Reasons for academic dishonesty during examinations among nursing students: Cross-sectional survey.
TL;DR: A questionnaire to develop and validate a questionnaire for investigating nursing students' perceptions about the reasons for academic dishonesty during examinations, whose identification can guide preventive strategies.
About: This article is published in Nurse Education Today.The article was published on 2020-03-01. It has received 24 citations till now. The article focuses on the topics: Academic dishonesty & Cheating.
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TL;DR: In this paper, the authors investigate the reasons for students' unethical behaviors during the Covid-19 pandemic and elicit students' and lecturers' perceptions of students' academic dishonesty during this period.
Abstract: The Covid-19 pandemic that entered our lives suddenly in 2020 compelled higher education systems throughout the world to transfer to online learning, including online evaluation. A severe problem of online evaluation is that it enables various technological possibilities that facilitate students' unethical behaviors. The research aimed to investigate these behaviors, as well as the reasons for their appearance, as practiced in exams held for the first time during the Covid-19 pandemic, and to elicit students' and lecturers' perceptions of students' academic dishonesty (AD) during this period. The sample included 81 students and 50 lecturers from several Israeli colleges and universities. The findings expand extant knowledge on academic dishonesty, identifying significant differences between the perceptions of students and lecturers concerning attitudes towards online exams and the reasons for dishonest behaviors. The findings among the students also indicate that younger students and Arab students tended to cheat more in online exams. Moreover, the findings indicated a lack of mutual trust between students and lecturers with regard to academic dishonesty, a deep distrust that will probably continue even after the Covid-19 crisis. This last finding should be a cause of concern for higher education policy-makers, affecting future policies for improving lecturer-student relations, especially during crises. Recommendations are proposed for addressing academic dishonesty in exams in general and during the pandemic in particular.
24 citations
30 Jun 2021
TL;DR: In this paper, the authors examined the behavior of academic dishonesty when online learning is applied, besides that it also examines the strategies of nursing students majoring in academic dishonestness.
Abstract: Since the implementation of online learning in various countries in the world, all educational institutions have made new learning adjustments. Universities are educational institutions that have also changed the online learning system. but online learning has an impact on academic ethical behavior.
Purpose. the aims of this study is to determine the behavior of academic dishonesty when online learning is applied, besides that it also examines the strategies of nursing students majoring in academic dishonesty.
Materials and methods. 150 college students participated in filling out an online academic dishonesty questionnaire and we randomly selected 5 nursing students to participate in a focus group discussion to discuss their dishonest behavior during online learning.
Results. Our research shows that academic dishonesty behavior in the form of collaboration is common in online learning. In the process, student learning has strategies for committing academic fraud in various ways, including by downloading a friend’s answer file in the online system by logging in using a standard username and password that is not changed by students. In addition, the student chose to behave dishonestly by imitating his friend’s work by simply changing the name rather than trying to answer the question. and take advantage of the whatsapp group application to collaborate in cheating.
Conclusions. Collaboration in academic dishonesty predominates: one way is by collaborating in online groups to cooperate with each other illegally. We describe several other forms in detail and discuss them.
17 citations
Cites background from "Reasons for academic dishonesty dur..."
...Previous studies emphasized the absence of punishment for the perpetrators (Burgason et al., 2019; Park, 2003) or the absence of severe consequences (Kiekkas et al., 2020)....
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TL;DR: In this article, a multiple case study approach examined how academic misconduct is perceived in universities in in Australia, New Zealand and the United Kingdom via interviews with academics and administrators and found that academic misconduct was a systemic problem that manifests in various ways and requires similarly diverse approaches to management.
Abstract: Academic misconduct is a problem of growing concern across the tertiary education sector. While plagiarism has been the most common form of academic misconduct, the advent of software programs to detect plagiarism has seen the problem of misconduct simply mutate. As universities attempt to function in an increasingly complex environment, the factors that contribute to academic misconduct are unlikely to be easily mitigated. A multiple case study approach examined how academic misconduct is perceived in universities in in Australia, New Zealand and the United Kingdom via interviews with academics and administrators. The findings show that academic misconduct is a systemic problem that manifests in various ways and requires similarly diverse approaches to management. Greater consistency in policies and procedures, including a focus on preventative education for both staff and students, is key to managing the mutations of academic misconduct that continue to plague the higher education sector globally.
13 citations
Cites background or result from "Reasons for academic dishonesty dur..."
...…in specific professional or disciplinary groups (Abdulghani et al. 2018; Birks et al. 2018; Brown et al. 2019; Cronan et al. 2018; Ewing et al. 2019; Kiekkas et al. 2020; Mohamed et al. 2018) or in respect of the influence of gender (Bokosmaty et al. 2019; Jereb et al. 2018; Zhang et al. 2018)....
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...In terms of students defending their behavior, findings from this study are very similar to others conducted around the world (Birks et al. 2018; Kiekkas et al. 2020; Mahmud et al. 2019; Moss et al. 2018)....
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TL;DR: By cultivating a learning environment that promotes honesty and integrity, rather than waiting until a dishonest act occurs to take action, the likelihood is lower that students will engage in academically dishonest behaviors.
Abstract: Background Much has been explored about academic dishonesty among nursing students. Problem Nursing students continue to engage in a variety of dishonest behaviors in the classroom and clinical settings. Concerned faculty members are seeking assistance in understanding the problem and finding suggestions for reducing students' engagement in academically dishonest behaviors. Approach Drawing on current literature, we discuss an expanded definition of academic dishonesty, explore motivating factors for students' academic dishonesty, and summarize common ways to reduce students' engagement in academically dishonest behaviors. We also provide guidance for development of policies concerning academic dishonesty. Conclusions By cultivating a learning environment that promotes honesty and integrity, rather than waiting until a dishonest act occurs to take action, the likelihood is lower that students will engage in academically dishonest behaviors.
8 citations
TL;DR: The authors conducted an exploratory quantitative study employing a cross-sectional survey to determine the type and prevalence of academic dishonesty engaged in by post-registration nursing students and their understanding of the relationship between academic honesty and professional conduct.
6 citations
References
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Book•
01 Jan 1983
TL;DR: In this Section: 1. Multivariate Statistics: Why? and 2. A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques.
Abstract: In this Section: 1. Brief Table of Contents 2. Full Table of Contents 1. BRIEF TABLE OF CONTENTS Chapter 1 Introduction Chapter 2 A Guide to Statistical Techniques: Using the Book Chapter 3 Review of Univariate and Bivariate Statistics Chapter 4 Cleaning Up Your Act: Screening Data Prior to Analysis Chapter 5 Multiple Regression Chapter 6 Analysis of Covariance Chapter 7 Multivariate Analysis of Variance and Covariance Chapter 8 Profile Analysis: The Multivariate Approach to Repeated Measures Chapter 9 Discriminant Analysis Chapter 10 Logistic Regression Chapter 11 Survival/Failure Analysis Chapter 12 Canonical Correlation Chapter 13 Principal Components and Factor Analysis Chapter 14 Structural Equation Modeling Chapter 15 Multilevel Linear Modeling Chapter 16 Multiway Frequency Analysis 2. FULL TABLE OF CONTENTS Chapter 1: Introduction Multivariate Statistics: Why? Some Useful Definitions Linear Combinations of Variables Number and Nature of Variables to Include Statistical Power Data Appropriate for Multivariate Statistics Organization of the Book Chapter 2: A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques Some Further Comparisons A Decision Tree Technique Chapters Preliminary Check of the Data Chapter 3: Review of Univariate and Bivariate Statistics Hypothesis Testing Analysis of Variance Parameter Estimation Effect Size Bivariate Statistics: Correlation and Regression. Chi-Square Analysis Chapter 4: Cleaning Up Your Act: Screening Data Prior to Analysis Important Issues in Data Screening Complete Examples of Data Screening Chapter 5: Multiple Regression General Purpose and Description Kinds of Research Questions Limitations to Regression Analyses Fundamental Equations for Multiple Regression Major Types of Multiple Regression Some Important Issues. Complete Examples of Regression Analysis Comparison of Programs Chapter 6: Analysis of Covariance General Purpose and Description Kinds of Research Questions Limitations to Analysis of Covariance Fundamental Equations for Analysis of Covariance Some Important Issues Complete Example of Analysis of Covariance Comparison of Programs Chapter 7: Multivariate Analysis of Variance and Covariance General Purpose and Description Kinds of Research Questions Limitations to Multivariate Analysis of Variance and Covariance Fundamental Equations for Multivariate Analysis of Variance and Covariance Some Important Issues Complete Examples of Multivariate Analysis of Variance and Covariance Comparison of Programs Chapter 8: Profile Analysis: The Multivariate Approach to Repeated Measures General Purpose and Description Kinds of Research Questions Limitations to Profile Analysis Fundamental Equations for Profile Analysis Some Important Issues Complete Examples of Profile Analysis Comparison of Programs Chapter 9: Discriminant Analysis General Purpose and Description Kinds of Research Questions Limitations to Discriminant Analysis Fundamental Equations for Discriminant Analysis Types of Discriminant Analysis Some Important Issues Comparison of Programs Chapter 10: Logistic Regression General Purpose and Description Kinds of Research Questions Limitations to Logistic Regression Analysis Fundamental Equations for Logistic Regression Types of Logistic Regression Some Important Issues Complete Examples of Logistic Regression Comparison of Programs Chapter 11: Survival/Failure Analysis General Purpose and Description Kinds of Research Questions Limitations to Survival Analysis Fundamental Equations for Survival Analysis Types of Survival Analysis Some Important Issues Complete Example of Survival Analysis Comparison of Programs Chapter 12: Canonical Correlation General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Canonical Correlation Some Important Issues Complete Example of Canonical Correlation Comparison of Programs Chapter 13: Principal Components and Factor Analysis General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Factor Analysis Major Types of Factor Analysis Some Important Issues Complete Example of FA Comparison of Programs Chapter 14: Structural Equation Modeling General Purpose and Description Kinds of Research Questions Limitations to Structural Equation Modeling Fundamental Equations for Structural Equations Modeling Some Important Issues Complete Examples of Structural Equation Modeling Analysis. Comparison of Programs Chapter 15: Multilevel Linear Modeling General Purpose and Description Kinds of Research Questions Limitations to Multilevel Linear Modeling Fundamental Equations Types of MLM Some Important Issues Complete Example of MLM Comparison of Programs Chapter 16: Multiway Frequency Analysis General Purpose and Description Kinds of Research Questions Limitations to Multiway Frequency Analysis Fundamental Equations for Multiway Frequency Analysis Some Important Issues Complete Example of Multiway Frequency Analysis Comparison of Programs
53,113 citations
Book•
01 Jan 1991
TL;DR: In this paper, the authors discuss the role of measurement in the social sciences and propose guidelines for scale development in the context of scale-based measurement. But, the authors do not discuss the relationship between scale scores and scale length.
Abstract: Chapter 1: Overview General Perspectives on Measurement Historical Origins of Measurement in Social Science Later Developments in Measurement The Role of Measurement in the Social Sciences Summary and Preview Chapter 2: Understanding the "Latent Variable" Constructs Versus Measures Latent Variable as the Presumed Cause of Item Values Path Diagrams Further Elaboration of the Measurement Model Parallel "Tests" Alternative Models Exercises Chapter 3: Reliability Continuous Versus Dichotomous Items Internal Consistency Relability Based on Correlations Between Scale Scores Generalizability Theory Summary and Exercises Chapter 4: Validity Content Validity Criterion-related Validity Construct Validity What About Face Validity? Exercises Chapter 5: Guidelines in Scale Development Step 1: Determine Clearly What it Is You Want to Measure Step 2: Generate an Item Pool Step 3: Determine the Format for Measurement Step 4: Have Initial Item Pool Reviewed by Experts Step 5: Consider Inclusion of Validation Items Step 6: Administer Items to a Development Sample Step 7: Evaluate the Items Step 8: Optimize Scale Length Exercises Chapter 6: Factor Analysis Overview of Factor Analysis Conceptual Description of Factor Analysis Interpreting Factors Principal Components vs Common Factors Confirmatory Factor Analysis Using Factor Analysis in Scale Development Sample Size Conclusion Chapter 7: An Overview of Item Response Theory Item Difficulty Item Discrimination False Positives Item Characteristic Curves Complexities of IRT When to Use IRT Conclusions Chapter 8: Measurement in the Broader Research Context Before the Scale Development After the Scale Administration Final Thoughts References Index About the Author
11,710 citations
Book•
05 Jun 1991
TL;DR: Measurement in the Broader Research Context Before the Scale Development After the Scale Administration Final Thoughts References Index about the Author.
Abstract: Chapter 1: Overview General Perspectives on Measurement Historical Origins of Measurement in Social Science Later Developments in Measurement The Role of Measurement in the Social Sciences Summary and Preview Chapter 2: Understanding the "Latent Variable" Constructs Versus Measures Latent Variable as the Presumed Cause of Item Values Path Diagrams Further Elaboration of the Measurement Model Parallel "Tests" Alternative Models Exercises Chapter 3: Reliability Continuous Versus Dichotomous Items Internal Consistency Relability Based on Correlations Between Scale Scores Generalizability Theory Summary and Exercises Chapter 4: Validity Content Validity Criterion-related Validity Construct Validity What About Face Validity? Exercises Chapter 5: Guidelines in Scale Development Step 1: Determine Clearly What it Is You Want to Measure Step 2: Generate an Item Pool Step 3: Determine the Format for Measurement Step 4: Have Initial Item Pool Reviewed by Experts Step 5: Consider Inclusion of Validation Items Step 6: Administer Items to a Development Sample Step 7: Evaluate the Items Step 8: Optimize Scale Length Exercises Chapter 6: Factor Analysis Overview of Factor Analysis Conceptual Description of Factor Analysis Interpreting Factors Principal Components vs Common Factors Confirmatory Factor Analysis Using Factor Analysis in Scale Development Sample Size Conclusion Chapter 7: An Overview of Item Response Theory Item Difficulty Item Discrimination False Positives Item Characteristic Curves Complexities of IRT When to Use IRT Conclusions Chapter 8: Measurement in the Broader Research Context Before the Scale Development After the Scale Administration Final Thoughts References Index About the Author
10,722 citations
TL;DR: Emp empirical data supports the conclusion that cheating is a significant issue in all disciplines today, including nursing, and some preliminary policy implications are considered.
Abstract: Academic dishonesty, whether in the form of plagiarism or cheating on tests, has received renewed attention in the past few decades as pervasive use of the Internet and a presumed deterioration of ethics in the current generation of students has led some, perhaps many, to conclude that academic dishonesty is reaching epidemic proportions. What is lacking in many cases, including in the nursing profession, is empirical support of these trends. This article attempts to provide some of that empirical data and supports the conclusion that cheating is a significant issue in all disciplines today, including nursing. Some preliminary policy implications are also considered.
147 citations