
has an impact upon exhaustion. Whilst this particular study did not attempt to measure individual
differences, it stands to reason that a person’s ability to manage their experience of stress and strain
might influence the impact of the training demands upon exhaustion. Further research is required to
better understand this interaction.
There was no relationship between demands of the academic skills training programme and either
student cynicism or effectiveness, although past research in the workplace tells us that prolonged
exposure to high demands can lead to these aspects of burnout (Cavanaugh et al. 2000; Demerouti et
al. 2001, 2003; Lepine, Lepine, and Podsakoff 2005). In particular, demands that do not help to meet
the individual’s goals have a strong relationship with burnout (Crawford, Lepine, and Rich 2010; Lepine,
Lepine, and Podsakoff 2005). The experience of structured education places high demands upon students across a long time frame, which has been shown to lead to burnout in both the high school setting
(Salmela-Aro et al. 2008, 2009) and the higher education sector (Galbraith and Merrill 2015; Schaufeli et
al. 2002), but of course the current study did not examine prolonged demands. In addition, despite the
fact that demands are an inevitable part of a learning environment, burnout need not be, even if the
demands are prolonged. The JD-R model proposes that, in the presence of demands, resources play an
important role in predicting engagement and mitigating the impact of those demands upon burnout.
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Resources predict engagement, but this is not mediated by demands
In partial confirmation of the hypothesis that resources would be related to engagement, resources had
a positive relationship with engagement. This indicates that, when participants felt that they had access
to resources, such as autonomy, support and feedback, they were more likely to be engaged. The amount
of effort required and the subjective experience of difficulty, which we measured as demands, however,
did not mediate the effect of resources upon engagement. Previous research suggests a mediating role
for demands in the resources-engagement connection, suggesting that demands actually increase
the impact of resources on motivation, particularly in the case of high demands (Bakker, Demerouti,
and Verbeke 2004; Bakker et al. 2007). The presence of high demands within a workplace have been
shown to increase the likelihood that staff take advantage of resources available, which in turn increases
engagement (Bakker, Demerouti, and Sanz-Vergel 2014). Hakanen, Bakker, and Demerouti (2005), for
example, found that, for dentists, resources had the greatest impact upon engagement when there was
a high level of demand. It may be the case that the short-term introductory nature of the training studied here was not challenging enough to lead to an increase in the utilisation of resources. In addition,
this study was limited by the fact that it only measured participant’s perception of available resources,
and did not account for a participant’s utilisation of those resources. This research supports the idea
that the availability of resources improves student engagement in training, but future research would
do well to investigate whether high demand training increases the likelihood of participants utilising
resources more than does low demand training.
This research has a number of limitations. The participants of the study were unquestionably highly
motivated, having both chosen to attend the training and having volunteered for the survey component. This may mean that the sample was not representative of the broader student body. The content
covered in this programme, and the perceptions of demands and resources that accompanied it, may
not necessarily generalise to other programmes. Future studies may seek to explore this further with
content that may be viewed as less satisfying by students in terms of usefulness and applicability, such as
a course in quantitative research methods. The fact that this particular study used burnout components
as a measure of the negative impact of demanding training, but in a very short-term context, provides
insight into the provision of training but has limited application to understanding the development
of burnout. In order to gain insight into the development of burnout at university it will be necessary
to conduct more longitudinal research, building on the results of this initial study. Finally, unlike the
ERI model, the JD-R model does not seek to measure the impact of participant motivation and coping
skills on the outcomes of engagement and burnout. This may be an area that needs to be explored in
future research to more comprehensively assess the impact of intrinsic characteristics.
In conclusion, this study found support for the JD-R model within the context of a time-limited
training programme. This is a unique finding, important for the development of more effective training.
Demands were associated with exhaustion but not effectiveness or cynicism, and resources were related
to engagement. The effect of demands upon exhaustion was mediated entirely by the subjective experience of those demands; the degree to which the person found the training stressful and the level of
strain experienced. This evidence provides support for the use of the JD-R model as a useful framework
for better understanding the dynamic nature of the training environment. By further investigating the
nature of demands and resources as they are perceived and appraised by students, it may be possible
to better articulate how these elements of training predict engagement, burnout and behavioural outcomes. This, in turn, will provide insight for the development of training which ensures maximum effort,
produces maximum engagement and reduces the negative impact of that effort upon the student.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes on contributors
Brad Hodge is currently a doctoral candidate at La Trobe University, Bendigo Australia. He is currently conducting a research
project looking into the elements of training that lead to increased engagement and decreased disengagement and
exhaustion. His work experience focusses around the development and delivery of innovative educational experiences
within the university sector.
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Brad Wright is a lecturer in the School of Psychological Science at La Trobe University. He has over 10 years’ experience in
the field of academia, worked in various roles in the disability sector, and continues to provide Sport Psychology advice on
a consultative basis for state level teams and athletes. His research interests are broad and include areas of Performance
and Health Psychology. He is also interested in Psychophysiology and Research Methods and Statistics.
Pauleen Bennett is the Director of Regional Operations in the School of Psychological Science at La Trobe University.
Pauleen heads the Anthrozoology research group (www.anthrozoology and is the president of the
International Society of Anthrozoology ( Pauleen’s work combines all aspects of teaching and learning in
conjunction with a research focus around improving human-animal relationships.
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Appendix 1. Training Demands-Resources Survey
Did you have to work quickly?
Did you have too much work to do?
Did you often have to work extra hard to finish in time?
Did you work under time pressure?
Did it require a lot of concentration?
Did it require that your work be exactly correct?
Was it mentally straining?
Did it require sustained attention?
Was the training emotionally demanding?
Was the training stressful?
never sometimes
Very often
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Did you have flexibility in the way went about your work?
Did you have control in how you complete your work?
Did you get to decide what kind of work you do?
If necessary, can you ask your classmates for help?
Can you count on your classmates to support you if difficulties arise
in your work?
Did you feel valued by your classmates?
Did you receive sufficient feedback about your work in class?
Did you find out how well you are doing in class?
Did you receive sufficient information about your work in class?
Were you able to develop your skills?
Did you learn new things?
Did you get positive feedback?
Were the staff friendly and open with you?
Very often
Appendix 2. Engagement survey
I felt bursting with energy.
I felt strong and vigorous.
I am enthusiastic about the topics taught.
This subject inspires me.
When I got up in the morning I looked forward
to this training.
I felt happy when I worked intensely in this
I am proud of the work I have done in this
I was often immersed in my work.
I got carried away when I was working on
never almost never
sometimes often very often
Appendix 3. Burnout survey
I felt emotionally drained during this training.
I felt used up at the end of this training.
I felt tired when I got to this training.
Being part of this training was a real strain
for me.
I felt burned out after this training.
I have become less interested in academic
skills following the training.
I have become less enthusiastic about this
topic since I did the training.
I have become more cynical about the usefulness of this training.
I doubt the significance of this training.
I was able to effectively solve problems that
arose during the training.
I believe that I made an effective contribution
during this training.
In my opinion I was a good student during
this training.
I felt stimulated when I achieve things in this
I have learned many interesting things in this
During this training I felt confident that I was
effective in getting things done.
never almost never
sometimes often very often
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