Researching Mobile Learning

2020 ◽  
pp. 33-53
Author(s):  
Eric Seneca
Keyword(s):  
Mobile Learning ◽  
Learning Experience ◽  
Potential Effect ◽  
Research Field ◽  
Learning Study ◽  
Human Computer Interactions ◽  
User Context ◽  
Confounding Variables ◽  
Computer Interactions

m-Learning (mobile learning) is an exciting research field but few studies address the potential effect of human-computer interactions on users' learning experience. A well-designed mobile learning study should consider the potential confounding variables of user context and usability. The purpose of this chapter is to inform readers of current research in m-learning and mobile HCI (human-computer interactions). The chapter will provide an example of a study that was designed to address the important factors of user context and usability.

Design Thinking for Gesture-based Human Computer Interactions

2019 ◽  
Vol 7 (3) ◽  
pp. 919-925
Author(s):  
Kelvin Wambani Siovi ◽  
Cheruiyot Willison Kipruto ◽  
Agnes Mindila
Keyword(s):  
Design Thinking ◽  
Human Computer Interactions ◽  
Computer Interactions

Multimodal emotion recognition with hierarchical memory networks

2021 ◽  
Vol 25 (4) ◽  
pp. 1031-1045
Author(s):  
Helang Lai ◽  
Keke Wu ◽  
Lingli Li
Keyword(s):  
Emotion Recognition ◽  
The Self ◽  
Global Memory ◽  
Human Computer Interactions ◽  
Accuracy Improvement ◽  
Local Memory ◽  
Hierarchical Memory ◽  
Multimodal Emotion Recognition ◽  
Novel Model ◽  
Computer Interactions

Emotion recognition in conversations is crucial as there is an urgent need to improve the overall experience of human-computer interactions. A promising improvement in this field is to develop a model that can effectively extract adequate contexts of a test utterance. We introduce a novel model, termed hierarchical memory networks (HMN), to address the issues of recognizing utterance level emotions. HMN divides the contexts into different aspects and employs different step lengths to represent the weights of these aspects. To model the self dependencies, HMN takes independent local memory networks to model these aspects. Further, to capture the interpersonal dependencies, HMN employs global memory networks to integrate the local outputs into global storages. Such storages can generate contextual summaries and help to find the emotional dependent utterance that is most relevant to the test utterance. With an attention-based multi-hops scheme, these storages are then merged with the test utterance using an addition operation in the iterations. Experiments on the IEMOCAP dataset show our model outperforms the compared methods with accuracy improvement.

scholarly journals Building an Emotionally Responsive Avatar with Dynamic Facial Expressions in Human—Computer Interactions

2021 ◽  
Vol 5 (3) ◽  
pp. 13
Author(s):  
Heting Wang ◽  
Vidya Gaddy ◽  
James Ross Beveridge ◽  
Francisco R. Ortega
Keyword(s):  
Facial Expressions ◽  
Descriptive Analysis ◽  
Uncanny Valley ◽  
Human Computer Interactions ◽  
Basic Emotions ◽  
Subjective Responses ◽  
Dynamic Facial Expressions ◽  
Nasa Tlx ◽  
Computer Interactions

The role of affect has been long studied in human–computer interactions. Unlike previous studies that focused on seven basic emotions, an avatar named Diana was introduced who expresses a higher level of emotional intelligence. To adapt to the users various affects during interaction, Diana simulates emotions with dynamic facial expressions. When two people collaborated to build blocks, their affects were recognized and labeled using the Affdex SDK and a descriptive analysis was provided. When participants turned to collaborate with Diana, their subjective responses were collected and the length of completion was recorded. Three modes of Diana were involved: a flat-faced Diana, a Diana that used mimicry facial expressions, and a Diana that used emotionally responsive facial expressions. Twenty-one responses were collected through a five-point Likert scale questionnaire and the NASA TLX. Results from questionnaires were not statistically different. However, the emotionally responsive Diana obtained more positive responses, and people spent the longest time with the mimicry Diana. In post-study comments, most participants perceived facial expressions on Diana’s face as natural, four mentioned uncomfortable feelings caused by the Uncanny Valley effect.

The Development of Mobile Learning (M-Learning) Apps Adik-Adik Jawi to Enhance Learning Experience in Arabic Calligraphy

2021 ◽  
Vol 02 (01) ◽  
Author(s):  
Mohd Hatta Mohamed Ali ◽  
◽  
Anwar Hafidzi ◽  
Juliana Mohamed ◽  
Mariam Abdul Hamid ◽  
...  
Keyword(s):  
Mobile Learning ◽  
Teaching And Learning ◽  
Development Process ◽  
Learning Experience ◽  
Learning Activities ◽  
Learning Approach ◽  
Application Development ◽  
Research Article ◽  
Arabic Calligraphy

History has proven the development of Jawi calligraphy is in line with the development of Islam in the archipelago. It is the root of the nation’s identity that must be defended and maintained. As with other subjects, the challenge to learning Jawi calligraphy at this time is that the whole world including Malaysia is affected by the COVID 19 pandemic. Therefore, all learning activities are now geared towards teaching and learning from home (PdPR) as methods to ensure the continuity of education. Therefore, it is very important that Jawi calligraphy is given a new breath in teaching and learning. This research article will discuss the mobile learning approach (M-Learning) for Jawi calligraphy. Important elements discussed include the application development process according to the needs of teaching and learning activities. The features of the application that contribute to the improvement of students’ learning experience as well as the results of tests performed on students are also stated. The success of this M-Learning application for learning Jawi calligraphy will certainly be able to be further expanded to the learning of other subjects in various fields.

As Human–Computer Interactions Go Global

2010 ◽  
pp. 15-33 ◽  
Author(s):  
Helen Klein ◽  
Katherine Lippa ◽  
Mei-Hua Lin
Keyword(s):  
Human Computer Interactions ◽  
Computer Interactions
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