Designing and eXperiencing smart objects based learning scenarios

Taamallah, Aroua; Khemaja, Maha

doi:10.1145/2669711.2669926

Cited by 11 publications

(8 citation statements)

References 15 publications

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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.

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show abstract

“…In addition to the above pedagogical facilities, our virtual lab environment integrates a learning analytics framework able to collect in the xAPI format [10] most of users interactions with the system. In this study, we focus on interactions between learners and the remote virtual resources they had to administrate, as this kind of activity can be considered as almost fully representative of the learning tasks completed by learners.…”

Section: The Learning Environment: Lab4cementioning

confidence: 99%

Using Sequential Pattern Mining to Explore Learners’ Behaviors and Evaluate Their Correlation with Performance in Inquiry-Based Learning

Venant

Sharma

Vidal

et al. 2017

Data Driven Approaches in Digital Education

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This study analyzes students' behaviors in a remote laboratory environment in order to identify new factors of prediction of academic success. It investigates relations between learners' activities during practical sessions, and their performance at the final assessment test. Based on learning analytics applied to data collected from an experimentation conducted with our remote lab dedicated to computer education, we discover recurrent sequential patterns of actions that lead us to the definition of learning strategies as indicators of higher level of abstraction. Results show that some of the strategies are correlated to learners' performance. For instance, the construction of a complex action step by step, or the reflection before submitting an action, are two strategies applied more often by learners of a higher level of performance than by other students. While our proposals are domain-independent and can thus apply to other learning contexts, the results of this study led us to instrument for both students and instructors new visualization and guiding tools in our remote lab environment.

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“…Neste sentido, o uso de ontologias tem demonstrado ser muito útil para verificar a consistência do modelo de dados quando os requisitos estão formalmente representados [Taamallah and Khemaja 2014].…”

Section: Limitaçõesunclassified

Descrição semântica de experiência de aprendizagem baseada na especificação xAPI

Rabelo

Lama²,

Amorim

et al. 2015

Workshops Do Congresso Brasileiro De Informática Na Educação

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Abstract. In this paper, we present an ontology that formally represents both the data model and the restrictions of the xAPI specification IntroduçãoEm Ambientes Virtuais de Aprendizagem (AVA), diversos aspectos da interação entre alunos e serviços podem ser considerados na avaliação de resultados. Por exemplo: a quantidade de posts propondo tópicos e/ou respondendo a estes na participação em fóruns de discussão, o tempo gasto em leituras de material, a quantidade de acertos e o tempo gasto ao responder a questionários de múltipla escolha, download/upload de materiais, e, também, estratégias de estudo utilizadas pelos alunos tais como as consultas a materiais online fora do AVA, formação de equipes, dentre outras.No entanto, para isso é necessário que o AVA disponibilize serviços não somente para a captura de dados quanto para a análise dos mesmos, considerando que as interações possíveis entre alunos e ambiente virtual são inúmeras e pode gerar uma imensa quantidade de dados. Para resolver esta situação, surgiu, no início de 2010, o conceito de Learning Analytics (LA), entendido como a medição, coleta e análise de dados sobre os estudantes e seu contexto, com o objetivo de compreender e facilitar o

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Designing and eXperiencing smart objects based learning scenarios

Cited by 11 publications

References 15 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

Using Sequential Pattern Mining to Explore Learners’ Behaviors and Evaluate Their Correlation with Performance in Inquiry-Based Learning

Descrição semântica de experiência de aprendizagem baseada na especificação xAPI

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