Emotional augmented reality-based mobile learning design elements: a kansei engineering approach

Redzuan, Fauziah; Khairuddin, An-Nur Atiqah; Daud, Nor Aziah

doi:10.11591/ijeecs.v14.i1.pp413-420

Cited by 11 publications

(4 citation statements)

References 23 publications

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“…The cloud governance can be initiated by creating guideline and policy, negotiating and having clear SLA, investigating about legal and act, data geographical and level of data privacy impact. There are growing research on blended learning, online learning and mobile learning [23][24][25][26][27][28][29]. The Malaysia Education Blueprint 2015-2025 roadmap towards higher income nation by producing holistic, excellent graduates and lifelong learners through independent and democratize access towards learning when higher education institutions adopted cloud computing services as the universities' IT service platform.…”

Section: Resultsmentioning

confidence: 99%

Cloud computing adoption reference model

Isa

Suhaimi

Noordin

et al. 2019

IJEECS

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<span>This paper presents a study to conceptualize a cloud computing adoption reference model. The case study on the cloud computing adoption was done in one local public university in Malaysia. The case study was conducted to understand in-depth and real context phenomenon by investigating the influencing factors of cloud computing adoption. The main objective of this study is to design a cloud computing adoption reference model. This study applied Technology-Organizational-Environmental (TOE) Framework by Tornatzky & Fleischer and Diffusion of Innovation by Rogers as the theoretical background of the Cloud Computing Adoption Reference. Ten interviews were conducted with key informants. The theme pattern analysis of data were done by using qualitative computer programs, Atlas.ti. The findings are shown in summarize patterns that supports the conceptualization of cloud computing adoption reference model. Future work include the adaption of cloud computing adoption reference model specifically for the niche area of mobile computing.</span>

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Section: Resultsmentioning

confidence: 99%

Cloud computing adoption reference model

Isa

Suhaimi

Noordin

et al. 2019

IJEECS

View full text Add to dashboard Cite

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“…Nine [12], [6], [46], [47], [48], [49], [50], [51] and [52] of the 50 studies indicated that there were challenges that related to a need for identifying and using instructional design guidelines to design mobile training content. Seventeen studies [6], [53], [54], [55], [44], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], and [67] mentioned challenges related to adapting and appropriating mobile learning technology to learn in a compatible way with suitable learning theories, objectives and actual training/learning needs.…”

Section: Discussionmentioning

confidence: 99%

Designing Mobile Training Content: Challenges and Open Issues

et al. 2020

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Mobile training is an evolution of electronic training and is based on mobile learning technology, which is used to design mobile learning courses. Due to the widespread deployment of mobile devices and the need to remain current with developments in mobile technology, it is important to consider the design of appropriate mobile training content to increase learners' engagement in mobile learning courses. However, studies have emphasized the challenges in this area. Therefore, we conducted a systematic mapping study that offers an overview of the current literature in this domain based on a thorough search of the literature by using a process of selection that involves criteria for inclusion and exclusion, data extraction and synthesis strategies. Of the 194 journal articles identified in the initial search stage, 58 were selected as primary studies; they were published between 2009 and 2019. We applied a classification scheme to answer our research questions. Our study examines the current challenges in the design of mobile training content, identifies the key open issues, determines the trends in publication and emphasizes the most widely researched topics in recent years related to the design of mobile training content. Our study identifies the existing challenges and suggests further work on key open issues. Our study also suggests that, considering the major issues related to pedagogical challenges, the research focus should shift toward the design of attractive, interactive and motivating mobile content that is based on a theoretical framework for mobile training courses, and other technological and managerial challenges that can be addressed should be investigated in order to overcome the existing difficulties in the design of mobile training content and to provide better solutions for the continuity of this research domain.

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“…Several factors are taken into consideration in generating the EEG dataset for emotion recognition, including [11]: a) Stimulus media: The literature studies showed several categories of stimuli to evoke emotions such as audio [16], [17], visual, and audio-visual media [14] as well as others including the ambient assisted living (AAL) technology [18], a combination of music, video, and game stimuli [19], mobile learning application [20], augmented reality (AR) [21], virtual reality (VR) [22], [23], and tactile enhanced multimedia (TEM) [24]. b) Proper stimuli presentation setup [11].…”

Section: Rq 1: What Factors Need To Be Considered To Generate and Distribute Eeg Data To Represent Emotional Reactions?mentioning

confidence: 99%

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

Wirawan

Wardoyo

Lelono

2022

IJECE

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Electroencephalogram (EEG) signals in recognizing emotions have several advantages. Still, the success of this study, however, is strongly influenced by: i) the distribution of the data used, ii) consider of differences in participant characteristics, and iii) consider the characteristics of the EEG signals. In response to these issues, this study will examine three important points that affect the success of emotion recognition packaged in several research questions: i) What factors need to be considered to generate and distribute EEG data?, ii) How can EEG signals be generated with consideration of differences in participant characteristics?, and iii) How do EEG signals with characteristics exist among its features for emotion recognition? The results, therefore, indicate some important challenges to be studied further in EEG signals-based emotion recognition research. These include i) determine robust methods for imbalanced EEG signals data, ii) determine the appropriate smoothing method to eliminate disturbances on the baseline signals, iii) determine the best baseline reduction methods to reduce the differences in the characteristics of the participants on the EEG signals, iv) determine the robust architecture of the capsule network method to overcome the loss of knowledge information and apply it in more diverse data set.

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Emotional augmented reality-based mobile learning design elements: a kansei engineering approach

Cited by 11 publications

References 23 publications

Cloud computing adoption reference model

Cloud computing adoption reference model

Designing Mobile Training Content: Challenges and Open Issues

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

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