The increase in online instruction makes business schools vulnerable to a surge in cheating behavior and in need of a framework to assess the potential effectiveness of exam proctoring conditions. A social facilitation theory framework was developed to assess how four prototypical proctoring conditions (1. web recording, 2. video summarization, 3. live online, 4. in-person) should be associated with decreasing levels of cheating. The framework was investigated with three studies. Study 1 assessed students' (n = 395) perceptions of being watched and monitored and instructors' (n = 113) perceptions of being able to control and monitor the testing environment. Study 2 showed students (n = 397) had weaker intentions to cheat for proctoring conditions posited as more actively watched and monitored. Consistent with social facilitation theory, evaluation apprehension and self-awareness were motivational deterrents to cheating. Study 3 replicated and extended prior findings of higher student scores for unproctored versus proctored exams with a field study (n = 224) of actual exam results for the same four proctoring conditions. Results were mostly consistent with the framework, with interesting asymmetries in how students and instructors perceived live online versus in-person proctoring conditions. This framework can help researchers classify proctoring effectiveness and help instructors align proctoring approaches with their assessment goals.

The COVID-19 pandemic transformed higher education globally, and even after in-person instruction resumed in Fall 2021, the number of online courses continued to grow. This shift, combined with ongoing socioeconomic and health challenges, impacted student learning. This study analyzed data from 212 students in an undergraduate online synchronous management science course at a public university in the western U.S. to examine how personality traits, cognitive factors like learning approaches, and meta-cognitive factors, such as self-regulation and goal orientations, influenced performance and satisfaction post-pandemic. Stepwise regression models showed that certain goal orientations affected learning, while others influenced satisfaction. Intrinsic value had a significant negative effect on both. Logistic regression revealed factors influencing future online course preferences, including goal orientations, agreeableness, controlled regulation, intrinsic value, GPA, and gender. Implications for online education and classroom teaching are discussed.

Abstract Family responsibilities, work commitments and financial stress are among the reasons why students might drop out of university. Sinjini Mitra and Yi Zhang used statistics at their minority-serving institution to explore the role of non-academic factors in student success

This study analyzes students' future preferences for online business courses based on responses from a spring 2021student sample from a California public regional university. The timing of this research takes advantage of the COVID-19 "sudden disruption" that provided a unique opportunity to examine factors (including challenges specific to COVID-19) that affect student choices about course delivery modality. Academic background, remote-learning experiences, and mental health significantly impacted future course enrollment decisions. Study results provide insights for both university administrators and faculty who will need to plan curriculum in the challenging postpandemic higher education setting.

The abrupt transition to online teaching and exams has made business schools especially vulnerable to a surge in cheating behavior. To address this problem, we conducted two studies investigating cheating behavior and motivation from a social facilitation perspective. Study 1 explored how alternative interpretations of social facilitation (mere presence vs. virtual presence) might predict potential cheating for online proctoring approaches ranging from more passive to more active. We found indirect evidence that active proctoring was more effective at reducing cheating behavior. Study 2 extended the continuum of passive to active proctoring with a scenario study that explored motivational factors to deter cheating through a social facilitation lens (evaluation apprehension, negative arousal, self-awareness, distraction). Consistent with study 1 results, cheating intentions were highest for passive proctoring (instructor monitoring an online Zoom room) and lowest for active proctoring (instructor monitoring in-person). We also found evaluation apprehension and self-awareness were effective cheating deterrents in different proctoring scenarios. We discuss the implications and suggest future research directions.

During the pandemic, tertiary institutions made an unprecedented shift to virtual teaching modalities. Administration of exams as learning assessments also shifted from in-person to online with a variety of approaches to proctoring and monitoring student behavior. Given that prior research shows business students are relatively more likely to cheat, the transition to online exams made business schools especially vulnerable to a potential surge in cheating behavior—something supported anecdotally by faculty experiences. To address this problem, we conducted a natural experiment to explore the differential impact of passive and active proctoring on potential cheating behavior during online exams in an introductory management course. Passive proctoring entailed the course instructor monitoring an online synchronous exam using Zoom with the instructor’s camera off. Active proctoring entailed an external proctoring service taking control of the students’ computer while they were aware their behaviors and activities were being tracked and recorded by the proctor with a live video feed. We use competing interpretations of a social facilitation framework to explore whether passive or active proctoring is more effective at reducing potential cheating. In support of a traditional social facilitation approach, we found nascent indirect evidence that active proctoring was relatively more effective than passive proctoring at reducing cheating behavior. We discuss the implications of our findings and suggest future research directions.

Online education has grown exponentially in the past few years. An important aspect of learning that is often missing in online classes is the feeling of social connectedness among students. This paper presents a mixed methods approach using both quantitative and qualitative means to understand the impact and long-term benefits of a collaborative project in an online bottleneck business course for underrepresented students (e.g., some ethnic minority categories like African Americans, Hispanics, etc.). Data analyzed from 80 students showed not only significantly better learning outcomes for this group (as compared with earlier semesters without such an activity) but also increased academic and professional skill acquisition and an enhanced sense of community.

Odds, log odds, and odds ratio concepts can be effectively applied in several machine learning algorithms and model evaluations. The use of these concepts has potential to make the algorithms simple, easy to interpret, and computationally more efficient. However, their implementation among the machine learning professional community has been concentrated mainly in the context of logistic regression. In this article, the authors discuss how odds, odds ratio, and log odds can be used in Bayes' theorem and multinomial naïve Bayes' classifiers. The authors will reformulate Bayes' theorem and multinomial naïve Bayes' classifiers in terms of “odds” and illustrate their applications with examples dealing with “loan application” approval.

From face recognition to fingerprint scans, Sinjini Mitra explains how statistical ideas inform the design and implementation of biometric security systems

Today, the healthcare industry is challenged with promoting health and disease prevention through various customer outreach initiatives. Social media tools enable customers to aquire health information without the need to visit their doctor. Yet, healthcare providers do not understand what motivates customers to use such technology for health-related purposes. In this study, an online survey was conducted with 4,058 participants. Using SEM techniques, the results indicate that previous experience with online health-related searches serves as a direct and indirect motivational driver. While the direct relationship between prior experience with online health-related searches can be mostly positive, it can be weakened and impacted by inhibitors that create online use concerns (e.g., privacy and confidentiality). Furthermore, health condition may determine the level of interest people may have for health-related social media sites as well. This research provides a deeper understanding about motivational factors that impact the intent to use health-related social media.

Infants born before 37 weeks of pregnancy are considered to be preterm. Typically, preterm infants have to be strictly monitored since they are highly susceptible to health problems like hypoxemia (low blood oxygen level), apnea, respiratory issues, cardiac problems, neurological problems as well as an increased chance of long-term health issues such as cerebral palsy, asthma and sudden infant death syndrome. One of the leading health complications in preterm infants is bradycardia - which is defined as the slower than expected heart rate, generally beating lower than 60 beats per minute. Bradycardia is often accompanied by low oxygen levels and can cause additional long term health problems in the premature infant. The implementation of a non-parametric method to predict the onset of bradycardia is presented. This method assumes no prior knowledge of the data and uses kernel density estimation to predict the future onset of bradycardia events. The data is preprocessed, and then analyzed to detect the peaks in the ECG signals, following which different kernels are implemented to estimate the shared underlying distribution of the data. The performance of the algorithm is evaluated using various metrics and the computational challenges and methods to overcome them are also discussed. It is observed that the performance of the algorithm with regards to the kernels used are consistent with the theoretical performance of the kernel as presented in a previous work. The theoretical approach has also been automated in this work and the various implementation challenges have been addressed.

Biometric authentication is a promising approach to securing the Internet of Things (IoT). Although existing research shows that using multiple biometrics for authentication helps increase recognition accuracy, the majority of biometric approaches for IoT today continue to rely on a single modality. We propose a multimodal biometric approach for IoT based on face and voice modalities that is designed to scale to the limited resources of an IoT device. Our work builds on the foundation of Gofman et al. [7] in implementing face and voice featurelevel fusion on mobile devices. We used discriminant correlation analysis (DCA) to fuse features from face and voice and used the K-nearest neighbors (KNN) algorithm to classify the features. The approach was implemented on the Raspberry Pi IoT device and was evaluated on a dataset of face images and voice files acquired using a Samsung Galaxy S5 device in real-world conditions such as dark rooms and noisy settings. The results show that fusion increased recognition accuracy by 52.45% compared to using face alone and 81.62% compared to using voice alone. It took an average of 1.34 seconds to enroll a user and 0.91 seconds to perform the authentication. To further optimize execution speed and reduce power consumption, we implemented classification on a field-programmable gate array (FPGA) chip that can be easily integrated into an IoT device. Experimental results showed that the proposed FPGA-accelerated KNN could achieve 150x faster execution time and 12x lower energy consumption compared to a CPU.

From biometric image acquisition to matching to decision making, designing a selfie biometric system is riddled with security, privacy, and usability challenges. In this chapter, we provide a discussion of some of these challenges, examine some real-world examples, and discuss both existing solutions and potential new solutions. The majority of these issues will be discussed in the context of mobile devices, as they comprise a major platform for selfie biometrics; face, voice, and fingerprint biometric modalities are the most popular modalities used with mobile devices.

In this article, we study a set of factors underlying student success in a bottleneck business course using statistical and data mining techniques. Factors included learning styles, motivational and other cognitive factors, personality traits, learning analytics, along with background demographic and academic ones. Our analysis yielded interesting insights that show some of these factors play significant roles in predicting both student performance and their propensity to utilize resources that help improve their performance, such as additional support services. The predictive accuracy of both of our models were over 95% (error rate <5%). Moreover, quantile regression models were used to determine factors that specifically affect the performance of low-performing students so that targeted intervention and support services can be developed specifically for them. In conclusion, deeper analytics via statistical models are crucial for forming an in-depth understanding of how to improve student performance in a bottleneck course and this has far-reaching implications for both educators and administrators in higher education.

Variations in facial appearance resulting from the application of ordinary makeup products, such as eyeliner and lipstick, are challenging for automated facial recognition. This work studies the potential of using the measurement of the inherent asymmetry between the two halves of a person's face presented in [18] as a helpful feature to overcome this challenge. We hypothesized that inherent facial asymmetry is not completely concealed by makeup or might even be increased by the application of makeup. To test our hypothesis, we applied the Eigenfaces algorithm to classify the faces of 67 ethnically diverse individuals in our labeled database, with and without makeup, based on the asymmetry feature. The database was designed so that all variations, except the application of makeup, were fixed. The use of Eigenfaces in this preliminary evaluation was intentional; the feature classification process of this method is simple, which makes the effects of asymmetry features on the classification process easier to observe, and Eigenfaces are widely used in popular computer vision packages, such as OpenCV. The results show that recognition accuracy improved by 42.36% when using our asymmetry feature compared to the classical Eigenfaces algorithm. These results are encouraging and will serve as the baseline for future experiments on new datasets with other more robust classifiers.

Many students in quantitative business courses are struggling. One technique designed to support such students is Supplemental Instruction (SI), which is most popular in the science, technology, engineering, and mathematics (STEM) disciplines. In this paper, we show the positive impact of SI on student performance in two bottleneck business courses in a large university. Our evaluation results establish that (i) SI has a statistically significant effect on students’ likelihood of passing both courses (after controlling for background variables), (ii) SI is more helpful for students identified as at risk than for those who are not, and (iii) it is important to consistently attend SI sessions for greater success. We also present models to predict consistent student attendance based on background factors with 90% accuracy and conclude with a brief qualitative study about students’ self-perception of SI and the professional development attained by SI leaders.

Increased growth of online education has created an additional issue of devising effective proctoring for remotely-administered online examinations. In this research project, we explore some popular proctoring methods for such exams such as live proctoring through a computer’s webcam and biometrics-based proctoring that monitors a student’s mouse movement, and head and eye movements in order to detect cheating attempts. Upon discussion of the various advantages and disadvantages of these two approaches, we propose an integrated proctoring technique combining the complementary strengths of both these methods. Along with potential challenges and solutions, we present an architectural design of this prototype that is currently being developed to be implemented, and mention ways of extending its benefits to exams in the traditional classrooms also. Finally, we present analyses of survey and interview data from faculty and students about their concerns for integrity in online exams and their opinions regarding our proposed proctoring technique.

Increased growth of online education has created an additional issue of devising effective proctoring for remotely-administered online examinations. In this research project, we explore some popular proctoring methods for such exams such as live proctoring through a computer's webcam and biometrics -based proctoring that monitors a student's mouse movement, and head and eye movements in order to detect cheating attempts. Upon discussion of the various advantages and disadvantages of these two approaches, we propose an integrated proctoring technique combining the complementary strengths of both these methods. Along with potential challenges and solutions, we present an architectural design of this prototype that is currently being developed to be implemented, and mention ways of extending its benefits to exams in the traditional classrooms also. Finally, we present analyses of survey and interview data from faculty and students about their concerns for integrity in online exams and their opinions regarding our proposed proctoring technique.