Fun and Learning
Research into understanding the role of fun in the learning process is found mainly in the areas of serious games and gamification in education (Chu et al. 2017), thus concerns mainly the non-formal and informal learning environment. In these fields, there is growing evidence in support of the importance of fun for learning; we review this body of research below.
Bisson and Luckner (1996) elaborated on the pedagogical benefits of fun. They saw fun as a powerful tool to enhance motivation and create a safe learning environment. They summarized that fun is beneficial as (a) it evokes intrinsic motivation, (b) it facilitates the suspension of the social reality, (c) it reduces stress and (d) it creates a state of relaxed alertness where “learners feel safe to take risks, be creative, make mistakes, and most importantly, keep trying” (Bisson and Luckner 1996, p. 111).
Rambli et al. (2013) stated that fun and interactive learning are one of the most powerful pedagogical factors which could yield to create an interactive and engaged learning environment (Rambli et al. 2013) and they added that this environment facilitates the memorization procedure of learners while keeping their attention, which ultimately enhances learning. Based on the PISA test (N > 400,000 15-year-old students), Ainley and Ainley (2011) concluded that the sense of fun and excitement has a huge importance for science learning.
While investigating the learning outcomes of an educational game, Long (2007) found that 87.5% of the participants joined the activity in the first place because of the promise of fun (Fun in programming games) and it improved the skills of 79.8% of the participants. Moreover, she found that computer games “lead to positive results in long-term learner retention by improving learning interest and more focused attention because the students enjoy the approach” (Long 2007, p. 280)—although she used the terms “fun” and “enjoyment” interchangeably. She also demonstrated that fun has a significant effect on the learning effort.
In a recent systemic review on emotions in design-based learning (DBL), Zhang et al. (2020) found that design-based learning had an overall positive effect on students’ interest and motivation to learn. Accordingly, enjoyment was among the most frequently mentioned emotions in DBL; however, this review also reflected that fun and enjoyment are barely distinguished or measured separately.
Vieira and da Silva (2017, p. 130) stated that “fun is an important element of life because it satisfies curiosity and fosters learning”. They encouraged designers to “make their artefacts fun” in order to stimulate users to use them. In their understanding, fun consisted in attention, flow, immersion and emotion. Tews and Noe (2019) even went further and said that “fun is an important component of high quality learning experiences” (Tews and Noe 2019, p. 226).
Chan et al. (2019) investigated the role of perceived fun in a collaborative learning scenario and the learning performance while using personal response systems (PRSs). Their results suggested that “the level of fun students experienced using PRSs was found to promote collaborative learning and learning performance” (Chan et al. 2019, p. 99).
Iten and Petko (2016) studied whether fun playing an educational game was a predictor for learning success. They used the terms “fun” and “enjoyment” interchangeably. They found that the experienced enjoyment and flow during the game had a significant effect on gaining motivation, increasing interest in the subject matter and upon choosing to play the game again. However, their study could not demonstrate any association between the experienced enjoyment and the learning gains, which is in contrast with previous findings. And, they questioned “whether ‘fun’ and ‘enjoyment’ are adequate constructs to grasp meaningful motivational processes in serious game experiences” (Iten and Petko 2016, p. 161). They referred to other authors who proposed instead “student engagement” to analyze positive emotions when learning with serious games. Similarly, Sim et al. (2006) did not find significant correlation neither between the observed nor the reported fun and the learning outcome.
Controversially, Tews et al. (2017) found that fun had a significant impact on informal learning in the working environment (i.e. learning from others and learning from non-interpersonal sources). They stressed that “researchers should not necessarily focus on fun as a unidimensional construct” (Tews et al. 2017, p. 52). Additionally, their findings suggested that the managers’ support for fun had a significant influence on learning (learning from oneself) as well.
Similarly with adults, but in the learning environment, Lucardie (2014) in her qualitative research found that “both adult learners and their teachers also believed that fun and enjoyment impacted on adults learning and they were able to articulate the role that fun plays in adult learning programs” (Lucardie 2014, p. 445).
Elton-Chalcraft and Mills (2015) summarized their study results as follows: “Learning which is enjoyable (fun) and self-motivating is more effective than sterile (boring) solely teacher-directed learning” (Elton-Chalcraft and Mills 2015, p. 482). This finding is supported by Aoki et al. (2004) who investigated how the education of children with type-1 diabetes could be improved. They developed three edutainment tools and tested them. Their findings suggested that children patients found the games fun (compared to the researchers’ previous study on traditional learning methods), 91.4% of the respondents showed more interest toward the edutainment method, and more than 60% of them found that this approach would be useful as an initial education for type-1 diabetes children. They thus concluded that “edutainment systems could have a significant potential for healthcare education especially for children” (Aoki et al. 2004, p. 859).
Willis (2007) wrote about the neuroscience of joyful education. “When students are engaged and motivated and feel minimal stress, information flows freely through the effective filter in the amygdala and they achieve higher levels of cognition, make connections, and experience ‘aha’ moments. Such learning comes not from quiet classrooms and directed lectures, but from classrooms with an atmosphere of exuberant discovery” (Willis 2007, p. 1). She added that “when classroom activities are pleasurable, the brain releases dopamine, a neurotransmitter that stimulates the memory centers and promotes the release of acetylcholinem, which increases focused attention” (Willis 2007, p. 2). Additionally, she claimed that despite “some schools have unspoken mandates against these valuable components of the classroom experience” (Willis 2007, p. 3), no neuroimaging or brain wave analysis data exist that would demonstrate any downshifting effect of “joy”—a term she used interchangeably with “fun”—in the classroom.
Thus, in sum, previous research provides a growing support for the view that fun has positive effects on learning. However, the above-introduced studies do not define and measure fun, and moreover, none of these studies is a controlled experiment which would compare the effects of introducing fun elements versus not. Only one study—of Iten and Petko (2016)—comes close by studying the effects of enjoyment but in that study, no distinction is made between fun and enjoyment. In order to be able to make such claims precise regarding the effect of fun on learning, or to evaluate such activities, we need to define clearly what fun is and have a reliable instrument to measure it.
Characteristics of Fun
Bisson and Luckner (1996) synthesized earlier scholarly attempts to define fun into four characteristics that were inherent to it. They argued that “fun” is a relative, situational, voluntary experience and natural/essential to all human beings. With relative and situational, they meant that fun depends on many factors, e.g. what one finds fun is not necessarily fun for another, nor is it certainly fun on another day. Fun is voluntary as “to experience fun one must consciously or unconsciously accept to feel good, to relax, to let go and to let the situation be perceived as enjoyable” (Bisson and Luckner 1996, p. 109). Fun cannot be “forced”, so, for an activity to be perceived as fun, the participation must be intrinsically motivated.
Glasser (1986) argued that fun is one of the five most essential human needs and emphasized its importance while learning and especially for child development. He stated that fun “is like a catalyst that makes anything we do better and worth doing again and again” (Glasser 1986, p. 28). Accordingly, Read et al. (2002) discussed returnance as a facet of fun in child-computer interaction; in this context, returnance meant the desire to do an enjoyable activity again and again.
Based on a 3-year-long study on attitudes towards physical education, Dismore and Bailey (2011) argued that the meaning attributed to the concept of fun changed in merit as children approached teenage years. While for younger children (7–11 years), fun was a critical factor for an activity to be enjoyable; teenagers (11–14 years) described fun in terms of a learning challenge rather than in relation to hedonic responses while playing games (Dismore and Bailey 2011). Along with the aforementioned properties of fun, its stress-reducing effect has to be mentioned as well (Caine and Caine 1991).
Fun and Play
During childhood, “fun” and “play” often concur. Despite that the two concepts are closely related, they should be distinguished from each other. Sutton-Smith (2011, p. 3) defined play as “an activity that is voluntary, intrinsically motivated, fun, incorporates free will/choices, offers escape, and is fundamentally exciting”. Gajadjar, de Kort and IJsselsteijn considered play as an “intrinsically motivated, physical or mental leisure activity that is undertaken only for enjoyment or amusement and has no other objective” (Gajadhar et al. 2008, p. 105). Especially in early childhood, “fun” and “play” are overlapping notions. However, as indicated already (Dismore and Bailey 2011), with adolescence, the hedonistic character of fun that is purely present during play gives way to challenge, which is a well-established dimension of fun among adults (Fu et al. 2009; IJsselsteijn et al. 2008; Ryan et al. 2006).
Intrinsic Motivation and Social Aspect
Bisson and Luckner argued that the promise of fun “can motivate learners to engage in activities with which they have little or no previous experience” (Bisson and Luckner 1996, p. 110). Therefore, fun is not only an experience but it can be itself a strong, intrinsic motivating factor to encourage children to try new challenges. Already back in the 1980s, Malone and Lepper (Malone 1981; Malone and Lepper 1987) emphasized the importance of intrinsic motivation that according to their theory, it could be evoked by the optimal level of challenge, curiosity and fantasy. Bisson and Luckner (1996) also suggested that the combination of “fun” and “play” can act as a catalyst to eliminate inhibiting factors inherent to our socialization. In their opinion, “the more genuine and intense the fun is, the greater the suspension of reality will be. Consequently, fun can transform social insecurity into trust and camaraderie, and a restrictive self-image into the freedom of expression” (Bisson and Luckner 1996, p. 110). This property of fun is closely related to the concept of Flow.
The concept, or rather the experience of Flow, was defined by Csikszentmihalyi (1990) as an optimal experience, where the following characteristics were present: (a) an intrinsically rewarding experience, (b) a loss of reflective self-consciousness, (c) a distorted experience of time, (d) an intense and focused concentration on the present, (e) a merge of actions and awareness, (f) an optimal balance between challenge and skills, (g) a sense of control over the situation and (h) clear goals and immediate feedback. Considering the definitions above, it appears that experiencing the fun and being in the psychological state of Flow overlap substantially.
Abbasi et al. (2019) argued based on structural modelling that the theoretical constructs “experience” (a.k.a. Flow) and “engagement” should not be used interchangeably to investigate the subjective experience of video gameplay. Rather, they proposed a model of playful-consumption experience, which consisted of different types of experience (emotional and sensory) and different types of engagement (cognitive, affective and behavioural), and they discussed “enjoyment” as one of the emotional experience factors, which, in their terminology, were used interchangeably with the term “fun”. Thus, they considered enjoyment or fun as a part of the emotional experience.
As Tasci and Ko (2016, p.167) described, “A distorted sense of time, in general, is taken as an indicator of engagement, desire, enjoyment, excitement and thus, having fun; when it feels as if time went more quickly than it actually did, this is a sign of fun and vice versa”. Rodríguez-Ardura and Meseguer-Artola (2017) also stated that when one feels that the activity is going smoothly and is fun, then time flies, and one undergoes a distortion of the temporal experience of time.
Caine and Caine (1991) showed how learning is maximized when combining fun and challenge, which they called a state of relaxed alertness and suggested that a major goal for educators should be to challenge students in a natural way so conceptual mapping (i.e. intellectual connections) could happen without evoking a downshifting response. Mellecker, Lyons and Baranowski have also found while evaluating video game design with children that “an engrossing story in which a player faces increasing challenges and can increase skills quickly enough to overcome the challenges, but not so quickly as to get bored by the challenges, appears to provide an important game design structure for enhancing fun or enjoyment” (Mellecker et al. 2013, p. 144). Chu et al. (2017) described how during curriculum-based making activity children had the most positive feelings and got engaged the most when the level of challenge matched their skills. It has been mentioned already how adolescents attributed experiencing fun to challenge (Dismore and Bailey 2011) which corresponds to the challenge aspect of the Flow experience. Rodríguez-Ardura and Meseguer-Artola (2017), p. 902) explained the effect of challenge on Flow in terms of the cognitive evaluation theory (Ryan et al. 1983). They suggested that as long as individuals have a psychological need to feel competent, activities that trigger positive challenge can lead to experiencing optimum experienced and intrinsic motivation—because they satisfy the individual’s need for competence.
Distinction Between Young Children and Adolescents
Approaching the childhood from the perspective of cognitive and psychological development, both Piaget and Erikson account for the shift that occurs on the edge of adolescence. According to Piaget (1964), the formal operational stage begins approximately at the age of 11–12 and lasts into adulthood when children develop the ability to think about abstract objects and to logically test hypotheses. In the theory of Erikson (1950), stage 5 is approximately between age 12 and 18, during which children search for a sense of self and personal identity exploring their own personal values, beliefs and goals. Without further delving into the characteristics of the teenagers, it can be summarized that during this age children’s identity is formed and the way they understand the world changes. They begin to shape their opinion and they learn how to express it as well. Their cognitive abilities—such as memory capacity, language skills and concentration span—are approaching quickly the level of an adult’s. De Leeuw (2011, p. 15) argued that the “age of 11 is seen as a turning point in memory capacity when children appear to function as well as adults”. From the beginning of this age, therefore, they are less prone to the typical response biases that are common for younger children (see section Attention Span). Ultimately, this means that when they are asked about their opinion, the answers will be more differentiated than in younger ages (this is also shown by Read 2008, 2012; Read and MacFarlane 2006) and are generally more valid (Mellor and Moore 2014). Moreover, Dismore and Bailey (2011) showed that the meaning and the content of the concept of “fun” altered during childhood. This shift was found to be around age 11 as well, which is in synchrony with the psychological and cognitive changes while approaching adolescence. On the basis of these arguments, the definition and measurement of fun will address children over the age of 11.
In designing measurement tools especially for children, their competencies and differences to adults have to be taken into account. Hall et al. (2016) emphasized that generally, children preferred Likert-type scales over similar simple response items and that free-recall questions were useful especially in spoken surveys (Read and MacFarlane 2006). De Leeuw (2011) found that in general, the older the child, the more reliable the answers will be and that children were better informants on topics directly related to them such as their feelings and other subjective phenomena. Furthermore, de Leeuw (2011, p. 6) stated that “Below the age of 7 children do not have sufficient cognitive skills to be effectively and systematically questioned” and added that individual (semi-) structured interviews were more suitable than questionnaires for children between 7 and 12 (see also Bell 2007). Mellor and Moore (2014) found that children below the age of 12 had difficulties in answering questions about abstract concepts such as their own behaviours, bodily states or emotional states. They related it to the theory of Piaget about the formal operational stage of development. Regarding adolescents, de Leeuw (2011) suggested that questionnaires could be used for adults; however, there should be special attention devoted against ambiguity in item wording. Therefore, using simple language and formulating items as exactly as possible is a must and ensuring language appropriateness by readability testing is highly recommended.
Read (2008) and de Leeuw (2011) discussed the challenges of designing measurement tools for children, and Mellor and Moore (2014) wrote specifically about the use of Likert-type scales with children. The main caveats which can jeopardize the reliability of surveys involving children are discussed below.
Children’s attention span (or sustained attention) is crucial for directly measuring children (i.e. not observing their behaviour). Attention span is defined as the time a person is able to selectively attend to relevant information, such as listening to a teacher and persisting on a task (McClelland et al. 2013, p. 315). Scientists have been studying children’s attention span for a long time. In a literature review dating back to the 1950s, Moyer and von Haller Gilmer (1954) reviewed nineteen studies which found that the attention span of young children ranged from 1 to 25 min. Additionally, they found that the attention span was lower in a group situation and that there was a difference “between work tasks, such as reading, and the activities in which a child engages in playing with a toy” (Moyer and von Haller Gilmer 1954, p. 466). Moreover, Sousa (2011) suggested that motivation had an effect on the attention span, and Bradbury (2016) even called this effect crucial.
Within the field of educational psychology, a generally accepted and referred rule of thumb for the length of the average student’s attention span is 10 to 15 min during lectures (Goss Lucas and Bernstein 2005; McKeachie and Svinicki 2006, and Wolvin 1983 in Davis 2009). Although formulas are available for approximating the length of the attention span per age, no supporting empirical evidence exists. Lin et al. (1999) showed that the sustained attention developed between ages 6 and 15. Nonetheless, since they have measured the sustained attention by the Continuous Performance Test (CPT), they only report on the hit and false alarm rates for the evaluation of sustained attention and did not provide the length of the attention span in minutes.
Controversially to the generally accepted 10–15 min, a study conducted at Microsoft (2015) claimed that the average attention span was only 12 s in 2000 and 8 s in 2013, and it is ever decreasing. Although the validity of these numbers has been contested (Bradbury 2016), it appears that people nowadays, and especially the younger generation, get distracted easily and hold their attention for less time than was the case in the past.
To safeguard the reliability of answers, it is important to consider children’s attention span adjusting the length of the inspection to the child’s attention span. Based on the above, a survey—that is not a particularly engaging task for a child—should not require sustained attention by adolescents for more than 10–15 min.
When working with children, the risk of introducing bias is high and different types of bias can be manifested compared to those concerning adults. The most common bias types concerning children are discussed below.
Suggestibility pertains to the influence of the researcher on the way the respondent encodes, stores, retrieves and reports events. This effect is due to a range of social and psychological factors. Social desirability bias is when the respondent provides the answer that s/he thinks the examiner asking the question wants to hear. Satisficing is a tendency of the respondent to select a good enough option, instead of the very best one. In the case of surveys, this phenomenon could be manifested as giving a superficial response that appears to be reasonable but without thoroughly considering all answer possibilities. Acquiescence bias is the tendency of the respondent to agree or respond positively. Extreme responding is the type of response bias when the respondent mainly selects the most extreme options/answers available. Straight-lining is the tendency of the respondent to provide answers in a way that the responses form a line or rather a visual pattern. Extreme responding is a sort of straight-lining; however, meanwhile, in extreme responding, it can be assumed that the respondent reads the question and considers the responses; in case of straight-lining, this assumption cannot be made.
Around the age of 11, the suggestibility of children decreases while the importance of peers increases. Therefore, peer pressure can be a serious issue with early adolescents (12–16 years) (de Leeuw 2011). However, contrary to adults, the item non-response appears not to be a problem with children and adolescents (Bell 2007). That is, the error size in responses by children and adolescents is approximately stable across different conditions and not dependent on the content of the question. This is assumed to be in relation to their cognitive abilities, namely that they cannot fully apply an optimizing strategy (Borgers 2003); therefore, they will not skip difficult questions. It has, however, a downside. The difficult or vague questions will not be indicated by a missing value pattern, but the quality of those responses remains doubtful. Therefore, the importance of simple and short questionnaire items is stressed and the application of think-aloud interviews to check whether any item is problematic is highly recommended.
Earlier research has shown that children are particularly prone to the above-described bias types (Bell 2007; de Leeuw 2011; Hall et al. 2016; Read 2008; Read and MacFarlane 2006) to very different degrees for different ages, which has to be considered when developing measurement instruments for any specific age group.
Existing Measurement Tools
Methods for evaluating fun derive primarily from the domain of human-computer interaction and especially child-computer interaction, where fun is seen as an essential component of children’s interactive experience whether they approach technology as users (e.g. of an application or consumer device), learners or players (Markopoulos et al. 2008).
There are a few existing measurement tools that have been designed to gather opinions on the “funness” of an experience or product. Where these exist, they target either young children or adults. Some studies report the use of a survey or a list of questions to be asked from children within the narrow scope of the study; however, they are not intended for further use nor are they validated. A list including the most-known tools for measuring (aspects of) fun is shown in Table 1.
The This or That (Zaman et al. 2013) method examines preference. Despite being a validated measurement tool, it is constrained by its comparative structure: it is only suitable when measuring the preference of one product/experience over the other, which is particularly suitable for its targeted age group of 2 to 7.
The Fun Semantic Differential Scales (Yusoff et al. 2011) is a measurement tool for evaluating games with nursery-aged children based on choosing between photos of a child expressing different emotions (love-do not know-hate). While it has been shown to work well for the target age group, it has not been psychometrically validated, and it addresses fun as a unidimensional construct and is not sufficiently refined for teenagers.
The Fun Toolkit (Read 2008) is a set of tools that targets a wide age range up until teenage to measure the “funness” (Smileyometer) and preference of products (Fun sorter and Again-again table). However, it handles fun as a unidimensional construct, and it faces a problem that younger children tend to use mainly the higher values of the Smileyometer. Despite being widely used, it has not been psychometrically validated.
The Five Degrees of Happiness (Hall et al. 2016) was introduced to address the extreme response bias of the Smileyometer discussed above. Its target audience is children between age 9 and 11, and like its predecessor handles fun as a unidimensional construct and has not been validated psychometrically. Furthermore, the emphasis on positive emotions makes it less suitable for assessing less pleasant experiences that might include frustration or disappointment.
A recent study in this domain proposed a list of Likert scales for the evaluation of a game and attitudes towards learning games (Iten and Petko 2016) which however has not been validated. Despite being suitable for teenagers and measuring multiple dimensions, those dimensions are not linked to fun and refer to the serious game rather than to the personal experience (i.e. How is the game instead of How do one feels while playing the game).
This limitation is avoided in the Physical Activity Enjoyment Scale (Kendzierski and DeCarlo 1991) which measures the personal experience of a physical activity, rather than the activity as such. However, it does not help conceptualize enjoyment which is measured as a unidimensional construct across bipolar scales.
The PENS (Ryan et al. 2006), UES and UES-SF scales (O’Brien et al. 2018) are validated measurement tools, made for adults and have a strong focus on the evaluation of games (usability, aesthetics, novelty, intuitive controls, in-game competence, etc.) rather than on the personal experience (flow, intrinsic motivation, etc.). This limits their applicability in different contexts and does not contribute to our purpose of defining and measuring fun as a psychological construct.
The GEQ (Poels et al. 2007) has the focus on how one feels while playing a game and measures enjoyment as a multidimensional construct; however, it has been validated only with adults in a gaming environment, which limits its applicability in different contexts. Besides, given that the scale is designed for adults, its vocabulary is quite advanced, so it is questionable whether children would be able to comprehend and rate the scale reliably.
In the same domain, a recent study (Abbasi et al. 2019) proposed a Playful-consumption experience questionnaire for the assessment of consumer video game engagement for adolescents. However, they measured enjoyment as a unidimensional construct and as a subdimension of emotional experience, and the questionnaire has not yet been validated.
The EGameFlow (Fu et al. 2009) measures the enjoyment of an e-learning game across the dimensions of Csikszentmihalyi’s Flow theory; thus, it equates enjoyment to the Flow experience, and it is validated for adults. Given the nature of the scale, its usability in different contexts with different ages is limited.
The FUN scale (Tasci and Ko 2016) handles fun as a multidimensional construct; however, it is validated to measure the fun value of a touristic destination as a product among adults, which is reflected in its vocabulary. Additionally, the focus of the scale is to evaluate whether a place, a hotel or a restaurant is fun and not to assess the personal experience.
The EmoForm (Zhang et al. 2019) is a recently developed tool for the assessment of emotions during design-based learning. While the instrument is designed for adolescents, it examines various emotions; however, it does not examine fun only enjoyment across a single item, and it has not yet been validated.
From this review of earlier work, we can conclude that there is currently no psychometrically validated inventory targeting adolescent respondents that is theoretically grounded and that treats fun as a multidimensional construct. The necessity of having multiple dimensions was not only shown by the theoretical review but it helps to conceptualize and define fun rather than treating it as an opaque descriptor or an umbrella term. The present study introduces an instrument designed to fill this gap by providing a tool that is psychometrically and theoretically sound, comprehensive yet parsimonious, practical and adolescent appropriate, and can be used in the learning environment across various fields of research.
For the development of the FunQ, only a fraction of the referred scales was relevant. It is important to mention that several items of different measurement tools overlap with each other (e.g. items measuring the Flow experience, the perceived competence, the enjoyment). Selected questionnaire items from the EGameFlow (Fu et al. 2009), the Evaluation of- and attitudes towards learning games list (Iten and Petko 2016), the FUN scale (Tasci and Ko 2016), the GEQ (Poels et al. 2007), the Physical Activity Enjoyment Scale (Kendzierski and DeCarlo 1991) and the Intrinsic Motivation Inventory (Ryan 1982) have been included in the initial item pool of FunQ, albeit, rephrased to be adolescent appropriate and to reflect a personal experience instead of evaluating the activity (e.g. I had fun instead of It’s [the activity] a lots of fun). Additionally, the pool of items was extended by further ones that reflect the underlying factors, and the adopted items were organized in the FunQ by the factorial structure proposed in this paper and not by the dimensions of the original instrument.