Abstract
Learner engagement, loosely defined as the extent of a student’s cognitive and emotional investment in both academic and co-curricular activities, is argued to make a positive impact on both the student experience at university and student learning outcomes. Universities often implement co-curricular activities and programs to drive learner engagement, yet it continues to be the most lagging metric in nationwide student evaluations of higher education experience in Australia. This raises questions of how to design and deliver effective and engaging co-curricular programs, however, there is scant praxis-oriented knowledge available. This article draws on the experience of a team of academics in an Australian business school tasked with creating a co-curricular program to foster learner engagement amongst an undergraduate cohort. Drawing upon engagement data from 1 year of delivery, as well as a series of semi-structured long-form focus group discussions with participating students at varying stages of their learning journey, this article questions assumptions about learner engagement and co-curricular design and challenges the use of learner engagement as a metric for evaluating educators and educational institutions given the nature of students’ relationship with their education.
Introduction
In early 2019, we were tasked by our university to develop and deliver a suite of co-curricular activities targeted at all commencing students who placed within the 98th percentile in their final year of high school. The university hoped to foster learner engagement and enhance students’ university experience. Given enrolment numbers at the time, the program would need to serve up to 1,000 students joining the university’s business school each year. Our team of academics was given substantial freedom to develop the activities, with the caveat that they must support academic success, develop employability skills, and establish social networks with peers, academics, and industry professionals. Other teams across the university were being tasked with a similar project for their respective schools and faculties. As a team we drew upon extant literature on co-curricular design, the prior experience of academic colleagues, and the knowledge of industry partners, designing a program consistent with university requirements ahead of Semester 1. The start of Semester saw strong attendance at the launch event, with around 300 students, but these numbers dropped rapidly over the course of the semester and only 17 students attended the final session. Despite making substantive changes based on student feedback for Semester 2, there were similar participation rates for the remainder of the year. By the end of 2019, we were disappointed and frustrated at having missed the mark with our students. In response, we looked for the reasons behind poor participation in the program so that we could better understand how to drive learner engagement.
Learner engagement has its conceptual roots in Astin’s (1984) student involvement theory (SIT). Astin (1984) argued that student involvement, defined as the “quantity and quality of the physical and psychological energy that students invest in the college experience” (p. 528), is a direct predictor of student outcomes, including academic achievement. There is a significant body of research suggesting that learner engagement is positively correlated with key educational outcomes “including academic achievement, persistence, satisfaction, and sense of community” (Halverson & Graham, 2019, p. 144). Therefore, most universities invest in initiatives to drive learner engagement such as study programs, on-campus activities, community events, and support of clubs and societies. Moreover, universities are often evaluated and ranked based on students’ campus experience (Quality Indicators of Learning and Teaching [QILT], 2023).
In Australia, students consistently give learner engagement a low ranking when evaluating their higher education experience (QILT, 2023). Australia’s annual Student Experience Survey (SES) gathers data from all graduating higher education students, measuring student satisfaction across five metrics: Skills Development, Learner Engagement, Teaching Quality, Student Support, and Learning Resources (QILT, 2023). Learner Engagement is measured across seven survey items that assess the extent to which students (i) felt prepared for study, (ii) had a sense of belonging to the institution, (iii) participated in discussions, (iv) worked with other students, (v) interacted with students outside of study, (vi) interacted with students who are different from them, and (vii) (for international students only), were given the opportunity to interact with local students. In 2019 (n = 277,868), 78.5% of students rated the overall quality of their entire educational experience as positive but only 60.2% of students rated Learner Engagement as positive (QILT, 2020, 2023). This low satisfaction rate has dropped slightly over the 4-year period, with only 55.2% of students (n = 233,916) rating Learner Engagement as positive in 2022 (QILT, 2023). This low rating compares to the percentage of students rating their overall experience as positive (75.9%) and the percentages for the other four survey items—Skills Development (80.5%), Teaching Quality (80.1%), Student Support (72.9%), and Learning Resources (83.6%) (QILT, 2023).
In this article, we first briefly explore the academic literature around co-curricular programs and how they relate to learner engagement and student outcomes. We subsequently provide an overview of the relevant literature on high-achieving students and their learning approaches. We then provide details about the context, initial program design, and our mixed successes in instructional innovation throughout 2019 and 2020. From there, we synthesize insights from student feedback and the scholarly literature to offer a set of principles for co-curricular program design for learner engagement. This article ultimately challenges assumptions about learner engagement and co-curricular design. In doing so it aims to provide guidance for those in higher education tasked with developing co-curricular programs and/or driving learner engagement in their institution.
Theoretical Framework
Learner Engagement in Co-Curricular Programs
Astin’s (1984, pp. 528, 529) SIT is a commonly employed theoretical lens used to help understand the role and value of learner engagement in higher education.
Student involvement refers to the quantity and quality of the physical and psychological energy that students invest in the college experience. Such involvement takes many forms, such as absorption in academic work, participation in extracurricular activities, and interaction with faculty and other institutional personnel. According to the theory, the greater the student’s involvement in college, the greater will be the amount of student learning and personal development.
Thus, it is unsurprising that universities invest in initiatives to foster learner engagement, or that they receive funding based on learner engagement metrics (Baron & Corbin, 2012). This also helps explain scholarly interest in exploring learner engagement and outcomes (see, e.g, Bowden, 2022; Brown et al., 2022; Chiu, 2022). This interest has tended to focus on academic activity, with studies a positive relationship between learner engagement in the classroom and academic outcomes (see, e.g., Clancy et al., 2021; O’Connor et al., 2021; Schöbel et al., 2023; Trinh et al., 2023).
Research also explores the impact of co-curricular activities (CCAs) on learner engagement (see, e.g., Eury & Treviño, 2019), with findings underscoring their value, particularly in enhancing graduate employability (Jackson & Bridgstock, 2021; Lau et al., 2014; Winstone et al., 2022). Fox and Sease (2019) found that CCAs designed to facilitate social networking, professional development, and direct application of in-curriculum material are the most effective at promoting higher levels of learner engagement and performance. Wresch and Pondell (2015) and Webber et al. (2013) also identified CCAs as facilitating better academic performance and student satisfaction.
However, Seow and Pan (2014) identified a curvilinear relationship between CCA participation and academic performance, with participation in less than five activities over 4 years producing negligible benefits and participation in any more than 14 negatively impacting academic performance. Further mixed findings include those of Leung et al. (2011), who studied a co-curricular program for first- and second-year business students, tracking and surveying 575 students over 2 years across six scales. They did not find a positive relationship between program engagement and academic performance, with their data suggesting those not fully engaged in the co-curricular program had the highest level of academic development. However, they noted that despite this, students reflected positively on the program and reported several benefits that were not investigated, including social networking. A follow-up study by Wong and Leung (2018) looked at business students’ perceptions of CCAs, finding that students believe CCAs add value to their experience and enhance their skills. However, they argued that practitioners should ensure that programs: (1) allow for opportunities to simultaneously develop their academic skills, employability, and social networks; (2) are specifically designed, and integrated, with students’ core studies and curriculum; (3) not clash with other key periods such as exams or holidays; (4) are advertised directly and through word-of-mouth; and (5) are no longer—or shorter than—3 hours.
Thus, CCAs appear to be theoretically and intuitively sound approaches to drive student outcomes including academic performance in higher education (Chiu, 2022; Kahu & Nelson, 2018). However, the drivers of learner engagement in CCAs and their impact on student outcomes require further investigation.
Conceptualizing High Achievers
As the program was open to incoming students who had placed in the 98th percentile in their final year of high school, we understood that the CCAs we designed needed to target academically successful students, defined in the literature as high achievers. High achievers are students with high academic success as reflected in measures of cognition, motivation, and self-regulation (Lee et al., 2017). However, few studies investigate the specific attitudes, values, and beliefs of university high achievers in terms of their education in general and CCAs in particular. Given this limitation, we employed the Learning Model (Biggs, 1978) as a conceptual lens to understand the approaches of students with differing levels of academic performance.
Biggs’ (1978) Learning Model frames students’ differing pre-existing presage factors (personal and institutional characteristics) as leading to different study processes (motives and strategies), which in turn lead to different product factors (objective and subjective). Biggs later categorized three different learning approaches, which he labeled surface, deep, and achieving (Beattie et al., 1997). Biggs argued that surface learners are motivated to meet minimum standards by focusing on essentials and relying on rote learning (Beattie et al., 1997). By contrast, deep learners read widely and engaged with broader knowledge bases (Beattie et al., 1997). Finally, achieving learners are competitive, driven to perform to the best of their ability regardless of interest by optimizing the resources available to them (Beattie et al., 1997). Importantly, Biggs emphasizes that students modify and choose different approaches to learning according to their desired outcome provided they possess a certain degree of awareness of their cognitive resources, described by Beattie et al. (1997, p. 6) as “metalearning” ability: Metalearning increases as the general cognitive ability of students increases. The other important factor is the locus of control, which describes the extent to which students believe themselves to have some control over their learning (an internal type) compared to those who believe their learning to be governed by external forces (an external type). Biggs (1987) presented evidence which supports the view that approaches to learning vary according to students’ capability for metalearning which, in turn, is related to individual student differences such as general ability and the locus of control.
In lieu of more robust research on high achievers in higher education, we based our assumptions on Biggs’ (1987) paradigm of deep and surface learning for our project. That is, we assumed that it was likely that the students in our high achievers’ program were deep and/or achieving learners with an internal locus of control over their learning.
Context and Site of Study
Our university’s High Achievers Program (HAP) [a pseudonym], the primary site of this study, was estimated to be open to around 1,000 incoming students per year. The HAP stream was comprised of both an in-curriculum component and a co-curricular component. The
Co-Curricular Activity Design
In the first semester of 2019, the HAP CCAs were launched, informed by the design principles suggested by Wong and Leung (2018). CCAs were distributed throughout the 13-week academic semester, offering students across undergraduate year levels opportunities to hone skills for academic success, develop employability skills, and establish social networks with peers, academics, and industry professionals. Four CCAs were offered:
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All sessions incorporated opportunities for participants to interact and network with peers, academics, alumni, and industry guests. Further, and consistent with the design principles of aligning CCAs with core studies while not clashing with exams or holidays (Wong & Leung, 2018), the sessions were timed carefully to coincide with key periods in the academic calendar and avoid major assessment periods as follows:
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In line with CCA design principles (Wong & Leung, 2018), all sessions were communicated to students through the university’s learning management system (LMS) and student leaders tasked with inviting others to the sessions. However, to fit with class schedules, sessions were kept to 1 to 1.5 hours.
The Student Response
In the first semester of 2019, a total of 300 students attended the first session (representing around 35% of the total population of the 850 students in the cohort). Feedback from this first session was exceptionally positive in the post-experience survey. Despite the initial positive response, only around 100 students (around 12% of the cohort) attended the second session. This sharp downward trend in attendance continued over the semester with around 24 students in the third session (around 3% of the cohort), and only 17 students in the final session (around 2% of the cohort).
The sharp decline in attendance prompted a redesign the following semester. Despite enhancing the experience by offering catering and gamified activities accompanied by structured academic mentoring sessions, student participation and engagement remained persistently low, averaging around 1.5% of the total cohort in each session. Faced with positive student feedback on the one hand and extremely low attendance on the other, we sought explanations for this contradiction, and for lack of engagement more broadly, by conducting in-depth focus group discussions among students. Our guiding research question was “What factors might explain the low student participation in the Business School’s HAP co-curricular activities?”
We designed a study to take place throughout 2020 with the existing and incoming HAP stream cohort to seek answers to our research question. The next section will discuss the methods and methodology, including details of the online survey and focus groups.
Methods
Given the apparent contradiction in positive student feedback on the one hand and extremely low attendance on the other, we elected to undertake an exploratory study with students involved in the HAP to better understand the students’ perspectives and to design a better CCA for current and future students. We selected qualitative interviewing because of the potential to facilitate “mutual discovery, understanding, reflection, and explanation via a path that is organic, adaptive, and oftentimes energizing” (Tracy, 2012, p. 132). Specifically, we selected focus groups as our interviewing approach for several reasons.
First, focus groups would allow us to leverage “the group effect,” a documented phenomenon wherein participants show less inhibition and greater self-disclosure than in one-on-one interviews (Tracy, 2012). Second, focus groups are particularly valuable for the generation of in-vivo terms which is vernacular speech used by that population (Tracy, 2012). Third, focus groups can serve as a micro-interaction laboratory in which the dynamics within a target population can play out in real-time (Tracy, 2012). Fourth, and finally, focus groups allow participants to discuss, poll, and rank the relative importance of different issues or items (Tracy, 2012). The focus group discussion questions were designed in line with Tracy’s (2012) approach to interviewing, focusing on themes of motivation, performance, identity, details of the CCA program itself, learning, and self-leadership.
We received ethics approval in late 2019 to undertake a study with undergraduate students taking part in the HAP. This included students who had enrolled in 2018 and 2019, and those who were commencing in 2020. It should be noted that per institutional policy we were not able to receive retroactive approval for experiential data collected until that point, including post-experience surveys. Recruitment for the study took place at the start of 2020, during our university’s orientation week activities. The researchers attended the first HAP event of the year and invited participation from all three cohorts. In addition, students who attended this event were sent a follow-up email inviting them to participate. Students who expressed interest in participating were provided information about the study via email and invited to take part in focus groups with peers. Participation was voluntary with students offered a small voucher for the campus bookstore in appreciation of their time. In total, we had 17 students participate across seven focus groups. Each student was a current undergraduate HAP student, from first to third year. No student participated in more than one focus group. Given the small size of the sample and the data provided by the students detailed later in this article, we have elected not to provide a demographic breakdown of the students to avoid the possibility of students being identified.
Students were asked to select a timeslot for participation, then randomly divided into smaller groups on the day. The first five focus groups took place in late February 2020, however the remaining two focus groups were delayed until late May 2020 due to institutional, state, and national COVID-19-related restrictions. The focus groups were semi-structured and in-depth, each taking an average of 57 minutes. Each interview was recorded and later transcribed via a transcription service, before being imported into NVIVO for analysis.
Data analysis was undertaken via a multi-step coding and analysis process in NVIVO employing Tracy’s (2012) iterative analysis approach. The first author of this article undertook primary-cycle coding independent of the other researchers, then the files were shared with the team for validation and confirmation. Following the completion of the primary-cycle coding and validation, each of the researchers independently undertook secondary-cycle coding before finally meeting to confirm and validate the final set of second-level codes. Coding results from the three researchers were highly similar and ultimately three core themes were generated, which will be discussed in the following section.
Results and Findings
In this section, we describe the three core themes that emerged from our coding processes that pertain to participants’ learner engagement with in-curriculum and co-curricular activities. The three key themes that emerged from the analysis of focus group transcripts were: (1) weak locus of control; (2) motivation to satisfy expectations; and (3) outcome (career) versus process (learning) orientation.
Weak Locus of Control
The first theme that emerged challenged the widely held institutional and theoretical assumption that high-achieving students demonstrate a strong locus of control. The participating students did not identify having strong control over their learning. For example, when asked why they chose to attend a specific university, students indicated that the decision was not necessarily the result of conscious reflection and active decision-making on their part; rather, it was simply a taken-for-granted path, framed by cultural and social expectations. A participant said: I came from a selective school and then there’s that instinct to just – that’s just how it works. You go to high school, and you go to university.
This sentiment was echoed by several other participants within this focus group, and in other focus groups. According to a participant, Zachary: Yeah, I would hundred per cent agree with that, actually. I would say . . . that it wasn’t exactly a choice, it wasn’t something that you ever considered not doing. I used to joke with my parents about dropping out in Year 11 and 12 and I’d just say, ‘the [High School Exam] is too hard, I want to drop out’. Then my mum would say, ‘yeah, sure, if you can find a job, go for it,’ in a very joking manner. For me growing up, that was always an expectation. I think if you ask a lot of people who would be in this program, as generally the traditional sense of high achievers . . . that’s also what’s expected.
In addition to this broad cultural sense of university attendance as a deterministic outcome of high school, many students added a layer of cultural expectations based on their familial background. For example, a participant, Jacob, identified how his family, including their cultural background, played a key role in his decision to attend university, specifically this university.
I chose to attend university purely out of – I come from an Asian household, so there’s a lot of that pressure that in order to be successful, I needed to get into university. I think what spurred me on, even just to get into [this university], was the fact that both my sisters went here. They both graduated with honours, so it was kind of like an expectation.
This sentiment was immediately echoed by Eve, in the same focus group, who similarly wove this narrative of cultural heritage into her story of university choice.
I’m actually in a very similar position because I also come from an Asian family. In my high school, we were all expected to go to university . . . we weren’t given options to go on to do vocational training, or to not go to university.
Another participant, Cameron said that even his degree program was selected for him by his family.
I didn’t actually pick commerce as my degree. It was something that was kind of recommended by my dad because he viewed it as a way that, oh, if you do commerce, it’s a really easy way to get a job, easy money, and that way you’ll live a really simple life. Yeah . . . that’s why I attended university.
For many students, this (lack) of agency extended not just to their decision to come to university, but also to their decision to become engaged with the HAP itself. Students such as Jacob pointed out that they signed up to the program not knowing what it was, but drawn to the idea that it was an exclusive opportunity that represented a challenge: My initial draw was I’m going to be part of this community, which is the best of the best, I’m going to be working with some of the smartest people that the university has to offer and that opportunity on its own motivated me to take it . . . I didn’t really know much about it in . . . beyond that . . . you needed a 98. I . . . wasn’t really sure of the opportunities that we were to be given. It’s just it was a . . . really exclusive community.
This sentiment was echoed by other participants who were drawn to the exclusivity and prestige of the program.
In summary, in just the first few minutes of our focus groups it became clear we had fundamentally misunderstood the decision-making processes that led to students being involved in the university, our school, and the HAP program.
Motivated to Satisfy Expectations
The next important theme became apparent as we moved into the second stage of our interview, which sought to better understand student outlook on performance and motivation. Throughout the program’s development process, we assumed that high-achieving students were motivated (be it intrinsically or extrinsically) to perform to the best of their ability and/or to increase their performance vis-à-vis deep or achieving learners. Indeed, many of the activities and programs offered in the program served to help drive and stimulate academic outcomes for students. However, for most students, it became clear that a key motivation was to avoid falling below minimum expectations rather than maximizing outcomes.
For example, Dylan outlined his motivation to perform well: For me . . . it started initially from primary school, high school, where I’ve been pressured to – hey, you need to be maintaining higher grades if you’re going to be getting anywhere. For me, at the moment, it’s more like I need to be maintaining these higher grades if I really want to differentiate myself, because I know a lot of grad programs right now have a cut-off of 75[%], 80[%]. If I want to get into those companies, then I need to maintain that performance . . . for me right now, that’s what the main motivation is to perform well.
This sentiment was echoed by Jacob, who responded that his motivation to act or adjust behavior is based on whether he is maintaining the 75% average to remain in the HAP and apply for graduate programs.
To be honest, the only time where I’ll look at my grades and think, ha, I need to do something, is when they dip below where they need to. Honestly, if it’s between 75[%] to 100[%], I’m not going to bat an eye . . . I don’t really have that motivation to be like, I’ve got 75[%], let’s push to an 80. Or literally, it’s just because of the [HAP]. It’s like I get my 75[%], that’s enough. Seventy-five is that magic number that a lot of grad programs are offering. It’s not like I’m intrinsically motivated to keep pushing and pushing and pushing myself to do really well. It’s more like once I hit that threshold, then I can just relax for a bit.
Jacob’s perspective is consistent with other students. For some students though, this pressure took a more intrinsic form. For example, Zachary identified his pride in not dropping below a particular standard of performance.
I think for me it’s about keeping up a streak, so I think it’s pride in that sense. If you’re doing something well, you want to do well again, you don’t want to feel like you’re dropping below your standard or anything. So, I think that’s what keeps me going.
Particularly interesting is that several students suggested that in high school they had been motivated to perform to the best of their ability, but that had changed since beginning university. Dylan claimed his sense of intrinsic motivation to perform to the best of his ability was no longer present.
. . . in terms of my marks, I’d say initially it was very intrinsic. I did have a lot of motivation, going into university, to do really well. However, I felt that as I went through my degree, it became more extrinsic in that – oh, it’s not that I was motivated to maintain . . . really high-level marks. It’s more that, oh, if I drop below a certain [Weighted Average Mark], then yeah, I’m going to get kicked out . . . that kind of relates to how I lack a lot of motivation, in that I don’t have this really good sense of direction. It’s not like, yes, I want to go here, I need to be doing these things. That way I could become intrinsically motivated, but that’s just not present for me right now.
In general, this idea that students were driven to maintain perceived minimum standards, be it their own or those of the program, as opposed to maximizing outputs either in terms of grades or experiences was shared amongst most of the participants. Some participants did point to some general intrinsic motivations, such as building social relationships or traveling whilst at university, however this sentiment was presented in an aspirational manner that lacked concrete examples.
Outcome (Career), Not Process (University) Oriented
Finally, and closely tied to the first two themes, many students’ perception of university was that it was a career requirement. When asked about her motivation for attending university, Kayla responded that her experience is deeply contextualized by her long-term career.
I guess it’s all to do with my career. It’s my passion towards my career and trying to reach my career in the best possible way. That’s what I . . . It’s very hard for me to have a clear distinction because I feel like everything to me is based on seeing myself in the position of my dream job rather than anything else, so that’s the main thing, yeah.
Almost every participant shared this view that their engagement with the university, and HAP, was to meet a career requirement. In line with the prior two themes, students demonstrated an acute sense that within their culture/community, it was an expectation and requirement that they attend university to secure a job. According to Zachary: I think it’s almost a prerequisite to attend university these days, just based on how competitive it is in the job market. At least with my interactions with people that I know and within my social level or whatever you may describe it . . .
Many students extended this logic to their participation in HAP itself. The clear motivation in engaging with the program was ultimately rooted in the assumption that it would help them to secure a job because they would be differentiated in the job market. According to Sophia: I guess it really helps us to stand out, especially when we are trying to get a job in our job interviews and also, I think it’s always nice to perform well because that gives you a sense of self-accomplishment and also you can reach towards your dream job and also your career.
Across all seven focus groups, each student articulated at some point that their motivation in either attending university, undertaking the program, or performing within these spaces was motivated by their career aspirations.
Discussion
Our analysis of the focus group data suggests that our design to foster learner engagement fundamentally misunderstood the desire or capacity to engage of the cohort. We assumed that students in the HAP program exercised agency to employ deep or achieving learning approaches (Beattie et al., 1997) and that they independently worked toward achieving their academic potential with limited need for external prompts, pressures, or rewards. However, student feedback data revealed that their motivation to attain high levels of academic performance was significantly driven by external expectations from their families, secondary school environment, social circles, and indeed from future employers. These students were historically immersed in environments where high benchmarks for current and future performance were explicitly and consistently communicated to them. These presage factors led to unconscious seeking of externally determined performance benchmarks that did not harness their capabilities to work toward them. The keen awareness of these performance benchmarks, coupled with the internalized expectation to aspire toward them, are what make these students high-achievers. Our students had limited agency in their decision to attend university, pursue a particular degree, or participate in the HAP. They may not necessarily be motivated by the sheer joy of learning discovery or self-improvement (or at least, not yet); instead their focus was meeting the grade requirements established by the HAP without exceeding the high minimum requirements of the program by a wide margin. This suggests a surface rather than deep or achieving learning approach (Biggs, 1987).
Employment of surface learning approaches can explain why these students were particularly compelled to engage either physically or psychologically with CCAs, where participation is voluntary and thus viewed as extra work, and where outcomes are not viewed as directly contributing to how their academic performance is measured (i.e., grades). Students who believed they had already found the optimum amount of work required to achieve at least the minimum grade requirement to remain in the HAP would be unlikely to voluntarily attend CCAs. Once students realized that they did not need to attend the CCAs to meet the minimum expectations of the program, other priorities took precedence. Given that students were unlikely to have a strong internal locus of control, it would be unlikely for them to see the value in the program.
Implications: Pedagogy and Policy
This article has several implications for researchers and university policymakers dealing with CCAs, high achievers, and learner engagement in higher education more broadly. Our findings offer further evidence that there may not be a clear correlation between academic achievement and learning approaches (Biggs, 1978). They may go some way to explaining why so much research has found mixed results when examining the relationship between CCA involvement and academic performance. Thus, this study provides further evidence to support Baron and Corbin’s (2012) argument that programs such as HAP that are based on assumptions about student expectations may foster disengagement.
For policymakers, this article may thus provide a solid basis to suggest that learner engagement is at best a problematic metric for evaluating higher education experience. We provide some evidence to suggest that academically successful students are not necessarily compelled to have a high degree of involvement with their program or institution. In turn, if we are to assume that learner engagement is heavily moderated by learning approaches but that neither is necessarily strongly correlated with student performance or outcomes, then it does beg the question of the utility of evaluating universities on the “quantity and quality of the physical and psychological energy that students invest in the college experience” (Astin, 1984, p. 528). Evaluative metrics, such as the national SES in Australia, assume that a high degree of learner engagement is both plausible and desirable for students. If we accept that some students, due to nature or nurture, engage in surface learning approaches and others in deep learning approaches, what does this imply for university-level initiatives to drive learner engagement, and attempts to measure it? Further, to what extent should universities be investing resources into CCAs if we know that, despite their best efforts, other factors, such as individual differences or economic circumstances, may heavily influence student involvement?
Implications: Advice for Practitioners
The first implication of our findings for practitioners engaged with CCA design is that it is useful to query assumptions about ourselves and about institutions. We made assumptions about the students in our business school HAP cohort, including about how and why they were there. Further, our institution’s core vision for HAP assumed that students involved in the program were interested in, or capable of, being highly psychologically and physically engaged with their learning experience. More specifically, our institution assumed that those students involved in the in-curriculum component would want to take part in an optional co-curricular component. While research does support the importance of linking in-curriculum learning with CCAs, it is wrong to assume that students want to spend their spare time on these activities. In hindsight these assumptions seem foolish, but they were informed by our aspirations for students, institutional preferences for student experience, and industry-wide imperatives for driving learner engagement.
Regardless, we recognize that CCAs as a method of fostering learner engagement are likely to remain an important part of the higher education student experience, offering significant opportunities to support the process of personal discovery and learning exploration. In turn, academic faculty will be encouraged to support and be involved in the design of such programs. Based on our experience and our findings, we offer three CCA design recommendations aimed at encouraging participation, enhancing the student experience, and ultimately enriching learning.
Establishing a Recognition System
It is critical to establish a system to formally recognize student participation and active engagement in CCAs. While CCA participation may not make a direct impact on students’ grades, a recognition system signals that CCAs are important to students’ overarching degree and/or program outcomes. The evidence of recognition can come in the form of digital badges, extra credit, bonus points, certificates, and prizes. Having an awards ceremony in the presence of peers and faculty reinforces the message that CCAs matter and encourages other students to participate in future activities.
Further, we argue that the types of prizes and forms of recognition matter in effectively reinforcing the skills a particular CCA fosters. Digital badges (e.g., for participation in leadership training or career development programs) may be valuable for senior students approaching the end of their university studies. Students can showcase these badges on their professional social media profiles, which are visible to recruiters and future employers. As another example, a career development program that incorporates a leadership skills component could incorporate industry networking opportunities where the university’s industry partners present leadership awards to students. This raises the industry profile of the students while giving them an opportunity to grow their professional network and signals that CCAs are valuable in strengthening future employment prospects.
For some types of CCAs, gamification or the introduction of a competitive element to activities may effectively motivate students to participate. Competition can be between teams or individuals. As above, the university’s industry partners can be invited to present prizes to the winners, further signaling the link between skills learned through CCAs and future employer expectations.
Embedding Co-Curricular Activities
The importance of CCAs can be reinforced by embedding them into academic courses. For example, if students undertake industry projects as a major assessment in a course, the final project presentations could be packaged as a conference to be attended by industry guests, faculty, and the broader student population. The conference could incorporate a keynote where an industry professional shares insights on the real-world significance of skills learned at university through both academic courses and CCAs. As above, industry partners can also be invited to present awards for the best presentations. The conference can include a networking reception for students, faculty, and industry guests.
Embedding CCAs into academic initiatives signals that they offer skills development opportunities that complement or reinforce the skills learned in the classroom and are thus just as important the university learning experience.
Creating Integrative Programs
Finally, there is an opportunity to further integrate CCAs, not only at the individual course level, but also at the program level. For example, CCAs can be designed progressively to be undertaken by students throughout their university program, where junior-level CCAs serve as a pathway to senior-level opportunities for internships. One option would be to offer teamwork skills and communication skills workshops for commencing university students, followed by case competitions embedded in second-year courses, and career development workshops designed to create internship opportunities for senior-year students. Participation in selected junior-level CCAs can then be recognized as an informal prerequisite for participation in senior-level CCAs.
The successful seamless integration of CCAs into other aspects of university life reinforces their importance while offering students greater opportunities to participate and benefit from a richer higher education experience.
Limitations
The most notable limitation of this article is that the primary data for this study was collected from participants of a single co-curricular program, within a single business school in the Australian context. Thus, the perspectives articulated by participants are subject to confounding variables such as localized behavioral, attitudinal, and motivational factors. Different schools and faculties across the university may have had different results with our program design, that is, it is unclear to what extent our data and findings are limited to business and management education contexts. Further, it is important to identify that while this article has drawn upon literature from across North America, East Asia, and Oceania—the primary research focus and site of study is definitively Australasian higher education and the specific learner engagement imperatives and measurements in Australia. Finally, we note the small sample size for the long-form focus groups at 17 participants. A larger number of participants would have added validity to our results but attracting more participants to provide feedback on a program with low engagement is challenging.
Conclusion
Our study makes clear that our initial co-curricular program design was fundamentally flawed due to the presence of several erroneous assumptions. Most notably, the ideological principles of our institution, the HAP, our own beliefs as scholars, and even SIT (Astin, 1984) provided a basis for the assumption that all students (including high achievers) have a capacity and/or desire to be deeply engaged with their university experience. A lack of nuance in research about high-achieving students in higher education in the literature and in professional networks means that high-achieving students are assumed to be deep learners but may also be surface learners (Biggs, 1987; Biggs & Rihn, 1984). Our findings reveal that the students in our cohort have a weak locus of control of their education, were motivated to satisfy expectations, and were outcome-oriented. Hence, our program, designed to provide learner engagement opportunities, did not appeal to students.
Our findings inform a reconsideration of co-curricular program design that aims to drive learner engagement. We make three recommendations for program design improvement. However, fundamentally, we see learner engagement as a goal and as a metric as problematic for educational institutions. While the efforts to engage high-achieving students were well-intentioned and based on our aspirations for students, institutional preferences for student experience, and industry-wide imperatives, they missed their target. We caution other scholars to query personal, institutional, and even national imperatives to privilege and drive modes of engagement that are potentially not aligned with our students’ preferences, needs, and wants.
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.
