Recommendations for emergency remote teaching based on the student experience

Wilcox, Bethany R.; Vignal, Michael

doi:10.1119/10.0001828

Cited by 32 publications

(38 citation statements)

References 2 publications

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“…The topic of synchronous versus asynchronous video presentation is addressed in many studies with the surprising results that either can be equally effective: Wilcox and Vignat (2020) found that both synchronous and asynchronous videos could be a very effective mode of presentation; the active agent was the quality of the instructor performance. Olson and colleagues ( 2020 ) supported this result with the finding that there was no difference in the effectiveness of learning environments—the quality of presentation was the determining factor.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For instance, student satisfaction was found to be lower due to difficulty in understanding the lectures ( Aboagye et al., 2020 ; Bai et al., 2020 ). Wilcox and Vignat (2020) added a new twist with the finding that one problem was that lectures moved too quickly because the instructor, in a virtual PowerPoint setting, does not need to write on the board. Finally, workload increases were an additional problem that students identified ( Aguilera-Hermida, 2020 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Aboagye and colleagues ( 2020 ) identified factors that detract from student learning: (a) low quality of online materials, (b) lack of clear learning expectations, (c) inadequate instructors in conveying information, and (d) delays of course materials. The necessity of instructors providing clarity is supported by student comments that irrelevant material on, for instance, a PowerPoint page is distractive and interferes with learning ( Wilcox & Vignat, 2020 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Students’ responses make it clear that opportunities for students to engage with their peers and the instructor need to be built into both synchronous and asynchronous instruction ( Wilcox & Vignat, 2020 ). Shim and Song (2020) found that the absence of interaction between students and professors reduced their satisfaction with ERT.…”

Section: Literature Reviewmentioning

confidence: 99%

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Student input on the effectiveness of the shift to emergency remote teaching due to the COVID crisis: Structural equation modeling creates a more complete picture

Buttler

George

Bruggemann

2021

International Journal of Educational Research Open

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A study was conducted to assess student reaction to the shift to Emergency Remote Teaching (ERT) due to the COVID crisis in March of 2020. Four hundred students were randomly selected from a small private university database in central Alberta, Canada. A 65.5% response rate resulted in a final N of 262. These students responded to a 32-item questionnaire that assessed a number of factors that impacted four criterion variables: professor performance, quality of learning, affect on the final grade, and likelihood of returning in the Fall if their university was online. Results showed that the greatest predictors of the criterion variables were: professor support, professor caring, satisfaction with the final exam format, a relaxed schedule, quality of presentation, emotional response, adequate technological resources, and student input. Structural equation modeling creates a model that sorts out the relative impact of predictors on each criterion variable.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Student input on the effectiveness of the shift to emergency remote teaching due to the COVID crisis: Structural equation modeling creates a more complete picture

Buttler

George

Bruggemann

2021

International Journal of Educational Research Open

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“…If the course content is not carefully and intentionally designed, "undergraduate students claim a lack of socialization with peers and low engagement with the course materials" as primary reasons for their dissatisfaction, according to a similar study (Fuchs, 2021a). Furthermore, Wilcox and Vignal (2020) discovered that the two most common difficulties students faced as a result of ERT were (1) course inception and (2) learning environment.…”

Section: The Emerging Paradigm Of Ertmentioning

confidence: 99%

Perceived Satisfaction of Emergency Remote Teaching: More Evidence from Thailand

Fuchs

2021

IJLTER

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The sudden shift from physical classroom education towards emergency remote teaching (ERT) in higher education during the unprecedented global pandemic caused an abrupt change in the learning environment for students and educators alike. The disruptive overnight change and conversion of entire courses to emergency remote teaching caused concern for not only educators, but also students that had little time to adapt to the new circumstances. While the embedment of technologies in the classroom is not a new concept, this quantitative research expands a case study that sought to examine the perceived satisfaction of undergraduate students with the emerging paradigm of ERT. Responses based on empirical data (n=450) as well as secondary data (n=219) were analyzed to conclude that, in particular, younger freshmen students struggled more with online emergency remote teaching than their older peers. Furthermore, the study identified numerous similarities between both data samples. The current research informs educators about student perceptions and preferences during these extraordinary circumstances of uncertain duration. Furthermore, the paper concludes with recommendations that aim to provide institutions and educators with practical guidance on how to tackle the outlined issues.

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Implementation of an Industrial Automation Remote Lab (IARL) and validation using a deep learning approach during the COVID pandemic

Sekaran,

Hildas

2023

Comp Applic In Engineering

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The purpose of this study is to demonstrate a novel remote lab development for improving student psychomotor skills in the Industry 4.0 course. In this work, the vision and Internet of Things‐based lab setup for third‐year undergraduates is proposed as Industrial Automation Remote Lab (IARL). The major motivation of this novel approach is to replicate Industry 4.0 in the laboratory for learning purposes to undergraduate students during pandemic condition. In industries, it is essential to collect all the data from the machines to make optimal use of resources and increase the efficiency of the system. The stored data are interfaced through the user datagram protocol to control the speed of the hydraulic actuator. In the proposed experimental work, a vision sensor‐based anydesk remote control software technique is employed to conduct programmable logic circuit (PLC) experiments. To collect the various output image data sets, numerous PLC experiments were conducted. The collection of images is classified to investigate the student performance using deep learning algorithms like YOLOv4, VGG, and YOLOv5. Based on the simulation and validation results, the IARL‐based approach with YOLOv5 outperforms the other two algorithms with 98% accuracy. Additionally, the validated statistical internal and external results show that the remote laboratory Conceive, Design, Implement, and Operate (CDIO) batch performance is highly improved over virtual laboratory CDIO batch students.

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Recommendations for emergency remote teaching based on the student experience

Cited by 32 publications

References 2 publications

Student input on the effectiveness of the shift to emergency remote teaching due to the COVID crisis: Structural equation modeling creates a more complete picture

Student input on the effectiveness of the shift to emergency remote teaching due to the COVID crisis: Structural equation modeling creates a more complete picture

Perceived Satisfaction of Emergency Remote Teaching: More Evidence from Thailand

Implementation of an Industrial Automation Remote Lab (IARL) and validation using a deep learning approach during the COVID pandemic

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