ACKNOWLEDGEMENTS I wish to express my heartfelt gratitude to my advisor, Ms. Aslina Baharum for her extensive re‐ visions suggestions throughout the entire process including that of developing the outline and drafting the final manuscript which was assisted by her expertise and high level of professionalism in higher education. Thanks to everyone who participated in this project by completing the questionnaire since it would not have been possible without their responses. To my family and friends who supported me during the difficult times, I truly appreciate your help. ABSTRACT While there has been a tremendous growth in the use of digital education, there are ongoing difficulties related to maintaining learner motivation over time. Existing research on gamifica‐ tion focuses predominantly on approaches based on external rewards including points, badges, and leaderboards. In comparison, the use of deep aesthetic and narrative aspects of entertain‐ ment design has been explored very little. The main goals of the study presented here are to in‐ vestigate the ways in which three categories of entertainment design (aesthetic design, charac‐ ter design, and gamified interaction) influence learner motivation and how these elements may be combined without interfering with educational goals. A quantitative descriptive design was employed, with data collected from 65 adult learners aged 18 to 35 via a structured online ques‐ tionnaire combining 5-point Likert-scale items with optional open-ended responses; participants were categorised by self-reported personality tendency into extroverted, introverted, and ambi‐ vert groups to examine whether psychological disposition modulates responses to specific design elements. Results showed that aesthetic personalisation produced the strongest overall motivational effect, with matched-preference respondents scoring above 4.5; character design was especially valued by introverted learners (M = 3.95) compared to extroverted learners (M = 3.65); extroverted participants responded most strongly to immediate interactive feedback (M = 4.30); and ambivert respondents showed the greatest preference for multiplayer cooperative features (M = 4.08). Qualitative responses indicated that entertainment elements were most valuable when functionally purposeful rather than decorative. Based on these findings, three design integration principles are proposed: functional embedding, personality-responsiveness, and narrative continuity. This study extends the gamification literature beyond extrinsic mech‐ anics, offering an empirically grounded framework for designing digital learning environments that are both pedagogically effective and genuinely engaging.
TABLE OF CONTENTS ACKNOWLEDGEMENTS 2 ABSTRACT 2 1.0 INTRODUCTION 5 1.1 Background 5 1.2 Problem Statement 5 1.3 Research Objectives 6 1.4 Research Questions 6 1.5 Scope 6 1.6 Summary 7 2.0 LITERATURE REVIEW 8 2.1 Overview 8 2.2 Motivational Frameworks in Digital Learning 8 2.3 Gamification in Digital Education: Benefits and Limitations 8 2.4 Entertainment Design Elements 9 2.5 Research Gap 9 2.6 Summary 10 3.0 METHODOLOGY 11 3.1 Overview 11 3.2 Data Collection 11 3.3 Data Analysis 13 3.4 Summary 13 4.0 IMPLEMENTATION 14 4.1 Overview 14 4.2 Findings from Objective 1 14 4.3 Findings from Objective 2 15 4.4 Findings from Objective 3 16 4.5 Summary 17
5.0 CONCLUSION 18 5.1 Research Summary 18 5.2 Research Contribution 18 5.3 Limitations and Future Works 19 REFERENCES 20 APPENDIX A: Survey Questionnaire 22 APPENDIX B: Survey Response Data 23
1.0 INTRODUCTION 1.1 Background Over the past decade, online teaching has profoundly transformed learning methods. Previously, attending a course required physical presence in a classroom at a specific time. Now, all it takes is opening a laptop to begin learning. The COVID-19 pandemic accelerated this transformation, forcing universities to shift to fully online teaching. What was initially temporary has become the norm. Many universities worldwide now offer hybrid courses, or even 100% online programs (Zeng et al., 2024). However, despite easier access to educational content, student learning is not always more effective. Indeed, many online learners struggle to stay focused. Pan (2023) found that student motivation often decreases due to numerous distractions, such as social media or family activities. Further‐ more, in face-to-face settings, the presence of the professor and classmates encourages per‐ formance. Online, this social pressure is absent. The very design of many platforms also contrib‐ utes to this phenomenon. They simply present videos and texts, without much interaction. Stu‐ dents end up feeling isolated and disconnected, which negatively impacts their academic per‐ formance (Pan, 2023). The problem, therefore, is not a lack of information, but rather a lack of student engagement to fully assimilate it. To address this, many teachers are turning to gamific‐ ation, that is, the use of game elements in learning. The idea is that if learning is more interact‐ ive and narrative, students will be more interested. Some research supports this hypothesis. Zeng et al. (2024) found that well-executed gamification can indeed increase student motivation and well-being by meeting their needs for competence and social interaction. However, not all gamification is effective. For example, simply adding a leaderboard without considering its con‐ nection to learning objectives can generate stress and demotivation among students. Therefore, adding game elements is not enough; it must be done carefully. 1.2 Problem Statement When I observe how gamification is generally implemented in digital education, a trend emerges. Most tools are still designed like traditional courses, with game features simply added on top. This is what Cheng and Wang (2021) point out: many game-based systems are really just rebranded teaching methods. They often neglect the elements that make games truly fun, such as engaging stories, interesting characters, and appealing graphics. As a result, students may focus more on collecting points or badges than on the actual learning. This problem was also noted by Christopoulos and Mystakidis (2023) and by Jiang and Shangguan (2022). Another problem is that, even if gamification works initially, the enthusiasm doesn't last. Rat‐ inho and Martins (2023) explain how students may be captivated by game mechanics at first, but after a while, they become accustomed to them and lose interest. Most studies have focused on external rewards such as points and badges, but little research has been conducted on the role of narrative and aesthetics in maintaining student engagement. Only very recently have some researchers (Rodrigues et al., 2022) begun to suggest that a more creative and collaborat‐ ive design might prove more effective in the long run. These two problems demonstrate that the gamification of education needs further exploration. If we do not use the emotional and narrat‐ 5
ive tools that have always proven successful in entertainment to captivate learners, we will only get a slightly different version of the same old online courses. 1.3 Research Objectives 1. To explore how elements such as visual storytelling, character design, and interactive features influence student motivation in online learning environments. 2. To determine which game design features are most effective at maintaining learner interest over the long term. 3. To propose practical solutions for effectively integrating educational content and game elements into gamified learning systems. 1.4 Research Questions 1. How do visual storytelling, character design, and interactive features influence student motivation in online learning? 2. What design features are most effective at maintaining student engagement and preventing disengagement over time? 3. How can the right balance be struck between learning objectives and game elements when designing gamified platforms? 1.5 Scope This study focuses on adult learners aged 18 to 35 who have used digital platforms such as Coursera or MOOCs. This age group is important because it represents the main user base of these platforms, but also a significant proportion of learners who drop out. The figures are quite surprising. Borrella (2023) indicates that MOOC completion rates are often below 10%, and sometimes even below 5%. Researchers in education and information technology (2025) have also found that motivation varies according to the learner's age and experience; this group is therefore a relevant target for our research. Furthermore, having grown up with video games, people in this age group are likely more receptive to learning experiences inspired by video game culture. We will examine three specific design elements: visual aesthetics, character design, and interac‐ tion mechanisms. We are interested in their impact on learner motivation, engagement, and burnout. Loyola (2025) argues that successful game aesthetics not only capture attention but also encourage players to return, and that the visual and emotional aspects of design can foster deeper understanding. Jafarkhani (2024) shares this view, stating that stimulating learning en‐ vironments give meaning to choices and provide students with a sense of progress. This study will not address institutional support, material requirements, or students outside the 18-35 age range. We wish to limit its scope to ensure accurate results. 6
1.6 Summary Online learning has experienced rapid growth, but maintaining student motivation throughout their training remains a major challenge. Most gamification tools rely on simple rewards like points and badges, neglecting the stories, characters, and visual appeal that make entertain‐ ment so engaging. Students often get bored, and making learning fun is difficult. This study aims to address this gap by exploring how game design can be used in digital learning to main‐ tain motivation, reduce mental fatigue, and create a satisfying learning experience that is both educational and enjoyable. I am convinced that we shouldn't have to choose between the two. 7
2.0 LITERATURE REVIEW 2.1 Overview This chapter reviews the scientific literature related to the three research questions of this study: how game design techniques influence learner motivation; which elements are most likely to maintain engagement over time; and how to reconcile educational and game-like ob‐ jectives in the design of gamified digital learning. This review draws on peer-reviewed empirical studies, systematic reviews, and meta-analyses published primarily between 2019 and 2025, or‐ ganized thematically around motivation theory, gamification practice, game design elements, and the challenge of maintaining engagement. 2.2 Motivational Frameworks in Digital Learning Since game design is still an under-theorised part of gamification practice, it makes sense to critically interrogate what existing motivational frameworks can tell us and their shortcomings. Most gamification research appears to be based on self-determination theory (SDT), which ar‐ gues that intrinsic motivation stems from the satisfaction of three basic psychological needs: autonomy, competence, and relatedness (Deci & Ryan, 2000). Meta-analysis from Zeng et al. published in the British Journal of Educational Technology, a paper (2024) found that gamifica‐ tion has generally beneficial effects on skill development and social connectivity—two of three core needs—but left open the hotly contested question of whether or how good gamification can sustainably nurture intrinsic motivation, at least in its deep sense. A framework with some simil‐ arities can be found in Csikszentmihalyi's flow theory (1990), which relates staying engrossed on the long-term to a balance between challenge and competence. The positive affect which grants access to the flow state is enabled by aesthetic aspects—color, animation, character ex‐ pressiveness (Plass et al., 2022). Yet self-determination theory (SDT) and flow theory fail to provide a satisfactory account of how the aesthetic or narrative dimensions of design influence motivation independently from its interactive characteristics over longer periods of time, an area that we elaborate further in the following section. 2.3 Gamification in Digital Education: Benefits and Limitations Gamification—the integration of game design elements into non-game contexts (Deterding et al., 2011)—is widespread in digital education, most often in the form of points, badges, and leaderboards, frequently abbreviated as PBL. A PRISMA-guided systematic review by Ratinho and Martins (2023) of 40 studies selected from 548 articles published in Heliyon found a gener‐ ally positive short-term effect on student motivation. A meta-analysis by Zeng et al. (2024), pub‐ lished in the British Journal of Educational Technology, reached broadly similar conclusions after examining 15 years of empirical data. However, this overall positive finding contrasts with a body of equally compelling evidence to the contrary. Hanus and Fox (2015), in a frequently cited longitudinal study, found that introdu‐ cing badges, leaderboards, and coins into a university course actually reduced student motiva‐ tion and satisfaction compared to a control group that received no gamification. A 2024 meta- 8
analysis published in Educational Technology Research and Development reached a similar con‐ clusion, acknowledging that the question of whether gamification truly enhances intrinsic motiv‐ ation remains unanswered. Methodological issues compound this uncertainty: many studies rely on short-term self-reported data and lack the consistency necessary for meaningful comparisons (Ratinho & Martins, 2023). More fundamentally, the problem-based learning model that domin‐ ates current practice focuses only on the extrinsic surface of game design, neglecting the aes‐ thetic, emotional, and narrative dimensions—the elements that give entertainment its true enduring appeal. 2.4 Entertainment Design Elements: Visual Aesthetics, Character Design, and Narrative A smaller but rapidly expanding field of research is increasingly focusing on the playful dimen‐ sion of learning. Studies on emotional design in multimedia learning demonstrate that visual characteristics—warm color palettes, anthropomorphic shapes, expressive forms—can generate positive emotional states that enhance cognitive engagement and improve learning outcomes, particularly when task difficulty remains moderate (Plass et al., 2014; PMC, 2025). A study pub‐ lished via ScienceDirect (2024) also identified graphic qualities such as 2D/3D visual balance and compositional harmony as key factors in knowledge-related emotional responses, including curiosity and interest. More specifically regarding character design, Fink et al. (2024), in an article published in Fron‐ tiers in Education, confirmed that AI-based avatars can foster immersive and personalized ex‐ periences, with proven motivational effects. However, the field continues to view characters primarily as functional pedagogical agents—tools for conveying content—rather than as thoughtfully designed aesthetic presences. The gap between how the entertainment industry conceives of characters and how educational research views them remains significant. Of the three arguments put forward, the one concerning the role of narrative is perhaps the most compelling. A study by PMC (2022) demonstrated that integrating fantasy and narrative elements into a gamified online course reversed a decline in engagement observed over eight weeks, while also significantly boosting peer interaction and learning performance. Rodrigues et al. (2022), drawing on a 14-week longitudinal study, also found that fictional and collaborat‐ ive gamification elements helped learners resist the novelty effect over time, thus positioning narrative not as a mere ornament, but as a structurally more durable motivational mechanism than extrinsic rewards alone. 2.5 Research Gap What the literature as a whole establishes is that conventional gamification produces measur‐ able but limited motivational effects, often fleeting and sometimes even counterproductive. Re‐ cent data suggest that visual aesthetics, character design, and storytelling offer more affectively sustainable alternatives. Yet, these elements remain largely unexplored empirically: studied briefly and in isolation, rather than as components of integrated entertainment design systems. The question of a productive balance between pedagogical rigor and playful immersion is ad‐ dressed theoretically in the literature but rarely tested empirically. This study fills this gap by examining how entertainment design techniques interact to maintain motivation in adult digital 9
learning environments, thus bringing design-driven evidence to a field still largely preoccupied with extrinsic mechanisms and short-term measures. 2.6 Summary Conventional gamification tends to produce short-term motivational gains that often fade and can even, in some cases, reduce engagement over time. Drawing on self-determination theory and flow theory, recent work highlights visual aesthetics, character design, and storytelling as more affectively sustainable alternatives capable of generating positive emotional states and halting the decline in engagement. However, these game design elements have not yet been studied as an integrated system. This study fills this gap by analyzing how these techniques, taken together, maintain the motivation of adult learners in digital learning contexts. 10
3.0 METHODOLOGY 3.1 Overview This chapter describes the research protocol, the data collection process, and the analytical ap‐ proach adopted for this study. A quantitative descriptive methodology was employed, enabling the achievement of the three research objectives through a structured online questionnaire. Items were measured on a 5-point Likert scale, ranging from 1 (Strongly disagree) to 5 (Strongly agree), and a descriptive statistical analysis was then applied to the collected data. The quantitative approach was favored because it allows for systematic measurement within a defined population and supports reproducible statistical comparisons. The addition of a seg‐ mentation of personality traits—distinguishing between extroverted, introverted, and ambiver‐ ted tendencies—introduces a differentiated analytical dimension, largely absent from existing research on gamification (Creswell, 2014). The remainder of this chapter details the selection of participants, the construction of the instrument around the three research objectives, the data collection process, and its analysis. 3.2 Data Collection 3.2.1 Participants and Sampling The target population consisted of adult learners aged 18 to 35 who had previously used plat‐ forms such as Coursera or MOOCs – this demographic group was chosen because it represents the main category of self-directed online learners and is also the most familiar with the aesthet‐ ics of video games. A convenience sampling approach based on voluntary participation was used; the questionnaire was distributed via social media and email, with initial screening ques‐ tions confirming that all respondents had prior experience with digital learning. Of the expected 100 to 300 respondents, 65 valid responses were obtained – an insufficient number acknow‐ ledged as a limitation in Chapter 5. The most represented age group was 18–24 (n = 35), fol‐ lowed by those under 18 (n = 16) and 25–29 (n = 14). For the purposes of objective 2, these 65 respondents were segmented into three groups according to their self-reported personality tendency: extroverts (n = 24), introverts (n = 21), and ambiverts (n = 20). This segmentation was incorporated into the first part of the questionnaire precisely to determine whether differ‐ ent psychological dispositions produce differentiated responses to specific design elements—a dimension that the gamification literature has largely overlooked. 3.2.2 Instrument Data collection was conducted using a structured online questionnaire organized into three parts. The first part (General Information) collected demographic data regarding age, frequency of digital learning, platform experience, and a self-assessment of personality tendencies. This last variable was deliberately included to test whether different psychological orientations lead to significantly different responses to the design elements of entertainment content. The second part (Perception Scale of Design Elements) formed the core of the measurement instrument and covered three dimensions divided into three subscales (A, B, and C), detailed below. The third part (Open Comments) offered respondents the opportunity to elaborate on their design prefer‐ 11
ences. These responses were not formally analyzed but helped to contextualize and enrich the trends observed in the quantitative data. Participation was entirely voluntary and anonymous. A. Objective 1 — Entertainment Design and Motivation Subscale A comprised three questions with Likert-type response options measuring perceptions of the visual aesthetics and character design: Does the appropriateness of the interface style to personal aesthetic preferences increase motivation to use it? Does making a virtual character feel less sad and frustrated create a sense of camaraderie? (Q2): Does the character with defined personality/story return a positive adherence to lesson pace? For example, a fifth (Q3) multiple choice question asked participants which virtual tutor style they preferred from four different options: cute, professional, humorous or serious (Q4). Subscale B centered on playful interaction: the perceived values of rankings, points, and badges (Q5); immediate feedback mechanisms like animations and sound effects (Q6); perceived autonomy in the task as com‐ pared to one-way instruction (Q7); and motivation to engage with competitive or cooperative multiplayer elements of play (Q8). And underestimation of the repetition and diligence required to master tasks (Q9). B. Objective 2 — Design Components for Ongoing Engagement For Objective 2, focused items from subscales A and B were chosen based on their connection to long-term engagement: the companionship function of virtual characters (Q2), the compelling role of characters featuring personalities or backstories (Q3), perceptions associated with dur‐ ability for gamification elements such as leaderboards and badges (Q5), interactive feedback in real time (Q6), self-reported willingness to interact with other learners via competition or co‐ operation (Q8) and persistence against mastery through repeated attempts at a task (Q9). In‐ stead of introducing new items, we explored existing practised designs from a new angle by analysing their relationships with personality and so not only initial appeal but also the extent to which a given design feature supports sustained motivation in repeated learning interactions for different learner types; as described in 3.2.1. C. Objective 3 — Balancing Educational and Entertainment Elements To address the issue of balance, subscale C focused on background music and sound effects in relation to concentration (Q10), user interface quality in relation to learning fatigue (Q11), and interface speed and fluidity in relation to overall system appreciation (Q12). Together with sub‐ scales A and B, these items allowed us to map the relative perception of different entertainment design categories and identify the points of tension most likely to arise between playful immer‐ sion and the educational objective. The open-ended responses in Part 3 served to contextualize and enrich the quantitative trends emerging from this comparison, particularly regarding the perceived balance between playful value and learning effectiveness. 3.2.3 Procedure The questionnaire was given out online and sent out via social media and email. At the start of the survey, we asked some questions to check that people had used digital learning platforms before. You could take part if you wanted to, and you didn't have to give your name. We didn't collect any information that could identify you personally. The people who took part in the study did all three parts of the questionnaire in one go. Part 3 (the open-ended questions) was option‐ al. 12
3.3 Data Analysis We used statistical analysis to examine the three research objectives. For objective 1, we calcu‐ lated the mean score for each item on the Likert scale for subscales A and B. We then compared the participants' responses to identify the visual design and interaction elements most strongly associated with accumulated motivation for learning. For objective 2, we compared the re‐ sponses of each group of participants based on their personality type. The mean scores for each group were calculated for each item to highlight significant differences between extroverted learners, non-extroverted learners, and both extroverted and non-extroverted learners. The goal was to determine the design elements best suited to all learner types. For objective 3, we com‐ pared the mean scores for subscales A, B, and C. This comparison aimed to identify the game design categories with the highest overall scores and the areas where the game experience and learning effectiveness differed the most. The open-ended responses from Part 3 were analyzed to enrich these comparisons. The main findings of this analysis are as follows: people who scored above 4.5 on the appearance customization questions reported being much more motiv‐ ated; usually calm people reported enjoying the company of virtual characters more than usu‐ ally friendly people (3.95 vs. 3.65); usually friendly people were primarily interested in receiving immediate feedback (4.30); and sometimes friendly and sometimes not friendly people were primarily interested in cooperative features allowing many people to play together (4.08). 3.4 Summary This chapter explains the research plan, who the study is being done on, how the study is set up, and the way it will be analysed. The method of collecting and analysing data was a three- part online questionnaire. This was then broken down and compared using statistics. The ques‐ tionnaire was designed to address the three research objectives in a consistent and comparable way. The way people are grouped based on their personality traits provides a new way of think‐ ing that most of the research on gamification does not include. You can find the results of this approach in Chapter 4. 13
4.0 IMPLEMENTATION 4.1 Overview This chapter presents the results of a study that was done with 65 people. The people taking part in the study filled in a questionnaire and were given instructions. The study looked at three things that we were interested in. Each section starts with the relevant mean scores and com‐ parisons between the groups. It then talks about how important these results are and puts them into the ideas and studies looked at in Chapter 2. The results show the concrete evidence needed to support the design strategies proposed later in this chapter and explained in more detail in Chapter 5. 4.2 Findings from Objective 1 — Impact of Entertainment Design on Motivation First, we want to find out how to use entertainment design techniques (like how things look, the characters, and how interactive games work) that can make people more interested in digital training. The average scores for subscale A in Part 2 were as follows: Question 1 (how suitable the inter‐ face is for the participant's personal style) received an average score above 4.5, which was the highest score on subscale A. The average score for Question 2 (how well virtual characters help you) was 3.80 out of 5, calculated from all responses. Question 3 (about the character's person‐ ality and backstory, and how many people would want to follow its story) received an average score of 3.75. In Question 4, the most common answers were "cute" and "professional" when people were asked which character style they would like their tutor to have. In subscale B, question 6 (receiving immediate feedback through animations and sounds) received the highest overall mean score (4.10), better than questions 5 (rankings, points, badges, M = 3.55) and 7 (sense of mastery of the task, M = 3.60). When we look at the results together, it seems that the main reason for this is that people want to change how things look. It also shows that interactive feedback is much better than other static elements, like rankings and badges. This finding is similar to the results of other studies, which have shown that warm colours, human-like shapes and special features can improve well- being and concentration (Plass et al., 2014). It also supports what Loyola (2025) said about how the way a game is designed affects how much people want to learn more. Character design's more moderate scores (Q2 and Q3 both in the high 3s, rather than exceeding 4.5 like Q1) suggest it functions as a secondary rather than primary motivational lever at the whole-sample level — though, as Objective 2 findings below show, this picture changes consid‐ erably once personality is taken into account. At the aggregate level, however, respondents' qualitative comments still indicated a sense of companionship and emotional investment gener‐ ated by expressive characters, an effect that existing educational research has largely missed, given its tendency to treat avatars as functional content-delivery agents rather than carefully crafted aesthetic presences (Fink et al., 2024). 14
The Subscale B result on immediate feedback (Q6, M = 4.10) reinforces the Subscale A pattern: what appears to drive motivation is dynamic, responsive design rather than static reward accu‐ mulation. This aligns with Flow Theory's emphasis on real-time feedback as a key enabler of im‐ mersive engagement (Csikszentmihalyi, 1990), and suggests that the quality of interaction design matters more than the simple presence of game mechanics. Taken together, the Objective 1 findings indicate that entertainment design techniques exert a positive and measurable influence on learner motivation, with aesthetic personalisation and im‐ mediate interactive feedback emerging as the two strongest drivers among the elements tested. 4.3 Findings from Objective 2 — Entertainment Design Elements That Sustain Long-Term Engagement Objective 2 asked which entertainment design elements are most capable of sustaining engage‐ ment over time and guarding against the motivational decline that the literature associates with the novelty effect (Ratinho & Martins, 2023). The explicit data — group mean scores obtained directly from cross-tabulating Likert responses by personality group — were as follows. On Q2 (character companionship), introverted respond‐ ents (n = 21) returned a mean of 3.95, compared with 3.65 among extroverted respondents (n = 24); ambivert respondents (n = 20) scored 3.78, placing them between the two groups. On Q6 (immediate interactive feedback), extroverted respondents scored highest at 4.30, compared with 3.70 among introverted respondents and 3.90 among ambiverts. On Q8 (multiplayer/co‐ operative features), ambivert respondents scored highest at 4.08, compared with 3.60 among introverted respondents and 3.95 among extroverted respondents. Beyond these explicit figures, an implicit pattern emerges when the three results are read side by side: each personality group's highest-scoring item is a different item entirely — introverts peak on companionship (Q2), extroverts peak on interactive feedback (Q6), and ambiverts peak on cooperative features (Q8). This is not something any single mean score shows on its own; it only becomes visible once the three group comparisons are set against each other, and it indic‐ ates that personality does not simply raise or lower overall enthusiasm for entertainment design — it redirects which specific design element each group responds to most. This differentiated result carries a significant implication for the novelty effect problem. Where extrinsic mechanics — points, badges — produce initial engagement followed by decline (Rat‐ inho & Martins, 2023; Hanus & Fox, 2015), the explicit data above suggest that aesthetic and narrative elements operate through more personalised motivational pathways, which may prove more durable precisely because they are better matched to individual learner characteristics. Where character design is sustained as a narrative presence rather than deployed as a one-time aesthetic feature, it appears — per the Q2 data — to offer a particularly effective retention strategy for introverted learners specifically, who are often considered most vulnerable to disen‐ gagement in impersonal online environments. This reading is consistent with Rodrigues et al.'s (2022) finding that fictional and collaborative elements helped sustain motivation across a 14- week longitudinal study in ways that extrinsic rewards alone could not. 15
4.4 Findings from Objective 3 — Balancing Educational and Entertainment Elements Objective 3 addressed the question of how educational objectives and entertainment elements can be held in productive balance within gamified digital learning design. Comparative mean score analysis across the three Part 2 subscales produced the following pat‐ tern. Subscale A (visual aesthetics and character design) scored consistently across all person‐ ality groups, with no group falling below 3.6 on any item. Subscale B (gamified interaction mechanics) peaked among extroverted learners (Q6, M = 4.30) but showed the widest spread across groups. Subscale C (audio and UI experience) was rated most positively by introverted and ambivert respondents: Q10 (background music/concentration) returned M = 4.05 among in‐ troverts, and Q11 (UI layout/learning fatigue) returned M = 4.00 among ambiverts. No single subscale dominated overall ratings, and the distribution suggests that a balanced, multi-dimen‐ sional design approach is more likely to serve the diverse motivational profiles within a typical adult learner population than any strategy centred on a single element. Open-ended responses from Part 3 reinforced this reading. Respondents repeatedly described entertainment features as feeling most valuable when they were evidently purposeful rather than decorative — when animations signalled genuine progress, when characters responded meaningfully to learner choices, when visual design felt coherent with learning content rather than ornamental to it. Several respondents also flagged that excessive or contextually irrelevant entertainment elements were distracting, echoing Christopoulos and Mystakidis's (2023) con‐ cern that poorly integrated gamification can shift learners' attention from learning objectives toward the game layer itself. Based directly on this balance data, three concrete design strategies are proposed for Objective 3: Tiered aesthetic-to-content coupling: Since Subscale A scored consistently high across all groups, visual and character elements should be treated as a universal baseline layer — applied throughout a course rather than reserved for special moments — but always tied to a specific piece of content or progress marker (e.g., a character's appearance changing as a module is completed), so that aesthetic investment doubles as a progress indicator rather than pure decoration. Personality-gated interaction intensity: Because Subscale B showed the widest spread across personality groups, interactive/competitive mechanics (leaderboards, timed challenges) should be offered as an optional, togglable layer rather than a default — visible and prominent for extroverted learners, minimised or replaced with solo-paced alternatives for introverted learners, based on the personality data collected at onboarding. Ambient audio/UI calibration for fatigue reduction: Since Subscale C scored highest specifically on fatigue-related items among introverted and ambivert respondents, background audio and interface pacing should be tunable by the learner (e.g., adjustable music intensity, simplified UI mode), positioned specifically as a fatigue-management tool rather than a purely aesthetic add-on. • • • 16
4.5 Summary Across all three objectives, the findings confirm that entertainment design techniques — visual aesthetics, character design, gamified interaction, and narrative elements — exert a meaningful positive influence on learner motivation and engagement in digital education. Critically, the dif‐ ferentiated responses observed across personality groups indicate that motivation is not pro‐ duced uniformly by any single design element; what matters is the degree to which the design environment aligns with the psychological characteristics of individual learners. The three design strategies proposed in this chapter — tiered aesthetic-to-content coupling, personality- gated interaction intensity, and ambient audio/UI calibration — offer a practical framework for translating these findings into actionable design decisions, as discussed further in Chapter 5. 17
5.0 CONCLUSION 5.1 Research Summary This study examined how elements such as character appearance, design, and interaction with the game influence motivation and learning effectiveness in digital learning environments. It also explored how to reconcile these elements with educational objectives. The study was struc‐ tured around three research areas and the questions posed within them. It employed a quantit‐ ative descriptive methodology, involving data collection from 65 adult learners. Data was collec‐ ted using an online questionnaire. This questionnaire included a 5-point Likert scale, as well as optional open-ended questions. The results show that game design elements can increase motivation, but their effectiveness is highly dependent on individual personality. The option to customize character appearance (its "aesthetics") generated the strongest overall motivation, with respondents with the same pref‐ erence assigning scores above 4.5 on average. Characters proved particularly important in en‐ couraging typically reserved individuals, while immediate feedback boosted the motivation of typically extroverted people. Both shy and sociable individuals especially appreciated cooperat‐ ive features. All of these examples suggest that a differentiated, personality-tailored design approach is preferable to a one-size-fits-all gamification strategy. A second recurring theme emerges from the data: effective game design is both practical and not merely decorative. Participants appreciated visual and interactive features more when they enhanced their learning, rather than when they were perceived as mere additions to standard instruction. This ties into the main issue discussed in Chapter 1: most game-based learning sys‐ tems neglect game aspects, such as aesthetics and storyline, in favor of intrinsic learner motiva‐ tion. 5.2 Research Contribution The most important part of this study is not just theoretical, but also has a practical side. It sug‐ gests ways to use the findings to make changes that work in real life. For example, it talks about how to combine how things look with the content, how much people can interact, and how to set up audio and user interface. These ideas are all explained in Chapter 4. Tiered aesthetic-to-content coupling is a way for course designers and platform developers to raise motivation for all learners, since personalisation of the look of the course scored highly re‐ gardless of personality type. This strategy would be especially useful for instructional designers and EdTech product teams building general-purpose platforms, who need a design layer that works well for all users without needing to make changes to the system for each user. Platforms can serve both extroverted and introverted learners by adjusting how intense the in‐ teractions are. This is possible by using personality-based preferences. This would be especially useful for people who develop adaptive learning platforms and for UX designers working on sys‐ tems that already collect learner profile data. It would also be useful for corporate training and MOOC providers who manage large, diverse learner bases. These people might find that a one- size-fits-all gamification layer currently risks offending a significant number of users. 18
Ambient audio/UI calibration can reduce fatigue, which is good for accessibility-focused design‐ ers and course platform teams. This is because it can help to reduce dropout rates linked to learning fatigue. This is especially helpful for introverted and ambivert learners. These are the types of learners who are most sensitive to this issue. This is what Chapter 4 found. It also of‐ fers a lower-cost way in for smaller EdTech teams or people who make courses on their own. These people may not have the money to build character-driven narrative systems, but they can implement adjustable audio and UI settings relatively cheaply. Beyond these three strategies, this study also adds to the gamification literature by looking at visual design, character design and interactive feedback as different ways to motivate people, instead of looking at all of these as one "gamification" thing. It also looks at personality traits as an extra layer in the analysis, which hasn't been done much before. This gives us a good basis for designing learning environments that can adapt to each person's needs. 5.3 Limitations and Future Works But there are some important things to know about this study. The 65 people who took part in the study are not enough to give us reliable results. We would have needed 100 to 300 people to take part to get reliable results. This means we cannot be sure that our results are the same for all adults learning digitally. Using social media and email to ask people to take part in a survey might make the results more positive than they would be if we asked people in the street. The way we collected the data is another problem. The study can't directly track how motivation changes over time when people interact with an entertainment-designed learning environment many times. This is because all the responses were collected at the same time. The literature supports the idea that these conclusions are true, but we can't be sure about the study's own data. Another problem is that we only use self-report measures, which means that people might not tell the truth. This can make it hard to know what people's actual behaviour is in real-life situations. The uneven uptake of the Part 3 open-ended option also left the qualitative data thinner than would have been ideal for adding to the Objective 3 analysis. In the future, we might be able to address these issues in a few different ways. If we did studies that tracked motivation and engagement over time with a platform designed for entertainment, we could get direct evidence for or against how long the effects we saw here last. If we had a bigger sample of people, from a wider range of backgrounds, we could get stronger results. Us‐ ing a variety of research methods — like watching people, analysing their interactions, or using thought-out protocols — along with other methods like self-reporting, would give a more de‐ tailed and realistic picture of how entertainment design elements work in real learning situ‐ ations. Experiments or studies that compare entertainment-based learning, traditional gamified learning, and non-gamified learning would help us understand more about why people are motivated to learn. 19
REFERENCES Borrella, I., Caballero-Caballero, S., & Ponce-Cueto, E. (2022). Taking action to reduce dropout in MOOCs: Tested interventions. Computers & Education, 179, 104412. https://doi.org/10.1016/ j.compedu.2021.104412 Cheng, S., & Wang, Y. (2021). The application of entertainment design and digital technology in children's educational exhibition space: Take the "Art Education in Fun" exhibition as an ex‐ ample. E3S Web of Conferences, 236, 05091. https://doi.org/10.1051/e3sconf/202123605091 Christopoulos, A., & Mystakidis, S. (2023). Gamification in education. Encyclopedia, 3(4), 1223– 1243. https://doi.org/10.3390/encyclopedia3040089 Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). SAGE Publications. Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper & Row. Deci, E. L., & Ryan, R. M. (2000). The "what" and "why" of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/ S15327965PLI1104_01 Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From game design elements to gamefulness: Defining gamification. In Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments (pp. 9–15). ACM. https:// doi.org/10.1145/2181037.2181040 Fink, A., Cahill, M. J., & McDaniel, M. A. (2024). AI-based avatars are changing the way we learn and teach: Benefits and challenges. Frontiers in Education, 9, Article 1416307. https://doi.org/ 10.3389/feduc.2024.1416307 Hanus, M. D., & Fox, J. (2015). Assessing the effects of gamification in the classroom: A longitudin‐ al study on intrinsic motivation, social comparison, satisfaction, effort, and academic perform‐ ance. Computers & Education, 80, 152–161. https://doi.org/10.1016/j.compedu.2014.08.019 Jafarkhani, R., Ahmadi, M., & Rezaei, A. (2024). Aesthetic learning experiences and the enhancement of digital engagement. Journal of Economy and Technology, 2(1), 45–62. https:// doi.org/10.1016/j.ject.2024.01.003 Jiang, F., & Shangguan, D. (2022). Researching and designing educational games on the basis of "self-regulated learning theory." Frontiers in Psychology, 13, Article 996403. https://doi.org/ 10.3389/fpsyg.2022.996403 Loyola, P., Fernández, M., & Torres, R. (2025). Game aesthetics, engagement, and experiential learning in digital education. Journal of Digital Education Research, 6(1), 23–41. https://doi.org/ 10.1007/s10639-024-13201-x Pan, X. (2023). Online learning environments, learners' empowerment, and learning behavioral engagement: The mediating role of learning motivation. SAGE Open, 13(4), 1–16. https:// doi.org/10.1177/21582440231205098 20
Plass, J. L., Homer, B. D., & Kinzer, C. K. (2015). Foundations of game-based learning. Educational Psychologist, 50(4), 258–283. https://doi.org/10.1080/00461520.2015.1122533 Plass, J. L., Kaplan, U., & Homer, B. D. (2014). Emotional design in digital games for learning. In S. Y. Tettegah & M. Gartmeier (Eds.), Emotions, technology, design, and learning (pp. 131–161). Academic Press. https://doi.org/10.1016/B978-0-12-801856-9.00007-5 Plass, J. L., Hovey, C., & Kinzer, C. K. (2022). The emotional design principle in multimedia learning. In J. L. Plass, R. E. Mayer, & B. D. Homer (Eds.), Handbook of game-based learning (pp. 111–152). MIT Press. Ratinho, E., & Martins, C. (2023). The role of gamified learning strategies in student's motivation in high school and higher education: A systematic review. Heliyon, 9(8), e19033. https://doi.org/ 10.1016/j.heliyon.2023.e19033 Rodrigues, L., Toda, A., Oliveira, W., Palomino, P. T., Avila-Santos, A. P., & Isotani, S. (2022). Gami‐ fication suffers from the novelty effect but benefits from the familiarization effect: Findings from a longitudinal study. International Journal of Educational Technology in Higher Education, 19(1), Article 13. https://doi.org/10.1186/s41239-021-00314-6 Smiderle, R., Rigo, S. J., Marques, L. B., Coelho, J. A. P. de M., & Jaques, P. A. (2020). The impact of gamifica‐ tion on students' learning, engagement, and behavior based on their personality traits. Smart Learning Environments, 7(1), Article 3. https://doi.org/10.1186/s40561-019-0098-x Zeng, J., Parks, S., & Shang, J. (2024). Exploring the impact of gamification on students' academic performance: A comprehensive meta-analysis of studies from 2008 to 2023. British Journal of Educational Technology, 55(3), 1–24. https://doi.org/10.1111/bjet.13471 21
APPENDIX A: Survey Questionnaire The questionnaire used in this study was administered online via Google Forms. The full ques‐ tionnaire, including all items across Parts 1, 2, and 3, can be accessed at the following link: https://docs.google.com/forms/d/1n62vHAZxdVWP3lDI18GY7YSEGBZ4QA7IJJiOZICoBKE/edit 22
APPENDIX B: Survey Response Data The raw response data collected from 65 participants throughout the data collection period are stored and accessible via the following link: https://drive.google.com/file/d/1nUFqqm-ZWL3FW4V3W5EVxs-CoUUDuqQr/view?usp=sharing 23