Measuring Students’ Acceptance to AI-Driven Assessment in eLearning: Proposing a First TAM-Based Research Model

Cruz-Benito, Juan; Prieto, José Carlos Sánchez; Therón, Roberto; García-Peñalvo, Francisco José

doi:10.1007/978-3-030-21814-0_2

Cited by 30 publications

(17 citation statements)

References 51 publications

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“…The TAM is a useful framework that is used to understand adoption of new technologies in diverse contexts, such as the smartphones (Park & Chen, 2007), social media (Choi & Chung, 2013), and virtual reality (Lin & Yeh, 2019;Sagnier et al, 2020). Although very limited, the TAM has been also employed to understand the adoption of AI-based assessment in education (Cruz-Benito et al, 2019;Sánchez-Prieto et al, 2019). However, the use of TAM in these studies was limited to proposing theoretical arguments without empirical support.…”

Section: Contributions and Implicationsmentioning

confidence: 99%

My Teacher Is a Machine: Understanding Students’ Perceptions of AI Teaching Assistants in Online Education

Kim

Merrill

et al. 2020

International Journal of Human–Computer Interaction

124

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An increase in demand for online education has led to the creation of a new technology, machine teachers, or artificial intelligence (AI) teaching assistants. In fact, AI teaching assistants have already been implemented in a small number of courses in the United States. However, little is known about how students will perceive AI teaching assistants. Thus, the present study investigated students' perceptions about AI teaching assistants in higher education by use of an online survey. Primary findings indicate that perceived usefulness of an AI teaching assistant and perceived ease of communication with an AI teaching assistant are key to understanding an eventual adoption of AI teaching assistant-based education. These findings provide support for AI teaching assistant adoption. Based on the present study's findings, more research is needed to better understand the nuances associated with the learning experience one may have from an AI teaching assistant.

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Section: Contributions and Implicationsmentioning

confidence: 99%

My Teacher Is a Machine: Understanding Students’ Perceptions of AI Teaching Assistants in Online Education

Kim

Merrill

et al. 2020

International Journal of Human–Computer Interaction

124

View full text Add to dashboard Cite

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“…The growth and popularity of e-learning depend on its use by educators and learners across the globe. The demand of e-learning is because of the learning innovations in various educational fields such as health and engineering (Rodrigues et al, 2019), analytics dashboard applications for feedback to support learning regulation (Sedrakyan et al, 2020), artificial intelligence-based assessment (Cruz-Benito et al, 2019) and so on. The supply of e-learning is carried out by various content developers, open education resources (OER), open courseware (OCW) and so on.…”

Section: Theoretical Foundationmentioning

confidence: 99%

Effectiveness of e-learning: the mediating role of student engagement on perceived learning effectiveness

Panigrahi

Srivastava

Panigrahi

2020

ITP

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PurposeThis study extends the literature on the effectiveness of e-learning by investigating the role of student engagement on perceived learning effectiveness (PLE) in the context of Indian higher education. Further, the impact of personal factors (Internet self-efficacy (ISE)) and environmental factors (information, system and service quality parameters) on various dimensions of student engagement (behavioral, emotional and cognitive) is studied through the lens of social cognitive theory (SCT).Design/methodology/approachAn online management information systems (MIS) course is delivered to a batch of 412 postgraduate students. An online survey was conducted to measure the factors affecting their PLE. In addition to the survey, a summative assessment is conducted to evaluate the students in terms of their marks to assess their achievements (actual learning). Covariance-based structural equation modeling (CB-SEM) is used to validate the developed research model.FindingsIt is discovered that the IS (information system) quality parameters (environmental factors) positively impact PLE. The ISE affects the PLE through the mediating effect of all the dimensions of student engagement. Furthermore, there exists a positive relationship between PLE and student marks.Originality/valueThis study develops a research model using personal and environmental factors to understand PLE through the lens of SCT and then empirically validates it. The psychological process from the students' ISE to the PLE is explained through the mediating effects of various dimensions of engagement. Further, it is found that the PLE is positively related to student marks.

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“…When integrating new technologies into the learning processes of students (in this case, our CVL in the context of cybersecurity), a set of factors that affect the acceptance of technology arise, such as attitude, social needs, resistance to its use, and so on. Many works can be found in the literature that address this fact considering different contexts [35]. A well-known model for defining and validating the user acceptance of a novel educational technology is the Technology Acceptance Model (TAM) [10,36].…”

Section: User Acceptance Modelsmentioning

confidence: 99%

Students’ Acceptance and Tracking of a New Container-Based Virtual Laboratory

et al. 2020

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Presently, the ever-increasing use of new technologies helps people to acquire additional skills for developing an applied critical thinking in many contexts of our society. When it comes to education, and more particularly in any Engineering subject, practical learning scenarios are key to achieve a set of competencies and applied skills. In our particular case, the cybersecurity topic with a distance education methodology is considered and a new remote virtual laboratory based on containers will be presented and evaluated in this work. The laboratory is based on the Linux Docker virtualization technology, which allows us to create consistent realistic scenarios with lower configuration requirements for the students. The laboratory is comparatively evaluated with our previous environment, LoT@UNED, from both the points of view of the students’ acceptance with a set of UTAUT models, and their behavior regarding evaluation items, time distribution, and content resources. All data was obtained from students’ surveys and platform registers. The main conclusion of this work is that the proposed laboratory obtains a very high acceptance from the students, in terms of several different indicators (perceived usefulness, estimated effort, social influence, attitude, ease of access, and intention of use). Neither the use of the virtual platform nor the distance methodology employed affect the intention to use the technology proposed in this work.

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Measuring Students’ Acceptance to AI-Driven Assessment in eLearning: Proposing a First TAM-Based Research Model

Cited by 30 publications

References 51 publications

My Teacher Is a Machine: Understanding Students’ Perceptions of AI Teaching Assistants in Online Education

My Teacher Is a Machine: Understanding Students’ Perceptions of AI Teaching Assistants in Online Education

Effectiveness of e-learning: the mediating role of student engagement on perceived learning effectiveness

Students’ Acceptance and Tracking of a New Container-Based Virtual Laboratory

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