Student engagement: The impact of positive psychology interventions on students

Abstract

Positive psychology interventions are widely used in primary and secondary education to enhance student engagement and well-being. When used in the higher education sector, interventions tend to be used in psychology, psychotherapy and mindfulness programmes with successful outcomes in terms of student engagement, learning and well-being. However, there is little evidence to support the effectiveness of such interventions in disciplines outside of psychology. This triangulated action research study utilised student self-reported feedback to explore the link between positive psychology interventions and student engagement in a non-psychology related classroom. The study involved two intervention and one control group. In contrast to results obtained from psychology students, findings in this study showed a lack of student engagement in both intervention groups and a significant increase in student engagement in the control group compared to one of the intervention groups. The findings suggest that further consideration needs to be given to identify positive psychology interventions that might achieve a better fit with non-psychology students.

Keywords

business degree engagement higher education interventions positive psychology well-being

Student stress, well-being and engagement

University students face many challenges. There are many who are first in their family to enter university, have struggled academically, are at higher risk of stress and anxiety disorders and work either part or full time while studying full time (Baik et al., 2015; Kahu and Nelson, 2018). Balancing these life demands contributes to students experiencing increased stress levels at university (Ryan et al., 2010). Stallman (2010) investigated the mental health of university students and identified high levels of psychological stress with almost 84% of students reporting elevated distress levels. In another study, Geertshuis (2019) explored changes in anxiety levels during a semester and discerned that anxiety increased towards the end of the semester. Pitt et al. (2018) also found that the beginning and end of the first semester was a time of increased stress for students. Pitt et al.’s research identified factors that initiated stress in non-traditional students such as a fear of failure, assessments and perceived inability to achieve desired results. These triggers for stress can lead students to adopt avoidance behaviours (Geertshuis, 2019). For instance, at the beginning of the semester students may feel a decreased sense of belonging (Lin and Huang, 2012), while end of semester stress levels may increase due to examinations (Fejes et al., 2020), leading to avoidance behaviour that inhibits students’ ability to adequately study for examinations. Avoidance behaviour can result in individuals doubting their ability to succeed academically (Kahu and Nelson, 2018; Vaez and Laflamme, 2008).

Feelings of stress and anxiety distract students from their studies and decrease their ability to fully engage academically. As student engagement is inextricably linked to academic success (Kahu et al., 2017; Krause and Coates, 2008), it is important that academics endeavour to create an engaging classroom environment. Kahu and Nelson (2018) outline four main educational interfaces that when aligned lead to student engagement: self-belief that you can succeed, having positive thoughts and emotions, a sense of belonging in both a social and course fit and a sense of well-being or lack of stress and anxiety.

As well-being is a multi-faceted construct it has differing definitions. Some research focuses on the hedonic or subjective aspect of well-being where individuals receive short-term pleasure from activities (McMahan and Estes, 2011). Conversely, eudaimonic well-being which focuses meaning and self-realisation, involves activities that are inherently good for individuals and will more likely provide longer-term enduring well-being for both the individual and the broader community (Steger et al., 2008). Research tends to support an eudaimonic approach to well-being as this has been found to be more positive for psychological functioning than hedonic (McMahan and Estes, 2011; Park et al., 2009; Peterson et al., 2005). The World Health Organisation’s (2014) definition of well-being identifies well-being as a state ‘in which every individual realises his or her own potential, can cope with the normal stresses of life, can work productively and fruitfully and is able to make a contribution to her or his community’. This definition aligns closely with the eudaimonic view of well-being, focusing on an individual’s functioning well, over the more ‘feeling good’ focus of hedonic well-being.

Individuals who have high levels of well-being display more pro-social behaviour and less racial bias (Johnson and Fredrickson, 2005), are less likely to smoke (Grant et al., 2009), have healthy eating habits (Boehm and Kubzansky, 2012) and take regular exercise (Huang and Humphreys, 2012). Individuals exhibiting these characteristics of well-being foster healthier and happier communities. Student well-being strengthens engagement and vice-versa (Kahu and Nelson, 2018), therefore, implementing interventions that support student well-being may assist student engagement.

Interventions to promote engagement

Positive psychology interventions have been found to contribute to student engagement (Leland, 2015), well-being (Giannopoulos and Vella-Brodrick, 2011; Sin and Lyubomirsky, 2009) and academic success (Noble and McGrath, 2015). An integral part is the identification of individual character strengths (Seligman, 2002). Identifying character strengths and applying them to personal situations is generally accepted as the foundation for engagement and well-being (Schutte and Malouff, 2019; Seligman, 2002). Waters (2011) revealed a strong link between strengths-based interventions and enhanced student well-being with positive outcomes such as; social-emotional competencies, academic performance, enjoyment and engagement. Likewise, Williams et al. (2018) suggest that a strengths-based approach can enhance a student’s sense of well-being which will positively impact their personal and professional success. However, a challenge is knowing which interventions to implement. Park and Biswas-Diener (2013) caution that when used in real-world environments interventions may not provide the desired result, but it is not clear why, with whom and which activity this happens.

The Synergistic Change Model (SCM) (Rusk et al., 2018) may assist with identifying which interventions lead to positive outcomes. This model identifies five domains of positive functioning which can help instructors identify paths to achieve well-being outcomes. The five domains are attention and awareness; comprehension and coping; emotions, goals and habits; and virtues and relationships. These domains are not separate entities but overlap and often build on each other. Oman et al. (2008) investigated a mindfulness intervention on health care workers and found the intervention positively impacted on 15 other elements and had a positive impact on self-efficacy. Students who are mindful are able to regulate their thoughts and actions (Glomb et al., 2011) which facilitates engagement and academic goals (Kahu and Nelson, 2018).

In another example, consider an intervention that asked students to watch motivational videos or read positive messages. The primary outcome is to increase positive emotions. Positive emotions are critical for student engagement (Pekrun and Linnenbrink-Garcia, 2012) and academic achievement (Pekrun et al., 2017). Not only does the intervention increase positive emotions, but a secondary outcome is increased attention and awareness and perhaps encouraging students to set goals. Thus, one intervention can have a positive influence on more than one domain, and these domains together encourage an upward spiral of student engagement that increases general well-being. Despite the positive outcomes that interventions can provide, people differ and what works for one will not necessarily work for another (Park and Biswas-Diener, 2013; Schuller, 2010). This may be influenced by an individual’s current level of perceived well-being and their need for the intervention (Lyubomirsky and Layous, 2013). For instance, an individuals’ social-well-being may be supported at home or in the community (Diener et al., 2010), therefore, students may feel social well-being support unnecessary at university and interventions aimed at increasing social well-being could have a negative outcome (Sin and Lyubomirsky, 2009).

To increase the success of interventions, Lyubomirsky and Layous (2013) developed the Positive-Activity Model (PAM) which identifies three elements that can influence an individual’s fit with positive psychology interventions. The three elements are: the activity, including variety and frequency; personal characteristics such as, current level of well-being and demographics; the fit between the person and the activity. These elements are not mutually exclusive and all need to be considered when planning activities that will engage students.

In terms of demographic age fit, interventions have seen positive results in well-being for children and young adults ranging in age 5–19 (Chodkiewicz and Boyle, 2017; Stockings et al., 2016; Waters, 2011). Age has been found to be a moderating factor in intervention outcomes, with individuals aged 18–35 gaining less from interventions then those aged 36–59 (Sin and Lyubomirsky, 2009). The older age group is more likely to self-select to be part of an intervention group which increases an intervention’s potential for success (Sin and Lyubomirsky, 2009). In addition, Seligman et al.’s (2005) research found that individuals who self-selected were more likely to have been slightly depressed prior to commencing the study but reported higher gains in happiness after than those who did not self-select and did not report any depression.

The ambiguity around how, when and with whom interventions should be used creates a challenge for instructors. Although evidence encourages the implementation of interventions in higher education to support individual well-being and engagement (Noble and McGrath, 2015), the implementations have been limited (Williams et al., 2018) to mainly positive psychology, psychotherapy and mindfulness courses. An exception includes a study by Lambert et al. (2019) who investigated changes in well-being from a happiness programme and found increased levels of well-being which was sustained at three months post-intervention. While the control group in Lambert et al.’s study (2019) study consisted of participants from various disciplines, the intervention group was enrolled in an introduction to psychology course and the participants ranged from second to final year undergraduate students. There is, then a need to investigate the influence positive psychology interventions has on engagement in disciplines other than psychology. Specifically, there is a need to explore participation in positive psychology interventions in the classroom, student perceptions of engagement, motivation, happiness and connection during the whole course and self-perceived levels of mindfulness in terms of being ‘present’ in the class.

Method

Setting

This study was situated in an Australian university that, in 2018, underwent a change in course delivery from the traditional 12-week delivery of four units running simultaneously to ‘block’ (hereafter called ‘course’) delivery where courses were delivered sequentially over a 4-week period and class size was capped at 35 students. One reason for this change in delivery was to enhance student engagement. Instructors were encouraged to use classroom techniques and online tools to enhance student engagement and motivation. The study implemented a triangulated action research methodology to assess the impact interventions had on first-year student engagement in an undergraduate business course. The study analysed data from two intervention and one control group. Data analysis was undertaken post intervention and interventions changed accordingly, prior to the delivery of the next course. The intervention groups were taught sequentially by the lead researcher and the control group was taught by an experienced marketing instructor.

Participants

Participants consisted of 58 first year students, studying a compulsory marketing course during the 2018–2019 academic year. In the first year of the business degree students are required to complete eight core papers prior to beginning their specialisation. Participants’ age ranged from 18 to 37 with a mean of 21 (SD = 3.8). Students’ existing levels of well-being were unknown. The research was approved by the University’s Human Ethics Committee and written consent received from participants.

Measurements and procedure

To assess student engagement the study collected three sets of data based on student self-perceptions. Data were collected at three points: one during and two after each course. At the end of the course students were asked to reflect on: the interventions used, and the extent the course made them feel, engaged, motivated, connected and happy. During the course, at the end of weeks 1–3, students were asked to reflect on the amount of time they felt they were ‘present’ in the classroom, and express this as a percentage. The above data were collected via paper-based surveys which were completed in-class. Students were encouraged to write comments to support their quantitative responses. Student comments are included in the findings. All data were anonymous. A more detailed description of the data collected from each group is provided below.

Intervention groups

Group one: Six positive psychology interventions

The first intervention group was exposed to six different interventions, all interventions were woven into course activities: watching motivational videos; positive messages on the learning management system (LMS); sending thank you texts; focus on mindfulness; applying strengths to situations; and talking about positive experiences.

Prior to every class, the course instructor emailed students a motivational video related to either academic or general motivation and a positive message was posted on the LMS. At the beginning of each week students were encouraged to send a thank you text, for a kindness they experienced in the last week, to a friend or family member. Students were also asked to be mindful of their use of technology and the amount of time they were cognitively ‘present’ in the classroom. At the end of each week students were asked to discuss in their table group something positive that happened that week and to think about what strengths they used in that situation. Participants were asked to identify the interventions they enjoyed. Each group, excluding the control group, experienced these interventions.

These interventions were chosen as they link to Rusk et al.’s (2018) research on the importance of focusing on interventions that can create sustained changes in well-being. For example, the interventions watching motivational videos and posting positive messages focus on developing positive emotions. The emotion domain is considered pivotal in that it has the potential to create synergistic change in other domains. Sending thank you texts focuses on gratitude which has also been identified as a pivotal element as it influences several domains: emotions, attention and awareness and virtues and relationships (Rusk and Waters, 2015). Focusing on mindfulness through applying strengths to situations and talking about positive experiences, can impact on all Rusk et al.’s (2018) five domains.

Group two: Four positive psychology interventions

Interventions for the second iteration included four of the six positive psychology interventions: talking about positive experiences, applying strengths to situations, focus on mindfulness and positive messages on the LMS. After analysing the data from the first iteration, the interventions ‘watching motivational videos’ and ‘sending thank you texts’ were discontinued. As Rusk et al. (2018) caution, overloading students with interventions may result in a negative outcome. The interventions included in the second iteration were retained as they have the potential to create synergistic changes in other domains (Rusk et al., 2018). Interventions were implemented in the same way as in the group who had been exposed to all six interventions.

End of course survey questions

Students were asked to comment on four questions based on research surrounding elements that affect student performance (Pekrun and Linnenbrink-Garcia, 2012). The survey asked students to what extent the course made them feel: engaged in the learning process; connected to people at university; motivated to do better academically; happy to be at university.

Items were rated on a 10-point Likert scale, from 1 = not at all, to 10 = very much. Reliability for the 4-item scale showed good internal reliability consistency, Cronbach’s alpha = 0.93 for the current sample (Pallant, 2016). The end of course survey question data were collected from the intervention and control groups.

Student mindfulness self-perceptions

Each week students were asked to be mindful of their use of technology in the classroom and try to avoid using technology when it was not needed for classroom tasks. When not using devices for class purposes students were explicitly asked to close their computers/tablets and put their phones in their bags or turn them over on the table. At the end of each week, students in the intervention groups were asked to write, on sticky-notes, how present they were in class that week. Responses were expressed as a percentage. Students posted the notes on a white board prior to leaving class. The percentages were collated each week and the average percentage for each group calculated. The mindfulness self-perception percentages data were collected from both intervention groups. Student participation was optional for all interventions.

Data analysis

After each intervention data were analysed to assess the impact of the interventions and changes made prior to the next implementation. A chi-square test (α = 0.05) assessed the proportion of students that enjoyed the interventions and was used to assess any statistical significance in responses to the interventions. A series of MANOVAs were used to assess any significant differences between groups in relation to the questions. Prior to conducting the MANOVAs normality and homogeneity of variance were checked using Zskewness (Ghasemi, 2012) and Fmax (Allen et al., 2014), assumptions were not violated. A mixed model ANOVA was used to investigate if any of the mindfulness self-perception percentages were significant. Prior to conducting the mixed ANOVA, assumptions were tested. The Shapiro-Wilk, and Fmax were used to check for normality and homogeneity of variance; these assumptions were not violated.

Findings

To recap, students in group one were exposed to six interventions (watching motivational videos; positive messages on the LMS; sending thank you texts; focus on mindfulness; applying strengths to situations; talking about positive experiences) and those in group two experienced four interventions (all six minus these two: watching motivational videos and sending thank you texts).

Group one: Students exposed to six interventions

Nine of the 19 who responded identified they found applying strengths to different situations was enjoyable (47%). Only two (11%) to three (16%) of the 19 respondents found the other interventions enjoyable (see Figure 1).

Figure 1.

Group one: student enjoyment of six interventions (N = 19).

The chi-square test was negatively significant with participants indicating a lack of enjoyment for all interventions except ‘applying strengths to situations’ which was not significant (p = 0.82). While not significant over 50% of students indicated disinterest in the intervention. Cohen’s w ranged from 0.05 to 0.79 with a large effect size for all questions except for ‘applying strengths to situations’ which was a small effect (w = 0.05).

Data indicated that most students were not interested in and did not take part in the interventions. Only two students indicated that watching the videos made them feel motivated and most did not watch any videos. One student reported resentment to the videos being ‘pushed’ on them stating ‘it was spam in my inbox’ while another said ‘if I was going to watch something I would go out and look for it myself’, and another, ‘It’s not really something that I would want to watch in the first place’. Self-motivation was another reason for not watching the videos with students responding ‘I don’t normally need external motivation. I am pretty motivated to do what is required’.

Group two: Students exposed to only four of the six interventions

There were 18 responses to the interventions in group two. Figure 2 highlights students’ enjoyment. Over 60% of participants indicated they did not enjoy the interventions. While most students did not find talking about positive experiences enjoyable (61%), comments from those that did found it promoted positive feelings. Such comments included ‘I think it gets you to think’ and ‘it made me feel good. It gets you in a good mood and lifts your spirits’. While others commented that it ‘puts you in a good mindset for the rest of the class’ and ‘makes you feel happy, more positive’.

Figure 2.

Group two: student enjoyment of four interventions (N = 18).

The chi-square test was negatively significant for all interventions except ‘talking about positive experiences’ which was not significant (p = 0.346). Cohen’s w ranged from 0.03 to 0.89 indicating a small effect size for the questions ‘applying strengths to situations’ (w = 0.03) and ‘talking about positive experiences’ (w = 0.22), but a large effect size for the other two questions (w = 0.89).

Although not significant, some students enjoyed talking about positive past experiences, specifically, what they did on the weekend. However, they did not enjoy the process of applying their strengths to these situations. One student commented that the process ‘wasn’t normal because we haven’t done that in any other block’ and another ‘it wasn’t really uncomfortable, it was more unusual’. The data indicate that, even with a decrease in the number of interventions, there was a negative activity-person fit in group two, who had been exposed to only four of the six interventions.

Students were asked via the end of course survey to self-reflect on the extent they felt engaged, motivated, connected and happy during the course. A total of 58 students responded from the two intervention groups and one control group. Findings show there was a significant effect in students’ perceptions of happiness between the control group (n = 21) and the groups that were exposed to either four (n = 18) or six interventions (n = 19) with a Bonferroni adjusted alpha level of 0.013, F (2, 55) = 3.94, p = 0.025, partial η² = 0.125. The control group recorded a significantly higher mean score (6.71) than the groups that were exposed to either four or six interventions (M = 5.26). No other significant differences were identified between the groups. Group means and standard deviations for each question are presented in Table 1.

Table 1.

Means, standard deviations: end of course survey questions.

	Engaged in learning		Motivated academically		Connected to people		Happy at university
	Mean	SD	Mean	SD	Mean	SD	Mean	SD
Group 1: Six interventions	5.74	1.82	6.05	1.43	5.26	1.82	5.26	2.08
Group 2: Four interventions	5.67	1.85	5.5	1.86	6.5	1.15	5.5	2.12
Control	6.29	1.62	6.43	1.21	6.14	1.68	6.71	0.9

Note. Group 1 n = 19, Group 2 n = 18, Control n = 21.

The only significant finding was with the control group, which showed a positive impact on students’ feelings of happiness at university compared to the groups who had been exposed to either four or six interventions. Students commented on the course’s 4-week delivery and how it decreased their feelings of anxiety ‘I look at my friends at other unis and they are so stressed, I don’t feel that way’ and how they were better able to connect with classmates ‘better for connecting with other people’ and their teacher ‘you are in the same room with the same teacher 3 days a week, you get to know them’.

Student mindfulness self-perceptions

In the last session of weeks 1–3 students were asked to consider how mindful they had been, on average, during that week. Figure 3 provides the data, expressed in percentages, for the group exposed to all six interventions and those exposed to only four of the six interventions.

Figure 3.

Student mindfulness self-perceptions.

Group one: Students exposed to all six interventions

The data reveal that group one students’ self-perceived mindfulness decreased as the course progressed from 68% in week 1 to 52% in week 3. One student summed up what many felt, ‘I think there was a displacement between so much wellness and engagement and not enough on the actual marketing content’. By the end of the course many students were feeling slightly disengaged with another student saying ‘I just felt I was overloaded with so much positivity and it started to deter me from actually being engaged’. These comments support the mindfulness self-perceptions data which decreased throughout the course.

Group two: Students exposed to four of the six interventions

Group two students’ mindfulness remained stable in weeks 1 and 2 and increased between weeks 2 and 3, from 63% to 72%. Many students in this group were open to the focus on mindfulness with a comment ‘after we did that (self-perceived mindfulness percentage) the first week I thought maybe I’m not putting 100% into the class and maybe I should start focusing a bit more’ and another stating ‘I think it is pretty important, like just being mindful, because it can make you feel more open to ideas’. Table 2 provides the mean and standard deviation scores for the intervention groups.

Table 2.

Means, standard deviations: student mindfulness percentages across groups.

	Week 1		Week 2		Week 3
	M	SD	M	SD	M	SD
Group 1: Six interventions	69.50	21.22	63.21	9.73	59.07	23.47
Group 2: Four interventions	61.26	14.87	70.00	22.09	63.21	18.42

Note. Group 1 n = 14, Group 2 n = 19.

No significant differences were found in student’s mindfulness percentages between the two intervention groups. The main effect for time was F (1, 31) = 0.665, p = 0.421, partial η² = 0.02, the interaction effect for time and group F (1, 31) = 1.42, p = 0.243, partial η² = 0.04, and the main effect for group was F (1, 31) = 0.06, p = 0.81, partial η² = 0.002.

Discussion and conclusions

The study investigated the relationship between positive psychology interventions and engagement in students. Three data sets were obtained on students’ self-perceptions. First, student engagement in interventions in the classroom; second, the link between engagement, motivation, happiness and feeling connected at university; and last, students’ level of mindfulness in the classroom. In short, students had been exposed to either six interventions (watching motivational videos; positive messages on the LMS; sending thank you texts; focus on mindfulness; applying strengths to situations; talking about positive experiences) or only four of the six (without these two: watching motivational videos and sending thank you texts). At the end of each course, students were asked to rate their level of interest in each. Data suggest a poor person-activity/ intervention fit which led to student disengagement.

Students in the intervention groups showed no significant engagement in the activity which identified their strengths and applying them to recent situations in discussions with classmates. This finding is in contrast to theory which suggests that the identification and implementation of signature strengths is fundamental to enhancing well-being and engagement (Schutte and Malouff, 2019; Seligman, 2002; Waters, 2011; Williams et al., 2018). It may be that students saw completing the character strengths survey as external to the course environment, something they did not choose and did not think they required. The lack of student engagement with this activity suggests the students were more interested in activities that provide short-term pleasure rather than anything that could lead to longer term well-being. Another factor that may have contributed to the disengagement was the length and time it took to complete the survey. The Character Strengths questionnaire consists of 240 questions and students took between 30 and 50 minutes to complete the survey, with many getting distracted by classmates during the completion. Sin and Lyubomirsky (2009) identify the importance of the activity being suited to an individual’s personal situation in order to maximise success. Therefore, if students feel the time spent identifying character strengths and implementing them in class activities is irrelevant to their current situation, this may lead to disengagement.

Intervention groups were provided a variety of activities that aligned with the interventions. Student engagement in the activities requires a good person-activity fit (Lyubomirsky and Layous, 2013). This study identifies a lack of person-activity fit with findings indicating a negative correlation with all interventions in both groups. This result is supported by further data from the end of class course survey questions and the student self-perceived mindfulness feedback during the course. Although the interventions chosen had the potential to enhance students’ overall well-being and therefore lead to increased engagement in the learning process (Rusk et al., 2018) the opposite was evident, suggesting a lack of fit with either the student cohort or environment. Some students commented on high levels of internal motivation inferring they felt happy with their current state of well-being and the interventions did nothing to engage them in their learning environment. This again shows a lack of person-activity fit displayed in students not participating in the activities.

The lack of person-activity fit may also be associated with student demographics. Students were in the 18–37 age group and most were not interested in the interventions. This could be due to the fact that individuals over 35, who are reaching middle-age, are thought to be emotionally wiser and be more likely to engage in and benefit from such interventions (Sin and Lyubomirsky, 2009). Although participants self-selected to be part of the research, the intervention activities were undertaken as part of a whole-class activity. All students were exposed to the activities and this possibly led to the decrease in engagement, especially if students had no underlying perceptions of feeling stressed, anxious or depressed. This reflects Seligman et al.’s (2005) findings that students experiencing feelings of depression and those that are able to self-select are more likely to engage in the process and benefit in terms of increased well-being.

The mindfulness data revealed no significant effect on students’ in-class presence during the course. The perceptions of mindfulness of the students in group one, who had been exposed to all six interventions, showed a non-significant decrease over the course. The students in group two, who had been exposed to only four interventions, showed similar levels in mindfulness in weeks 1 and 2 and a non-significant increase in week 3. This shows students mindfulness levels remained relatively stable through the course. Mindfulness has an influence on student engagement (Leland, 2015), therefore, perceiving the mindfulness percentages as an indication of student engagement, this research suggests students’ engagement levels remained relatively stable through the course. The use of technology in the classroom has been shown to enhance engagement, however, the increase in technology use for non-academic tasks detracts students’ ability to focus. Therefore, it is suggested that further research utilising mindful processes that encourage students to self-regulate their technology use to improve engagement would be beneficial.

There are several limitations of this study. First, data were based on self-report measures which relies on participants being able to accurately and honestly recall and report on previous activities. A second limitation surrounds course delivery. The course in this study was delivered intensely over 4 weeks. Four weeks is a short period of time to see any systematic behavioural change. The fact that the mindful percentages tended to decrease in week 3 questions any long-term behavioural change. Lally et al. (2010) suggest that it takes an average of 66 days for an individual to change a behaviour. More research in a traditional 12-week semester environment and a longitudinal study looking at students’ behaviour at the end of their degree would be beneficial. In addition, the control group and intervention groups were taught by different instructors, and so it may be the case that each instructor used a different teaching style; this may have impacted the interventions themselves and also the students. Therefore, teaching styles may need to be considered in future research.

Thirdly, the context. The study was conducted across one discipline, namely, business and they were first-year students, taking a marketing course, at a university in a particular cultural context, that is, in Australia. Kahu et al. (2017) found that students experience a sense of emotional belonging, which encourages engagement, when the content they are studying is connected to their self-interests. Therefore, students planning on majoring in marketing will more likely feel a connection with the course content which will enhance engagement. As the majority of students in the control group planned to major in marketing, did this result in higher levels of engagement in this group? The link between future career, course content and level of engagement is an area that could be explored in future research. In addition, undertaking further research in other disciplines and also with students other than first-year undergraduates would be useful, as would studies in different cultural contexts. A fourth limitation may be that the activities used to support the interventions were not a good fit with business students. In assessing personality traits between higher education disciplines, Vedel (2016) identified business students to be low in neuroticism and agreeableness. This would indicate students have good emotional control but may be less likely to extend themselves for others and possibly be uncooperative. If this profile accurately fits the study’s business students, then having a good fit in terms of the person-environment-activity is crucial to the success of the intervention. This was not investigated and future research could explore students’ personality traits and align these with intervention activities. Lastly, the small number of participants in each group (18–21). Conducting further research with larger cohorts would also be useful.

The findings support the importance of person-activity fit (Lyubomirsky and Layous, 2013). While the interventions used have the potential to positively affect student engagement (Rusk et al., 2018) they appear not to be a good fit in the study described in this article. The lack of fit may be due to participants’ demographic in terms of age or be related to activity frequency, variety or the activity itself. It could be that it just was not the correct environment. The only significant finding was with the control group which identified a significant increase in students’ happiness in the course. The research described investigated the impact of interventions on students’ engagement. Although no significant change in engagement levels were identified, further research will attempt to identify a more suitable person-activity fit.

Interventions such as those described in this article have been found to positively impact student engagement in the discipline of psychology. However, the research described in this article showed no significant impact on student engagement in a different, non-psychology, discipline. The findings indicate that implementation of such activities may lead to student disengagement. For educators, this means caution needs to be taken when implementing well-being interventions. In order for them to be effective, students need to see the relevance to their personal and academic environment and where possible students should be able to opt in to participation to avoid academic disengagement. Therefore, it may be more important to focus on a positive person-activity fit, where the intervention fits with the students’ interests and motivation. This may enhance student engagement and promote general well-being.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Jackie Hammill

Author biographies

Jackie Hammill is a lecturer at Victoria University, Melbourne Australia. Her research focuses on the development of higher education students to be future community leaders. Current research investigates active learning approaches to enhance student engagement.

Associate Professor Thinh Nguyen has worked in the University sector for 23 year and lectures in the areas of physics, engineering, and project management Thinh’s current research interests include technology-enhanced learning, problem-based learning, gamification and student mindsets. Thinh has received a number of learning and teaching awards including a national citation, a Victoria University Award for Excellence in Learning & Teaching and twice voted Lecturer of the Year at Victoria University.

Associate Professor Fiona Henderson has worked for 25 years in the university sector, principally in the field of Academic Language and Learning pedagogy. She has received several national grants for research and innovative teaching related to Academic Literacies and Academic Integrity, a national citation in 2007, a Victoria University College Award in 2011 and Victoria University Vice-Chancellor’s Awards for Excellence in Programs that Enhance Learning in 2015 and 2016. Her main research focuses on internationalisation of the curriculum with Malaysian, Vietnamese, Thai and Chinese partners.

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