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While role-playing games and personalized learning have been regarded as effective tools to improve students’ learning, incorporating personalized learning into role-playing games is challenging and approaches are limited to cognitive and motivational variables. Aiming at expanding approaches to incorporate personalization into role-playing games,...
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... Although decision trees, useful in classifying variables into visual models, can support this personalization in digital game-based learning. Their ability to analyze and understand student patterns can be integrated into educational games (Zhong, 2022); however, providing tailored feedback based on individual learning is still needed and challenging to create in the AI-driven gaming approach. In other words, decision trees have offered some potential in the context of personalized digital game-based learning. ...
... The effective integration of AI-driven gaming with fuzzy logic and decision tree methodologies supports and extends existing learning theories. It suggests that personalized, interactive experiences can enhance learning outcomes, aligning with constructivist theories emphasizing the importance of active engagement and personalization in learning (Zhong, 2022). The research extends the principles of multimedia learning theory by demonstrating the effectiveness of combining textual and graphical elements, such as storytelling and facial emotion graphics, in educational games. ...
Previous studies have designed educational methods to cultivate digital citizenship behavior and support the construction of knowledge. However, these methods have not well incorporated personalized feedback mechanisms for enhancing digital citizenship knowledge. Therefore, this study proposed an algorithm that combines concept-effect propagation, fuzzy logic, and decision tree methods to address this drawback and create a personalized, contextual gaming experience. This personalization ensures an engaging and contextually relevant learning experience, addressing learning challenges related to digital citizenship scales. The game was tailored to individual learning experiences and decision-making patterns, with fuzzy logic interpreting nuanced student responses and decision trees guiding learning paths. A digital citizenship knowledge test and an affection questionnaire measured the game’s impact. Moreover, eye tracking was used to ensure attention in the experimental group. Therefore, a quasi-experimental design was conducted to evaluate the influence of a digital citizenship game on 110 students. ANCOVA and the Chi-square tests were performed to analyze students’ knowledge of digital citizenship. Moreover, eye-tracking metrics were used to gain deeper insights into students’ visual attention and engagement. The experimental results reveal that the proposed game enhanced the students’ digital citizenship achievement and promoted their perceptions. Additionally, eye-tracking data showed that the proposed gaming environment positively influenced students’ engagement. Findings indicate that using fuzzy logic and decision trees in educational games significantly promotes affection and alters attention in learning digital citizenship. This study contributes to educational technology by showcasing the potential benefits of personalized educational experiences. The insights gained are valuable for educators and educational game developers focused on digital citizenship education.
... Many journals have revealed the added value of using games in conveying learning contexts and carrying out educational scenarios [8]. Games with the Role-Playing Game (RPG) genre are effective in improving student performance [9]. In the PLE that we will develop, we need a game model that makes it easier for students to see the game broadly to make it easier for them to explore in the game. ...
This research aims to develop a top-down game to provide a comfortable learning environment for students with various learning styles. We use the visual, auditory and kinesthetic (VAK) learning style model. Top-down game development meets learning content needs within a Personalized Learning Environment (PLE). PLE is a system that is able to adapt students' characteristics in learning so that they can learn optimally. In this case, we chose Boolean algebra material presented to students taking Discrete Mathematics and Computer Architecture courses. The development of this learning environment uses the 4D method from Thiagarajan up to the third stage (Define, Design, Develop). The Define stage produces an instructional design consisting of Boolean algebra symbol material, Boolean algebra operations, and logic gate operations. The Design stage produces nine mini game designs representing three materials and each available for three learning styles. The nine game designs have been declared in accordance with the objectives of instructional design based on material expert validation. The Develop stage produces a digital game, media experts state that the game meets the parameters of digital game validity.
... For instance, our environment GAMOLEAF did not take into consideration the existence of students with different levels of knowledge, different learning styles, and different personalities in the recommendation process. However, knowing how individual characteristics such as different learning styles and personality traits will impact the experience of gamification (Buckley and Doyle, 2017), and will inform the effective design of gamified learning interventions and enable its effective integration into the learning environment (Zhong, 2022). Therefore, the authors suggest a lot of perspectives that will have to be realized in the future as an extension and improvement of our environment. ...
In this research, a GAmified Mobile Leaning Framework (GAMOLEAF) developed as a new intelligent application designed for mobile devices to ensure learning, assessing, and advancing learners’ knowledge in programming complex data structures in Java programming language. GAMOLEAF adopted motivational strategies to solve motivational problems during the COVID-19 pandemic by employing a gamification module, that integrates levels, scores, badges, leaderboard, and feedback. Furthermore, in order to assist learners to find useful and relevant lessons and best solutions for each data structure, GAMOLEAF incorporated personalized recommendations through two intelligent modules: a Lessons Recommendation Module (LRecM) and a problem-solving Solutions Recommender Module (PSSORecM). LrecM aims to provide learners with personalized lessons depending on the ratings collected explicitly from them. Whereas, PSSORecM bases on learners’ behaviors and directs them to consult other solutions. Both modules were based on the collaborative filtering method and used Matrix Factorization (MF) applying Singular Value Decomposition (SVD) and Negative Matrix Factorization (NMF) algorithms, respectively. To explore how the integration of personalized recommendations and gamification impact on students motivation and learning achievements in higher education to learning programming complex data structures course using mobile technologies, especially in difficult times like COVID-19, an experiment was carried out to compare the learning achievement and motivation of 90 students divided into three groups (control group, first experimental group, and second experimental group) using three versions of GAMOLEAF respectively: GAMOLEAF-V1 without gamification and without recommendation, GAMOLEAF-V2 integrating gamification only and GAMOLEAF-V3 integrating both gamification and recommendation. The One-way ANOVA (analysis of variance) test and Post hoc Tukey test were employed to analyze the performances of the three groups before and after the learning activity. The results suggested that the students who learned with GAMOLEAF-V3 using gamification and recommendation achieved significantly better learning achievement than those who learned with GAMOLEAF-V2 and GAMOLEAF-V1. From the experimental results, it was found that the gamification applied in GAMOLEAF-V2 and GAMOLEAF-V3 had significantly better effectiveness in improving only students’ motivation without improving their learning achievement. Moreover, the analysis result of the learning achievement indicated that the students in the second experimental group showed significantly higher learning achievement using GAMOLEAF-V3 in comparison with those in both the control group and the first experimental group who used GAMOLEAF-V1 and GAMOLEAF-V2 respectively. Such findings indicate that the personalized recommendations offered by the Lessons Recommendation Module (LRecM) and the problem-solving Solutions Recommender Module (PSSORecM) in GAMOLEAF-V3 may be one of the reasons why the learning achievement of students was increased.
... AI chatbots have emerged as a powerful tool in education, revolutionizing the way students learn and teachers teach. By analyzing vast amounts of data, AI chatbots can personalize the educational experience for each student, adapt to their unique needs, and maximize their potential (Zhong, 2022). AI chatbots in education serve multiple roles . ...
AI chatbots, e.g. ChatGPT, are becoming increasingly popular in education as a means to enhance student learning experiences and improve teaching efficiency. This study utilizes NVivo 12 Plus to examine the role of AI chatbots in education, ethical considerations, and sentimental analysis regarding the utilization of ChatGPT in education. ChatGPT has revolutionized education, but their use raises ethical concerns. They can enhance language learning, but may lead to plagiarism and information overload. Students may not develop discrimination skills and may rely on ChatGPT, leading to concerns about academic dishonesty and a failure to develop cognitive and analytical skills. The use of ChatGPT in clinical education also raises accountability and liability concerns regarding the use of patient information for educational purposes. Guidelines should be established to ensure privacy rights are upheld. Finally, the positive sentiment category was populated by predominantly positive sentiments, followed by neutral and negative sentiments. Future research on ChatGPT in education should focus on its application effectiveness in various educational settings and ethical considerations.
