Online Assessment Research Articles

An automatic Q-factor matching method for eliminating 77% of the ZRO of a MEMS vibratory gyroscope in rate mode

Mismatching quality factors (Q-factors) is one of the main factors causing zero-rate output (ZRO) in degenerate (DE) Micro-Electro-Mechanical Systems (MEMS) vibratory gyroscopes. To eliminate the ZRO of the DE MEMS gyroscope, this study introduces a method for real-time identification and automatic matching of Q-factors in rate mode. By leveraging the vibration characteristics of the DE MEMS vibratory gyroscope in rate mode, dedicated online test methods are designed to determine the Q-factors for both the drive and sense modes, enabling online identification of the Q-factor mismatching. Furthermore, an automatic Q-factor matching system is designed utilizing the mechanical-thermal dissipation mechanism of the resistive damper. The effectiveness of this proposed method is validated through simulations and experiments conducted on a MEMS disk resonator gyroscope (DRG). The results show a measurement error within 4% for Q-factor identification, and automatic Q-factor matching effectively reduces the ZRO by 77%. Employing this automatic Q-factor matching method successfully reduces the ZRO that is caused by the mismatching of Q-factors in the MEMS DRG from 0.11°/s to 0.025°/s and improves the bias instability (BI) from 0.40°/s to 0.19°/s.

The influence of online education on pre-service teachers’ academic experiences at a higher education institution in the United Arab Emirates

Online education has gained widespread adoption in recent years due to several factors, including the COVID-19 pandemic, which has accelerated the growth of online education, with universities transitioning to online platforms to continue their activities. However, this transition has also impacted the preparation of pre-service teachers, who receive training to become licensed or certified teachers. This study investigates the influence of online education on the academic experiences of 130 pre-service teachers attending the Postgraduate Diploma Program at Al Ain University in the UAE. It also explores the relationships between pre-service teachers’ demographics and five academic experiences. A quantitative questionnaire consisting of five newly-developed scales was used for data collection. Pre-service teachers’ demographics were found not to impact effective teaching and learning, skill development, or satisfaction. Age and employment status were found not to influence pre-service teachers’ views of faculty online assessment and feedback or course organization and management. However, online course organization and management and faculty online assessment and feedback were significantly correlated with marital status as engagement and motivation with employment status was, but not with age or marital status. Effective teaching and learning, faculty assessment, and feedback positively impacted pre-service teachers’ engagement and motivation, while effective teaching and learning and course organization correlated with pre-service teachers’ skill development and satisfaction. Research on online education and pre-service teachers’ experiences post-pandemic is limited. Thus, future studies should explore this relationship to understand better pre-service teachers’ online learning experiences, involvement, and success.

Understanding the log file data from educational and psychological computer-based testing: A scoping review protocol.

With the advancement of computer-based testing, log file data has drawn considerable attention from researchers. Although emerging studies have begun to explore log file data, there is a gap in the exploitation of log file data for capturing and understanding participants' cognitive processes. The debate on how to maximize insights from log file data has not yet reached a consensus. Therefore, we present this protocol for a scoping review that aims to characterize the application of log file data in current publications, including the data pre-processing techniques, analytical methodologies, and theoretical frameworks used by researchers. This review will also aim to illuminate how log file data can enhance psychological and educational assessments. Our findings will highlight the opportunities and challenges presented by log file data as an emerging and essential source of evidence for future advancements in psychological and educational assessment.

Online assessment of train hunting stability by monitoring dynamic wheel–rail displacement: why and how?

Online assessment of train hunting stability by monitoring dynamic wheel–rail displacement: why and how?

Large scale implementation of effective early literacy instruction

IntroductionLarge-scale implementation of Multi-Tiered Systems of Support (MTSS) to enhance children's early literacy success is critically important to address global literacy challenges. This paper describes one such initiative, the Better Start Literacy Approach (BSLA), which was specifically designed for large-scale implementation in New Zealand.MethodsBetween February 2020 and May 2023 over 3,000 teachers in 819 schools across New Zealand implemented BSLA, with baseline data available for 29,795 5-year-old children. Teachers implemented novel online assessments to monitor children's early literacy growth.ResultsIn comparison to an internal control group, accelerated progress in children's phonic, and phoneme awareness skills was evident after 10 weeks of BSLA Tier 1 (universal) teaching. After 30 weeks, there were significant gains in word reading, spelling, listening comprehension and oral narrative abilities; growth did not differ based on gender, ethnicity, or socioeconomic status. Children with greater learning needs who received BSLA Tier 2 (small group targeted teaching) caught up to their peers in word reading and spelling skills.ConclusionsWith appropriate resourcing, planning and engagement with communities, successful large-scale implementation of evidenced-based early literacy approaches is possible within a relatively short time. Culturally responsive implementation within MTSS frameworks holds much promise for raising literacy achievement for all children.

Comprehension of English Conditionals by Advanced Arabic-English Bilingual Speakers

The aim of this research is to determine the comprehension level of advanced Arabic L2 speakers of English conditionals, types I, II, and III. An online test was used which consisted of a total of 60 questions. The test questions were divided into two sections: syntactic structure questions and inference of meaning questions. These were grouped into 20 questions for each conditional type. Thirty-five participants, 29 females and six males, were recruited. The findings demonstrated that the participants' comprehension level was not high. Furthermore, conditional type II was observed to be the most difficult type of conditional. The findings also revealed that participants performed better in inference of meaning questions than in syntactic structure questions. Investigating such conditional constructions offers us a deeper understanding of the relationship between form and meaning.

Using an Online Assessment Tool to Teach Authentic Assessment to Early Childhood Teacher Candidates

Early childhood teacher candidates (TCs) need preparatory experiences using authentic assessments to inform their instructional practices with rural learners and their families. The shift to virtual field placements in response to COVID-19 restrictions pushed faculty in teacher preparation programs to reimagine how to engage TCs in meaningful experiences using authentic assessments with children and families. This study explores how TCs experienced authentic assessment practices using an online assessment management system, Assessment, Evaluation, and Programming System Interactive (AEPS i), during virtual field experiences. Fifty-five undergraduate and graduate TCs from 11 universities (10 U.S. & 1 international) completed a 45-item survey about their experience. Teacher candidates articulated knowledge and skills aligned with best practices underpinning authentic assessments. The virtual learning environment created opportunities for faculty to reimagine practices for promoting TCs’ understandings of assessment in early childhood special education contexts. We discuss implications for training TCs in rural areas to use authentic assessment.

ETHICAL ASPECTS OF SOCIAL AND PSYCHOLOGICAL RESEARCH IN THE FORM OF ONLINE TESTING, QUESTIONNAIRES, SURVEYS, INTERVIEWS. RELEVANCE IN THE POST-COVID PERIOD

During the COVID-19 pandemic, online socio-psychological research has become an important tool that is actively used everywhere and in the post-COVID period to study various aspects of psychology and social interaction in the context of restrictions on offline meetings. However, conducting such research involves ethical considerations and the need to adhere to certain principles.This article aims to explore the ethical dimensions of online socio-psychological research when employing online testing, surveys, and interviews. The authors delve into significant themes such as confidentiality, anonymity, informed consent, fairness, and the responsibility of researchers operating within the online realm. Additionally, the article presents practical recommendations for conducting ethical research.

Online Detection of Dry Matter in Potatoes Based on Visible Near-Infrared Transmission Spectroscopy Combined with 1D-CNN

More efficient resource utilization and increased crop utilization rate are needed to address the growing demand for food. The efficient quality testing of key agricultural products such as potatoes, especially the rapid testing of key nutritional indicators, has become an important strategy for ensuring their quality and safety. In this study, visible and near infrared (Vis/NIR) transmittance spectroscopy (600–900 nm) was used for the online analysis of multiple quality parameters in potatoes. The study concentrated on comparing three one-dimensional convolutional neural network (1D-CNN) models, specifically, the fine-tuned DeepSpectra, the fine-tuned 1D-AlexNet, and classic CNN, with UVE-PLS (uninformative variable elimination–partial least squares) models. These models utilized spectral data for the real-time detection of dry matter (DM) content in potatoes. To address the challenges posed by limited data from Vis/NIR, this study strategically implemented data augmentation techniques. This approach significantly enhanced the robustness and generalization capabilities of the models. The 1D-AlexNet and DeepSpectra models achieved 0.934 and 0.913 R2P and 0.0603 and 0.0695 g/100 g RMSEP for DM, respectively. Compared to UVE-PLS, the R2P value improved by 21.31% (0.770 to 0.934) for the 1D-AlexNet model and 18.64% (0.770 to 0.913) for the DeepSpectra model. The RMSEP value was reduced by 47.31% (0.114 to 0.0603) for 1D-AlexNet, and 39.30% (0.114 to 0.0695) for the DeepSpectra model. As a result, this study would be helpful for researching the online Vis/NIR transmission determination of potato DM using deep learning. These results highlighted the immense potential of employing specific spectral features in deep-learning models for a more precise and efficient online assessment of agricultural quality. This advancement provided some insight and reference for further contributing to the evolution of more targeted and efficient quality assessment methods in agricultural products.

A study on the design of a deep learning model for classroom based on user participation and game chemistry processes

In gamified learning, user engagement has always been an important concern in the field of education. For mass learning, the integration of classroom learning and gamification can make users well-stimulated and help them to improve and cultivate their comprehensive abilities. With the characteristics of efficiency, fairness, and objectivity, online assessment systems (OJ systems) have become an important tool for learning. However, the following limitations still exist in current OJ systems: First, most OJ systems cannot perceive and quantify the user’s learning level at the knowledge point level. This can lead to a mismatch between the difficulty of study questions adapted by the system to the user and the user’s learning ability; second, most OJ systems lack research and design of motivation methods. These limitations can negatively affect the enhancement of students’ motivation and the consolidation and improvement of their learning ability. Given this, the article designs and implements a gamified online assessment system (Game_OJ) based on user participation and the game chemistry process. The online assessment system uses a modified Bayesian knowledge tracking model (CC-BKT). The application of the Bayesian knowledge tracking model can accurately perceive and quantify students’ learning of each knowledge point. The online assessment system introduces the gamification idea to design the motivation methods in the OJ system, including the design of gamification elements and the design of the challenge process of gamified learning, aiming to enhance students’ motivation to learn. It promotes students’ deep learning in the classroom and outside the classroom. The CC-KPT model proposed in this paper has an average increase of 2.15 % in AUG value and an average increase of 3.25 % in accuracy compared to the same type of model, and the speed of response and the median speed of processing demand of this system is within 1 s when 500 users use the system at the same time, which meets the needs of on-campus teaching scenarios.

Online assessment in higher education: a mapping review and narrative synthesis

Online assessment takes many forms. While there have been reviews on a particular online assessment approach (e.g., online examinations or tests), there has not been a knowledge synthesis that considers online assessment research as a whole. A holistic understanding of online assessment research is important, as it recognizes differences in assessment approaches and the role of technologies in assessment. This understanding helps researchers navigate the heterogeneous body of research and allows educators to make research-informed improvements. To establish such an understanding, we analyzed 235 articles, following a mapping review and a narrative synthesis method. The findings revealed tests, assignments and skills assessments are major online assessment approaches. While research into tests reported using online technologies mainly to substitute or augment existing assessment, research into assignments and skills assessments was more likely to report using online technologies to modify or redefine assessment. Further, we identified disparities across the three approaches regarding academic misconduct, assessment validity, and reliability. We also identified a dearth of comparative research and a reliance on overall satisfaction and short-term self-reported impact measures as indicators of success. We discuss the implications of this review to provide insights for institutions and educators seeking to improve online assessment practices.

In silico design and evaluation of a multiepitope vaccine targeting the nucleoprotein of Puumala orthohantavirus.

The Puumala orthohantavirus is present in the body of the bank vole (Myodes glareolus). Humans infected with this virus may develop hemorrhagic fever accompanying renal syndrome. In addition, the infection may further lead to the failure of an immune system completely. The present study aimed to propose a possible vaccine by employing bioinformatics techniques to identify B and T-cell antigens. The best multi-epitope of potential immunogenicity was generated by combining epitopes. Additionally, the linkers EAAAK, AAY, and GPGPG were utilized in order to link the epitopes successfully. Further, C-ImmSim was used to perform in silico immunological simulations upon the vaccine. For the purpose of conducting expression tests in Escherichia coli, the chimeric protein construct was cloned using Snapgene into the pET-9c vector. The designed vaccine showed adequate results, evidenced by the global population coverage and favorable immune response. The developed vaccine was found to be highly effective and to have excellent population coverage in a number of computer-based assessments. This work is fully dependent on the development of nucleoprotein-based vaccines, which would constitute a significant step forward if our findings were used in developing a global vaccination to combat the Puumala virus.

An online intervention for carers to manage behavioral symptoms in motor neuron disease (MiNDToolkit): a randomized parallel multi-center feasibility trial

Background Evidence on management of behavioral symptoms in motor neuron disease (MND) is lacking. The MiNDToolkit, an online psychoeducational platform, supports carers dealing with behavioral symptoms (BehSymp). The study objectives were to ascertain recruitment and retention rates, carer and healthcare professional (HCP) use of the platform, and completion of online assessments, to inform a full-scale trial. Design: Randomized, parallel, multi-center, feasibility trial. Setting England and Wales, across diverse MND services; recruitment from July/21 to November/22; last participant follow-up in March/23. Participants Carers of people with motor neuron disease (PwMND) with BehSymp, recruited through MND services. After confirming eligibility, participants completed screening and baseline assessments online via the MiNDToolkit platform and were randomized centrally in a 1:1 ratio to MiNDToolkit or control. Intervention MiNDToolkit offered tailored modules to carers for the 3-month study period. Carers in the intervention group could receive additional support from MiNDToolkit trained HCPs. The control group was offered access to the intervention at the end of the study. Data were collected on platform usage and psychosocial variables. Main outcomes One hundred and fifty-one carers from 11 sites were invited to join the study (letter, face-to-face); 30 were screened; 29 were randomized. Fifteen people were allocated to the control arm; 14 to intervention. Carers were mostly female; median age for was 62.5 (IQR: 58, 68; intervention) and 57 (IQR: 56, 70; controls). Study retention was high (24/29 = 82.76%); carers engaged with the platform on average 14 times (median (IQR):14.0 (10.0, 18.5)) during the study period. Conclusion The MiNDToolkit study was feasible and well accepted by carers and trained HCPs. A definitive trial is warranted.

Does Online Testing Make the Grade? Evidence from a Randomized Controlled Trial

Abstract As a result of the increase in online course offerings at colleges and universities, the prevalence of online and computerized examinations has increased over the last two decades. Beyond academia, online examinations have been used to assess competence for certifications by both governments and private firms. Despite this importance, a randomized controlled trial that randomizes individual test takers into online and in-person examinations has not previously been conducted. In this paper, we rectify this by conducting this experiment in a large economics class at a large research university in the American southeast. We supplement our experiment results with a novel methodology of using screening surveys administered to potential volunteers which solicit preference information. We find that online testing reduces examination grades by over a tenth of a standard deviation, relative to in-person assessment. Additional exploratory exercises provide evidence that this effect is driven by students who prefer in-person examinations.

A level playing field? Evaluation of the virtual Objective Structured Clinical Examination in Psychiatry and Addiction Medicine: A mixed methods study.

The Objective Structured Clinical Examination (OSCE) has been used for clinical assessment of a broad range of medical student competencies in Psychiatry and Addiction Medicine. However, there has been little research into online assessments. We investigated the virtual OSCE (v-OSCE) from the user perspective to better understand its acceptability, usefulness, benefits, challenges and potential improvements. At the conclusion of the v-OSCE, all participants (medical students, examiners and simulated patients) were invited to participate in a brief online survey, based on the Technology and Acceptance Model. Freeform qualitative feedback was also obtained to explore participants' experiences and attitudes. Participants reported the v-OSCE was acceptable, efficient, convenient and easy to use. It was perceived as useful for demonstrating students' interviewing skills and interacting with the simulated patient. Benefits included greater convenience, reduced stress and travel time. Challenges were similar to those experienced in 'real world' telepsychiatry, primarily related to assessment of non-verbal cues and emotional prosody. Our findings inform recommendations for improving online examinations. These include increased preparation, practice and professionalism, to better simulate the in-person experience. Study credibility was strengthened by the triangulation of qualitative, quantitative and psychometric data.

ANALYSIS OF PARTICULAR SYSTEMS AND APPLICATIONS IN THE MODEL OF BLENDED LEARNING IN SECONDARY EDUCATION INSTITUTIONS

ANALYSIS OF PARTICULAR SYSTEMS AND APPLICATIONS IN THE MODEL OF BLENDED LEARNING IN SECONDARY EDUCATION INSTITUTIONS

Online Exam Portal

An online test is one that is conducted via the internet or an intranet utilizing a computer system. It is a web-based exam system. It works well as a mass education evaluation solution. Our system for online exams is built on a Browser/Server architecture. The multiple choice questions that are fed into the system are examined and automatically graded by the system. Even while it is comfortable, the classic pen and paper exam format has drawbacks in the current digital learning environment. IT students find these limits even more noticeable because they are used to using internet resources and platforms. In response, this proposal suggests creating an efficient, user-friendly, and safe online assessment system especially for IT students. This project goes beyond practicality. It increases assessment quality with interesting formats, empowers students with freedom, and promotes efficiency with quicker administration and grading. Teachers receive insightful data-driven information for bettering their curricula. In the end, it welcomes the digital age and uses cutting-edge evaluation techniques to get IT students ready for success

Early identification of macrophage activation syndrome secondary to systemic lupus erythematosus with machine learning

ObjectiveThe macrophage activation syndrome (MAS) secondary to systemic lupus erythematosus (SLE) is a severe and life-threatening complication. Early diagnosis of MAS is particularly challenging. In this study, machine learning models and diagnostic scoring card were developed to aid in clinical decision-making using clinical characteristics.MethodsWe retrospectively collected clinical data from 188 patients with either SLE or the MAS secondary to SLE. 13 significant clinical predictor variables were filtered out using the Least Absolute Shrinkage and Selection Operator (LASSO). These variables were subsequently utilized as inputs in five machine learning models. The performance of the models was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), F1 score, and F2 score. To enhance clinical usability, we developed a diagnostic scoring card based on logistic regression (LR) analysis and Chi-Square binning, establishing probability thresholds and stratification for the card. Additionally, this study collected data from four other domestic hospitals for external validation.ResultsAmong all the machine learning models, the LR model demonstrates the highest level of performance in internal validation, achieving a ROC-AUC of 0.998, an F1 score of 0.96, and an F2 score of 0.952. The score card we constructed identifies the probability threshold at a score of 49, achieving a ROC-AUC of 0.994 and an F2 score of 0.936. The score results were categorized into five groups based on diagnostic probability: extremely low (below 5%), low (5–25%), normal (25–75%), high (75–95%), and extremely high (above 95%). During external validation, the performance evaluation revealed that the Support Vector Machine (SVM) model outperformed other models with an AUC value of 0.947, and the scorecard model has an AUC of 0.915. Additionally, we have established an online assessment system for early identification of MAS secondary to SLE.ConclusionMachine learning models can significantly improve the diagnostic accuracy of MAS secondary to SLE, and the diagnostic scorecard model can facilitate personalized probabilistic predictions of disease occurrence in clinical environments.

A Robust Examination of Cheating on Unproctored Online Exams

The rapid growth of online education, especially since the pandemic, is presenting educators with numerous challenges. Chief among these is concern about academic dishonesty, especially on unproctored online exams. Students cheating on exams is not a new phenomenon. The topic has been discussed and debated within institutions of higher learning, and significant levels of cheating have been reported in the academic literature for over sixty years. Much of this literature, however, has focused on student behavior in a classroom utilizing proctored, in-class exams. Grades on exams usually determine most of a student’s final grade in a course, and GPAs are used by employers and graduate schools to indicate a student’s subject matter mastery. As more conventional colleges and universities expand their online course offerings it is natural to wonder if academic dishonesty is more prevalent in online classes than in face-to-face classes. In particular, are students more likely to cheat when no one is watching (i.e., on unproctored assessment assignments) than they do when someone is watching (i.e., on proctored assessment assignments)? The purpose of this study is to investigate whether students cheat more on unproctored online exams than they do on proctored in-classroom exams, and if so, is there any pattern to their cheating behavior. Our findings are derived from careful empirical analysis of 741 undergraduate students who completed three unproctored online exams, several collaboration-encouraged assignments, and a proctored in-class comprehensive final exam in the same course with the same instructor. Additionally, we collected demographic and human capital data for every student. Using bivariate and regression analysis, we find significant evidence of more cheating on unproctored online exams than on proctored in-class exams even though students were given stern honor code violation warnings. Moreover, we discover that student cheating increased with each unproctored online exam, implying that students learn how to cheat as they become more familiar with taking online assessment assignments. Finally, we find that students with certain demographic and human capital characteristics tend to cheat more than others. This research strongly supports the use of proctoring for all evaluation assignments in online classes to ensure that grades in these classes properly reflect student aptitude as opposed to merely reflecting their ability to cheat.

Eating Disorder Treatment Access in the United States: Perceived Inequities Among Treatment Seekers.

Although eating disorders are associated with high rates of psychological and physical impairments and mortality, only about 20% of individuals with eating disorders receive treatment. No study has comprehensively assessed treatment access for those with these disorders in the United States. The authors examined access to eating disorder treatments and how it might vary among some populations. Seekers of treatment for eating disorders (N=1,995) completed an online assessment of clinical demographic and anthropometric characteristics, barriers to eating disorder treatment access, and eating disorder symptomatology. Analyses were conducted to identify treatment access barriers, compare barriers to treatment access across demographic groups, and investigate relationships between barriers to treatment access and eating disorder symptoms. Financial barriers (e.g., lack of insurance coverage) were the most frequently reported barrier to treatment access. Participants with historically underrepresented identities and with a diagnosis of other specified feeding or eating disorder (OSFED) reported more barriers related to financial challenges, geographic location, eating disorder identification, sociocultural factors, and treatment quality compared with those with historically represented identities (e.g., White and cisgender persons). Higher frequencies of reported barriers to treatment access were associated with more severe eating disorder symptoms and poorer illness trajectories. Financial barriers were the most significant impediment to accessing treatment among individuals seeking eating disorder treatment. Barriers to treatment access disproportionally affected underrepresented groups and those with an OSFED diagnosis.