Affective computing in education: A systematic review and future research

Yadegaridehkordi, Elaheh; Noor, Nurul Fazmidar Mohd; Ayub, Mohamad Nizam Bin; Affal, Hannyzzura; Hussin, Nornazlita

doi:10.1016/j.compedu.2019.103649

Cited by 147 publications

(68 citation statements)

References 69 publications

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“…For instance, efforts to algorithmically detect mental states such as boredom, frustration, and confusion (Baker et al, 2010) must be supported by the operational definitions and constructs that have been prudently evaluated. Additionally, the affective data collected by AI systems should take into account the cultural differences combined with contextual factors, teachers' observations, and students' opinions (Yadegaridehkordi et al, 2019). Data need to be informatively and qualitatively balanced, in order to avoid implicit biases that may propagate into algorithms trained on such data (Staats, 2016).…”

Section: Big Data and Ai In Education: Researchmentioning

confidence: 99%

Challenges and Future Directions of Big Data and Artificial Intelligence in Education

et al. 2020

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We discuss the new challenges and directions facing the use of big data and artificial intelligence (AI) in education research, policy-making, and industry. In recent years, applications of big data and AI in education have made significant headways. This highlights a novel trend in leading-edge educational research. The convenience and embeddedness of data collection within educational technologies, paired with computational techniques have made the analyses of big data a reality. We are moving beyond proof-of-concept demonstrations and applications of techniques, and are beginning to see substantial adoption in many areas of education. The key research trends in the domains of big data and AI are associated with assessment, individualized learning, and precision education. Model-driven data analytics approaches will grow quickly to guide the development, interpretation, and validation of the algorithms. However, conclusions from educational analytics should, of course, be applied with caution. At the education policy level, the government should be devoted to supporting lifelong learning, offering teacher education programs, and protecting personal data. With regard to the education industry, reciprocal and mutually beneficial relationships should be developed in order to enhance academia-industry collaboration. Furthermore, it is important to make sure that technologies are guided by relevant theoretical frameworks and are empirically tested. Lastly, in this paper we advocate an in-depth dialog between supporters of "cold" technology and "warm" humanity so that it can lead to greater understanding among teachers and students about how technology, and specifically, the big data explosion and AI revolution can bring new opportunities (and challenges) that can be best leveraged for pedagogical practices and learning.

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Section: Big Data and Ai In Education: Researchmentioning

confidence: 99%

Challenges and Future Directions of Big Data and Artificial Intelligence in Education

et al. 2020

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“…Cost [30][31][32][33][34] Neuromarketing is reported to be more expensive than traditional marketing research due to requiring specialized equipment [30,32]. However, providing new equipment and technologies require massive investment in early stage, the cost of equipment becomes less when it becomes available.…”

Section: Criteria Author Explanationmentioning

confidence: 99%

Decision to Adopt Neuromarketing Techniques for Sustainable Product Marketing: A Fuzzy Decision-Making Approach

et al. 2020

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Sustainable products and their marketing have played a crucial role in developing more sustainable consumption patterns and solutions for socio-ecological problems. They have been demonstrated to significantly decrease social consumption problems. Neuromarketing has recently gained considerable popularity and helped companies generate deeper insights into consumer behavior. It has provided new ways of conceptualizing consumer behavior and decision making. Thus, this research aims to investigate the factors influencing managers’ decisions to adopt neuromarketing techniques in sustainable product marketing using the fuzzy analytic hierarchy process (AHP) approach. Symmetric triangular fuzzy numbers were used to indicate the relative strength of the elements in the hierarchy. Data were collected from the marketing managers of several companies who have experience with sustainable product marketing through online shopping platforms. The results revealed that the accuracy and bias of neuromarketing techniques have been the main critical factors for managers to select neuromarketing in their business for advertising and branding purposes. This research provides important results on the use of neuromarketing techniques for sustainable product marketing, as well as their limitations and implications, and it also presents useful information on the factors impacting business managers’ decision making in adopting neuroscience techniques for sustainable product development and marketing.

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“…The last two decades have witnessed a surge of research on affective computing systems that aim to detect human emotion and stress based on physiological signals [1], [2], [3]. Such systems have been used in a variety of applications with the aim of inferring, with some specificity, the occurrence of emotional and/or cognitive states that in turn affect human user engagement, performance, and health [4], [5], [6], [7]. Especially important for operational domains such as the military, principles of affective computing can be implemented in virtual reality (VR) systems for the purpose of training human performance outcomes (e.g., shooting marksmanship) [8], [9], [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Multilevel Longitudinal Analysis of Shooting Performance as a Function of Stress and Cardiovascular Responses

Spangler

Alam

Rahman

et al. 2021

IEEE Trans. Affective Comput.

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Virtual reality (VR) systems are increasingly using physiology to improve human training. However, these systems do not account for the complex intra-individual variability in physiology and human performance across multiple timescales and psychophysiological demands. To fill this gap, we propose a theory of multilevel variability where tractable neurobiological mechanisms generate complex variability in performance over time and in response to heterogeneous sources. Based on this theory, we also present a study that examines changes in cardiovascular activity and performance during a stressful shooting task in VR. We examined physiology and performance at three important levels of analysis: task-to-task, block-to-block, session-to-session. Findings indicated joint patterns of physiology and performance that notably varied by the level of analysis. At the task level, higher task difficulty worsened performance but did not change cardiovascular activation. At the block level, there were nonlinear changes in performance and heart rate variability. At the session level, performance improved while blood pressure decreased and heart rate variability increased across days. Of all the physiological metrics, only heart rate variability was correlated with marksmanship performance. Findings are consistent with our multilevel theory and highlight the need for VR and other affective computing systems to assess physiology across multiple timescales.

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Affective computing in education: A systematic review and future research

Cited by 147 publications

References 69 publications

Challenges and Future Directions of Big Data and Artificial Intelligence in Education

Challenges and Future Directions of Big Data and Artificial Intelligence in Education

Decision to Adopt Neuromarketing Techniques for Sustainable Product Marketing: A Fuzzy Decision-Making Approach

Multilevel Longitudinal Analysis of Shooting Performance as a Function of Stress and Cardiovascular Responses

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