Understanding the Role of Student Stress, Personality and Coping on Learning Motivation and Mental Health During a Pandemic

The aims explored associations between stress ratings and influences on coping on student mental health and motivation and compared defensive-pessimism against optimism as a strategy for learning motivation. Most research construes stress as distress, with little attempt to consider positive ‘eustress’ experiences. Undergraduate psychology students (N=162) were surveyed on student and pandemic-related stressors, personality, support, control, mental health and learning motivation. Overall, lack of motivation and procrastination were acute. Uplifting ratings of teaching and optimistic thinking were associated with good mental health, but context control was key. Hassle ratings of teaching lowered learning motivation. Support and conscientiousness bolstered learning motivation, with the latter an important buffer against hassle experiences on motivation. Openness was associated with the stress involved in learning. For those anxious-prone, defensive-pessimism was as effective as optimism was for those not anxious-prone, in stimulating learning motivation. Developing context control, support and strategies linked to personality could bolster student resilience during and post Covid-19.


Introduction
Stress has variously been defined as a physiological and psychological response (e.g. Cannon, 1939, andSelye, 1946) as well as the external stimuli that trigger that reaction (e.g. Holmes & Rahe, 1967). This early stimulus-response framework saw psychological factors as largely a consequence of the stress response. In contrast, Lazarus and Folkman (1987), in their Transactional model of stress, place psychological and social factors front and centre in recognizing and interpreting demands (the primary appraisal) and in managing those demands (the secondary appraisal).
The primary appraisal refers to the initial perception and assessment of the stressor. This can lead to the judgment that it is irrelevant (or benign), a challenge or a threat. As illustrated in figure 1, sources of stress that are interpreted as demands in which one can achieve are called eustress (B) and those that are perceived as associated with apathy or boredom (A) or, more often, as exceeding one's capacity to cope (C), are sources of distress (Kanner, Coyne, Schaefer & Lazarus, 1981). The traditional health psychology approach construed stress in terms of degrees of distress. This study adopted a positive psychology framework with university demands measured using an adapted National Student Survey (HEFCE, 2017), employing a response scale that allowed stress demands to be rated as hassles (that hold the potential to have an adverse effect on well-being) and as uplifts (that hold the potential to enhance well-being). This is consistent with the 'threat' and 'challenge' or distress and eustress primary appraisal judgments in the Transactional model. This study measured daily and ongoing demands, rather than life-events. This is consistent with Moos and Swindle's (1990) argument that daily and ongoing stressors are important influences on well-being.

Sources of student stress
These include stressors related to academic demands, such as coursework, assessment, exams and work-life balance (e.g. Robotham & Julian, 2006;Ansari et al. 2011;Ansari, Oskrochi & Haghgoo, 2014); to fear of failure and lack of timely feedback on assessments and to the quality of teaching (Gibbons, 2008(Gibbons, , 2010(Gibbons, , 2015. Personal sources of stress include financial concerns, managing apparent free time, frequently working part-time while studying, and concerns about future careers (Gibbons 2015). The changes students experience as they transition to university are frequently a source of acute stress. For most, they are learning to live independently, meet new people and often live in close confines with strangers, as well as managing their own finances, and all along with the challenges posed by a course that may leave them feeling overwhelmed (Denovan & Macaskill, 2017).

Stress effects in students
Well-being is defined as: '…a state of complete physical, mental and social well-being and not merely the absence of disease and infirmity' (World Health Organization, 2006). The experience of stress can affect student well-being, including depression (Zhang et al. 2015); happiness (Denovan & Macaskill 2017) and even suicidal ideation (Abdollahi et al. 2015). Macaskill (2012) reports that students under 26 suffer most because they are still transitioning into adulthood. A widely used measure of self-reported mental health is the General Health Questionnaire (GHQ), with approximately, 15-9% of the general population categorised as 'at risk' of developing a stress-related illness based on this measure (Morris, Earl & Neave, 2017). This is not a life-threatening illness but complaints ranging from tension headaches, back problems, mouth ulcers and cold sores to digestive and intestinal problems, mood swings and irritability. Among student populations this can range from 30% to over 60% (Gibbons, 2008, Turner et al. 2015. These stress effects have been observed in students in the UK; in North America (Adlaf, Demers, & Gliksman 2005, Blanco et al., 2008; Australia (Stallman, 2010) and Sweden (e.g. Vaez, Kristenson, & Laflamme 2004) and the experience of stress has been directly linked to student attrition and retention issues (e.g. Chemers, li-tze & Garcia, 2001).

Pandemic stressors and effects
Following the global spread of the Covid-19 virus, a UK national lockdown was declared on 23 rd March 2020. This led to a dramatic change in students' university experience. For the respondents in this study, learning and teaching during the pandemic became a virtual experience, with students receiving online pre-recorded lectures, live virtual seminars and tutorials and where all live contact was virtual. Elmer, Mepham and Stadfeld (2020) measured the sources of stress and well-being in students (n=212) before and after the onset of the pandemic in Sweden. Within sample comparisons showed marked increases in depression, anxiety, loneliness and distress. Key sources of stress included the health of family and friends and uncertainty about their future, along with physically and emotionally isolation.
Since October 2020, the Office for National Statistics (ONS) have carried out three pilot surveys of university students (in mid-October and the start and end of November) with over 100 000 students in England and Scotland invited to participate via emails from the National Union of Students. Between 2016-to pre-pandemic 2020, student personal well-being measures had already declined compared with matched cohorts in the general population, for example, for those high in well-being in the two groups (operationalised as high in life satisfaction, life worthwhile, happiness and low anxiety) (Neves & Hewitt, 2020). The differences are likely to be influenced not just by the increased demands and life changes students face but that students are typically more willing, than non-students, to share mental health issues and university cultures are more supportive and focused on addressing student mental health.
In the Student Academic Experience Survey (Neves & Hewitt, 2020), taken in March, after most students had stopped face-to-face teaching, there was a drop in the number of students high on happiness but there were no differences in the other well-being measures. These results are dealing with a resilient group (those high in well-being) and across the cohort there was a marked increase in loneliness and decline in mental health (e.g. in depression and anxiety) compared to pre-pandemic levels (e.g. see Hewitt, 2020;the NUS Insight 2020, andONS, 2020). Whilst these results are based on different sampling procedures and questionnaire designs and while all used non-probability sampling and often lacked nonstudent comparison groups, the similarity in the findings from several large surveys lays testament to the adverse impact Covid-19 has had on students' lives and mental health.

Coping with stress
The secondary appraisal in the Transactional model refers to individual coping resources, personality and the past experiences drawn on to perceive and manage stress demands. Key student coping resources include support (Taylor, 2011) and control (Gibbons, 2012a(Gibbons, , 2012b(Gibbons, , 2015. While dispositional control is a strong predictor, so is context control or the skills one acquires to feel in control in a given situation (Maddi, 2002, Gibbons, 2008. Given the potential context control has over dispositional control in improving coping, it is this type that is measured. Important personality ingredients related to coping include those measured by the Big Five (McCrea & Costa, 2004), including extraversion (e.g. Kuijpers et al., 2021) and conscientiousness, levels of emotional stability and openness (e.g. Vollrath & Torgersen, 2000)in education contexts, openness is important if learning is to expand; and optimistic thinking strategies have been associated with improved well-being, performance and health (e.g. Schwarzer, 1994, Giltay et al., 2004, Seligman, 2008. Those scoring high on optimism construe stress demands in a way that makes success more likely. They tend to perceive change and stress demands as opportunities to grow and achieve, for example good copers more frequently score stress demands as higher on uplifts and lower when rated as hassles (Gibbons 2010). They are biased to attend more to positive events over negative events (defensive optimism) and they are more active in learning from their coping mistakes (Lazarus and Folkman, 1987). Norem and Cantor (1986) dispute the claim that adopting optimistic thinking strategies offers a panacea to the downside of stress. Norem and Cantor (1986) argue that for those anxiousprone, a more effective strategy is defensive pessimism. This involves setting yourself unrealistically low expectations in situations that cause you anxiety. Setting a high expectation of success could add to already heightened anxiety and inhibit performance.

Aims
Most of the research into student stress and coping comes from pre-pandemic findings and the pandemic forced universities to turbo-charge their digital learning provision, providing a different environment to explore the role of stress and coping on mental health and learning motivation. Despite the education potential that digital and remote learning holds, its impact on learning motivation in higher education is mixed (Saadé, He & Kira, 2007, Kauffman, 2015, Alhammadi, 2021. This underscores the need to explore its effect on student motivation during a pandemic along with the coping and moderating influence played by personality, support and control on motivation and mental health. The factors affecting the rating of stress as eustress is a relatively under-researched area in student populations (Gibbons, 2015) and this study aims to explore the relationship between sources of stress (rated as hassles and again as uplifting opportunities) and mental health, and between influences on coping (ratings on support, context control and personality) and mental health and to see if defensive pessimism, compared against optimism, is an effective strategy to harness anxiety as motivation towards learning goals.

Design
A survey-based, correlational design was employed. The predictor variables were: courserelated demands (rated as hassles and as uplifts), amended from the National Student Survey; pandemic-related stressors, including social media use and changes in diet and exercise; and, finally, aspects and influences on coping, namely support, context control and personality.
The outcome variable for the first regression analysis was mental health (measured using the GHQ) and the second, learning motivation. To test the efficacy of defensive pessimism compared to optimism on learning motivation, measures on course satisfaction and anxiety were used to identify individuals whose pessimism was likely to be defensive and who were anxious-prone.

Participants
A sample of 162 university students (81% of the cohort) were recruited from the second-year of a psychology BSc programme. On demographics, 86.4% were female (n=140) and 13% male (n=21). Participants' average age was 22 years (SD= 4.55 and range 18-59 years). The focus was on the experience of stress during a pandemic and so the inclusion criteria avoided first year students because of the additional demands faced by first year students in adjusting to university and increased independence.

Materials
Students completed an online survey that included a brief and instructions and 89 items gathering information on demographics; sources of student stress, influences on copingcontrol, support and personality and on anxiety, course satisfaction, learning motivation and mental health.

Procedure
The cohort was made aware of the study via email and in links on their course homepage.
Participation was voluntary and respondents were told they could stop at any time without penalty. The survey took approximately 12 minutes to complete. They were given the opportunity to complete this in class.

Measures
The National Student Survey (NSS) (HEFCE, 2017) NSS items were adapted so participants could rate each item twice -once as a "hassle" (a perceived source of distress) and once as an "uplift" (a perceived source of eustress). A continuous response scale, from 0 to 5, was used to rate each item as a hassle or uplift -0 indicating that the item caused no source of distress or eustress and 5 indicating an extreme source. A range of factors were measured using 23 items from the NSS, such as teaching demands, assessment and feedback, time management etc. An example item is: 'The extent to which teaching staff explain things'. Banked items from the NSS were selected to measure learning motivation. This was a two-item measure with a 5-point Likert scale. An example item is: 'I have found the course motivating'. The Alpha coefficients for all factors ranged from .64-.85

Pandemic-related stressors (generated by the author)
This scale contained six items that split into two sub-scales: time on devices and lack of motivation. They were generated following focus group interviews with three groups of second year students. Respondents rated each item on a 10-point response scale from 1 (Not at all True) to 10 (Very True). Sample items included: 'During the period of Covid-19 restrictions, have you found that you have been: '…using social media more than usual' (time on devices), '…losing your mojo' (lack of motivation) The Alpha coefficient ranged from .67-.85.
Context control (Gibbons, 2010) This scale, of three items, aimed to measure how much participants had developed control in specific contexts. A 5-point Likert scale was used. A sample item is: 'The pace of learning often leaves me with little feeling of control.' Two of the three items are reverse scored. The Alpha coefficient was .80. (Seligman, Park & Peterson, 2005) This eight-item scale measures levels of optimistic thinking. Participants respond on a fivepoint Likert scale. A sample item is: 'I always look on the bright side'. The Alpha coefficient was .81.

The Values in Action scale
Big Five Inventory -10 (BFI-10) (Rammstedt & John, 2007). This is a ten-item scale using a 5-point Likert scale. Respondents are asked to rate statements that describe their personality. A sample item is: 'I see myself as someone who is reserved'.
Two items measure each of the Big Five traits, with one of those two being reversed. Alpha coefficient ranged from .58-.74.
Defensive pessimism scale (Norem & Cantor, 1986). This is a twelve-item scale using a 7-point response scale from 'Not at all true of me' (1) to 'Very true of me' (7). A sample item is: 'I often start out expecting the worst, even though I will probably do okay'. The Cronbach's alpha was .87.
General Health Questionnaire (GHQ) (Goldberg & Williams, 1988) This a twelve-item scale and respondents answer on a four-point frequency scale. GHQ measures general levels of self-confidence, happiness, anxiety, depression and sleep disturbance and, taken together, this comprises a general measure of mental health. An example item is: 'Have you recently been able to concentrate on whatever you're doing?' Response options include: 'Better than usual', 'Same as usual', 'Less than usual', 'Much less than usual'. The scale measures transitory distress. A scoring key of 0-3 was used to determine totals for the analysis and a scoring key of 0, 0, 1, 1 was used to determine caseness or those 'at risk', where totals on the measure above 3 indicated a risk of developing a stress-related illness. The Alpha coefficient was .89. (Zigmond & Snaith, 1983) The anxiety sub-scale of the HADS was used to measure anxiety. Respondents rated seven statements, each on a scale from 0-3, where 0 is "not at all" and 3 is "most of the time". An example item is: "I feel tense or wound up". The Alpha coefficient was .87.

The Course Satisfaction Scale (abridged from the National Student Survey, HEFCE, 2017)
This is a three-item scale, using a 5-point Likert scale. Respondents are asked to rate statements that describe their course, such as: 'I enjoy my studies.' The Alpha coefficient was .89.

Ethics
The study received ethical approval from the Ethics committee at the host university. All participants received a brief and a point of contact for further clarifications. All were informed that participation was voluntary and they were free to stop at any time and all acknowledged informed consent before participating. All ethical considerations and methods were executed in accordance with the Declaration of Helsinki.

Results
The GHQ results in this sample (M = 18.44, SD = 7.40) were compared with and significantly higher than normative data from James, Yates and Ferguson (2013) with a cohort (n=251) of UK (medical) students (M = 13.39, SD = 5.77), t(159) = 8.63, p<.001. The scores were computed for caseness and 68.5% (n=111) were 'at risk', 30.2% (n=49) 'not at risk'. This compares with 19% 'at risk' in the Health Survey for England report (n=8034) (Morris, Earl & Neave, 2017). Table 1 compares those 'at risk' and 'not at risk' on stress ratings: There were significant differences in nine out of eleven stress demands, when rated as a hassle, with those 'at risk' rating the demands higher than those 'not at risk'. There were no significant differences in the uplifting ratings between these two groups.

Optimism and pessimism as predictors of learning motivation
Results of the independent sample t-tests indicated that there were no significant differences in learning motivation between the 11 participants selected for being in the upper quartile on  teaching demands rated as hassle, Beta = -.37 (p < .0001); social opportunities rated as an uplift, Beta = .22 (p < .001); lack of motivation, Beta = -.22 (p < .001); and conscientiousness and teaching on my course hassle-conscientiousness moderator, Beta = .13 (p < .05) were predictors in the model and offered partial support for the primary hypothesis.

Figure 2 Slope graph testing the interaction between conscientiousness and teaching
demands rated as hassle on learning motivation.

The 'at risk' caseness analysis
A striking finding is that the 68.5% of respondents 'at risk' of developing as stress-related illness exceeded that reported in pre-pandemic populations of students and non-students of similar age (Gibbons, 2015;Morris, Earl, & Neave, 2017;Turner, 2015) and the average GHQ score was higher compared with pre-pandemic normative student populations (e.g. James, Yates and Ferguson, 2013). This reflects the marked stress associated with living and learning during a pandemic.
The NSS items were sub-divided into eleven factors or stress demands, rated once as a hassle and again as an uplift. For nine of eleven of these factors, those 'at risk' scored higher on hassles ratings compared with those 'not at risk' (Table 1). Some of these differences may, in part, be attributable to actual differences in the demands. For example, there may be differences in the quality of support offered between peers or from tutor to tutor or from one's family and friends.
However, for other demands, such as the teaching experience, the workload and the opportunities for intellectual stimulation through course resources, these were the same or similar i.e. students were following the same modules, received the same pre-recorded lectures and faced the same assignment workload. So, the differences in hassles ratings for these demands was more likely to be attributable to differences in student appraisals, with those 'at risk' and by implication not coping well, more likely to interpret those demands as distressing. It is possible that some of those in the 'at risk' group were not, de facto, bad at coping but given these different appraisals to the same stressors, the contention offered here is that most in the 'at risk' group could improve in how they cope. That it was differences in the individual coping rather than material differences in the stressors faced, is supported by the finding in relation to pandemic-related stress: Those 'at risk' spent more time on their devices and they were more likely to struggle to find the motivation to be productive and they more often reported changes in sleeping habits i.e. they engaged in behaviours that impacted on their coping or reflected poor coping.

The mental health regression analysis
In this analysis ( Table 2), lack of motivation was the strongest predictor of adverse mental health and it referred to the loss of mojo towards learning demands during a pandemic. It appears that apathy and a lack of energy to undertake necessary tasks was a major source of stress. Procrastination is a perennial problem for most people from time to time and frequently for students. It is a state that is negatively reinforcing but avoidance adversely impacts on learning and well-being (Gibbons, Dempster & Moutray, 2011, Gibbons, 2015.
The challenge of studying during a pandemic has created a set of circumstances where, despite one's aspirations, struggling to overcome a state of procrastination proved especially difficult and this was the strongest predictor of adverse mental health.
Those students that are worry-prone or anxious by nature appear to suffer most. This was suggested by the positive correlation between neuroticism and GHQ. Consistent with earlier research (Gibbons 2012a(Gibbons , 2015, developing a sense of control in specific contexts is a powerful coping mechanismhigh scores on context control were associated with low scores on GHQ. Optimism was predictive in the same way but weaker than context control. Several studies lay testament to the value of optimism, but others show that it either does not feature or is only weakly related to measures of well-being (Gibbons 2012a(Gibbons , 2015. Context control is a frequent robust predictor of health and well-being and this measure of control is learnt, not dispositional (Maddi, 2002). Developing context control holds potential to help students cope in the face of changing pandemic challenges. Both context control and optimistic strategies can be developed through learnt strategies but based on these findings, it is the former that may offer more benefits.
Openness was a significant predictor. Higher scores on openness were associated with higher scores on GHQ. On the face of it, this suggests that openness has an adverse effect on mental health. However, it is important to remember that stress is not always distress and if one is to learn and develop new knowledge and new skills, one has to be willing to move out of one's comfort zone. Feeling vulnerable and accepting that one might get things wrong and make mistakes and accepting that one's self-esteem may take a hit in some disappointing marks or critical feedback are best interpreted as the growing pains of a growth mindset (Dweck,2015).
Hand-in-hand with this, is the feeling that one may occasionally doubt that one can meet the learning challenge. This is consistent with the large number of students who eventually succeed, if not shine, in their performance but who experience imposter syndrome en route (Denver, 2019). Being open-minded is integral if one is to effectively master new learning, and the contention here is that so too is its association with heightened stress.
'Teaching on my course', when rated as an uplift, was associated with lower scores on GHQ.
This is likely to reflect the efforts by faculty to engage their students remotely and to provide effective teaching through pre-recordings, live seminars and more frequent live tutorials (these were held weekly instead of fortnightly, the pre-pandemic format). It also reflects the tendency by those scoring high on optimism, to more readily interpret stress demands as opportunities to achieve.
Openness and high scores on idealism, as opposed to cynicism, have been associated with more frequent and more intense experiences of 'elevation'. This is an uplifting emotion, where one feels inspired, experiences awe or a general feeling of emotional warmth (Sparks, Fessler & Holbrook, 2019). Teaching and learning experiences are more likely to be elevating if one adopts an open-minded perspective and this might be part of the explanation behind the dominance of the uplifting ratings for teaching and the openness predictor in the model. Support, as a coping resource, was removed in the process of arriving at the most parsimonious regression model. This is not to suggest that support is not important. The literature supporting its efficacy is strong (Taylor, 2011). Its absence here could be attributed to various reasonsthere was no in-person support during the pandemic and its significance was over-shadowed by the importance of those predictors in the model. In the second analysis it did feature, in the form of social opportunities. This suggests that it remains important, but less so in predicting mental health as measured by the GHQ.

Is there a place for defensive pessimism in coping?
Defensive pessimism was helpful for those anxious-prone in relation to learning motivation: There was no difference in levels of learning motivation between those respondents high on defensive pessimism and anxiety compared with those high on optimism. This suggests that for individuals who are anxious-prone, rather than adopt those ubiquitous optimistic thinking strategies, setting unrealistically low learning expectations, might relieve them of the pressure to achieve and actually (ironically) enhance performance. Only those in the upper quartile on defensive pessimism, anxiety and course satisfaction were selected and compared against those in the upper quartile on optimism. Selecting those high in course satisfaction was used because it made it more likely that their pessimism was defensive not realisticthe satisfaction rating was an indicator that they had been achieving. Had those with lower scores in course satisfaction been included it would make it more likely that their pessimism was, for some, a realistic reflection of a disappointing course performance.

A cautionary note
It is noteworthy that defensive pessimism does not offer the same dividends for happiness and satisfaction with life. The defensive pessimist group scored significantly lower than the optimists on these measures. Moreover, optimism remained a significant predictor of happiness and life satisfaction when context control was tested as a potential mediator. This suggests that for anxious-prone individuals, defensive pessimism offers an effective strategy for harnessing motivation towards learning goals, but optimistic thinking strategies and context control should be employed to help bolster these other well-being ingredients.
Consistent with the tenets of positive psychology, one does not always need to work directly on one's coping deficits, such as trying to lower measures of neuroticism. Rather, if one focuses on building one's coping strengths, such as improved techniques in context control and in optimistic thinking strategies, and in defensive pessimism for those anxious-prone, it can buffer against the costs of neuroticism on mental health (Norem & Cantor, 1986;Steen, Peterson, Seligman, 2010).

Regression analysis for learning motivation
As illustrated in Table 3, teaching demands was the strongest predictor of learning motivation but not in the positive way observed in the first regressionthe more these demands were rated as a hassle, the more learning motivation declined. The benefit of asking participants to identify the distress and eustress elements of demands allows one to identify their subtle and disparate influences. The nature of learning and teaching took on a new meaning when students did it virtually and in isolation, and in a way that involved many more hours sat in front of a computer screen. Where faculty introduced changes that helped, it significantly improved mental health (Table 2). However, so dramatic were the changes in learning that this inevitable shift in practice is likely to be associated with added hassle ratings. If there are other added disappointments, perhaps related to teaching variability or in the levels of effort faculty engaged in to support students, then it is understandable that these combined influences had an adverse impact on learning motivation.
Previous research justified testing the role of personality, support and control but several key influences -extraversion, neuroticism, control and optimism, did not feature in this second analysis. Conscientiousness did however, and it was the most effective in maintaining learning motivation. It is likely that the isolation of the pandemic meant there was little scope to derive the same well-being benefits (for example in happiness and general motivation) that extraversion is normally associated with (Lee, Dean & Jung, 2008). Studying remotely and virtually put an increased importance on how learning and teaching was delivered and rated and, not unsurprisingly, when the experience was positive it was rated very favourably (its uplifting rating in the first regression) and when it was disappointing, it had a greater adverse impact on learning motivation because the pandemic induced isolation took away most of the coping benefits that come from being extraverted.
The items underpinning the social opportunities predictor asked respondents to rate opportunities to interact with other students on the course and in university clubs and societies. The predictor represents a proxy for support. Its positive relationship with learning motivation shows that, despite the restricted opportunities imposed by the pandemic, having the contact and support of other students, whether course-related or recreationally, increased learning motivation.
Consistent with the mental health regression, students who reported losing general motivation as a fall-out of the prolonged Covid restrictions, found this carried over to the motivation towards their studies. In both regressions, all the predictors were tested for moderation effects and the slope graph in figure 2 illustrates the moderating influence of conscientiousness on learning motivation in response to teaching demands: For those low in conscientiousness (the bottom line), the more teaching demands were experienced as a hassle the more dramatically learning motivation declined. For those average in conscientiousness (the middle line) the decline in learning motivation was less dramatic. For those high in conscientiousness (the top line), increased ratings of teaching as a hassle had only a nominal influence on rates of learning motivation compared to the other two groups. This suggests conscientiousness was an important buffer for learning motivation against the adverse changes in the nature and quality of teaching.

Limitations
The NSS was used because it is recognized as the, de facto, measure of student experience.
However, the evidence of its validity does not yet match the frequency of its use (e.g. Sabri, 2013). The use of a survey method and volunteer sample are not without limitations and while the sample size was good, relative to target population, a larger sample across all cohorts in the psychology department would have allowed more insights into the difference demands faced in each year of study.
Norem and Cantor (1986) used upper quartile measures on GPA to benchmark those respondents whose pessimism was likely to be defensive not realistic. Here, course satisfaction was used. While past performance is likely to be an influence on course satisfaction, it is not the only influenceso is the quality of teaching and how engaging learning resources might be. This may question the validity of using course satisfaction alone to identify those that are defensive rather than realistic pessimists. Using course satisfaction and GPA, rather than either alone, would be a useful way to increase the confidence in identifying those whose pessimism was defensive.
Identifying the sources and experience of stress that are likely to enhance performance and are thereby uplifting as opposed to a hinderance or hassle, is a key challenge for those of us who explore this aspect of positive psychology. The stress that helps you achieve may be experienced as unpleasant and unwanted at the time and, because of that, be more likely to be rated as a hassle. This was the argument offered to explain the relationship between openness and GHQ. A fuller explanation on the distinction between the sources of stress that can help and that can inhibit performance was added to the participant brief in this study, compared to similar earlier studies, but, as an online survey, it was difficult to drive home this distinction.
An improvement might be to adopt different labels for 'hassles' and 'uplifts' such as sources of stress that 'hinder' performance and that are 'necessary to facilitate' performance.

Recommendations
Studying during a pandemic imposed dramatic and significant changes in student learning and coping. The interpretation offered here suggests specific pointers to help students cope; to improve mental health and learning motivation. During induction and early in their studies, students could be offered resilience training that includes tips on the thinking strategies adopted by optimists (for example, that change can be construed as a challenge even if one's initial reaction is one of threat; in defensive optimism, active disputing, problem-based coping) and in defensive pessimism for those high in anxiety or who experience situations associated with high anxiety, such as the first semester for most new students. It would be useful to raise awareness to re-interpret 'stress and change' in a positive light. Understanding our evolved tendency to perceive change as a threat is, to that end, likely to improve coping.
Control, in an education context, could be developed by empowering students with an HE skill-set that goes beyond exercises in time and task management, important though they are, and that incorporates apps that imbed daily and weekly schedules anchored around assignment deadlines; for better time management, and that utilize evidence-based positive psychology techniques. Students can be supported in their learning independence by using some of the psychology-based apps designed for this purpose; along with selected subjectspecific podcasts to help enthuse them in their learning and to help move them from a lay understanding to a progressively more academic and in-depth understanding at a pace that leaves them feeling in control.
As universities move to return to in-person teaching they are more likely to retain some elements of virtual learning. Both regression analyses showed this can be associated with uplifting and hassle ratings. It is important, therefore, to look to maximise its positive impact.
For example, by recording virtual learning for students to revisit; using transcript options to facilitate (not replace) student note-taking; allowing student participation through chat features and break-out rooms. Many faculty drew on these elements and are getting better at doing this. However, during this study, there was a mixed take up in encouraging students to turn on their cameras during learning and where some educators did not turn on their camera when presenting. Evidence in multi-sensory processing (Tiene, 2000) and the animacy effect in memory (Félix, Pandeirada & Nairne, 2019), support the benefit to learners if they can see as well as hear each other and the presenter. Finally, support opportunities should continue to be enhanced through extended freshers' fayre events; student inductions with a strong peer networking focus, along with peer mentoring initiatives.
These are just some suggestions to help develop specific personality ingredients; student control and support and which, in turn, increases the likelihood that a conscientious approach is one that quickly translates into effective learning and coping. These initiatives hold the potential to combat procrastination, improve learning motivation and mental health.

BMC Psychology Journal submission:
Declaration section (insert before references)

· Ethics approval and consent to participate
The study received Ethics approval from the Ethics committee in the Engineering and Physical Sciences Faculty, QUB. As part of the review, the panel considered the participant brief and consent form and all participants gave informed consent before participating.
Further information can be obtained from the Ethics committee, EPS Faculty, QUB using the the reference: EPS20_01 The study received ethical approval from the Ethics committee at the host university. All participants received a brief and a point of contact for further clarifications. All were informed that participation was voluntary and they were free to stop at any time and all acknowledged informed consent before participating, in accordance with the Declaration of Helsinki.
The use of the survey method and collection of data was performed in accordance with

Declaration of Helsinki
The above information is detailed in the manuscript.

· Consent for publication
Not applicable

· Availability of data and materials
The data set is available at: https://orcid.org/0000-0001-6631-721X The question items are subject to copyright but the sources for all the measures used are referenced and interested parties can contact any of these sources. The authors vary on their decisions to make their tests available for free for educational purposes.

· Competing interests
The author has no competing interests

· Funding
Not applicable

· Authors' contributions
Dr C Gibbons undertook all elements of this study, including questionnaire distribution, data analysis, manuscript preparation and review.

· · Authors' information (optional)
Dr Chris Gibbons is a lecturer in psychology at Queen's university Belfast. His research focus is on health psychology, positive psychology, including the influences on student wellbeing and performance in higher education. He has been Chair of the Association for Psychology Teachers (https://www.associationforpsychologyteachers.com/) since it was founded in 1995 and is the recipient of numerous teaching awards. In August 2021 he received a Teaching Hero Award from the National Forum For The Enhancement Of Teaching And Learning In Higher Education.