AJEE_16_2_Lindsay

167

Institution of Engineers Australia, 2010

* Paper D09-070 submitted 3/08/09; accepted for publication after review and revision 20/04/10.

Corresponding author Dr Euan Lindsay can be contacted at [email protected].

The relationship between reported workload, stress and employment levels in fi rst-year engineering students*

ED Lindsay and H RogersCurtin University, Perth, Western Australia

ABSTRACT: Engineering degree programs are notorious for placing considerable demands upon their students. Balancing study and work is a challenge faced by an increasing number of undergraduate students, and this balancing act can be stressful. This paper presents data gathered from fi rst-year engineering students regarding their perceptions of their levels of stress and workload throughout a semester of study. Stress is investigated both as an absolute measure, and also as a measure relative to the students perception of normal. These data show that there is considerable variation in the perceptions of the cohort. There is a proportion of the cohort that are always highly stressed; similarly there is a proportion that never fi nd themselves stressed at all. More importantly, the data shows that while stress and workload are linked, they are not equivalent. Relative stress does not always match absolute stress there are students who are very stressed, but for whom this is normal; similarly there are students who are only slightly stressed, but for whom this is an increase on their usual non-stressed state. Students reported levels of workload were more variable than the measures of stress, suggesting that the relationship between stress and workload is more complex than simply more work equals more stress. Their self-reported levels of workload and stress are compared to each other and to the number of hours reported for study and paid employment. This comparison shows that while in general workload and stress are indeed linked, there is a substantial proportion of the cohort for whom these factors appear to be independent. In particular the link between absolute stress and workload appears weaker, suggesting that the issue may not be the actual level of stress, but rather the students perceptions of what constitutes a normal workload at a university level.

1 INTRODUCTION

Engineering degree programs are notorious for placing considerable demands upon their students. Balancing study and other commitments, such as paid employment, is a challenge faced by an increasing number of undergraduate students (McInnis, 2001). These challenges are particularly diffi cult for fi rst-year students who are also dealing with the transition from high school student to the university environment.

The Graduate Course Experience Questionnaire is a key indicator of the teaching performance of Australian universities, and engineering has historically underperformed against other degree programs. Average ratings on the Good Teaching

Scale are consistently 10-20% lower for engineering programs than the overall national average (Graduate Careers Australia, 2006), with excessive workload issues being a common theme in graduate responses.

There are a wide range of factors that cause stress in undergraduate students (Garrett, 2001). In addition to academic-related issues, a signifi cant number of non-academic-related factors also contribute heavily to the stress levels of students (Ross et al, 1999). In order to understand students stress levels, it is necessary to understand the factors in their lives and academic workload is often misunderstood.

The concept of workload is potentially misleading as students self-reporting of workload does not necessarily represent their ability to cope with their learning load. Jonkman et al (2006) showed students perceptions of workload are not correlated to the amount of work that they do, but instead show some correlation to the number of assignments that they are required to complete. Other studies have shown

Australasian Journal of Engineering Education, Vol 16 No 2

168

Australasian Journal of Engineering Education Vol 16 No 2

The relationship between reported workload, stress and employment ... Lindsay & Rogers

that the extent to which the work is perceived as meaningful impacts upon the students ratings of workload (Kember, 2004; Marsh, 2001).

This paper explores the relationship between students perceptions of their workloads and their stress levels. The study was carried out with a large cohort enrolled in a first-year Engineering Foundation Principles and Communication (EFPC) unit at Curtin University. Each week students were asked to complete an online survey to refl ect on their weeks activities. The surveys were available from Thursday until the Tuesday of the week following. The students responses regarding their workload, stress and employment levels over a full semester (semester 2, 2007) are presented in this paper.

2 THE SURVEY INSTRUMENT

The EFPC unit includes an activity called the Weekly Workload Refl ection. This activity is an 11-question survey that students are encouraged to complete as a refl ective exercise to help them with developing their time and workload management skills.

This weekly refl ection serves two purposes: fi rstly (and most importantly), as a tool for developing the students professional skills; and secondly as a source of research data regarding student workloads. This multiple purpose approach places meant that the weekly survey was optimised for ease of use and for promoting student learning, rather than being optimised for the purposes of data collection. This constraint has the potential to confound the signifi cance of any research fi ndings.

2.1 The survey questions

The students were asked a range of questions dealing with the nature of their workload: How many tasks, of what size, diffi culty and relevance? How many hours did they invest in their study? In paid work? What factors have contributed to their workload this week? Which one contributed the most? What strategies worked well this week? What will you do differently next week?

The students were also asked to provide a measure of their workload and of their stress levels. These were each implemented on a fi ve point scale.

Q: How does your academic workload this week compare to your typical weekly academic workload?

A. This week requires much less work than normal

B. This week requires a little less work than normal

C. This week is pretty typical

D. This week requires a little more work than normal

E. This week requires much more work than normal

Q: How would you characterise your current level of overall stress?

A. Not at all stressedB. A little stressedC. Somewhat stressedD. Very stressedE. Extremely stressed

Q: How does your current stress level compare to your typical stress level?

A. Im much less stressed than normalB. Im a little less stressed than normalC. My current stress level is pretty typicalD. Im a little more stressed than normalE. Im much more stressed than normal

In addition, students were asked to report on their time commitments:Q: How many hours did you put into your study

this week?Q: How many hours of paid employment did you

do this week?

Time commitment responses were collated into fi ve hour categories 0, 1-5, 6-10, 11-15, etc.

2.2 Implementation of the survey

The survey questions provided a measure of the overall cohorts perception of their workload and their stress levels. The data was gathered online, through the units WebCT interface. Students were given from the Thursday of the week until the Tuesday of the following week to complete the survey for that week. Completion of the survey was voluntary, but encouraged.

The overall data gathered from WebCT was anonymous no individual student could be identifi ed. While this prevents the ability to follow a student longitudinally throughout the semester, it does promote honest and authentic engagement on the students part they know that this cannot be used as an assessment tool. Aggregation of the anonymous data allowed generalisations to be made about the overall cohort.

Students were asked to complete the survey every week, including the non-teaching breaks after teaching weeks four and seven. The overall response rates varied throughout the semester, with numbers dropping off towards the end of the semester and during the examination period (fi gure 1).

The variations in response rate illustrated in fi gure 1 are a potential concern for the validity of the data. Presumably the drop-off rate is in some way infl uenced by the students workloads, however, there is no data available as to why the students did or did not participate in the survey.

169



A total of 1392 useable data points were collected from the 238 students enrolled in the unit.

2.3 Validity of the data

The choice to make the survey voluntary was a balance between two issues: the ability to generalise from volunteers to non-volunteers versus the authenticity of responses given only because responding is compulsory. The responses are only meaningful if they are authentic; the risk of introducing meaningless data which cannot be managed by careful analysis was deemed greater than the risk of over-generalising which can be managed.

The relationships between the responses for each student are less affected by the number of responses each week. The comparisons of workload and stress are made independent of week; as such it was only important to ensure that each category of responses is adequately represented.

One obvious potential weakness of this study is that the survey collects self-reported data. Students are asked to estimate their workloads and the number of tasks that they have to complete each week. Relying upon this self-reported data introduces the risk of misreporting, however, the steps necessary to avoid this risk are prohibitive to the exercise. The survey is intended as a non-invasive, quick, once-a-week activity. Monitoring over 200 students throughout the 14 weeks of semester to ensure accurate reporting would require a substantial investment of resources, as well as placing a much larger burden upon the participants. Completion of the survey itself represents an increase in the students workloads; this addition, presented at a time where their awareness of

the extent of their workload is inherently heightened, can lead to frustration and resentment.

In order to make the results meaningful, students need to engage objectively with the exercise. There was a concern that if the purpose of the survey was seen to shift from a teaching exercise to a research instrument, students would be less likely to engage properly with the survey. Rather than seeing it as a learning opportunity, it would instead be viewed as an unfair burden using them as research guinea pigs. For this same reason it was felt that compulsory participation would be unhelpful, as well as complicating the process of acquiring ethics approval for the work.

Consistency between responses was a major concern for the analysis of results, as it is for most self-reported data. A student who has done some forward planning may report a major project as a set of fi ve small tasks rather than as one large task; a student who is not up to date on lecture material may report the hours of catch-up as part of the time taken to do an assignment. This potential weakness in the data is inherent in the nature of how it is collected; remedying this weakness, however, was deemed prohibitive for the context of the work.

For all that self-reported data may be a weakness of the data collection approach, however, many of the quantities of interest are inherently subjective. Stress levels are unique to each person, and the ultimate goal of this work is to reduce the negative effects of stress and workload upon students. Ultimately it does not matter whether a student moves from very stressed to somewhat stressed, or from somewhat stressed to a little stressed the key is that they have in fact become less stressed.

0

20

40

60

80

100

120

140

160

180

1 2 3 4 NT 5 6 7 NT 8 9 10 11 12stu

dy

exam

1

exam

2

Fig ure 1: Response numbers by week of semester.

170



3 THE RESULTS SINGLE VARIABLES

Given the drop off at the end of semester, the analysis of this data is confi ned to the 1392 data points for which all three variables (relative and absolute stress, and relative workload) are represented, in the fi rst 14 weeks (12 teaching and two non-instruction) of the semester.

The data does not allow for individual students to be tracked throughout the semester, and so no conclusions can be drawn regarding how workload and stress changes for an individual. It is useful, however, to consider a representative student based upon a percentile ranking. From week to week, different students will occupy different percentile ranks, however, it is illustrative to consider the experience of a student who occupies a particular rank. In this way a representative longitudinal experience can be assumed, made up of the collective experience of the overall cohort. This representative student approach provides a useful technique to consider each of the individual variables more closely.

3.1 Absolute stress versus week

The absolute stress follows the overall pattern of increasing from the first week until the second break, then peaking again at week 10 and decreasing towards the exams (fi gure 2, normalised to show proportions of respondents in each week who fi t in each category).

It is signifi cant to note that there is a proportion of the cohort who are not at all stressed at any point throughout the semester, and that all throughout the semester there are students who are extremely stressed. For most of the semester, the median reported absolute stress is somewhat stressed. It is only in three of the weeks 9, 10 and 11 that the median response is very stressed or higher.

It is also signifi cant to note the small variations in the reported level of absolute stress. There is no point in the semester in which a representative student will be two responses different in consecutive weeks; for instance, if the student at the 65th stress percentile reports C, somewhat stressed in one week, then the student at the 65th stress percentile in the next week will report in the range B-D. Indeed, there are very few instances in which a two-step change is made inside two weeks, and these mostly deal with the wind-down in stress at the end of the semester.

Again, because no student can be tracked longitudinally, this does not mean that no individual student experiences these fl uctuations. What it means is that the cohort overall makes a relatively gradual transition from relaxed to stressed. This data suggests that for the overall cohort, there is a fairly appropriate balance of stress. Whether this stress matches their expectations, however, is better considered with the relative stress variable.

3.2 Relative stress versus week

The relative stress built up over semester, peaking at teaching week 10, then dropping off towards exams (fi gure 3).

The relative stress variable is more volatile than the absolute stress variable, with more frequent changes in the median percentile response throughout the semester. There are also more instances of greater changes in response (ie. two steps) between weeks at a given percentile rank, although these are primarily linked to the non-teaching breaks. This decrease in stress at breaks contradicts Ross et als (1999) fi ndings that breaks were a source of stress, however, it could be that workload in the lead up to breaks is stressful, but the breaks themselves are not. There is a signifi cant decrease in relative stress at the end of the semester, with some representative students

Fig ure 2: Absolute stress versus week.

171



experiencing two-response changes from C to A in both the fi nal weeks.

It is signifi cant to note that in all but the fi rst week there are students that report that they are much more stressed than normal, and that for nine of the 14 weeks there are students that report they are much less stressed than normal. This suggests that there are some substantial differences in the students perception of what constitutes normal a phenomenon that is also observed in the data regarding their relative workload.

3.3 Relative workload versus week

The relative workload builds up over semester, peaking at teaching week 10, then dropping off towards exams (fi gure 4).

The relative workload variable displays signifi cant variability in the weeks adjacent to a non-teaching break. There is a huge decrease in the reported workload in the second non-teaching week, with the lowest 60 percentiles showing a two-category decrease in reported workload. There is also a much greater variability in the median response, which varies through all of the weeks throughout the semester, and which remains constant for at most two consecutive weeks.

The biggest changes are located near the breaks, which show that the students clearly perceive that time off from classes provides a decrease in their workload. It is uncertain whether this is because they have fewer in-class commitments and as such have less work; or it could be an artefact of staff using the Friday before the break as a deadline; or it

Fig ure 3: Relative stress versus week.

Fig ure 4: Relative workload versus week.

172



could simply be a recalibration of what constitutes normal for the semester.

Again it is signifi cant to note that for every single week of semester there is at least one student who considers the workload to be much more than normal, and that in seven of these 14 weeks there is also at least one student who considers the workload to be much less than normal.

It is clear from the analysis of individual variables that there is a variation in perceptions between students. What is more meaningful is to determine whether there is a variation in perceptions within the students; that is to say whether or not their view of workload corresponds to their view of their stress levels. This comparison appears in the next section.

4 THE RESULTS COMPARINGSTRESS TO WORKLOAD

Each of the three individual variables shows distinct trends throughout the semester, and there is some

consistency between these trends. It is also informative to look at pairs of variables together, to determine the extent of the coupling between workload and stress.

4.1 Relative stress versus relative workload

One of the key goals of this work is to determine whether stress actually correlates with workload is it workload that causes stress, or is it other factors that have a greater impact? To do this, it is useful to compare the students reported levels of relative stress to those of relative workload (fi gure 5).

Figure 5 shows that there is a good degree of overall correlation between the perceptions of workload and stress, but that there are outliers. There are responses for which students responded that their workload was much higher than normal, but their stress levels much lower than normal, and vice versa. The responses are cross-tabulated in table 1.

Overall, 52% of the recorded responses have a matching value for relative stress and relative workload, which are highlighted in green in table 1. 37% of responses

Much lessLittle less

TypicalLittle more

Much more

Much less

Little less

Typical

Little more

Much more

0

50

100

150

200

250

Relative Stress

Relative Workload

Fig ure 5: Relative stress versus relative workload.

Table 1: Relative stress versus relative workload.

Relative stress

Much less Little less Typical Little more Much more Total

Relative workload

Much less 59 44 38 10 2 153

Little less 35 68 57 22 3 185

Typical 15 52 234 77 8 386

Little more 3 20 109 206 30 368

Much more 2 4 24 117 153 300

Total 114 188 462 432 196 1392

173



have a one-step difference (highlighted in yellow) and 11% of responses (highlighted in red) have a two-or-more category difference between relative workload and relative stress.

The mismatch is relatively evenly spread between students who rate stress higher and those who rate workload higher. 27% of responses rate their relative workload greater than their relative stress, of which 4.9% rate the difference at two steps or greater (A-C, B-D, C-E, etc.). 21% rate their stress greater than their workload, of which 6.0% rate the difference at greater than one step.

This data shows that while relative stress may correlate well with relative workload, the students responses matched for just over half of their responses, suggesting that other factors infl uence the students perceptions of workload and stress.

4.2 Absolute stress versus relative stress

One key distinction made in this work is the difference between absolute stress and relative stress

whether the student is negatively impacted by their stress is different to whether their stress levels are higher or lower than usual. In order to explore this distinction, it is useful to compare the students reported levels of relative stress to those of relative workload (fi gure 6).

Figure 6 shows that there are responses representing almost all pair-wise combinations of absolute and relative stress values, with some combinations being much more common than others. The responses are cross-tabulated in table 2.

In table 2, three clusters of responses are highlighted, each representing a theme in the responses essentially the aggregated version of the representative student model used earlier.

The first cluster, highlighted in red, represents students who consider not at all stressed to be their typical level of relative stress. For these students, a little stressed is a little more than usual, and somewhat stressed is much more than usual. These responses represent students for whom any

Much lessLittle less

TypicalLittle more

Much more

Not at all

A little

Somewhat

Very

Extremely

0

20

40

60

80

100

120

140

160

180

200

Relative Stress

Absolute Stress

Fig ure 6: Absolute stress versus relative stress.

Table 2: Relative stress versus absolute stress.

Relative stress

Much less Little less Typical Little more Much more Total

Absolute stress

Not at all 64 43 122 7 2 238

A little 47 118 141 76 2 384

Somewhat 1 24 169 192 12 398

Very 0 2 26 147 76 251

Extremely 2 1 4 10 104 121

Total 114 188 462 432 196 1392

174



stress is unusual perhaps unchallenged at high school, they are not used to stress in any form. A total of 210 responses fi t into this category, 15% of the overall dataset.

The second cluster, highlighted in yellow, represents students who represent a little stressed to be their typical level of stress. For these students, not at all stressed represents less than normal, while somewhat and very constitute more than normal. A total of 516 responses fi t into this cluster, 37% of the overall dataset.

Taken together, these two clusters represent more than half of the responses responses for whom the typical level of stress is not at all stressed or a little bit stressed. The perception of what is normal plays a big part in students responses to stress and workload, and this data suggests that the majority of students perceive normal to be a predominantly relaxed state.

Conversely, of the 372 responses indicating that the student is either very stressed or extremely stressed, the third cluster (highlighted in green) of 35 responses reported that this was typical or less than normal. For these students (2.5% of the overall cohort, or 9.4% of those who were highly stressed) it is clear that stress is a common part of their student experience, and their defi nition of normal is diametrically opposed to that of the fi rst two clusters.

This difference in opinion is clearly representative of the diversity of engineering student cohorts, however, it also raises defi nite challenges for the teaching of these students. For half of the students any notable level of stress will be more than they are used to; for a minority of students highly stressed is their normal way of life.

5 IMPACT OF EMPLOYMENT HOURS

When considering the impact of employment hours on relative workload (fi gure 7), relative stress (fi gure

8) and absolute stress (fi gure 9), only responses up to the 30-34 hour category were considered. The higher categories contain only six of the 1392 responses, and are not representative of the dataset overall.

Figure 7 shows that relative workload appears to be mostly independent of the number of hours worked, with the distribution of responses mostly unchanged across the different categories of responses. This suggests that the students perceptions of relative workload are largely independent of how many hours they report working.

Figure 8 shows that the distribution of responses remains similar for students who report less than 25 hours of paid employment. Above 25 hours of paid employment there is a tendency for students to report more extreme responses both much more and much less than usual.

While the conventional wisdom is that students who work more will be more stressed, fi gure 8 also shows that there is a subset of the cohort who are able to cope well with a high employment workload. Indeed, it is quite possible that it is the requirements of working so many hours that requires students to be organised, and that this organisation helps keep their academic work in check.

Figure 9 shows a strong similarity in the distribution of absolute stress responses between categories. There is a slight increase in the proportion who report higher than usual stress levels as the number of hours worked increases to 20 and beyond, however, overall the distribution is similar. This suggests that the students overall levels of stress are largely independent of the number of hours of paid employment that they report.

6 IMPACT OF STUDY HOURS

When considering the impact of study hours on relative workload (fi gure 10), relative stress (fi gure 11)

Fig ure 7: Relative workload versus employment hours.

175



and absolute stress (Figure 12), only responses up to the 40-44 hour category were considered. The higher categories contain only 34 of the 1392 responses, and are not representative of the dataset overall.

Figure 10 shows that there is a difference between the distribution of responses for students studying fi ve or less hours and those who study six or more. For students studying fi ve or less hours, half of the responses indicate that the week is less work than normal. For students studying more than fi ve hours, this proportion is less than 20%.

The distributions are similar across all of the 6+ hour categories, with a gradual increase in the proportion who report being much more stressed than normal.

Figure 11 again shows differences between the 0 and 1-5 categories and the 6+ categories. Around 40% of students studying fi ve hours or less report less than usual stress levels, whereas this proportion is around 20% for the 10-20 hour categories, and around 10% for the categories where students are studying 21+ hours.

The proportion of students who report being more stressed than normal also increases as the number of hours studied in the week increases. The proportion who report being much more stressed than usual is constant at around 20% for students reporting more than 15 hours per week.

It is interesting to note that the overall proportion of students who report being more stressed than usual increases from the 5-hour category to the 20-hour category, then drops for the 25-hour category and rises again to the 40-hour category.

Figure 12 shows that students who report low number of study hours also report low levels of absolute stress. As the number of study hours increases from zero to 15, the distribution of responses moves away from predominantly low-stress responses through to a balance of low- and high-stress responses. The distributions in the categories above 20 hours are fairly similar, although there is an ongoing increase in the proportion of students who respond that they are extremely stressed.

Fig ure 8: Relative stress versus employment hours.

Fig ure 9: Absolute stress versus employment hours.

176



It appears that there is a difference in the distributions of responses for categories where students spend more than 20 hours on a given activity. Figure 8 suggests that the distribution of relative stress

responses indicating more than 20 hours of paid employment were different to those for less than 20 hours; fi gure 12 shows that the distribution of absolute stress responses is different for those who

Fig ure 10: Relative workload versus study hours.

Fig ure 11: Relative stress versus study hours.

Fig ure 12: Absolute stress versus study hours.

177



report more than 20 hours when compared to those who study less than 20. There also appears to be a breakpoint at 20 hours for the way in which levels of relative stress increase in fi gure 11.

7 DISCUSSION

The results of this study have identifi ed a number of interesting artefacts in the relationships between stress, workload, study and paid work. The constraints of this study, however, limit the ability to generalise the results. In order to more thoroughly explain these artefacts, further investigation is necessary.

An evolution of the survey instrument is necessary to capture more accurately the range of activities in which students participate, rather than just study and paid employment. Family commitments are an issue for many mature age students; students who participate in sport at a competitive level have compulsory training commitments. Furthermore, a distinction between in-class and out-of-class study time will provide insight to study patterns; it may also indicate whether students distinguish study from attending class.

For more meaningful results, it will be necessary to abandon anonymity. This will complicate the implementation of the survey, with additional ethical concerns as well as having to ensure that students continue to volunteer to participate. The ability to track respondents week-by-week will allow for a clearer picture of how students stress and workload levels evolve throughout a semester. For instance, there is the possibility that non-participation is a masking factor for excessive workload, with students opting out because they are too busy. By tracking students throughout the semester such patterns of behaviour would become apparent.

The ability to identify participants would also allow for matching of responses to demographic information such as age, gender, domestic/international status, etc. Exploring whether demographic-based sub-cohorts give similar results to the overall cohort will allow for an analysis of the impact of these factors upon stress and workload to be considered.

It would also be helpful to expand the study beyond the fi rst year of the degree, however, this would introduce the potentially confounding factor of an evolution in the perceived normal stress and workload levels.

8 CONCLUSION

The underlying motivation for this work was to equip students with the skills to manage their workloads, and to provide academics with information as to the factors affecting their perceptions of these workloads and their stress levels. Stress and workload certainly appear to be related, however, the data shows that

they are not interchangeable. It is clear from the aggregated data that perception of workload varies more than perception of stress, and that perception of relative stress varies more than perception of absolute stress. The general trends in the variables are similar, however, there are differences when it comes to the students perception of normal.

Almost every week of the survey has both much more and much less responses for both relative stress and relative workload. There is a percentage of the cohort who are never stressed; there is similarly a percentage that are always stressed. Why these students are robust to changes in the workload throughout the semester warrants investigation. Are they unstressed because they are coping well, or are they unstressed because they have not realised how much work they have to do?

The majority of the cohort does undergo changes in stress and workload levels throughout the semester, with a defi nite link between the week of semester and their reported levels of workload and stress. The non-teaching weeks have a clear impact upon both of these variables, with both workload and stress decreasing in the weeks in which no classes are held. Clustering of assessments prior to a break can contribute to the pre-break peaks; the distribution of workload throughout the semester should be considered to ensure that these peaks do not climb too high.

Nearly half of the responses (48%) rated relative workload differently than their relative stress; 11% of all responses had a two or more category difference between the two variables. Some students reported workload, but not stress; others stress, but not workload. It is clear that more factors are at play. The students perceptions of normal also clearly varied among responses. For some students, very high levels of stress are normal; for other students any level of stress whatsoever was more than usual. These students need to learn how to manage their stress levels; it is incumbent on academics to provide them with opportunities to develop these skills.

Overall, it appears that while there is a link between workload and stress levels for the overall cohort, the relationship is not as simple as more work equals more stress. Some students are always stressed; some are never stressed. More study is required to truly identify what factors lead to students perceptions of their workloads.

REFERENCES

Garrett, J. B. 2001, Gender differences in college related stress, Undergraduate Journal of Psychology, Vol. 14.

Graduate Careers Australia, 2006, Course Experience Questionnaire Data, www.avcc.edu.au/content.asp?page=/policies_programs/graduates, retrieved 13 April 2007.

178



Jonkman, M., Boer, F. D. & Jagielski, J. 2006, Are we over-assessing our students? The students view, paper presented at the 17th Conference of the Australasian Association for Engineering Education, Auckland, 11-13 December.

Kember, D. 2004, Interpreting student workload and the factors which shape students perceptions of their workload, Studies in Higher Education, Vol. 29, No. 2.

Marsh, H. W. 2001, Distinguishing between Good (Useful) and Bad Workloads on Students Evaluations

of Teaching, American Educational Research Journal, Vol. 38, No. 1, pp. 183-212.

McInnis, C. 2001, Signs of Disengagement? The changing undergraduate experience in Australian universities, Inaugural Professorial Lecture: Centre for the Study of Higher Education, The University of Melbourne.

Ross, S. E., Niebling, B. C. & Heckert, T. M. 1999, Sources Of Stress Among College Students, College Student Journal, Vol. 33, No. 2, pp. 1-5.

179



EUAN LINDSAY

Dr Euan Lindsay is a mechatronic engineer, a discipline that integrates computers, electronics and physical hardware. His PhD investigated whether remote and simulated access alternatives to the traditional in-person laboratory experience could provide the same learning outcomes for students. Euans work in remote and virtual laboratory classes has shown that there are signifi cant differences not only in students learning outcomes, but also in their perceptions of these outcomes when they are exposed to the different access modes. These differences have powerful implications for the design of remote and virtual laboratory classes in the future, and also provide an opportunity to match alternative access modes to the intended learning outcomes that they enhance. Euan is a Senior Lecturer in Mechatronic Engineering at Curtin University of Technology, in Perth, Western Australia. His research interests include engineering education, telecontrol (particularly internet-based telecontrol), artifi cial neural networks, and rehabilitative technologies for people with sensing impairments. He is the President of the Australasian Association for Engineering Education, and co-edits the Australasian Journal of Engineering Education. He is a Fellow of the UK Higher Education Academy and was the recipient of a 2007 Carrick Award for Australian University Teaching. In 2005 he was named as one of the 30 Most Inspirational Young Engineers in Australia.

HELEN ROGERS

Helen Rogers is a Communication Skills Coordinator and Lecturer for the School of Media, Culture and Creative Arts (MCCA) at Curtin University, Western Australia. Presently she is working collaboratively with the Unit Coordinators of the Engineering Foundation Units (fi rst-year engineering) in the School of Engineering at Curtin University. Helen has many years experience working with undergraduate students in Australia, the UK and the Middle East. She has worked as a course coordinator at Stirling University (Scotland), Higher College of Technology (Dubai) and at Curtin University (Australia). She lectured and coordinated language and communication skills units to fi rst-year petroleum and chemical engineering students at Higher College of Technology (HCT) in Dubai. She became involved with the English Department and helped to create short English Language programs (specifi c to engineering) to help second language students with their communication/study skills. Helen was also involved in curriculum reviews and teaching and learning development. She established and directed the fi rst Womens Branch of the British Council in Saudi Arabia, where she taught, examined and supervised over 500 female students. Helen was also Director of Studies of Inlingua Language Institute in Cheltenham, UK, where she managed a medium-sized language school and developed ESP courses. She was a coordinator for the British Council in Athens, Greece, where she also taught many off-shore courses to petroleum and chemical engineers. Helen studied in Scotland, where she obtained an MPhil in Shakespearean Studies and a MA in Applied Linguistics. She is also a qualifi ed IELTS examiner.

180



Documents

AJEE_16_2_Lindsay