The article highlights the need for enhanced user interaction and personalization in virtual reality (VR) systems for cultural heritage. As cultural sites become accessible through VR, delivering meaningful and tailored user experiences is crucial for effective learning and engagement. Traditional VR systems often fail to address varying levels of user expertise, limiting the accessibility of cultural content. To solve this, the study introduces SculptMate, a fuzzy logic-based VR system that estimates user proficiency in computers and VR, delivering context-sensitive assistive messages. In a study with 64 participants, SculptMate was compared to a standard VR system. Results demonstrated significantly higher engagement and comprehension of cultural artifacts with SculptMate, offering a personalized, immersive experience. The study emphasizes the potential of integrating fuzzy logic into VR to improve educational outcomes and user satisfaction. Future enhancements include dynamic machine learning, multisensory components, and platform optimization for broader accessibility and better user experiences.

Education for sustainability refers to educational policies that focus on shaping a sustainable future. Mobile learning software refers to the use of mobile applications through smart devices for promoting online learning. However, the effectiveness of such software in learning for sustainability depends on users’ intention to adopt it. While there is growing interest in using mobile technology for sustainability education, empirical evidence on how personalized mobile software can influence users’ attitudes and behaviors toward sustainable practices is limited. Thus, this study investigates the factors that affect users’ acceptance of mobile learning technology in sustainability education, by integrating environmental awareness and personalization strategies into a modified model based on the unified theory of acceptance and use of technology (UTAUT). A total of 120 users participated in the survey. An empirical analysis of data was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the relationship between the variables of the model. The results showed that both environmental awareness and personalization strategies had a significant effect on users’ behavioral intentions. In addition, hedonic motivation and habit exerted an effect on behavioral intention, contrary to effort expectancy and social influence. These findings confirm that a personalized mobile learning application related to environmental issues reveals as a powerful tool for supporting environmental education since such software appears to have high acceptance by the user. The significance of the study lies in its potential to inform the design and development of personalized mobile applications for sustainability education. The study contributes to the creation of more effective mobile applications that promote sustainable behavior and contribute to a more sustainable future by identifying the elements that influence user acceptance.

Abstract During the last decade, there has been a substantial increase of interest in studies related to Virtual Reality (VR) as a learning tool. This paper presents a systematic literature review of personalization strategies utilized in immersive VR for educational objectives in the classroom. For the purposes of this review, 69 studies between 2012 and 2022 were analyzed in terms of their benefits, limitations and development features. The novelty of the study mainly arises from the in-depth analysis and reporting of personalization strategies as well as gamification techniques used in VR applications. The significance of this research lies in the observation that earlier studies’ applications did not sufficiently incorporate adaptive learning content, indicating the necessity for more research in this field and revealing a research gap. In conclusion, as it encourages future research of this field, this study may be a beneficial reference for those interested in researching the implementation of Virtual Reality in education, including academics, students, and professionals.

Digitalization in education is of great importance, especially in era of COVID-19 pandemic. Augmented Reality can help to this direction, bringing a range of benefits in the field of education. Prior researches reveal that AR enhances the students’ learning outcomes offering significant pedagogical affordance when it is used in the tutoring of different domains, such as astronomy, biology, geometry, physics etc. However, the exploration of the factors associated with the acceptance of the technology of AR in education, is yet limited. This article aims to present valuable information to researchers, tutors and AR application developers concerning the learners’ behavioral intention to use such technology in the learning process. The motivation of this study is the increasing use of AR in education, offering significant room for future research, and its novelty is the analysis of the most significant factors affecting the actual AR system use. This study is based on a modified Technology Acceptance Model, consisting of the four core constructs and extended by two external variables, namely playfulness and quality output, in order to consider both pedagogy and technology. The population that participated in this research includes 220 secondary school students. The results show that the intention to use AR is positively influenced directly by quality output, perceived usefulness and perceived ease of use, and indirectly by playfulness. The findings help AR developers to understand the factors that maximize the user’s experience prior to the application of AR technology in the digital era of education.

Abstract Group formation is a complex task requiring computational support to succeed. In the literature, there has been considerable effort in the development of algorithms for composing groups as well as their evaluation. The most widely used approach is the Genetic Algorithm, as, it can handle numerous variables, generating optimal solutions according to the problem requirements. In this study, a novel genetic algorithm was developed for forming groups using innovative genetic operators, such as a modification of 1-point and 2-point crossover, the gene and the group crossover, to improve its performance and accuracy. Moreover, the proposed algorithm can be characterized as domain-independent, as it allows any input regardless of the domain problem; i.e., whether the groups concern objects, items or people, or whether the field of application is industry, education, healthcare, etc. The grouping genetic algorithm has been evaluated using a dataset from the literature in terms of its settings, showing that the tournament selection is better to be chosen when a quick solution is required, while the introduced gene and group crossover operators are superior to the classic ones. Furthermore, the combination of up to three crossover operators is ideal solution concerning algorithm’s accuracy and execution time. The effectiveness of the algorithm was tested in two grouping cases based on its acceptability. Both the students participated in forming collaborative groups and the professors participated in evaluating the groups of courses created were highly satisfied with the results. The contribution of this research is that it can help the stakeholders achieve an effective grouping using the presented genetic algorithm. In essence, they have the flexibility to execute the genetic algorithm in different contexts as many times as they want until to succeed the preferred output by choosing the number of operators for either greater accuracy or reduced execution time.

The explosive growth in the amount of available digital information has increased the demand for recommender systems. Recommender systems are information filtering systems that deal with the problem of information overload by filtering vital information fragment out of large amount of dynamically generated information according to user's preferences or interests. Recommender systems have the ability to predict whether a particular user would prefer an item or not based on his/her personal profile. To this direction, this paper presents multi-algorithmic techniques, such as content-based filtering and collaborative filtering, which increase the efficiency of recommender systems. Moreover, a hybrid model for recommendation, employing content-based and collaborative filtering, is introduced. The presented recommender system takes as input information about users from their profile in Facebook, one of the most well-known social networking services. Examples of operation are given and they hold promising results for the described techniques. Finally, the paper attests that the aforementioned techniques can be used for different kind of software, such as e-learning, e-commerce, etc.

Towards the last decade, digital education has become a burning issue in the related scientific literature and involves the production of Intelligent Tutoring Systems (ITSs). ITSs are adaptive educational applications that enrich the tutoring and learning processes with "intelligence" by divulging the abilities and weaknesses of each student, in order to provide him/her with a personalized learning experience. A crucial factor of adaptive learning systems is testing, and indeed adaptive testing. It is a challenge to create an adaptive test that includes the most suitable exercise/question/activity of a large pool of test items for a particular learner taking into consideration her/ his particular learning characteristics, needs and ability. In this paper, a framework for creating automated adaptive tests using multiple-criteria decision analysis and the weighted sum model is presented. The presented framework takes into consideration multiple students' criteria along with the types of exercises and the desirable learning objective. The aforementioned assessment framework was incorporated in two adaptive e-learning systems and was fully evaluated. The evaluation results are very encouraging.

Social Networking Services can serve as a great platform for learning. As such, the use of Facebook in learning contexts can be proved beneficial. Following this direction, this paper presents a prototype Facebook application for learning which is supported by Latent Dirichlet allocation (LDA). LDA is a generative model that allows sets of observations to be explained by unobserved groups that clarify why some parts of the data are similar. Hence, making automatic predictions about the interests of students can be made by collecting their preferences and characteristics. Hence, by tracking user interests, accurate recommendations can be made. The experimental results, presented in this paper, reinforce the view that automatic predictions using LDA to students in social networks can be a powerful idea in personalizing instruction.

Conversational interfaces are used for a variety of applications. They are constructed to offer useful services and to interact with the users in order to assist them. Towards this direction, the current paper presents the incorporation of the interactive chatterbot ALICE in a mobile-assisted English language learning application. This chatterbot is further enriched with mechanisms in order to support students while learning vocabulary in English. As such, apart from making conversation with this conversational agent, they can practice their vocabulary or even be evaluated by the chatterbot. Hence, this study offers a fertile ground to enhance pedagogical results such as fostering motivation and engagement, incrementing crucial language learning and assisting in the acquisition of cognitive skills. Finally, the students can chat with the pedagogic conversational agent either orally or by writing.

Towards the creation of a multiple language learning environment which supports and enhances collaboration among its students we propose an approach that uses user modeling and machine learning. The well known theory of user modeling is used to collect user characteristics and as second step a classical machine learning approach is incorporated in order to intelligently use these characteristics to create student groups. The resulting student groups promote win-win collaboration, thus support the learning process and provide additional educational benefits for the learners.