Neurophysiological Measurements in Higher Education: A Systematic Literature Review

Darvishi, Ali; Khosravi, Hassan; Sadiq, Shazia; Weber, Barbara

doi:10.1007/s40593-021-00256-0

Cited by 25 publications

(32 citation statements)

References 152 publications

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“…While we have shown examples of how AI and analytics can assist in addressing concerns with peer assessment, there are also increasing concerns about the FATE (Fairness, Accountability, Transparency, and Ethics) of AI‐based systems (Shin, 2020; Darvishi et al, 2021). Here we provide one example of a potential pitfall for each of the four peer assessment processes we have studied to indicate that further exploration and evaluation are required before we aggressively incorporate AI and analytics in peer assessment.…”

Section: Discussionmentioning

confidence: 99%

Incorporating AI and learning analytics to build trustworthy peer assessment systems

Darvishi

Khosravi

Sadiq

et al. 2022

Brit J Educational Tech

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Peer assessment has been recognised as a sustainable and scalable assessment method that promotes higher‐order learning and provides students with fast and detailed feedback on their work. Despite these benefits, some common concerns and criticisms are associated with the use of peer assessments (eg, scarcity of high‐quality feedback from peer student‐assessors and lack of accuracy in assigning a grade to the assessee) that raise questions about their trustworthiness. Consequently, many instructors and educational institutions have been anxious about incorporating peer assessment into their teaching. This paper aims to contribute to the growing literature on how AI and learning analytics may be incorporated to address some of the common concerns associated with peer assessment systems, which in turn can increase their trustworthiness and adoption. In particular, we present and evaluate our AI‐assisted and analytical approaches that aim to (1) offer guidelines and assistance to student‐assessors during individual reviews to provide better feedback, (2) integrate probabilistic and text analysis inference models to improve the accuracy of the assigned grades, (3) develop feedback on reviews strategies that enable peer assessors to review the work of each other, and (4) employ a spot‐checking mechanism to assist instructors in optimally overseeing the peer assessment process. What is already known about this topic Engaging students in peer assessment has been demonstrated to have various benefits. However, there are some common concerns associated with employing peer assessment that raise questions about their trustworthiness as an assessment item. What this paper adds Methods and processes on how AI and learning analytics may be incorporated to address some of the common concerns associated with peer assessment systems, which in turn, can increase their trustworthiness and adoption. Implications for practice Presentation of a systematic approach for development, deployment and evaluation of AI and analytics approaches in peer assessment systems.

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Section: Discussionmentioning

confidence: 99%

Incorporating AI and learning analytics to build trustworthy peer assessment systems

Darvishi

Khosravi

Sadiq

et al. 2022

Brit J Educational Tech

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“…Tao et al [60] emphasised that crowdsourced labelling systems should utilise a weighted majority vote method to aggregate the noisy labels so that higher competent annotators are counted more in the final decision. In educational systems, students competency is commonly utilised to adapt instructions [5], [8], [61] or build learner models in the system [62], [63]. Abdi et al [57] used auxiliary data from student performance in an unsupervised learnersourcing consensus approach to improving the accuracy of determining the quality of learning resources.…”

Section: B Consensus Approachesmentioning

confidence: 99%

Assessing the Quality of Student-Generated Content at Scale: A Comparative Analysis of Peer-Review Models

Darvishi

Khosravi

Rahimi

et al. 2023

IEEE Trans. Learning Technol.

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Engaging students in creating learning resources has demonstrated pedagogical benefits. However, to effectively utilise a repository of student-generated content (SGC), a selection process is needed to separate high-from low-quality resources as some of the resources created by students can be ineffective, inappropriate, or incorrect. A common and scalable approach is to use a peer review process where students are asked to assess the quality of resources authored by their peers. Given that judgements of students, as experts-in-training, cannot wholly be relied upon, a redundancy-based method is widely employed where the same assessment task is given to multiple students. However, this approach introduces a new challenge, referred to as the consensus problem: How can we assign a final quality to a resource given ratings by multiple students? To address this challenge, we investigate the predictive performance of 18 inference models across five well-established categories of consensus approaches for inferring the quality of SGC at scale. The analysis is based on the engagement of 2,141 undergraduate students across five courses in creating 12,803 resources and 77,297 peer reviews. Results indicate that the quality of reviews is quite diverse, and students tend to overrate. Consequently, simple statistics such as mean and median fail to identify poor quality resources. Findings further suggest that incorporating advanced probabilistic and text analysis methods to infer the reviewers' reliability and reviews' quality improves performance; however, there is still an evident need for instructor oversight and training of students to write compelling and reliable reviews.

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“…A second benefit of adopting WBT in education is the value of the implicit information offered by the collected physiological data. In [ 23 ], the term “neurophysiological measurement” is introduced, which refers to an exclusive type of physiological data that are related to the Central Nervous System (CNS) or the Autonomic Nervous System (ANS). On this note, neurophysiological measurements (NPMs) related to the ANS include measurements such as eye-related measurements (blink rate and pupil dilation), electrodermal activity (EDA) or galvanic skin response (GSR), blood pressure, and electrocardiography (ECG), while NPMs related to the CNS include electroencephalography (EEG) and electromyography (EMG) [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

“…In [ 23 ], the term “neurophysiological measurement” is introduced, which refers to an exclusive type of physiological data that are related to the Central Nervous System (CNS) or the Autonomic Nervous System (ANS). On this note, neurophysiological measurements (NPMs) related to the ANS include measurements such as eye-related measurements (blink rate and pupil dilation), electrodermal activity (EDA) or galvanic skin response (GSR), blood pressure, and electrocardiography (ECG), while NPMs related to the CNS include electroencephalography (EEG) and electromyography (EMG) [ 23 ]. From this list, EEG is of particular interest in an educational context as it measures brain activity, which can be used to infer fluctuations in cognitive processes [ 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Wearable Biosensor Technology in Education: A Systematic Review

Hernández-Mustieles,

Lima-Carmona,

Pacheco-Ramírez

et al. 2024

Sensors

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Wearable Biosensor Technology (WBT) has emerged as a transformative tool in the educational system over the past decade. This systematic review encompasses a comprehensive analysis of WBT utilization in educational settings over a 10-year span (2012–2022), highlighting the evolution of this field to address challenges in education by integrating technology to solve specific educational challenges, such as enhancing student engagement, monitoring stress and cognitive load, improving learning experiences, and providing real-time feedback for both students and educators. By exploring these aspects, this review sheds light on the potential implications of WBT on the future of learning. A rigorous and systematic search of major academic databases, including Google Scholar and Scopus, was conducted in accordance with the PRISMA guidelines. Relevant studies were selected based on predefined inclusion and exclusion criteria. The articles selected were assessed for methodological quality and bias using established tools. The process of data extraction and synthesis followed a structured framework. Key findings include the shift from theoretical exploration to practical implementation, with EEG being the predominant measurement, aiming to explore mental states, physiological constructs, and teaching effectiveness. Wearable biosensors are significantly impacting the educational field, serving as an important resource for educators and a tool for students. Their application has the potential to transform and optimize academic practices through sensors that capture biometric data, enabling the implementation of metrics and models to understand the development and performance of students and professors in an academic environment, as well as to gain insights into the learning process.

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Neurophysiological Measurements in Higher Education: A Systematic Literature Review

Cited by 25 publications

References 152 publications

Incorporating AI and learning analytics to build trustworthy peer assessment systems

Incorporating AI and learning analytics to build trustworthy peer assessment systems

Assessing the Quality of Student-Generated Content at Scale: A Comparative Analysis of Peer-Review Models

Wearable Biosensor Technology in Education: A Systematic Review

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