Beyond the Classroom: An Analysis of Internal and External Factors Related to Students’ Love of Learning and Educational Outcomes

Abstract

This study explores the multifaceted factors influencing student learning motivations and educational outcomes. Utilizing a diverse student body from Franklin University Switzerland, the study emphasizes the impact of internal factors, such as the psychological state of flow and a self-reported love of learning, alongside GPA and student cohort influences like year of study, academic discipline, country of origin, and academic travel. Through a cross-sectional survey of 112 students, the study evaluates how these factors correlate with and diverge from each other and student GPAs, aiming to dissect the influences of intrinsic motivations, demographic variables, and educational experiences. Our analysis revealed significant correlations between students’ self-reported love of learning, experiences of flow, and academic performance. Conversely, academic travel did not show a significant direct impact, suggesting that while such experiences are enriching, they do not necessarily translate into a greater love of learning, flow, or higher academic achievement in the short term. However, demographic factors, particularly discipline of study and country of origin, significantly influenced the students’ love of learning, indicating varied motivational drives across different cultural and educational backgrounds. This study provides valuable insights for educational policymakers and institutions aiming to cultivate more engaging and fulfilling learning environments.

Dataset: Underlying data: Mendeley Data: Love of Learning Survey. doi: 10.17632/xjsrpk5xv7.1, accessed on 19 April 2024. This project contains the underlying data file: Learning_Survey.xlsx (data file). Extended data: Learning_Questionnaire.doc (blank questionnaire file).

Dataset License: Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC_BY 4.0).

1. Introduction

Educators are perpetually seeking innovative methods to improve learning outcomes by increasing student engagement, motivation, and interest in the learning process. Meta-analyses conducted by Zepke and Leach [1,2] demonstrate that student engagement is affected by several variables, ranging from broad and external, like institutional fit or support, to narrow and individual, like a student’s own intrinsic motivation to study and be active in school citizenship.

Further research has examined how student motivation could be enhanced by disruptions to the traditional classroom setting. Specifically, active learning—learning by doing rather than through passive absorption of knowledge through instruction—has been found to increase students’ motivation to learn [3]. Other disruptions to the traditional learning process extend to student assessment, specifically the use of alternative grading methods to standard letter and grade point scales (e.g., using pass/fail metrics), which has been found to increase student motivation [4].

Moreover, in the digital age, the widespread and frequently excessive use of mobile phones amongst students is recognized as a significant distractor, one which interferes with their ability to enter a deep state of concentration during the learning process. This diminishes their prospects of experiencing the more enjoyable psychological state of attention that occurs when deeply focusing on a task. This psychological state is conceptualized as flow, as defined and described by psychologist M. Csikszentmihalyi [5].

Finally, research from McFarlane [6] concluded that it is important to broadly consider whether students ‘like to learn’ when assessing their engagement, motivation, or interest in studies. Rather than assessing specific motivations, this variable could be considered comparable in nature to more general measures of individual wellbeing, like life satisfaction. Today, assessing and promoting a student’s ‘like’ or ‘love of learning’ is particularly important as we accept a growing and increasingly diverse cohort of students into higher education, in an era where global uncertainties in employment necessitate that the spark of learning be maintained across an individual’s lifespan [7].

The theoretical basis for this study is grounded in these teaching, learning, and educational psychology variables, drawing inspiration from earlier research on fostering and evaluating student motivation. Thus, we aimed to collect and analyze data across a spectrum of potential motivating factors, bringing together a diverse set of measures into a single survey instrument. These include students’ self-reported GPA, their love of learning, amount of academic travel, and their self-reported state of flow during learning and study.

The survey sample was drawn from the student body of Franklin University Switzerland (FUS), a dually accredited American and Swiss university of Liberal Arts and Sciences located in Lugano, Switzerland. FUS students are uniquely required to take one academic travel course per semester for their first four semesters. Academic travel entails a two-week long experiential learning component held during a travel break each semester. Students enrolled in the course travel to a destination chosen by the instructor, connecting what they have previously learned in the classroom to the real world. In addition, students have the option to continue enrolling in academic travel as often as once per semester for their remaining undergraduate experience. Thus, the frequency of a student’s enrollment in these courses is used in our study as a proxy measure of active, experiential learning.

In addition to self-reported intrinsic motivation, flow, GPA, and experiential learning, we paired responses to these questions with student demographic variables across several dimensions to create cohorts. Here, too, the setting of Franklin University Switzerland offers unique data gathering opportunities, since the American–Swiss institution offers several majors and attracts students in near equal measure from secondary schools located both within and outside the United States and Europe.

In the resulting student sample, we find varying cohorts based upon year of study, amount of academic travel, and chosen academic discipline. Additionally, a significant cohort of American, European, and students from the rest of world (ROW) are present, and since the sample stems from several instructors, significant cohorts of Business and Economics students, Liberal Arts students (Art, History, Political Science, etc.), Science students (Environmental Science, with minors in Math, Biology, Chemistry, etc.), and Undeclared majors can be found.

Considering the diversity of student motivation variables, a measure of experiential learning frequency, and varying academic disciplines as well as countries of origin, we believe we offer researchers a rich dataset to investigate. It is against this sampling background that we evaluate relationships amongst the variables love of learning, flow, and GPA, as well as differences in their means by demographic cohort (year, travel, discipline, and country of origin).

2. Literature Review

2.1. Introduction

This literature review explores the multifaceted concept of student motivation within the contexts of teaching and learning and educational psychology. Excluding this brief introduction, the review is structured into five distinct sections, each focusing on specific aspects of student motivation, their interconnections, and outcomes.

Section 2.2 categorizes the broad fundamental factors influencing student motivation, distinguishing between intrinsic motivations, such as personal interest and enjoyment, and extrinsic motivations, such as grades, external rewards, and recognition. This section emphasizes the significant role that both intrinsic and extrinsic factors have in enhancing student engagement, performance, as well as short- and long-term educational outcomes.

Section 2.3 examines how the classroom setting and dynamics influence intrinsic student motivation, specifically love of learning, and extrinsic learning outcomes, namely GPA. Key aspects include teacher support, classroom dynamics, and a positive learning environment, which collectively foster both intrinsic and extrinsic motivations and better learning outcomes. The section highlights the importance of creating a supportive and engaging classroom atmosphere to boost student motivation and GPA.

Section 2.4 delves into the factors that influence learning while students are engaged in the act of study. One such factor is the psychological state of flow that students experience when effectively focused on learning tasks. Another factor to consider is the active, experiential learning that is offered outside the classroom via outlets like academic travel or study abroad programs. This section attempts to better understand how these ‘in the moment’ and ‘in situ’ factors can more broadly influence student motivation and outcomes.

Section 2.5 focuses on the influence that differing student demographics and cultural influences have on motivation and GPA. Students’ year of study, major discipline, and place of origin all affect the student experience, in turn influencing students’ fundamental drive to learn. As a result, certain student cohorts have been found to be more motivated by intrinsic than extrinsic factors and vice versa. This section underscores the importance of exploring these demographic factors relative to student motivation.

Section 2.6 synthesizes several recent findings on student motivation, from which emerges a drive by educators to develop pro-social, innovative classroom environments, and to integrate new technologies, including, most recently, Artificial Intelligence (AI). In short, the rise of digitally connected classrooms, accelerated post-COVID-19, has necessitated new approaches to reignite diminishing student motivation in recent years. This final section briefly touches on the potential benefits and challenges of developing new pedagogies and incorporating technologies like AI in the context of fostering student motivation in the modern classroom learning environment.

By weaving together these topics, this review provides a comprehensive understanding of the diverse factors that drive student motivation and offers insights into how educators can foster a more motivating and effective learning environment.

2.2. Intrinsic and Extrinsic Motivations to Learn

Understanding the factors that influence student motivation is crucial for enhancing the learning experience. These factors can be categorized into intrinsic (internal) and extrinsic (external) motivations [8].

Intrinsic motivation, such as interest and enjoyment in the subject, has been shown to significantly enhance student engagement and learning outcomes [6,9]. This form of motivation is associated with deeper learning approaches and better retention of knowledge [10,11]. Studies show that intrinsic motivation is linked to higher academic achievement and wellbeing, as well as student persistence and long-term engagement in learning activities [12,13]. Broadly speaking, when we think about our own or discuss an individual’s ‘love of learning’, we are colloquially encapsulating such intrinsic motivations to learn.

Extrinsic motivation, on the other hand, involves external rewards such as grades, praise, credentials, career prospects, or other forms of recognition [8]. These rewards can enhance motivation, particularly in the absence of intrinsic interest, by providing clear goals and incentives. Such extrinsic motivation has been found to improve performance and task completion in the short-term [13,14]. Outside of clear external goals, the learning environment itself can be an extrinsically motivating factor. Aspects of the learning environment include the level of teacher support and the classroom dynamics, and each plays a significant role in motivating students. In short, a supportive classroom environment is important and can foster both intrinsic and extrinsic motivation [15].

When observing an activity like learning, it can be particularly challenging to distinguish between the factors that motivate a student’s behavior [16]. The topic of motivation becomes even more complex if one aims to distinguish motivation to learn from achievement motivation, which refers to doing something to a high standard [3]. We can assume, however, that both intrinsic and extrinsic motivation are at work simultaneously in relation to a student’s learning capacity to some degree [17,18].

In consideration of these studies, we designed our survey to collect data on both intrinsic and extrinsic motivations, formulating a questionnaire that captured manifold factors including demographic backgrounds, self-reported love of learning, GPA, opportunities for experiential learning, and the psychological flow experienced during study-related activities.

2.3. GPA and Love of Learning

The selection of these two variables for our survey was driven by the goal to capture the primary influences on student learning. The most obvious external influence on student motivation is grades, or more broadly their Grade Point Average (GPA). GPA is an interesting variable, as it is simultaneously a motivating factor and a measurable outcome of learning. Moreover, despite its role as an external goal, it has been found to be a strong proxy for many intrinsic learning traits like self-efficacy and self-regulation.

Students with higher self-efficacy and motivation tend to have better academic performance [19,20]. Additionally, self-regulation, or self-discipline, is an important predictor of GPA, as self-regulated learning and intrinsic motivation are particularly important for academic achievement [21]. Moreover, self-efficacy and self-regulation strongly relate to a student’s conscientiousness, a personality trait that can mediate the relationship between motivation and academic performance [22]. Deep learning approaches and self-perceived competence are associated with a higher GPA, and students who adopt deep learning strategies and believe in their abilities tend to perform better academically [23]. As a result, GPA is both an important correlate and dependent variable for any study of student motivations.

While GPA can act as a powerful variable in and of itself, it is difficult to capture a multitude of intrinsic learning factors in one survey. Given this constraint, we select a potentially unique variable to measure that multitude—love of learning. Love of learning is a variable that educational researchers have long attempted to conceptualize and operationalize in studies [6]. Students with a strong love of learning exhibit higher levels of engagement and set more meaningful learning goals, leading to better academic performance and persistence [24]. A supportive and enriching learning environment enhances students’ love of learning, and positive perceptions of the learning environment correlate with higher academic motivation and performance [15]. As mentioned previously, when we evaluate our own or speak to others’ love of learning, we are potentially encapsulating many intrinsic motivations and traits. Thus, like GPA, we believe that self-reported love of learning can act as a powerful proxy for many intrinsic motivations and traits, and anticipate that GPA and love of learning are highly likely to be interrelated.

2.4. Flow and Experiential Learning

While love of learning is mostly brought to a classroom a priori, and grades are achieved ex post, the psychological state of flow occurs in situ in the classroom in the present moment, where and when learning takes place. Thus, learning flow is another factor to consider when examining student motivation.

The idea of an optimal in situ experience, or ‘flow’, was originally described, studied, and formalized by the Hungarian–American psychologist Mihaly Csikszentmihalyi [5]. A state of flow requires the subject’s active engagement in an activity for which the goals are clear and feedback is immediate, and for which the individual’s existing level of skill appropriately matches the level of challenge the activity presents. With these factors in place, the individual feels in control of the activity, experiencing such enjoyment and profound concentration that all irrelevant stimuli are erased from consciousness and the sense of self and time is altered [5,25,26].

In the process of learning, frequent experiences of flow in the classroom create a virtuous cycle encouraging student engagement, promoting growth, and potentially leading to higher levels of overall wellbeing [16,27]. Specifically, the enjoyment one experiences during classroom flow represents an intrinsic reward which leads to more frequent and eager study and a desire to take on more challenging courses [17]. In turn, better grades from these experiences create an extrinsic reward that further enhances the flow experience—a virtuous cycle [27,28].

Another in situ factor impacting the overall learning experience is the ability to partake in active, experiential learning opportunities such as field trips and study abroad programs. Experiential learning can significantly impact student motivation and learning outcomes by providing real-world experiences that enhance inside-the-classroom engagement. Study abroad programs can significantly boost students’ intrinsic motivation by providing hands-on learning experiences and by exposing them to new cultures and environments. This contributes to cognitive growth, multicultural awareness, and commitment to social justice, as evidence shows that those participating in experiential learning programs demonstrate significant gains in these areas compared to those who do not [29]. Moreover, the benefits of experiential learning extend beyond immediate academic outcomes, influencing students’ career aspirations, global competencies, and overall educational experience [30].

2.5. Demographic Influences on Motivation

In addition to intrinsic, extrinsic, and in situ factors, several other external demographic variables may also influence student motivation. These include a student’s major discipline of study, their place of origin, and their year of study.

First, a student’s chosen area of study can influence motivation and learning outcomes. Different fields present unique challenges and opportunities, and students in different majors exhibit varying levels of intrinsic and extrinsic motivation. For instance, students in Science and Engineering may be more extrinsically motivated by job prospects, while Arts students may be driven more by intrinsic interests [22]. The impact of motivation on GPA also varies by major. Studies indicate that motivation strategies effective in one field may not be as impactful in another, highlighting the need for tailored motivational approaches [19]. As a result, students in different disciplines often adopt varied learning strategies, which may affect their academic success. For example, deep learning approaches are more common in the humanities, while strategic learning is more prevalent in Business and Science fields [23].

Second, the origin of students, whether domestic or international, can impact their motivation and academic outcomes due to cultural, social, and educational differences. Cultural backgrounds significantly influence motivational orientations and learning behaviors. International students often face unique challenges, including language barriers and cultural adjustment, which affect their motivation [31]. The level of social support and integration into the academic community plays a crucial role in motivating international students. Strong support systems can enhance motivation and academic performance [32]. Self-efficacy and perceived competence can differ based on the student’s origin. Domestic students may benefit from familiarity with the educational system, while international students might need additional support to build self-efficacy [20].

Third, students’ year of study can impact student motivation and learning outcomes, with various factors influencing this relationship over time. Motivation levels can vary by year of study, with first-year students often experiencing higher intrinsic motivation, which may decline in later years. This decline can be mitigated by supportive learning environments and curriculum changes [33]. Motivation and learning strategies are crucial for student persistence and academic success, especially in the first year. Effective motivation and self-regulation strategies are predictors of academic achievement and persistence for first year students [34]. Finally, positive student–faculty interactions significantly enhance and help maintain academic motivation across different years of study. Frequent and quality interactions with faculty members are associated with higher motivation and better academic outcomes across students’ academic careers [35].

2.6. Recent Studies on Student Motivation

Recent studies on fostering student motivation emphasize the use of integrating innovative teaching strategies and technologies. This is largely driven by classroom digitization and faltering student motivation post-COVID-19 [36,37]. In turn, developing an interactive and pro-social learning environment is extremely important, both in the online and in-person context, with innovative teaching being a primary driver of in-classroom motivation to learn in the modern classroom context [38]. One such example of an innovative teaching strategy is classroom gamification, where presently pedagogical frameworks are being developed for novel classroom environments like the educational escape room [39].

Artificial Intelligence is another emerging technology that warrants further study regarding its impact on both students’ and teachers’ willingness to integrate and adapt to such tools. Early evidence suggests that AI has had a positive impact on motivation by providing personalized feedback and tailored learning approaches to students [40]. Moreover, teachers appear to have a positive attitude and willingness towards adopting AI, but most teachers’ AI literacy is presently low [41]. However, unlike past technological divides where older cohorts lagged, students’ ability to work with AI has also been found to be lagging [42].

Out of this extensive literature review of student motivation and motivating factors, we build our conceptual model and, in turn, our framework of analysis (see Figure 1 below). Pairing this framework with the unique higher educational setting of Franklin University Switzerland, we believe that our data and analysis make a novel contribution to the greater body of higher education literature.

3. Data and Methods

3.1. Survey

In Spring Semester 2023 at Franklin University Switzerland, a cross-sectional survey was administered to 120 university students. Upon removal of erroneous or non-responses, a total of 112 students remained in the sample. Students were invited to voluntarily complete a five-minute “Learning Research Study” online survey while in class. To begin the online survey, information regarding students’ current year of institutional study (e.g., freshmen, sophomore, junior, senior, or other), university major, and current grade point average (GPA) were gathered. Students were then asked to note how many academic travel courses they had completed.

Outside demographic data gathering, the key factors captured in the survey are students’ GPA, flow, and love of learning. Rather than relying on student records, GPA was reported by each student from memory. Students’ experience of flow was measured by the Core Dispositional Flow Scale (C DFS). The C DFS [43] is a 10-item instrument used to assess the frequency with which flow is experienced during an activity. In this case, students were asked to consider how often they experienced flow-related thoughts and feelings while “Learning or studying new things”. Responses were provided using the C DFS 5-point Likert scale ranging from “Never” to “Always”. Once complete, all 10 items of the C DFS were averaged to provide an overall flow scale score or Flow Index.

Next, to assess students’ self-perceived love of learning, a 10-point scale researcher-derived item asking, “On a scale of 1–10, how would you rate your love of learning?” was included in the survey. Responses could range from 1 = little to no love of learning, up to 10 = very strong love of learning. Like the Cantril [44] Life Ladder, the most used one-factor measure of Subjective Wellbeing, this single-item question was designed to be broad and reflective, assessing students’ general love of learning, not overtly defined, allowing the students themselves to individually define and assess their own perceived love of learning.

3.2. Analysis

Our analysis began with computing descriptive statistics for each of the variables captured by the survey. This includes the frequency and percentages of demographic dimensions (year, travel, discipline, and country of origin), and the mean, median, standard deviation, maximum, and minimum for each of these cohorts related to each of the self-reported measures of GPA, flow, and love of learning.

Next, we use Spearman’s Rho correlation analysis to test the strength, direction, and significance of relationships between several factors including year of study, travel, GPA, love of learning, and flow. Spearman’s Rho was utilized as the data are ordered and not normally distributed, as highlighted by the high mean and median of self-reported love of learning (~8 out of 10).

In addition to identifying significant relationships between measures, we also tested for significant differences between demographic cohorts. For this, we utilized Analysis of Variance (ANOVA) to test for statistically significant differences for each primary variables of interest (GPA, flow, and love of learning) amongst each cohort. The analysis treated each of the categorical cohorts explicitly to separate their influence, utilizing the F-test to compare the variance between groups against within-group variance.

While ANOVA is a broad test of significance amongst several groups, Tukey’s Honestly Significant Difference (HSD) conducts post hoc pairwise comparisons to pinpoint where exactly the significant differences lie. This test adjusts for multiple comparisons to control the type I error rate, providing confidence in the differences observed among specific groups.

Lastly, to measure the effect size of the differences observed, giving context to the magnitude of these differences beyond mere statistical significance, we utilized Cohen’s d. Cohen’s d is calculated as the difference between two means divided by the pooled standard deviation, offering insights into the practical significance of the findings.

The combined use of ANOVA, Tukey’s HSD, and Cohen’s d provides a comprehensive analysis, from detecting differences to understanding their relevance and magnitude. These methods are particularly well suited for educational research where understanding nuances between different student cohorts is crucial for policymaking and educational improvements. Over the past two decades, many studies in the field have utilized a similar methodology, employing these same statistical tools to uncover cohort differences. Such studies evaluate a range of differences, from mean scores for grades, academic life satisfaction, to student adjustment periods, and explore cohorts that differ based upon their socio-demographic characteristics, learning techniques, and personality traits [45,46,47,48,49].

3.3. Conceptual Framework

Figure 1 depicts the conceptual model captured by the survey and evaluated in our analysis. Different student demographics influence intrinsic motivation and GPA differently, and thus we expect significant differences to appear in our analysis between year, major discipline, and place of origin cohorts. GPA completes the learning process, influenced by many factors, external, internal, and in situ. We expect that student GPA, love of learning, and flow will all exhibit a strong correlation with one another, as a high GPA would reinforce love of learning and psychological flow during study. Considering our literature review, the short-term impact of experiential learning on factors like GPA, flow, and love of learning is less clear. However, we do anticipate at least a moderate correlation between flow, love of learning, and the frequency of student engagement in academic travel (experiential learning).

From this conceptual model, we should expect to see statistically significant relationships between GPA, flow, and love of learning, as well as statistically significant differences between groups based on external factors such as year, country of origin, and frequency of academic travel.

4. Results

Table 1. Demographic profiles of sampled students.

Variable	Cohort	Frequency	Percent
Year	Y1	40	35.7
	Y2	29	25.8
	Y3	21	18.7
	Y4	22	19.6
Travel	T2	53	47.3
	T3	43	38.3
	T4	16	14.2
Discipline	Business and Economics	35	31.2
	Liberal Arts and Sciences	58	51.7
	Undeclared	19	16.9
Origin	EU	36	32.1
	ROW	19	16.9
	USA	57	50.8

shows the proportion of each student cohort present in the survey, and

Table 2. Descriptive statistics of measures.

Variable	Cohort	Count	Mean	Median	SD	Max	Min
Flow	Y1	40	3.24	3.2	0.52	4.1	2
	Y2	29	3.49	3.5	0.38	4	2.6
	Y3	21	3.34	3.3	0.32	3.8	2.4
	Y4	22	3.40	3.35	0.43	4.3	2.6
	Business and Economics	35	3.37	3.4	0.49	4.3	2
	Liberal Arts and Sciences	58	3.33	3.3	0.37	4.1	2.4
	Undeclared	19	3.39	3.6	0.57	4.1	2.3
	USA	57	3.30	3.3	0.47	4.1	2
	ROW	19	3.42	3.3	0.41	4.3	2.9
	EU	36	3.39	3.45	0.42	4	2.4
GPA	Y1	32	3.43	3.5	0.39	4	2.8
	Y2	23	3.46	3.5	0.42	4	2.5
	Y3	18	3.50	3.5	0.37	3.9	2.6
	Y4	21	3.33	3.35	0.29	3.89	2.8
	Business and Economics	28	3.29	3.3	0.37	3.95	2.5
	Liberal Arts and Sciences	50	3.52	3.5	0.32	4	3
	Undeclared	16	3.38	3.25	0.46	4	2.6
	USA	27	3.33	3.3	0.35	3.9	2.6
	ROW	15	3.34	3.4	0.42	4	2.5
	EU	52	3.50	3.6	0.36	4	2.9
Learning	Y1	40	7.53	8	1.92	10	2
	Y2	29	8.48	8	1.33	10	6
	Y3	21	8.05	8	1.50	10	5
	Y4	22	7.82	8	1.56	10	4
	Business and Economics	35	7.51	7	1.95	10	2
	Liberal Arts and Sciences	58	8.36	8	1.35	10	5
	Undeclared	19	7.37	7	1.67	10	4
	USA	57	8.13	8	1.72	10	2
	ROW	19	8.37	9	1.46	10	6
	EU	36	7.33	7	1.53	10	4

shows descriptive statistics for each of these groups regarding their flow, GPA, and love of learning. Most students sampled were in their first year, and as a result most students have not experienced significant experiential learning opportunities. More equal group divisions, as mentioned previously, occur between students’ disciplines and country of origin. Several ‘NaN’ values were recorded in the survey for GPA, hence the lower count. This again may be due to the oversampling of first-year students, who would not have achieved an initial GPA score.

Interpreting Table 2, most students report a moderate to high level of flow in the classroom. According to authors Kawabata and Mallett [50], median flow scores above three are considered above average. Moreover, students report a median love of learning score of eight, suggesting that the sample is drawn from highly engaged, highly motivated students and/or some social desirability bias is present.

Table 3. Spearman’s Rho correlation coefficients (* p < 0.05).

	Year	Travel	GPA	Learning	Flow
Year	1	x	x	x	x
Travel	0.81 *	1	x	x	x
GPA	−0.08	−0.02	1	x	x
Learning	0.01	0.02	0.35 *	1	x
Flow	0.03	−0.02	0.27 *	0.59 *	1

illustrates the Spearman’s Rho correlation coefficients between several variable pairs and their potential significance (denoted by an *). Intuitively, the strongest relationship exists between students’ year of study and their frequency of academic travel. Additionally, also as expected, moderately strong and statistically significant correlations exist between GPA, flow, and love of learning. Contrary to initial expectations, no relationship exists between students’ frequency of academic travel and their self-reported flow or love of learning. Neither does more frequent academic travel correlate with student grades.

Table 4. ANOVA group differences by measure (* p < 0.10).

Variable	Cohort	sum_sq	df	F	PR(>F)	Significant
GPA	C(Year)	0.170	3	0.427	0.734
	C(Discipline)	0.631	2	2.377	0.099	*
	C(Travel)	0.176	2	0.663	0.518
	C(Origin)	0.268	2	1.010	0.369
	Residual	11.146	84
Flow	C(Year)	1.361	3	2.307	0.081	*
	C(Discipline)	0.117	2	0.298	0.743
	C(Travel)	0.269	2	0.684	0.507
	C(Origin)	0.125	2	0.318	0.728
	Residual	20.056	102
Love of learning	C(Year)	14.020	3	1.922	0.131
	C(Discipline)	19.702	2	4.052	0.020	*
	C(Travel)	3.389	2	0.697	0.500
	C(Origin)	19.135	2	3.935	0.023	*
	Residual	247.982	102

highlights the significant differences amongst cohorts regarding GPA, flow, and love of learning (* p < 0.10). First, it should be noted that some cohort differences approach borderline significance if the significance threshold set is p < 0.10. Such a threshold is reasonable given the small sample size.

Upon setting this threshold, unsurprising differences appear between majors regarding GPA (p = 0.099). As shown in Table 2 above, Liberal Arts and Sciences students have the highest mean GPA of all disciplines at FUS (3.52). This is not unexpected, as FUS is a Liberal Arts institution.

Intriguingly, significant differences in psychological flow emerge across students’ year of study (p = 0.081). This suggests that at certain points, more in-classroom flow is experienced by students than at other periods over their academic career. Illustrated by Figure 2, sophomore students experienced the highest mean flow (3.48), compared to lower mean scores for juniors and seniors (3.4), and lowest mean scores for first years (3.2). This may be the result of a needed adjustment period for freshman to adapt, feel comfortable, and then experience flow in the classroom. However, it should be noted that many FUS students originate from first-year study abroad programs, potentially contributing to feelings of general discomfort. These students only remain at the FUS for one year before returning to their home institution in the US.

Alternatively, though, it may highlight a growing trend of in-class distractions and lack of attention amongst newer student cohorts, as freshman students in this sample would be entering university classroom environments post-COVID-19. Regardless, our sample seems to suggest that students feel more comfortable in their environment as a sophomore than a freshman, although some of that flow seems to dissipate after the second year. This may be the result of external pressures and extrinsic motivations on students to graduate, secure a job, and enter the workforce as time goes on.

Perhaps it is most interesting to note from our sample that statistically significant differences lie between multiple cohorts’ self-reported love of learning. Regarding student majors,

Table 5. Tukey’s HSD test of between-group differences by discipline.

Cohort 1	Cohort 2	Meandiff	p-adj	Lower	Upper	Reject
Business and Economics	Liberal Arts and Sciences	0.85	0.04	0.03	1.67	TRUE
Business and Economics	Undeclared	−0.15	0.95	−1.24	0.94	FALSE
Liberal Arts and Sciences	Undeclared	−0.99	0.06	−2.00	0.02	FALSE

shows significant differences between Business and Economics (7.5) and Liberal Arts and Sciences (8.3) students relative to their mean love of learning scores. Figure 3 further illustrates the differences between disciplines by comparing those two majors with a Sciences student cohort. Sciences students had the highest mean love of learning score (8.6). However, like Undeclared majors (n = 19), their sample size was too small to reliably report (n = 22).

Table 6. Tukey’s HSD test of between-group differences by origin.

Cohort 1	Cohort 2	Meandiff	p-adj	Lower	Upper	Reject
EU	ROW	1.035	0.067	−0.056	2.127	FALSE
EU	USA	0.825	0.048	0.005	1.644	TRUE
ROW	USA	−0.211	0.876	−1.230	0.809	FALSE

highlights that US students self-report a statistically higher mean love of learning (8.15) than European students (7.33). Figure 4, in turn, illustrates the complete picture, showing that students originating from the ROW have the highest mean love of learning scores of any cohort. Unfortunately, this cohort suffers too from a small sample size (n = 19).

It should be noted that regardless of cohort sample size, none of the density plots approximate a true normal distribution, and there is evidence of bimodality within cohorts. Thus, future studies should employ stratified random sampling, rather than just random sampling, to improve the cohort sample size. This limitation is discussed more in the concluding section of this paper.

Finally, we employ Cohen’s d to measure the effect size between cohorts.

Table 7. Cohen’s d effect size for significant differences.

Cohort Comparison	Cohen’s d
Business and Economics vs. Liberal Arts and Sciences	−0.53
EU vs. USA	−0.50

shows that despite the small cohort samples, moderate effect sizes are present between cohorts, with the negative sign indicating the direction of the relationship between the first and second group, e.g., lower love of learning for Business and EU-origin students. As a result, we can consider our findings somewhat reliable and broadly generalizable beyond our local sample and FUS. Thus, we can assume that Business and Economics students have less intrinsic love of learning than Liberal Arts and Science students, at least when attending a Liberal Arts institution. Moreover, Americans are likely to self-report a higher love of learning than Europeans, at least those studying at American universities located within Europe. Speculation regarding the driving factors behind these results are discussed in the section below.

5. Discussion

The goal of our study was to gather and analyze data to better understand the factors motivating student learning and GPA at Franklin University Switzerland and beyond. Prior research and a conceptual model that includes external and internal influences on students acted as a guide for data collection and analysis. The survey setting offered unique opportunities to capture variables such as students’ frequency of experiential learning, country of origin, chosen discipline, and year of study on factors such as flow, love of learning, and GPA. Our findings suggest that year of study influences student flow in the classroom, and that two demographic dimensions, discipline and origin, influence love of learning.

First, we found significant differences in student flow by year of study. Sophomore students reported higher mean flow scores than all other cohorts, with freshman exhibiting the lowest flow scores. A similar study compared the mean scores of students’ subjective wellbeing by year, finding that first-year students (64.9%) had higher instances of low subjective wellbeing than fourth-year students (62%), although those differences were not statistically significant [51]. It should be noted, however, that only first and fourth-year students were compared, whereas sophomore and junior cohorts were also included in our study. Regardless of year-on-year comparison, studies show that adjustment to university freshman year is imperative for student motivation and positive learning outcomes [52,53]. As mentioned previously, as an overseas college for many students, FUS may require a longer period of adjustment that only takes hold in a student’s sophomore year, resulting in and captured by higher sophomore flow scores.

Regarding discipline, students in the Liberal Arts and Sciences demonstrated a higher love of learning than those studying Business and Economics. This may relate to the varying levels of intrinsic and extrinsic motivations that research has shown are exhibited by different majors [54]. Similar to Business students, Science and Engineering majors exhibited more extrinsic motivation based upon future job prospects [22]. In yet another comparison, Economics students showed stronger extrinsic aspirations and weaker intrinsic motivations than students in other streams of the Social Sciences [55]. These findings, including ours, potentially highlight a difference in love of learning versus love of earning.

For country-of-origin cohorts, it may be more difficult to speculate on the factors driving between-group differences for students’ self-reported love of learning. One study found that German and American college students exhibited remarkably similar goal structures, but that Americans placed a slightly higher emphasis on extrinsic rewards [56]. On the other hand, Taiwanese students held stronger extrinsic motivations than American students, finding that Americans’ academic performance correlated more strongly with intrinsic motivations [57]. It should be further noted that our study found that students from the ROW had the highest mean love of learning scores of all groups, although the sample size was small. To explain this result, although dated, an earlier study found Russian students tend to place greater importance on intrinsic goals like personal growth and community betterment compared to American students [58]. Looking at the data, many ROW students from our sample are enrolled in FUS from former Eastern Bloc countries like Russia, Ukraine, Kazakhstan, and Georgia.

Suffice it to say, untangling the cultural influences on student learning motivations is complicated, and our findings may simply be the cultural artifact of some form of desirability bias. Our one-item, 10-point love of learning scale is similar in nature to ‘happiness’ questionnaires (e.g., “On a scale of 1–10 how happy are you?”). On these questions, Americans tend to rate themselves more highly than other cultures [59].

It should also be noted that all cohorts, regardless of major, origin, or year of study, had moderately high love of learning scores, with a few exceptions. Anecdotally, FUS students from outside the US make clear distinctions between their prior educational experiences, which they believe was much more rigid, and an ‘American’ classroom. They describe an American classroom as more flexible and often speak fondly about the learning environment they are provided. Perhaps the reputation as a small-class-size Liberal Arts college with a flexible classroom environment helps self-select those with a high love of learning and may provide an explanation for the generally high median love of learning and flow scores outside of mere social desirability bias.

Finally, it is important to note one significant non-finding—namely, that an increasing number of academic travel experiences did not significantly correlate with higher student GPAs, love of learning, nor flow scores. Earlier studies have suggested that active, experiential learning has a positive effect on students’ intrinsic motivations and learning outcomes, including grades [60,61]. However, such studies were confined to one individual classroom experience and not the frequency of experiential learning across the entire curriculum on student motivation and GPA. It may be that such an integration of experiential learning across the curriculum likely has broader implications, like a student’s career choice or working abroad post-graduation [30].

6. Conclusions

In conclusion, these data and findings are relevant to a wide range of educational researchers and policymakers, especially those who wish to examine relationships and group differences in student motivation and learning outcomes. This study’s findings might be taken into consideration by those contemplating changes to the classroom environment, such as by adopting an active learning travel component (although not found to be significant) or removing mobile phones from the classroom to promote better in-classroom flow, a variable which held a significant correlation with both love of learning and GPA. More broadly, the data and findings may also be of interest to sociologists, human geographers, and social scientists researching different cohort motivations and dynamics.

Although this current study opens multiple paths for future research into the interrelationships between student motivation, demographics, and learning outcomes, the generalizability of the study’s findings is limited by its small sample size, especially when the sample is disaggregated into the smaller subsamples that provide us with our cohorts. Temporally our study is also limited, as it is a snapshot of student motivation at one point of one semester. Although our year-on-year cohort analysis provides some measure of temporal change, our survey oversampled year-one students. Additionally, timing is an important factor for GPA, as even small changes in GPA may affect our results. Thus, the measure of a student’s GPA would ideally be recorded from current transcripts rather than student memory. Furthermore, the geographical scope of the study is limited to only one small liberal arts university in Switzerland. Student motivation and outcome may also be influenced by institutional factors, including setting, size, and prestige, and thus cohort comparisons across multiple institutions would enrich this study’s findings.

In the future, collecting larger, stratified samples is suggested, allowing for the analysis of specific cohorts by origin, year, major, and/or academic travel, to be analyzed with greater generalizability. Moreover, researchers may not wish to be limited by the cohorts found in this study, but expand their analysis to other student demographics, such as gender.

Finally, love of learning, though a promising variable of interest, may be further developed beyond its current self-reported one-item, ten-ordered scale. Researchers may wish to add more open-ended questions to help uncover the possibility that different perceptions of love of learning are shaped by different cultural aspects, especially differing educational systems. Reflecting on both our findings and limitations, we remain strong advocates for researchers to adopt some measure of love of learning as a variable of importance when collecting data for the purposes of analyzing student motivations and educational outcomes.