Music students’ behavior on using learning objects closer to the domain characteristics and the social reality

Pinhati, Fernando; Siqueira, Sean W. M.

doi:10.1016/j.chb.2013.10.039

Cited by 6 publications

(8 citation statements)

References 39 publications

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“…Hedonic motivation refers to the pleasure or happiness obtained from using a system [9,48]. Prior studies showed that hedonic motivation plays a vital role in technology acceptance and use as well as having a positive relation with the intention of consumers to use IT [9].…”

Section: Hypothesis 4 (H4) Facilitating Conditions Of Users Have a Positive Influence On Continuance Intention Towards Personal Cloud Sermentioning

confidence: 99%

Assessing Determinants of Continuance Intention towards Personal Cloud Services: Extending UTAUT2 with Technology Readiness

Chen

Liu

et al. 2021

Symmetry

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In addition to the rapid development of global information and communications technology (ICT) and the Internet, recent rapid growth in cloud computing technology represents another important trend. Individual continuance intention towards information technology is a critical area in which information systems research can be performed. This study aims to develop an integrated model designed to explain and predict an individual’s continuance intention towards personal cloud services based on the concepts of technology readiness (TR) and the unified theory of acceptance and use of technology 2 (UTAUT2), moderated by gender, age, and experience of personal cloud services. The key results of the partial least square test largely support the proposed model’s validity and the significant impact of effort expectancy, social influence, hedonic motivation, price value, habit, and technology readiness on continuance intention towards personal cloud services. In addition to providing symmetric theoretical support with the proposed model and transforming the individual characteristics of TR into UTAUT2, this study could be used to enhance and analyze users’ adoption of personal cloud services and also increase the symmetry of the model’s explanation and prediction. The findings from this research contribute to providing practical implications and academic resources as well as improving our understanding of personal cloud service applications.

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Section: Hypothesis 4 (H4) Facilitating Conditions Of Users Have a Positive Influence On Continuance Intention Towards Personal Cloud Sermentioning

confidence: 99%

Assessing Determinants of Continuance Intention towards Personal Cloud Services: Extending UTAUT2 with Technology Readiness

Chen

Liu

et al. 2021

Symmetry

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show abstract

“…The digital objects used in the program were designed to coincide with the characteristics of the student’s sociocultural environment. This was intended to motivate the students to use the digital objects from their perception of the learning opportunities, social influence and ease of use (Pinhati & Siqueira, 2014).…”

Section: Methodsmentioning

confidence: 99%

Educational effectiveness analysis of the use of digital music learning objects. Comparison of digital versus non-digital teaching resources in compulsory secondary education / Análisis de la eficacia educativa del uso de objetos digitales de aprendizaje musical. Comparación con los recursos didácticos no digitales en educación secundaria obligatoria

Quiles

Vílchez-Fernández²,

Torres

2015

Infancia y Aprendizaje

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This article analyses the effect of the implementation of an educational intervention program based on digital learning objects for music in first year compulsory secondary education students, versus teaching focused on non-digital resources, such as textbooks. A pre-test, post-test, quasi-experimental study was performed with two comparison groups. The results confirm higher academic-disciplinary achievement as a result of the intervention program implemented within the experimental group as compared to the control group. The effects of this program on the transfer of learning are also confirmed.

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“…The analysis of learning behaviour features includes two parts -group feature analysis and individual feature analysis. The latter is the basis and refinement of the former, and the two types of features analyses support each other and develop together [19][20][21][22][23][24][25]. Luckily, with the increase of online open course learning platforms, online open courses and online learners, the learning behaviour data generated in the learning process of learners are gradually accumulating, further supporting the exploration of the learning behaviour features and patterns of learners in the online learning environment.…”

Section: Introductionmentioning

confidence: 90%

Features of Group Online Learning Behaviours Based on Data Mining

Zhu¹,

Wang²,

Wang³

2022

Int. J. Emerg. Technol. Learn.

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With the development of information technology, how to scientifically and properly organizing and guiding learners to learn actively and efficiently has become a research subject for domestic and foreign scholars. However, existing research on online learning behaviours studied little about learning attitudes, learning preferences, student-student interaction, teacher-student interaction and so on. To this end, this paper studies the features of group online learning behaviours based on data mining. In this paper, a K-means-based group online learning behaviour feature selection model and an AdaBoost-based group online learning behaviour classification model were constructed, and the processing methods, execution processes and algorithm functions of the two models were described in detail. Finally, the effectiveness of the constructed models was verified through an experiment.

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Music students’ behavior on using learning objects closer to the domain characteristics and the social reality

Cited by 6 publications

References 39 publications

Assessing Determinants of Continuance Intention towards Personal Cloud Services: Extending UTAUT2 with Technology Readiness

Assessing Determinants of Continuance Intention towards Personal Cloud Services: Extending UTAUT2 with Technology Readiness

Features of Group Online Learning Behaviours Based on Data Mining

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