Evaluation of the ARTutor augmented reality educational platform in tertiary education

Lytridis, Chris; Tsinakos, Avgoustos

doi:10.1186/s40561-018-0058-x

Cited by 31 publications

(29 citation statements)

References 19 publications

(13 reference statements)

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“…For example, smartphones are used as clickers to complete questionnaires and assignments [26], [38]- [41]. Alternatively, smartphones are used to visualize learning contents [32], [42], [43].…”

Section: Characterization Of Slesmentioning

confidence: 99%

“…With respect to the concept of smart teacher, Preston et al [46] presented a kitchen equipped with cookware (smart [26], [32], [34], [38]- [43], [49]- [51] Smart classroom 9 (13.24%) [20], [33], [34], [44], [48], [52] objects), and high resolution screens. An avatar guided the user with audio messages to cook while learning the vocabulary in the selected foreign language.…”

Section: Characterization Of Slesmentioning

confidence: 99%

See 1 more Smart Citation

Affordances and Core Functions of Smart Learning Environments: A Systematic Literature Review

Tabuenca

Serrano-Iglesias

Martín

et al. 2021

IEEE Trans. Learning Technol.

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Smart learning environments (SLEs) have gained considerable momentum in the last 20 years. The term SLE has emerged to encompass a set of recent trends in the field of educational technology, heavily influenced by the growing impact of technologies such as cloud services, mobile devices, and interconnected objects. However, the term SLE has been used inconsistently by the technology-enhanced learning (TEL) community, since different research works employ the adjective "smart" to refer to different aspects of novel learning environments. Previous surveys on SLEs are narrowly focused on specific technologies, or remain at a theoretical level that does not discuss practical implications found in empirical studies. To address this inconsistency, and also to contribute to a common understanding of the SLE concept, this paper presents a systematic literature review (SLR) of papers published between 2000 and 2019 discussing SLEs in empirical studies. Sixty eight papers out of an initial list of 1,341 papers were analyzed to identify: 1) what affordances make a learning environment smart; 2) which technologies are used in SLEs; and 3) in what pedagogical contexts are SLEs used. Considering the limitations of previous surveys, and the inconsistent use of the SLE concept in the TEL community, this paper presents a comprehensive characterization Manuscript sent for review July 30, 2020. Revised month day, year; Accepted month day, year. Date of publication month day, year.

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Section: Characterization Of Slesmentioning

confidence: 99%

Section: Characterization Of Slesmentioning

confidence: 99%

Affordances and Core Functions of Smart Learning Environments: A Systematic Literature Review

Tabuenca

Serrano-Iglesias

Martín

et al. 2021

IEEE Trans. Learning Technol.

View full text Add to dashboard Cite

show abstract

“…Hence, it has been predominantly evaluated to assess its usability, correctness and player experience. Several existing games that have been designed with similar aim of supporting teaching and learning, have been evaluated based on similar user experience such as system interaction, learning content, terminology, appearance, user satisfaction, likeability and so on (Lytridis & Tsinakos, 2018;Tlili, Essalmi, & Jemni, 2016). Models such as EGameFlow (Fu, Su, & Yu, 2009), MEEGA and MEEGA+ (Petri, von Wangenheim, & Borgatto, 2016) have also been used to extensively evaluate educational games in various aspects of user experience.…”

Section: Discussionmentioning

confidence: 99%

G4D - a treasure hunt game for novice programmers to learn debugging

Venigalla

Chimalakonda

2020

Smart Learn. Environ.

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Visual Programming Environments (VPEs) are predominantly being used to teach programming concepts through interactive games with interesting narratives. Games have been developed to teach basic concepts of programming such as deriving logic, writing code, debugging the code and so on. Debugging code is one of the most important activities that can improve the skill of tackling a problem. In programming, one needs to identify the correct location of an error and fix it, which is usually learned through experience. Games have been developed to teach debugging to novice programmers. Syntactical errors occur frequently in the early stages of programming. The existing debugging games aim to support users in debugging the logic of the problem, but do not target on correcting the code snippets based on syntax. To address this challenge of providing syntactical support, we propose a treasure hunt based debugging game, in which users pass through various levels of the game by debugging code snippets written in C language. We have evaluated G4D based on MEEGA+ model, with 20 volunteers, having different programming backgrounds. The results of the user survey indicate that G4D has a good quality level and about 75% of the volunteers have either strongly agreed or agreed to recommend G4D to their colleagues.

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“…The teaching aid available is only 1 unit while the number of students in the class is approximately 30 children. Of course, this will create an atmosphere that is less support for students whilst in process learning can be made more rigid result (Lytridis & Tsinakos, 2018). In addition, using Augmented Reality can change and make the virtual world shown on the screen turn into a real-world and can even try to transform all detected objects into object representations in 3D (Gweon et al, 2018).…”

Section: Previous Researchmentioning

confidence: 99%

Augmented Reality Mobile Application as Learning Media in Science Subject for the Post Gen Z Generation

Feryanto¹

2021

TURCOMAT

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The total population of Indonesia as of September 2020 is based on the official statistical news number 07/01/th. XXIV, 21 January 2021 issued by the Indonesian Statistics Center, totaling 270.20 million people and 10.88% or 29,397,760 people are the post Gen Z generation born from 2013 onwards with an estimated age up to 7 years of age. The post Gen Z generation is a generation that is categorized as early childhood. Moreover, learning from an early age is important so that children are familiarized with the knowledge they are most interested in and mastered so that it makes it easier for them to understand it. Especially if the learning material is delivered electronically via a smartphone which is associated with the application of the Augmented Reality (AR) method so that they are more interested in learning and with a different atmosphere in terms of delivering face-to-face subject matter in class which is sometimes boring. In this paper, we design and implement a learning model equipped with AR technology to make it easier for young children to learn subject matter that is important for their future and limited to science subject matter. In this paper, a mobile application with AR technology is proposed where users can learn from the images they capture and gain learning from the perspective of these images. What's more, the application of AR technology is also more practical and reduces the burden of carrying heavy and thick books in studying a subject matter. The model was designed using a use case diagram as a business process design and class diagrams for database design including the user interface as a representation of communication between users and applications.

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Evaluation of the ARTutor augmented reality educational platform in tertiary education

Cited by 31 publications

References 19 publications

Affordances and Core Functions of Smart Learning Environments: A Systematic Literature Review

Affordances and Core Functions of Smart Learning Environments: A Systematic Literature Review

G4D - a treasure hunt game for novice programmers to learn debugging

Augmented Reality Mobile Application as Learning Media in Science Subject for the Post Gen Z Generation

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