Final Marks Research Articles

Cardiac Autonomic Activity, Personality Traits, and Academic Performance in First-Year Medical Students: A Gender-Specific Relation.

Background It is not always the sincere or hardworking or intelligent student that gets the highest grades. Exploring unknown dimensions that may distinguish academic performance in adolescents/youth migrating from a high school study environment to that of a professional school and in a learning environment without parental supervision for the first time remains important. We hypothesized that cardiac autonomic activity influenced by cognitive domain factors and emotions would predict academic success in them. Further, we investigated which of their personality traits related to academic performance. Exploratory gender-based analysis was included. Methods A prospective cohort study measured first-year medical students' resting heart rate, heart rate variability (HRV), and personality traits (from the self-reported NEO Five-Factor Inventory-3). Spearman's correlation coefficient tested the correlation between the year-end final aggregate marks and assessed parameters, including subgroup analysis based on gender. Regression analyses of variables with academic marks were performed in the entire cohort. Results The aggregate marks of 81 volunteering students (Mage = 18.7, SD = 0.8 years; 42 females, 39 males) as a cohort did not correlate with their resting heart rate or HRV indices. Subgroup analysis revealed a positive correlation between marks and high-frequency power (r = 0.33, p= 0.03) and total power (r = 0.37, p= 0.02) of HRV in females. The marks positively correlated with the personality conscientiousness score (r = 0.32, p= 0.04) and extraversion score (r = 0.34, p= 0.03) in females.Multivariable regression analysis in the entire cohort revealed no significant interactions. Conclusion Academic performance was significantly related to cardiac autonomic modulation and personality traits of conscientiousness and extraversion in female but not male first-year medical students. These resultsindicate a gender-specific difference in the relation between scholastic performance and HRV in adolescents/youth transiting from high school to professional schools and entering a study environment without parental supervision for the first time. Further our data expands the knowledge base of educational psychology among them.

Implementation of a radio frequency identification and detection technology based digital class attendance system for university students

Class attendance is a key consideration when assigning final marks for courses taught, student attendance is a critical issue for higher institutions. Some institutions choose to regulate attendance with paper sheets, while others prefer to verify students' attendance with paper sheets and then manually enter this information into a computer. This mode of taking students attendance delays time and reduces the time needed for the lecture to be delivered. A system that eases the burden of time delay in taking attendance, promotes the universities, and supports parents is presented in this paper. The radio frequency identification and detection (RFID) student monitoring system has been developed to track student’s attendance in the classroom using passive RFID technology. The system uses an RFID reader and a contactless smart card to ensure time management during student’s attendance. A reader at a fixed place delivers a signal to a passive RFID chip for detection and identifies a huge number of chips in a short amount of time. The biometric thumb detection module for security was applied to improve the system. The applied enhanced technology sends a message to the parent of any students that fails to attend lectures.

E-Learning Interactions and Academic Outcomes: an Analysis of Undergraduates in Sri Lanka


 The shift to online teaching and learning following the onset of the COVID-19 pandemic resulted in a large increase in the usage of online learning platforms. Student interactions with these platforms provide an important source of information on student progress on a course during periods of distance learning. However, in resource constrained settings where students face difficulties in accessing stable and quality internet, it is unclear to what extent interactions with the learning platform influence academic outcomes, especially for programmes that were not originally intended to be delivered online. This study makes use of data on four cohorts of undergraduate students at the University of Moratuwa, where each cohort was exposed to different periods of online teaching and learning. The data covers interactions with Moodle course pages and assessment marks for multiple courses with diverse subject contents offered in two faculties. Interactions with the learning platform are measured using clicks on different types of objects on the course page as well as the distribution of clicks over the course of semester. To account for the confounding effect of prior ability on the relationship between learning platform interactions and academic outcomes, we also use results from courses that the students have taken a priori. We find that while the switch to online teaching and learning led to a dramatic increase in the level of student interactions with the learning platform, the level of interaction has remained above the pre-COVID levels even after resuming on-site, face-to-face delivery. While differences among the subjects and cohorts exist, results from a multiple regression model suggest that the association with certain types of learning platform interactions and academic outcomes are significant even after controlling for prior ability and differences in course design. Specifically, the volume and consistency of access both improve outcomes with the timing of clicks more strongly associated with the final examination mark whereas the type of content clicked on is more associated with continuous assessment marks. The findings have important implications for the continued adoption of blended learning methods and course design for the programmes under study.

Predicting student final examination result using regression model based on student activities in learning management system

<p><strong>Background:</strong> Learning analytics (LA) is the measurement, collection, analysis, and reporting of data about learners and their contexts to understand and optimize learning and the environments in which it occurs. Most teaching and learning (T & L) data was obtained from learning management systems (LMS), such as Moodle platform. However, this data is not utilized for teaching purposes, for example, learning how students’ activities can influence the final exam marks. <strong>Methods:</strong> Therefore, this study aims to find a correlation and develop a prediction model between students’ activities, independent variables known as LMS factors, against dependence variables, which are students’ final results. Besides, four non-LMS factors (race, sponsorship, admission requirements and final coursework marks) were also included in the research to obtain the best model. The regression analysis, models are used to predict the outcomes by evaluating the accuracies of testing data. <strong>Results:</strong> The findings reveal that the best model utilizes Simple Linear Regression (SLR) with coursework as an independent variable, resulting in an average error difference (AED) of only 1.8. The remaining experiments produced AED results ranging from 2.74 to 7.58 using Multilinear Regression (MR). <strong>Conclusions:</strong> in summary, this study provides a significant finding that demonstrates the potential of utilizing LMS activities to predict final marks, enabling lecturers to enhance their students’ results.</p>

Impact of a peer-to-peer escape room activity in the learning of Human Physiology of medical students from the university of Málaga.

Escape room's popularity has raised over the past years among young adults. It creates a distended competitive environment, where participants collaborate to achieve a common objective through teamwork. We decided to apply this format as a teaching method for medical students at the University of Malaga, Spain. A peer-to-peer physiological cardiorespiratory escape room was designed by intern undergraduate students, collaborating within the Department of Human Physiology. This activity integrated the contents of the Human Physiology syllabus, which were organized into four stages that culminated in a final medical case. Intern students oversaw the design, promotion, preparation and execution of the activity, and were in charge of conducting the evaluation and follow up. The escape room was done in mid-December, after all theoretical and practical contents had been delivered, for four consecutive years, improving from each year's experience. The target group for this activity were second year medical students, who were asked to team up freely in groups of four to six students before the start of the activity. The students in each group cooperated with each other while trying to solve the different puzzles and questions in each stage of the escape room. After the activity, the results of the final evaluation exam of these participants were compared against non-participants, who served as a control group. Qualitative feedback was also received from the participants via a special survey that was designed for this task. Results between 2020 and 2023 (three last activities) show that the final mark of the participants was significantly higher than in non-participants (6.39 ± 0.14 vs. 5.04 ± 0.2; p < 0.0007). The global exam mark also increased in the participants (5.43 ± 0.10 vs. 4.44 ± 0.15; p < 0.0007). A significant difference was observed in the performance in cardiovascular (p < 0.0007) and respiratory-related questions (p < 0.0007), which was substantial in the participants. The qualitative feedback received from the participants was mainly positive, indicating an overall acceptance of the format by the students. We conclude that escape room format with a peer-to-peer structure is an efficient teaching tool for medical students performed by medical students in the field of Human Physiology.

A Longitudinal and Repeated Cross-sectional Study on the Effectiveness of the Transition of Clinical Teaching from Traditional to Simulation Era

BackgroundNursing students must possess considerable knowledge and expertise in clinical procedures, particularly in advanced courses such as critical care, along with proper competence in dealing with various scenarios in real clinical settings. This study examined the effectiveness of integrating high-fidelity simulation (HFS) training in the skills laboratory and clinical hours of a critical care nursing course, with four cohorts of nursing students from the academic year (AY) 2019-2020 to AY 2022-2023. MethodThis was a longitudinal study, wherein a repeated cross-sectional analysis was conducted. Participants comprised 139 nursing students who were randomly selected. ResultsThe findings show remarkable progress and an increase in students’ final marks in the critical care nursing course for the AY 2019-2020 cohort, with a 10% simulation exposure reporting an average of 80.82 with a standard deviation (SD) of 7.48 compared to those with 40% simulation exposure in the AY 2022-2023 cohort, who reported an average of 93.24 with an SD of 4.29. ConclusionThe study results indicate that HFS can replace up to 40% of traditional skill lab sessions and clinical training only if the clinical simulated scenarios are well-planned and properly structured. However, the effectiveness of HFS also depends on students’ exposure to it. HFS can help refine students’ knowledge and skills.

Competence Development in an Undergraduate Physiotherapy Internship Program during the COVID-19 Pandemic: A Blended Learning Approach

Introduction: The COVID-19 pandemic has led to the creation of different teaching adaptations shared in health degrees, such as blended teaching methods, including face-to-face learning combined with online learning. Objectives: One objective was to compare the academic performance scores aligned to worked competencies during physiotherapy internships between two groups: one exposed to a blended learning (b-learning) educational model and another exposed to a face-to-face teaching system during internships. Another objective was to study the correlation of the marks of the following year’s internship subject. Design: Analytical study of retrospective cohorts. Settings: Physiotherapy university degree program. Participants: Three hundred students working towards attaining Physiotherapy degrees. Methods: Two groups were studied: the Non-Exposed Cohort, which had 100% face-to-face attendance at the clinical center, and the Exposed Cohort, which experienced b-learning and had 50% attendance at the clinical practice center and 50% completion of asynchronous online complementary training during lockdown. Results: The results show that both in the Exposed Cohort and in the Non-Exposed Cohort, the qualifications and, therefore, the competence development achieved by the students were similar. A stronger correlation with skills activities developed in the Exposed Cohort in relation to the final mark of the following year’s internship subject has been found. Discussion and Conclusions: For all the competencies developed during CS II, participants obtained the same grades in both groups, so it is concluded that the online activities implemented during confinement mixed with face-to-face teaching were useful for the achievement of these skills. Thus, b-learning is a good method for developing clinical practice competencies in physiotherapy students. Contribution of the Paper: Blended teaching (b-teaching) is useful for developing internship competencies. It is necessary to align the activities developed in internships with the evaluated skills.

Hybrid Nature-Inspired Optimization and Encryption-Based Watermarking for E-Healthcare

With the growth and popularity of the utilization of medical images in smart healthcare, the security of these images using watermarks is one of the most recent research topics. This algorithm is based on the joint use of dual watermarking, nature-inspired optimization, and encryption schemes utilizing redundant-discrete wavelet transform (RDWT) and randomized-singular value decomposition (RSVD). The key idea of the proposed method is to embed system encoded media access control (MAC) address in patient's ID card image via discrete wavelet transform (DWT) to generate the final mark. Afterward, embed the generated watermark into computed tomography (CT) scan images of the COVID-19 patient and general images through employing the RDWT and RSVD. Further, we use a hybrid of particle swarm optimization (PSO) and Firefly optimization techniques to determine the optimal scaling factor for embedding purposes. After that, the watermarked CT scan image is encrypted using an encryption technique based on a nonlinear-chaotic map, random permutation, and singular value decomposition (SVD). Extensive evaluations establish the benefit of our proposed algorithm over the traditional schemes. The optimal robustness is more effective than the five traditional schemes at lower computational efficiency.

The Discrete Exponentiated-Chen Model and Its Applications

A novel discrete exponentiated Chen (DEC) distribution, which is a subset of the continuous exponentiated Chen distribution, is proposed. The offered model is more adaptable to analyzing a wide range of data than traditional and recently published models. Several important statistical and reliability characteristics of the DEC model are introduced. In the presence of Type-II censored data, the maximum likelihood and asymptotic confidence interval estimators of the model parameters are acquired. Two various bootstrapping estimators of the DEC parameters are also obtained. To examine the efficacy of the adopted methods, several simulations are implemented. To further clarify the offered model in the life scenario, the two applications, based on the number of vehicle fatalities in South Carolina in 2012 and the final exam marks in 2004 at the Indian Institute of Technology at Kanpur, are analyzed. The analysis findings showed that the DEC model is the most effective model for fitting the supplied data sets compared to eleven well-known models in literature, including: Poisson, geometric, negative binomial, discrete-Weibull, discrete Burr Type XII, discrete generalized exponential, discrete-gamma, discrete Burr Hatke, discrete Nadarajah-Haghighi, discrete modified-Weibull, and exponentiated discrete-Weibull models. Ultimately, the new model is recommended to be applied in many fields of real practice.

Digital tools and active leaning in an online university: Improving the academic performance of future teachers

Digital tools are part of any online educational context. When it comes to training future teachers, this point is crucial because they will transfer this technology to their classrooms. Additionally, incorporating these digital resources as part of an active learning proposal will contribute to future teachers' motivation, self-regulation, and autonomy and positively influence their academic performance. This piece of research refers to the evaluation of an educational active learning proposal in which a pre-experimental design without a control group was used in order to study the possible relationship between the use of digital tools and the academic performance of the students. A sample of 908 students participated, distributed in three academic years. Results show that those who used more tools as part of the active learning proposals obtained better final marks, as well as a higher grade in the continuous assessment activities, which they also completed in a higher percentage (p&lt;0.001). It can be concluded that the use of digital tools within an active learning proposal contributes to an improvement in the academic performance of future teachers as well as in the participation and engagement of the students.

An evaluation of the inter-rater reliability in a clinical skills objective structured clinical examination

Background. An objective structured clinical examination (OSCE) is a performance-based examination used to assess health sciences students and is awell-recognised tool to assess clinical skills with or without using real patients.Objectives. To determine the inter-rater reliability of experienced and novice assessors from different clinical backgrounds on the final mark allocationsduring assessment of third-year medical students’ final OSCE at the University of the Free State.Methods. This cross-sectional analytical study included 24 assessors and 145 students. After training and written instructions, two assessors per station(urology history taking, respiratory examination and gynaecology skills assessment) each independently assessed the same student for the same skill bycompleting their individual checklists. At each station, assessors could also give a global rating mark (from 1 to 5) as an overall impression.Results. The urology history-taking station had the lowest mean score (53.4%) and the gynaecology skills station the highest (71.1%). Seven (58.3%) ofthe 12 assessor pairs differed by &gt;5% regarding the final mark, with differences ranging from 5.2% to 12.2%. For two pairs the entire confidence interval(CI) was within the 5% range, whereas for five pairs the entire CI was outside the 5% range. Only one pair achieved substantial agreement (weightedkappa statistic 0.74 ‒ urology history taking). There was no consistency within or across stations regarding whether the experienced or novice assessorgave higher marks. For the respiratory examination and gynaecology skills stations, all pairs differed for the majority of students regarding the globalrating mark. Weighted kappa statistics indicated that no pair achieved substantial agreement regarding this mark.Conclusion. Despite previous experience, written instructions and training in the use of the checklists, differences between assessors were found inmost cases.

ACTIVE LEARNING AND ACADEMIC PERFORMANCE: THE CASE OF REAL-TIME INTERACTIVE STUDENT POLLING

Active-learning pedagogical practices are encouraged in the education literature to improve academic outcomes. The research reported in this paper explored the effect of real-time interactive polling—a type of active learning—on three measures of academic outcome: final marks, student engagement, and failure probability. To provide credible impact estimates, a cluster randomized controlled trial in an introductory statistics class was conducted. With a little over 500 students, nine out of the 24 tutorial classes were conducted with real-time quizzes; the other 15 tutorial classes served as control classes. The results showed that this active-learning technique did not impact final marks and the probability of failing the subject. It did, however, increase tutorial attendance by about 24%, which corresponded to about 1.6 additional hours compared to the control classes. The findings support the literature on the benefits of classroom response systems that increase student engagement, student interest, and enjoyment from smaller group discussions.

Student semester marks prediction using linear regression algorithms in machine learning

Student Mark prediction using the previous semester's internal marks and semester marks project using a multivariate linear regression algorithm. Using the real-time data sets gathered from department, and analyze the data. The project's goal is to forecast the grade for the upcoming semester so that, by taking the necessary steps, for forecast the grade and enhance the outcomes in the upcoming semester. Designer developed three machine learning models for this project using the multivariate linear regression algorithm. Among these 3 models, two models are used to predict semester marks and another model is to predict the final internal or model exam marks. With the treatment of the data points and its specifies, to increase the machine learning model's effectiveness in this project.

Unveiling the Relationship between Internal Assessment and External Assessment in the International Baccalaureate Diploma Program

This study aimed to explore the relationship between internal assessment (IA) and external assessment (EA) scores of the high school students in the International Baccalaureate Diploma Program (IBDP). Methods: Simple linear regression analysis was conducted to explore to what extent the IA scores predict the EA scores in the Turkish, English, Physics, Chemistry, Biology and Mathematics courses. Findings: The quantitative analysis revealed that only a small percentage of the variation in IBDP EA scores can be explained by the model containing only IA scores in the IBDP standard level (SL) and high level (HL) courses. Furthermore, the correlation coefficient was utilized to explain the degree to which the IA scores are associated with the EA scores. This analysis revealed that there is a moderate relationship between IA and EA in HL English and SL biology course, but a weak relationship between IA and EA in SL and HL biology, chemistry, mathematics, physics, English (only SL) and Turkish courses. Value: This research highlights some important implications for educators about the consistency and reliability of IA as a tool for forming a final mark in the IBDP assessment and the role the IA plays in predicting the students’ general academic achievement.

Identifying At-Risk Students for Early Intervention—A Probabilistic Machine Learning Approach

The utilization of learning analytics to identify at-risk students for early intervention has exhibited promising results. However, most predictive models utilized to address this issue have been based on non-probabilistic machine learning models. In response, this study incorporated probabilistic machine learning for two reasons: (1) to facilitate the inclusion of domain knowledge, and (2) to enable the quantification of uncertainty in model parameters and predictions. The study developed a five-stage, probabilistic logistic regression model to identify at-risk students at different stages throughout the academic calendar. Rather than predicting a student’s final or exam mark, the model was focused on predicting the at-risk probabilities for subsequent assessments—specifically, the probability of a student failing an upcoming assessment. The model incorporated student engagement data from Moodle, as well as demographic and student performance data. The study’s findings indicate that the significance and certainty of student engagement and demographic variables decreased after incorporating student-performance variables, such as assignments and tests. The most effective week for identifying at-risk students was found to be week 6, when the accuracy was 92.81%. Furthermore, the average level of uncertainty exhibited by the models decreased by 60% from stage 3 to 5, indicating more reliable predictions at later than earlier stages. The study highlights the potential of a probabilistic machine learning model to aid instructors and practitioners in identifying at-risk students, and thereby to enhance academic outcomes.

Kahoot! and its effect on financial accounting marks at the university

New technologies have led to digital educational tools to improve learning. Game-Based Learning makes it possible to test whether there is a positive impact on official grades. We have collected the scores of the Kahoot! games, the marks of the continuous assessment, the final exam, and the final mark of the subject, to create an ad-hoc database. Finally, we have analyzed a database consisting of 392 marks of higher students from the first semester of the 2018/2019 academic year. We have calculated a correlation matrix, tested a hypothesis for differences in mean paired samples, and proposed three regression models to obtain that there is a positive influence of the games on the official marks. Therefore, Kahoot! is a didactic tool that improves students’ learning and is reflected in their official marks.

The Effect of Binary Interaction Parameters and Induced Association on the Performance of the PC-SAFT Equation of State: Validation against a Benchmark Database of High-Quality Binary-System Data

The performance of the Perturbed Chain - Statistical Associating Fluid Theory (PC-SAFT) equation of state (EoS) using classical mixing rules, induced-association schemes for mixtures exhibiting cross-association phenomena between two components that do not self-associate, and one regressed temperature-independent binary interaction parameter (kij) per mixture is assessed using a high-quality reference database containing binary-system data for various properties. The binary mixtures that are included in the database are divided into nine categories (Binary Association Codes, BACs) according to their ability to be involved in a hydrogen bond. Grading of the EoS is done by applying an already published procedure with certain modifications that were also published recently. An important feature is the inclusion of a performance indicator, called the “Success Ratio” (SR), that quantifies the percentage of experimental data points which the model can qualitatively reproduce. The original marks of the PC-SAFT EoS when implemented with classical mixing rules, no induced-association scheme, and a kij parameter set equal to zero are recalculated with the modified grading procedure and are directly compared with the new marks obtained in this work. The effect of including induced-association effects in PC-SAFT for mixtures that belong to BAC6, BAC7, and BAC8 (mixtures in which cross-association takes place without both components being self-associating) is also investigated. The effect of regressing cross-association parameters vs regressing kij parameters for mixtures belonging to the BAC6 group (mixtures in which cross-association arises although the two components do not self-associate) is showcased. The final optimized PC-SAFT EoS utilizes classical mixing rules, induced-association effects (using regressed cross-association parameters for some binary systems belonging to BAC6 and a predictive method for BAC7 and BAC8), and one temperature-independent kij parameter per binary mixture (except for binary systems belonging to BAC6 for which cross-association parameters were adjusted). The final average mark of the optimized EoS is 9.3/20.

Impact of Students’ Vocabulary Level to their Reading and Writing Performance

This study aimed to identify the impact of vocabulary level to performance in reading and writing. The study used the descriptive correlation research design to establish the relationship of the students’ vocabulary size to their reading and writing skills. Pearson's correlation coefficient (r) was used to measure the strength of relationship between the two variables- vocabulary level and reading and writing skills. Vocabulary Level Test, adapted from Nation (1990) [1], was used to measure vocabulary level, and the reading and writing final mark was used for students’ performance in reading and writing. Based on the findings, vocabulary level has significant relationship to reading and writing performance of students. The result shows that students who have average vocabulary level can achieve average performance in reading and writing courses. Vocabulary level is a valid indicator of reading and writing performance.

Using WeChat as an educational tool in MOOC-based flipped classroom: What can we learn from students' learning experience?

Despite its importance, interaction remains limited in MOOC-based flipped classroom (MBFC) Grounded in social learning theory, we proposed an MBFC approach supported by social media to facilitate students' interaction with peers and learning performance. A quasi-experiment was conducted to compare the MBFC approach (N = 58) based on WeChat with the conventional MBFC approach (N = 52). The results revealed that the use of WeChat in an MBFC approach led to better performance in terms of watching video lectures and completing online exercises before the class; however, it did not significantly enhance student learning performance compared to the conventional MBFC approach. In addition, the study found that students were moderately satisfied with the MBFC approach supported by WeChat. According to a WeChat interaction quantity and quality analysis, students' non-substantive postings are much higher than students' substantive postings in WeChat interaction groups, but students' contributions to the postings have no significant effect on the final marks. Findings from this study could be of valuable reference for practitioners and researchers who plan to leverage social media tools such as WeChat to support student MOOC learning.

Correlation of Students’ Vocabulary Range to their Listening and Speaking Performance

This study aimed to identify the correlation of vocabulary range to performance in listening and speaking. The study used the descriptive correlation research design to establish the relationship of the students’ vocabulary size to their Listening and Speaking Skills. Pearson's correlation coefficient (r) was used to measure the strength of relationship between the two variables- vocabulary size and Listening and Speaking Skills. Vocabulary Level Test, adapted from Nation (1990) [1], was used to measure vocabulary range, and the listening and speaking skills final mark was used for students’ performance in listening and speaking. Based on the findings, vocabulary range has no significant relationship to listening and speaking skills of students. The result shows that students who have average vocabulary range can still achieve high performance grade in speaking and listening courses.