scholarly journals Academic Integrity in Online Assessment: A Research Review

2021 ◽  
Vol 6 ◽  
Author(s):  
Olivia L. Holden ◽  
Meghan E. Norris ◽  
Valerie A. Kuhlmeier

This paper provides a review of current research on academic integrity in higher education, with a focus on its application to assessment practices in online courses. Understanding the types and causes of academic dishonesty can inform the suite of methods that might be used to most effectively promote academic integrity. Thus, the paper first addresses the question of why students engage in academically dishonest behaviours. Then, a review of current methods to reduce academically dishonest behaviours is presented. Acknowledging the increasing use of online courses within the postsecondary curriculum, it is our hope that this review will aid instructors and administrators in their decision-making process regarding online evaluations and encourage future study that will form the foundation of evidence-based practices.

2021 ◽  
Vol 11 (3) ◽  
pp. 129
Author(s):  
Gabrielle Wilcox ◽  
Cristina Fernandez Conde ◽  
Amy Kowbel

There are longstanding calls for inclusive education for all regardless of student need or teacher capacity to meet those needs. Unfortunately, there are little empirical data to support full inclusion for all students and even less information on the role of data-based decision making in inclusive education specifically, even though there is extensive research on the effectiveness of data-based decision making. In this article, we reviewed what data-based decision making is and its role in education, the current state of evidence related to inclusive education, and how data-based decision making can be used to support decisions for students with reading disabilities and those with intellectual disabilities transitioning to adulthood. What is known about evidence-based practices in supporting reading and transition are reviewed in relationship to the realities of implementing these practices in inclusive education settings. Finally, implications for using data-based decisions in inclusive settings are discussed.


2021 ◽  
Vol 6 ◽  
Author(s):  
Anne H. Verhoef ◽  
Yolandi M. Coetser

Background: This article examines the phenomenon of academic integrity during the coronavirus disease 2019 (COVID-19) pandemic, with particular reference to emergency online assessments in 2020.Aim: It explores academic dishonesty, cheating and plagiarism of university students during emergency remote online assessment, from the perspective of South African students.Setting and Methodology: The authors explore the approaches of different universities worldwide, as well as the extant literature on the topic. An examination of the current literature related explicitly to the COVID-19 online assessments reveals a dearth of engagement by researchers in the South African context. In order to address this lacuna, the authors rely on data generated from an institutional forum on academic dishonesty at a University in South Africa. It focuses specifically on the voices of students presented during the forum, which explained both why students are dishonest and ways to curb dishonesty.Results and Conclusion: The data generated show whilst some students were dishonest due to pandemic-related issues (like lack of monitoring), there are also other reasons, such as lack of time management, feeling overwhelmed and stressed and struggling with technology that contributes to student dishonesty. Students suggest that assessments be approached differently online to curb academic dishonesty. The paper concludes by providing some fundamental changes needed to address academic dishonesty.


2010 ◽  
Vol 4 (1-2) ◽  
pp. 53-58 ◽  
Author(s):  
Catherine Laurent ◽  
Marielle Berriet-Solliec ◽  
Marc Kirsch ◽  
Pierre Labarthe ◽  
AurélieT AurélieTrouvé

Various theoretical models of public policy analysis are used to treat situations of decision-making in which public deciders have to take into account the multifunctionality of agriculture. For some, science-society relations are not really problematical. Others acknowledge the current attempts of these policy-makers to find adequate scientific knowledge, and the difficulties they encounter. These difficulties stem partly from the very content of knowledge produced by research. Could other modes of production be more efficient? The status of the knowledge produced by these approaches is a subject of debate. Bridging the divide between science and policy more effectively is not only a question of knowledge brokerage.Accessibility and reliability of the existing evidences are also problems to be addressed. The debates around evidence-based practices may provide some landmarks in this new situation although they also emphasize the limits of the tools that can be built for this purpose.  


2021 ◽  
Author(s):  
Adrian Ahne ◽  
Guy Fagherazzi ◽  
Xavier Tannier ◽  
Thomas Czernichow ◽  
Francisco Orchard

BACKGROUND The amount of available textual health data such as scientific and biomedical literature is constantly growing and it becomes more and more challenging for health professionals to properly summarise those data and in consequence to practice evidence-based clinical decision making. Moreover, the exploration of large unstructured health text data is very challenging for non experts due to limited time, resources and skills. Current tools to explore text data lack ease of use, need high computation efforts and have difficulties to incorporate domain knowledge and focus on topics of interest. OBJECTIVE We developed a methodology which is able to explore and target topics of interest via an interactive user interface for experts and non-experts. We aim to reach near state of the art performance, while reducing memory consumption, increasing scalability and minimizing user interaction effort to improve the clinical decision making process. The performance is evaluated on diabetes-related abstracts from Pubmed. METHODS The methodology consists of four parts: 1) A novel interpretable hierarchical clustering of documents where each node is defined by headwords (describe documents in this node the most); 2) An efficient classification system to target topics; 3) Minimized users interaction effort through active learning; 4) A visual user interface through which a user interacts. We evaluated our approach on 50,911 diabetes-related abstracts from Pubmed which provide a hierarchical Medical Subject Headings (MeSH) structure, a unique identifier for a topic. Hierarchical clustering performance was compared against the implementation in the machine learning library scikit-learn. On a subset of 2000 randomly chosen diabetes abstracts, our active learning strategy was compared against three other strategies: random selection of training instances, uncertainty sampling which chooses instances the model is most uncertain about and an expected gradient length strategy based on convolutional neural networks (CNN). RESULTS For the hierarchical clustering performance, we achieved a F1-Score of 0.73 compared to scikit-learn’s of 0.76. Concerning active learning performance, after 200 chosen training samples based on these strategies, the weighted F1-Score over all MeSH codes resulted in satisfying 0.62 F1-Score of our approach, compared to 0.61 of the uncertainty strategy, 0.61 the CNN and 0.45 the random strategy. Moreover, our methodology showed a constant low memory use with increased number of documents but increased execution time. CONCLUSIONS We proposed an easy to use tool for experts and non-experts being able to combine domain knowledge with topic exploration and target specific topics of interest while improving transparency. Furthermore our approach is very memory efficient and highly parallelizable making it interesting for large Big Data sets. This approach can be used by health professionals to rapidly get deep insights into biomedical literature to ultimately improve the evidence-based clinical decision making process.


2014 ◽  
pp. 137-147
Author(s):  
Judith Aufenthie

Creating optimal well being is a multifaceted, complex process. It involves many biological, psychological, physical, behavioral, emotional as well as neurobiological factors which all interact and effect the choices we make and changes that we are able to implement. Research has begun to connect with the decision making process to better understand how our decisions and choices are made. This research coupled with research and evidence based models provides integrative nurses and patients with validated tools to optimize change and wellness.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hossein Azarpanah ◽  
Mohsen Farhadloo ◽  
Rustam Vahidov ◽  
Louise Pilote

Abstract Background Vaccine hesitancy has been a growing challenge for public health in recent decades. Among factors contributing to vaccine hesitancy, concerns regarding vaccine safety and Adverse Events (AEs) play the leading role. Moreover, cognitive biases are critical in connecting such concerns to vaccine hesitancy behaviors, but their role has not been comprehensively studied. In this study, our first objective is to address concerns regarding vaccine AEs to increase vaccine acceptance. Our second objective is to identify the potential cognitive biases connecting vaccine hesitancy concerns to vaccine-hesitant behaviors and identify the mechanism they get triggered in the vaccine decision-making process. Methods First, to mitigate concerns regarding AEs, we quantitatively analyzed the U.S. Vaccine Adverse Event Reporting System (VAERS) from 2011 to 2018 and provided evidence regarding the non-severity of the AEs that can be used as a communicable summary to increase vaccine acceptance. Second, we focused on the vaccination decision-making process. We reviewed cognitive biases and vaccine hesitancy literature to identify the most potential cognitive biases that affect vaccine hesitancy and categorized them adopting the Precaution Adoption Process Model (PAPM). Results Our results show that the top frequent AEs are expected mild reactions like injection site erythema (4.29%), pyrexia (3.66%), and injection site swelling (3.21%). 94.5% of the reports are not serious and the average population-based serious reporting rate over the 8 years was 25.3 reports per 1 million population. We also identified 15 potential cognitive biases that might affect people’s vaccination decision-making and nudge them toward vaccine hesitancy. We categorized these biases based on the factors that trigger them and discussed how they contribute to vaccine hesitancy. Conclusions This paper provided an evidence-based communicable summary of VAERS. As the most trusted sources of vaccine information, health practitioners can use this summary to provide evidence-based vaccine information to vaccine decision-makers (patients/parents) and mitigate concerns over vaccine safety and AEs. In addition, we identified 15 potential cognitive biases that might affect the vaccination decision-making process and nudge people toward vaccine hesitancy. Any plan, intervention, and message to increase vaccination uptake should be modified to decrease the effect of these potential cognitive biases.


1997 ◽  
Vol 170 (S32) ◽  
pp. 35-36 ◽  
Author(s):  
Michael Harris

Risk assessment has always been an essential part of all medical practice, and doctors have always been trained to make rapid assessment of risk. Much of the early training of doctors in both medicine and surgery centres on risk assessment. However, the method of acquiring that knowledge is predominantly through the apprenticeship model with observation by the trainee of the trainer's decision-making process. Those decisions, however, are often skewed and biased by a whole variety of influences, rather than always being based on scientific evidence. Clearly the increasing influence of evidence-based medicine will help this. At one extreme, however, there are heroic surgeons taking unnecessary risk or taking on cases which might more appropriately have been left without treatment, and at the other extreme, consultants who may feel demoralised or depressed might well become nihilistic about medicine and therefore might not attempt to treat cases that are treatable.


2016 ◽  
Vol 82 (3) ◽  
pp. 259-265 ◽  
Author(s):  
Victoria Serpico ◽  
Amy E. Liepert ◽  
Kenneth Boucher ◽  
Diane L. Fouts ◽  
Layla Anderson ◽  
...  

To enhance shared decision-making for patients with breast cancer, we developed an evidence-based educational breast cancer video (BCV) providing an overview of breast cancer biology, prognostic indicators, and surgical treatment options while introducing health care choice. By providing patients access to a BCV with information necessary to make informed surgical decisions before seeing a surgeon, we aimed to increase patient participation in the decision-making process, while decreasing distress. Patients with a new diagnosis of breast cancer were provided a link to the BCV. Group 1 participated in online pre- and postvideo questionnaires, with the BCV embedded in between. The questionnaires evaluated self-reported baseline knowledge of breast cancer and perceived distress related to the diagnosis. Changes in self-reported responses were analyzed using the Wilcoxon matched pairs test. Group 2 received a survey collecting demographics, decision-making information, and perceptions of the BCV at the time of clinic visit before meeting the surgeon. Group 1 included 69 subjects with 62 per cent reporting improved knowledge and 30 per cent reporting reduced distress in regard to their breast cancer diagnosis. Group 2 included 87 subjects; 94 to 98 per cent felt the BCV provided information and stimulated thoughts and questions to assist in breast cancer treatment decision-making. The BCV was positively received by participants and feasible to implement into clinical practice. Evidence-based media tools improve knowledge and reduce distress in patients with a new diagnosis of breast cancer as well as contributing to the shared decision-making process.


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