A Systematic Review of Gamification Within E-Learning

The focus of this study was to review and evaluate the effectiveness of gamification within e-learning platforms. The study deployed systematic literature review methodology to evaluate how effective gamification has been used within e-learning platforms. The study used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Approach (PRISMA), starting with 366 articles, shifting to a final 34 articles for consideration. It was established that gamification positively influences and enhances learning within the e-learning platform. Therefore, the study recommends policy makers, designers, and implementers of e-learning platforms to consider incorporating gamification elements in order to increase user motivation and engagement for enhanced learning.

The Rank of Silaturrahmi-Assimilated Collaboration Parameter Based on Core Drive Using Octalysis Gamification Framework and Fuzzy AHP

This study aims to build SME collaboration parameters from the assimilation of the "silaturrahmi" culture that considers user motivation in collaborating. Parameter validation involves linear regression statistical methods, followed by determining the ranking of collaboration parameters using a combination model of Octalysis and Fuzzy AHP frameworks. The test resulted in 4 collaboration parameters from the assimilation of "Silaturrahmi" ranked based on the user's level of motivation (core drive) with details: Relationship Building-RB (52.86%), Reciprocal Sustainment-RS (25.44%), Reciprocal Assistant-RA (21.77%) and Active Support- US (0.83%). It can be concluded that the four parameters potentially measure the performance of SME collaboration. The combination model can determine the user's motivation (core drive) for collaboration through these parameters. The ranking results serve as a reference for developing a collaborative framework by prioritizing activities related to the highest weight percentage parameters and evaluating the lowest weight percentage.

How Question Characteristics Impact Answer Outcome on Social Question-and-Answer Websites

Inducing more and higher-quality answers to questions is essential to sustainable development of Social Question-and-Answer (SQA) websites. Previous research has studied factors affecting question success and user motivation in answering questions, but how a question’s own characteristics affect the question’s answer outcome on SQA websites remains unknown. This study examines the impact of the characteristics of a question, namely readability, emotionality, additional descriptions, and question type, on the question’s answer outcome as measured by number of answers, average answer length, and number of “likes” received by answers to the question. Regression analyses reveal that readability, additional descriptions, and question type have significant impact on multiple measurements of answer outcome, while emotionality only affects the average answer length. This study provides insights to SQA website builders as they instruct users on question construction. It also provides insights to SQA website users on how to induce more and higher-quality answers to their questions.

How Question Characteristics Impact Answer Outcomes on Social Question-and-Answer Websites

Inducing more and higher-quality answers to questions is essential to sustainable development of Social Question-and-Answer (SQA) websites. Previous research has studied factors affecting question success and user motivation in answering questions, but how a question’s own characteristics affect the question’s answer outcome on SQA websites remains unknown. This study examines the impact of the characteristics of a question, namely readability, emotionality, additional descriptions, and question type, on the question’s answer outcome as measured by number of answers, average answer length, and number of “likes” received by answers to the question. Regression analyses reveal that readability, additional descriptions, and question type have significant impact on multiple measurements of answer outcome, while emotionality only affects the average answer length. This study provides insights to SQA website builders as they instruct users on question construction. It also provides insights to SQA website users on how to induce more and higher-quality answers to their questions.

Gamification Framework for Programming Course in Higher Education

This paper presents a gamification framework for higher education, especially for programming language courses to increase user motivation, pleasure and satisfaction so that learning objectives can be achieved. Although student and lecturer motivation, pleasure, and satisfaction tend to increase compared to conventional techniques, gamification is not a panacea. The success of its application depends on the skill of the lecturer in choosing a game mechanic to give a sense of playing to the learning process in a more interesting way. The technique is done by dividing the class into two parts where one class uses the conventional method and one class uses the gamification method and the results will be evaluated through the assessment results before using the gamification method and after using the gamification method. The framework in this paper adds to the existing framework activities, namely adding in the field of baseline analysis, learning materials and tools used in gamification, where previous papers from three activities have not been discussed in detail. The results obtained turned out that using the gamification technique of student learning outcomes on average 15 to 25 better than using conventional techniques.

Investigating the Influence of Personalised Gamification on Mobile Survey User Experience

Gamification in healthcare has shown to increase user motivation and treatment adherence. Personalisation strategies have the potential to create sustainable health change and user motivation. In striving for personalisation, surveys are often used as an easy tool to collect information about the user. But lengthy surveys are often tedious and demotivating, provoking bad respondent behaviour which results in a loss of data quality. Gamified surveys can enhance respondent behaviour but are labour-intensive to develop. This study explores the effect of a re-usable mobile survey application, using personalised gamification, on user experience. In an A/B study setting with 28 participants, The Hexad Player Type Framework is used to determine the player type of the user and personalise the survey accordingly. Results have shown that the overall user experience of the gamified application is higher than the traditional survey and 37.5% of gamified users perceived the duration of the survey as shorter than the actual time, compared to 20% of the respondents of the traditional survey. No significant difference in data quality has been detected, as loss in data quality remained limited in both versions. Future work should explore the influence of more elaborate game elements in a larger population.

Self-Escapism Motivation: A Determinant of E-Cart Use and Abandonment

E-shopping engagement has become a significant user activity in cyberspace. Prior scholarship has found self-escapism a crucial user motivation to engage in e-shopping. The current research identifies dimensions of self-escapism motivation to engage in e-shopping and their influence on shoppers’ attitude, e-cart use, and abandonment. Data were collected from 305 shoppers in Pakistan via an offline survey. Results reveal self-suppression and self-expansion as dimensions of shoppers’ self-escapism motivation to engage in e-shopping. These dimensions of self-escapism motivation determine shoppers’ positive affective attitude towards e-shopping. Nonetheless, they have a differing influence on shoppers’ e-cart use and abandonment. Keywords: Self-Escapism Motivation, E-Shopping, Affective Attitude, E-Cart Use; E-Cart Abandonment.

Intrinsic motivation in virtual assistant interaction for fostering spontaneous interactions

With the growing utility of today’s conversational virtual assistants, the importance of user motivation in human–artificial intelligence interactions is becoming more obvious. However, previous studies in this and related fields, such as human–computer interaction, scarcely discussed intrinsic motivation (the motivation to interact with the assistants for fun). Previous studies either treated motivation as an inseparable concept or focused on non-intrinsic motivation (the motivation to interact with the assistant for utilitarian purposes). The current study aims to cover intrinsic motivation by taking an affective engineering approach. A novel motivation model is proposed, in which intrinsic motivation is affected by two factors that derive from user interactions with virtual assistants: expectation of capability and uncertainty. Experiments in which these two factors are manipulated by making participants believe they are interacting with the smart speaker “Amazon Echo” are conducted. Intrinsic motivation is measured both by using questionnaires and by covertly monitoring a five-minute free-choice period in the experimenter’s absence, during which the participants could decide for themselves whether to interact with the virtual assistants. Results of the first experiment showed that high expectation engenders more intrinsically motivated interaction compared with low expectation. However, the results did not support our hypothesis that expectation and uncertainty have an interaction effect on intrinsic motivation. We then revised our hypothetical model of action selection accordingly and conducted a verification experiment of the effects of uncertainty. Results of the verification experiment showed that reducing uncertainty encourages more interactions and causes the motivation behind these interactions to shift from non-intrinsic to intrinsic.