An interactive test dashboard with diagnosis and feedback mechanisms to facilitate learning performance

Chen, Chih‐Ming; Wang, Jung-Ying; Hsu, Lewis

doi:10.1016/j.caeai.2021.100015

Cited by 9 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The concept of a human-machine interface was proposed by Norman, and Picard [12] laid the foundation for affective computing. Following these foundational works, the application of affective computing in human-machine interfaces received considerable attention [13].…”

Section: Affective Computingmentioning

confidence: 99%

“…Affective computing is an interdisciplinary field that focuses on computer models and methods for recognizing and expressing emotions [14]. Affective computing was originally proposed in 1997 by Rosalind Picard from the MIT Media Lab [12]. It results from biomedical engineering, psychology, and artificial intelligence.…”

Section: Affective Computingmentioning

confidence: 99%

“…According to the concept of multidimensional emotion generation, emotions can be generated through various methods. Multidimensional emotions were generated in this study through the adoption of the reason-generated operation and the quick and dirty method proposed by Picard [12]. Specifically, this study's system operates as follows.…”

Section: Principles For Multidimensional Emotion Generationmentioning

confidence: 99%

See 2 more Smart Citations

May Computers have Emotions? Establishing an AI Device based on Affective Computing Technologies

Lin¹

2022

Which Factors Contribute to Innovative Performance? A Case Study Applied to the Food and Beverage Industry

View full text Add to dashboard Cite

In the AI blueprint, allowing computers to have emotions is an important milestone in building emotional machines. In Picard's announcement, such an idea was framed. In this paper, we describe how to develop an emotion system and install it on smartphones to enable them to express emotions. This study also examines users' attitudes and emotions toward emotional information sent by machines, and the influence of emotional systems on user behavior. The purpose of this study is to explore the factors that developers should focus on when developing emotional machines. According to the results of this study, the level of attention paid to emotional information determines the quality of the emotional system, which triggers certain behaviors in users. The study recruited more than 100 people with smartphone experience. During the experiment, participants were free to operate the system and interact with system agents. Most of the participants were interested in, influenced by, and believed that the developed system enabled their smartphones to express emotion. A fairly high percentage of participants viewed emotional notifications provided by AI devices, enabling participants to address adverse smartphone conditions in a timely manner and for system agents to provide users with positive emotional feedback. The findings of this study provide an important contribution to affective computing, realizing the idea of allowing computers to have emotions.

show abstract

Section: Affective Computingmentioning

confidence: 99%

Section: Affective Computingmentioning

confidence: 99%

Section: Principles For Multidimensional Emotion Generationmentioning

confidence: 99%

See 1 more Smart Citation

May Computers have Emotions? Establishing an AI Device based on Affective Computing Technologies

Lin¹

2022

Which Factors Contribute to Innovative Performance? A Case Study Applied to the Food and Beverage Industry

View full text Add to dashboard Cite

show abstract

“…Jia and Zhang [3] proposed clear navigation, simple layout, linear display, harmonious colouring, smooth video, and full interaction. An interactive test dashboard with high-quality visualized test feedback, including response time and answer correctness, can enhance learning performance, and increase technology acceptance levels [4]. To provide insights, data analytics is performed on the data where it is cleaned, processed, and presented in the dashboard, which in this sense is known as learning analytics since the data is from the mobile learning environment and the learning context.…”

Section: Introductionmentioning

confidence: 99%

A review on learning analytics in mobile learning and assessment

Sun,

Lakulu,

Mohd Noor

2024

IJEECS

View full text Add to dashboard Cite

Employers are facing difficulties in selecting the most suitable candidates for employment and the transition from education to work is challenging for young graduates. Therefore, it is important to have indicators that could show the suitability of a potential candidate for his/her chosen job. A person who possesses knowledge but lacks confidence may struggle to perform assigned tasks, while an overly confident person with limited knowledge is likely to make errors in their job. Although there is existing research on learning analytics related to assessments, the research on learning analytics specifically focused on the confidence-knowledge relationship based on assessment data is still lacking. This article aims to examine the application of analytics in providing insights based on assessment data that can be utilized by potential employers. To achieve this, a systematic review was carried out, analyzing a total of 141 articles. The findings contribute to a better understanding of the use of assessment analytics in identifying the knowledge-confidence quadrants of students.

show abstract

“…They play multi-dimensional roles in building a digital education ecosystem, such as designing digital curriculum, smart attendance systems, feedback, learners' progress tracking intelligent devices, interactive mechanisms for students, etc. (Baker & Ga, 2021; C. M. Chen, Wang, & Hsu, 2021;Cox, 2021;Srinivasan & Murthy, 2021). opined that the imitation of human cognitive ability and behavioural traits makes AI and ML offer emotional support and enhances learners' communication skills.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Artificial Intelligence in Education Research: A Comprehensive Overview of 22 Years of Research indexed in the Scopus Database

Okagbue¹,

Ezeachikulo

Nwigwe

et al. 2022

Preprint

View full text Add to dashboard Cite

Objectives:Machine Learning (ML) and Artificial Intelligence (AI) remain crucial in education research and pedagogical development. The study provides an overview of ML and AI in education and research.Methods:In order to explore the field of ML and AI, a bibliometric approach was employed to extract and analyze data from the Scopus database. Bibliometrics analysis tools, VOS Viewer, Bibliometrics, and R packages were used to analyze the retrieved data.Results:A total of 1138 documents retrieved were written by 2800 authors, and published in 635 journals from 2000 to 2022, with average citations per document of 5.984 from 62 countries.Conclusion:The study identified an interesting gap that the previously published articles did not address: the implications of ML and AI in classroom learning activities and other pedagogical-related programmes.

show abstract

An interactive test dashboard with diagnosis and feedback mechanisms to facilitate learning performance

Cited by 9 publications

References 18 publications

May Computers have Emotions? Establishing an AI Device based on Affective Computing Technologies

May Computers have Emotions? Establishing an AI Device based on Affective Computing Technologies

A review on learning analytics in mobile learning and assessment

Machine Learning and Artificial Intelligence in Education Research: A Comprehensive Overview of 22 Years of Research indexed in the Scopus Database

Contact Info

Product

Resources

About