Skewness Values Research Articles

DUS Topp–Leone-G Family of Distributions: Baseline Extension, Properties, Estimation, Simulation and Useful Applications

This study introduces the DUS Topp–Leone family of distributions, a novel extension of the Topp–Leone distribution enhanced by the DUS transformer. We derive the cumulative distribution function (CDF) and probability density function (PDF), demonstrating the distribution’s flexibility in modeling various lifetime phenomena. The DUS-TL exponential distribution was studied as a sub-model, with analytical and graphical evidence revealing that it exhibits a unique unimodal shape, along with fat-tail characteristics, making it suitable for time-to-event data analysis. We evaluate parameter estimation methods, revealing that non-Bayesian approaches, particularly Maximum Likelihood and Least Squares, outperform Bayesian techniques in terms of bias and root mean square error. Additionally, the distribution effectively models datasets with varying skewness and kurtosis values, as illustrated by its application to total factor productivity data across African countries and the mortality rate of people who injected drugs. Overall, the DUS Topp–Leone family represents a significant advancement in statistical modeling, offering robust tools for researchers in diverse fields.

A supergene affects androgen concentrations during early development in a bird with alternative reproductive morphs

Species with alternative reproductive tactics typically show pronounced phenotypic variation between and within sexes. In some species, this variation culminates in discrete reproductive morphs that are genetically determined, facilitating studies on how genetic variation translates into phenotypic variation. In ruffs (Calidris pugnax), an autosomal inversion polymorphism underlies three reproductive morphs (Independents, Satellites and Faeders), which differ in circulating steroid concentrations in adults. Yet, it remains unknown whether morph differences in steroid concentrations already arise before adulthood. We examined variation in circulating testosterone, androstenedione and progesterone concentrations between morphs and sexes in ruff chicks and juveniles and compared the differences to those in adults. Since measured hormone concentrations only provide momentary states and show high within- and between-individual variation, we took repeated measurements to compare means, variances and skewness between groups. We found clear differences between morphs but not the sexes in early life. Between morphs, androgen concentrations in young ruffs differed in variance and skewness, but not in their means. For testosterone, Independents had a higher variance than Satellites/Faeders, whereas for androstenedione, we observed the opposite pattern. For progesterone, we did not detect clear differences between groups. Skewness values mirrored differences in morph variances. Compared to adults, premature ruffs had lower androgen concentrations. In both life stages, we detected morph-specific associations between androgen concentrations: androstenedione concentrations increased with testosterone concentrations more in Satellites/Faeders than in Independents. These observed morph differences during early life are consistent with a supergene-mediated regulation of androgen variation that underlies the diversification of adult behavioural phenotypes.

Fractal analyses of AlxGa1−xN thin film surfaces on AlN at different annealing temperatures

The determination of heteromorphological structures formed on the surface during annealing of AlxGa1−xN thin film grown on sapphire substrate using the metal organic chemical vapor deposition technique at different temperatures was investigated by fractal analysis method. The images of the surfaces of the thin films were taken by atomic force microscopy (AFM) at temperatures of 900, 1000, 1050 and 1200 °C respectively. AFM images were digitised in bitmap format according to the annealing temperatures. It was determined that the fractal dimensions obtained a linear correlation with the annealing temperatures. The results confirm the hypothesis that surface relaxation by the thermal action can produce fractal-like structures at particle or cluster boundaries. It is found that the observed cluster formation of superficial particles decreases as increasing temperature. The increase in temperature reduces the rate of superficial particle coating. To determine the surface roughness of the AlxGa1−xN thin film according to the annealing temperature, the AFM images were digitized in tagged image file format and the statistical root mean square, mean value, mean roughness, skewness and kurtosis values of the films were calculated. The roughness is a result of the thin film surface heteromorphology formed due to the specific annealing process. It is proved that the fractal dimensions of the AlxGa1−xN thin film increase as the annealing temperature rises. The particles coalesce on the surface and cluster in different types and sizes at each different annealing temperature, forming islets of different sizes. The skewness and kurtosis values show a different and irregular change.

Incomplete ordinal panel responses with initial condition and skew-normal random effect

This paper presents a model for the ordinal categorical panel data with skew-normal random effects, endogenous initial condition, and non ignorable (NI) non monotone missing data. We assumed three separate models for the initial condition, subsequent responses, and missing procedure. The baseline or initial response variable and subsequent response variables are modeled as ordinal logistic, whereas the logistic binary model is considered for the missing process where the maximum likelihood estimates are obtained using Monte Carlo methods. The results show that in the presence of missing data, the skew-normal random effects produce better results compared to the normal one, and considering the endogenous initial conditions is effective in modeling the results of the observed and missing responses such that the patients whose severity of schizophrenia is higher at the beginning of the process have a higher chance of missing on the subsequence times. In this research, schizophrenia data and then simulation studies have been used to check the performance of the proposed model. The results show that the skew-normal random effect using the NI non monotone missing mechanism by increasing the skewness value produces better results in the analysis of simulation studies.

Asymmetric Laplace scale mixtures for the distribution of cryptocurrency returns

AbstractRecent studies about cryptocurrency returns show that their distribution can be highly-peaked, skewed, and heavy-tailed, with a large excess kurtosis. To accommodate all these peculiarities, we propose the asymmetric Laplace scale mixture (ALSM) family of distributions. Each member of the family is obtained by dividing the scale parameter of the conditional asymmetric Laplace (AL) distribution by a convenient mixing random variable taking values on all or part of the positive real line and whose distribution depends on a parameter vector $$\varvec{\theta }$$ θ providing greater flexibility to the resulting ALSM. Advantageously concerning the AL distribution, our family members allow for a wider range of values for skewness and kurtosis. For illustrative purposes, we consider different mixing distributions; they give rise to ALSMs having a closed-form probability density function where the AL distribution is obtained as a special case under a convenient choice of $$\varvec{\theta }$$ θ . We examine some properties of our ALSMs such as hierarchical and stochastic representations and moments of practical interest. We describe an EM algorithm to obtain maximum likelihood estimates of the parameters for all the considered ALSMs. We fit these models to the returns of two cryptocurrencies, considering several classical distributions for comparison. The analysis shows how our models represent a valid alternative to the considered competitors in terms of AIC, BIC, and likelihood-ratio tests.

Scalar mixing and entrainment in an axisymmetric jet subjected to external turbulence

The present study aims to understand the process of turbulent entrainment into a jet, as affected by background turbulence, using scalar statistics. Planar laser-induced fluorescence was employed to capture the orthogonal cross sections of the jet at a fixed downstream station with varying background turbulence intensities and length scales. The conditional scalar profiles revealed that the thickness of the scalar turbulent/turbulent interface is greater than that of the traditional turbulent/non-turbulent interface, and the interfacial thickness is an increasing function of the background turbulence intensity. Although nibbling remains the primary entrainment mechanism in the far field, increased occurrence of concentration “holes” within the interfacial layer in the presence of ambient turbulence suggests a more significant role of large-scale engulfment in the turbulent/turbulent entrainment process (although still below 1% of the total mass flux). Enhanced contribution of the area of detached jet patches (i.e., “islands”) to that of the main jet is hypothesized to be evidence of intense detrainment events in the background turbulence. This can potentially contribute to a reduced net entrainment into the jet, which manifests as less negative values of scalar skewness within the jet core.

Microstructure and corrosion properties of Al1-xWxN coatings for potential use in gas turbine blades

Herein, a numerical approach of Al1-xWxN thin film coatings on Inconel 738LC superalloys for thermal barrier applications has been studied and the experimental analysis includes deposition of Al1-xWxN films on Si (100), glass and transparent quartz substrates at 40 % N2 flow rate using a dual target DC reactive magnetron sputtering technique to investigate the morphology, microstructure, chemical bonding and corrosion behaviour. AFM analysis revealed the rms roughness, Sq and average roughness, Sa increased with the increasing W content from 22 to 70 at.%. It was found that Al1-xWxN films tend to form tiny valleys on the film surface because the values of surface skewness, Ssk lie below zero. The TEM image showed a branched snowflake structure for the specimen of Al0.58W0.42N films with discrete spots in the SAED pattern, confirming the microcrystalline nature. The blue-shifting of Ecorr towards positive potential confirmed the lower corrosion resistance of WN films than those of AlN. XRD spectra of Al0.58W0.42N films showed an amorphous nature devoid of peaks, which is also confirmed by the SAED pattern. The quasi-passive oxide layer formation was spontaneous for WN films compared to the AlN films. The bending vibrations of AlN and WN showed the coordination environment of the metal centre in the Al1-xWxN films. The computational results indicated that the thermal barrier properties of Al1-xWxN films decreased with the increasing W content, i.e., AIN > Al0.58 W0.42N > WN.

DETERMINING THE RELATIONSHIP BETWEEN SOCIAL MEDIA APPEARANCE CONSCIOUSNESS AND SELF-ESTEEM IN ADOLESCENTS

Introduction: Adolescents' active use of social media raises curiosity about how they appear on social media platforms. This situation may cause changes in their personality. This study was conducted to determine the relationship between social media appearance consciousness and self-esteem in adolescents. Method: This cross-sectional study was conducted between January and March 2023. The study involved 1103 students from three different high schools located in a province in eastern Turkey. As part of the study process, data were collected using the "Questionnaire", "Appearance-Related Social Media Consciousness Scale" and "Rosenberg Self-Esteem Scale". To analyze the data, percentages, averages, independent sample t-tests, one-way ANOVA, Pearson correlation, and regression analysis were performed. The significance level in all tests was set at 0.05. Shao's method was employed to assess the conformity of the data to normal distribution. As a result, the Skewness value was 0.409, the Kurtosis value was -0.370, and it was determined that the data showed a normal distribution between -3 and +3. Results: The mean age of the students in the study was 26.8% 15 years old, 62.2% were male, and 27% were 9th-grade students. The mean total score of the students was 41.11±17.74 for the Appearance-Related Social Media Consciousness Scale and 29.36±6.35 for the Rosenberg Self-Esteem Scale. It was determined that there was a positive correlation between the scores of the Appearance-Related Social Media Consciousness Scale and the Rosenberg Self-Esteem Scale (p<0.05). Conclusions: In conclusion, it was observed that increasing social media appearance consciousness scores was associated with higher self-esteem levels.

Real-time identification of noise type contaminated in surface electromyogram signals using efficient statistical features

Different types of noise contaminating the surface electromyogram (EMG) signal may degrade the recognition performance. For noise removal, the type of noise has to first be identified. In this paper, we propose a real-time efficient system for identifying a clean EMG signal and noisy EMG signals contaminated with any one of the following three types of noise: electrocardiogram interference, spike noise, and power line interference. Two statistical descriptors, kurtosis and skewness, are used as input features for the cascading quadratic discriminant analysis classifier. An efficient simplification of kurtosis and skewness calculations that can reduce computation time and memory storage is proposed. The experimental results from the real-time system based on an ATmega 2560 microcontroller demonstrate that the kurtosis and skewness values show root mean square errors between the traditional and proposed efficient techniques of 0.08 and 0.09, respectively. The identification accuracy with five-fold cross-validation resulting from the quadratic discriminant analysis classifier is 96.00%.

Characterization of Multi-Layer Rolling Contact Fatigue Defects in Railway Rails Using Sweeping Eddy Current Pulse Thermal-Tomography

Railways play a pivotal role in national economic development, freight transportation, national defense, and regional connectivity. The detection of rolling contact fatigue (RCF) defects in rail tracks is essential for railway safety and maintenance. Due to its efficiency and non-contact capability in detecting surface and near-surface defects, Eddy Current Pulsed Thermography (ECPT) has garnered significant attention from researchers. However, detecting multi-layer RCF defects remains a challenge. This paper introduces a sweeping Eddy Current Pulsed Thermal-Tomography system (ECPTT) to detect multi-layer RCF defects effectively. This system utilizes varying excitation frequencies to heat defects, altering skin depth and facilitating feature extraction to distinguish multi-layer RCF defects. Skewness and thermographic signal reconstruction (TSR) values are employed as features in the experiments. These features are qualitatively analyzed to differentiate the layers and depths of multi-layer RCF defects. Additionally, five different coils were compared and analyzed quantitatively. The results indicate that the ECPTT system can effectively detect and distinguish multi-layer RCF defects, thereby providing more detailed defect information and enhancing railway safety and maintenance efficiency.

The Framework of Quantifying Biomarkers of OCT and OCTA Images in Retinal Diseases.

Despite the significant advancements facilitated by previous research in introducing a plethora of retinal biomarkers, there is a lack of research addressing the clinical need for quantifying different biomarkers and prioritizing their importance for guiding clinical decision making in the context of retinal diseases. To address this issue, our study introduces a novel framework for quantifying biomarkers derived from optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images in retinal diseases. We extract 452 feature parameters from five feature types, including local binary patterns (LBP) features of OCT and OCTA, capillary and large vessel features, and the foveal avascular zone (FAZ) feature. Leveraging this extensive feature set, we construct a classification model using a statistically relevant p value for feature selection to predict retinal diseases. We obtain a high accuracy of 0.912 and F1-score of 0.906 in the task of disease classification using this framework. We find that OCT and OCTA's LBP features provide a significant contribution of 77.12% to the significance of biomarkers in predicting retinal diseases, suggesting their potential as latent indicators for clinical diagnosis. This study employs a quantitative analysis framework to identify potential biomarkers for retinal diseases in OCT and OCTA images. Our findings suggest that LBP parameters, skewness and kurtosis values of capillary, the maximum, mean, median, and standard deviation of large vessel, as well as the eccentricity, compactness, flatness, and anisotropy index of FAZ, may serve as significant indicators of retinal conditions.

Exploring Teacher Awareness of Artificial Intelligence in Education: A Case Study from Northern Cyprus.

This study investigates the level of awareness among teachers regarding the use of artificial intelligence (AI) in education, focusing on whether this awareness varies according to socio-demographic characteristics, access to technology, and specific knowledge and beliefs about AI. Conducted in Northern Cyprus during the 2023-2024 academic year, this study employed a survey model with purposive and snowball sampling methods, involving 164 teachers. Teachers at different levels, namely, primary school, secondary school, high school, and university, were included in this study. The "Artificial Intelligence Awareness Scale", developed by Ferikoğlu and Akgün (2022), was used to measure AI awareness. Data normality was verified through skewness and kurtosis values, allowing for parametric statistical tests such as t-tests, one-way ANOVA, logistic regression, and chi-square analysis. This study explored the distribution of AI use across different school types and educational levels and assessed the impact of sub-dimensions of AI awareness on its application in teaching. Findings revealed no significant influence of teacher demographics (age, gender, education level, type of school, institution level, and monthly income) on AI awareness. However, usage patterns indicated that university lecturers were more likely to incorporate AI in their teaching, followed by primary and high school teachers, with secondary school teachers using it the least. A Multilayer Neural Network Analysis identified practical knowledge as the most critical factor influencing the use of AI in teaching (importance weight of 0.450), followed by beliefs and attitudes (0.298), relatability (0.148), and theoretical knowledge (0.104). These results highlight the importance of practical knowledge for fostering AI integration in educational practices, underscoring significant implications for teacher training and professional development programs.

Oral Health Literacy Status, Knowledge Levels, Beliefs, and Behaviors of Pregnant Women.

Pregnancy may exacerbate the frequency and severity of some dental problems. However, most pregnant women avoid going to the dentist. To examine the oral hygiene and dental care behaviors of women during pregnancy, to measure their knowledge levels, to reveal their oral and dental health literacy status, and their beliefs about treatment. The study was conducted for a period of 1 month with pregnant women over the age of 18 who presented to the obstetrics outpatient clinic and agreed to participate in the study and who had no known anomalies or complications. A survey was completed by face-to-face interviews with 317 pregnant women of different ages and gestational weeks. Participants were asked questions regarding their sociodemographic characteristics, pregnancy characteristics, oral hygiene-related beliefs, behaviors, and knowledge levels. The data obtained in the study were analyzed with SPSS 21.0 program. Since the kurtosis and skewness values were between +3 and -3, parametric tests were used. In total, 317 pregnant women between the ages of 18 and 43 participated in the study. The most common beliefs are listed as; babies receive the necessary calcium from the teeth, antibiotics given during dental treatment harm the baby, and panoramic radiography taken during treatment harms the baby. 91.5% of the participants did not visit the dentist after planning a pregnancy, and 89% of them did not visit the dentist during pregnancy. The most common reason not to visit the dentist is not having a dental problem. The rate of knowing that dental infections during pregnancy will affect the baby is 50.5%. In the present study, it is seen that pregnant women do not have adequate oral hygiene. The reasons were listed as various false beliefs and lack of sufficient knowledge. Health professionals should provide information about oral hygiene to pregnant women and direct them to the dentist.

Do Fair Play Behaviors Reduce Aggression and Anger in Sport?

The sample group of the study, which examined the relationship between the fair play behaviors of student athletes and their levels of aggression and anger in sports, consisted of a total of 333 student athletes, 140 female and 193 male, studying at Alanya Alaaddin Keykubat University Faculty of Sports Sciences in the 2021-2022 academic year. The aim of the study, in which both screening (descriptive) and correlational models were used, was to examine the relationship between student athletes' fair play behaviors and their levels of aggression and anger in sports. The Multidimensional Scale of Sportsmanship Orientation (MSOS-25) and the Scale of Aggression and Anger in Sport (CAAS) were used to collect data. The data were collected from the participants through face-to-face forms in April 2022. In the analysis of the data obtained in the study, Cronbach Alpha reliability coefficient was calculated to examine the internal consistency of the measurement tools. According to the various demographic characteristics of the participants, t-test and ANOVA tests were applied to evaluate the levels of the Multidimensional Sportsmanship Orientation Scale and the Aggression and Anger in Sport Scale. In addition, the level of relationship between the scales was evaluated by Pearson correlation analysis. Whether the data met the prerequisites of parametric tests was decided by examining the Skewness and Kurtosis (normal distribution of data) values and Levene (equality of variance) test results. As a result of the research, it was seen that there was a significant negative relationship between the Multidimensional Sportsmanship Orientation levels of student athletes and the levels of Aggression and Anger in Sports. Accordingly, it can be said that as the fair play behaviors of student athletes increase, their levels of aggression and anger in sports decrease.

THE MODERATING ROLE OF GENERAL ATTITUDE TOWARDS ARTIFICIAL INTELLIGENCE IN THE IMPACT OF DIGITAL TRANSFORMATION ON EMPLOYEE SATISFACTION

This study focuses on understanding the effects of digital transformation processes in the business world on employee satisfaction. The purpose of this study is to determine whether the general attitude towards artificial intelligence plays a moderating role in the effect of digital transformation on employee satisfaction. At the same time, the study was also reinforced and elaborated with demographic questions directed to the employees. The population of the study consists of private enterprises operating in the retail sector in Istanbul. The sample is the decision-making white- collar (N= 522) current employees working in these retail sectors. SPSS 24.0 statistical package programe was used to analyze the data. Normality test was performed to determine whether the data set was suitable for parametric tests. Kurtosis and skewness values were used to evaluate normality. Pearson correlation analysis was performed to determine the direction and severity of the relationship between the variables. Moderating analysis was performed to determine how the relationship between an independent variable and a dependent variable is affectedy a third variable. According to the results obtained from the study, there are quite high and significant correlations between digital transformation and other variables in the correlation analysis. According to the moderating effect analysis, it was observed that the general attitude towards artificial intelligence did not moderate the effect of digital transformation on the variables. In demographic variables, significant differences are observed in all variables and sub-dimensions.

A Study on the Measurement of Relative Poverty in Developing Countries with Large Populations

Under the goal of global poverty eradication, it has become a forward-looking research aim to establish relative poverty criteria and identify people experiencing relative poverty in countries with different demographic characteristics. This paper introduces a new method to measure the relative poverty standard, which is to use the fuzzy decision tree algorithm to objectively estimate the relative poverty standard. The advantage of this algorithm lies in (1) it not only maintaining the regression idea of measuring absolute poverty, but also emphasizing the nonlinearity when the demand is increasing, which can reflect the change in human needs. (2) It overcomes the division of the traditional method which clearly distinguishes between those experiencing poverty and those who are not by means of a subjective threshold, and it also avoids the subjectivity of the selection of multidimensional indicators. (3) It overcomes the problems of data skewness and extreme value issues that traditional methods have, and can exhibit multi-dimensional characteristics. (4) Most importantly, this method can overcome the gap problem caused by the complex population structure in developing countries with huge populations, and is more adaptable under big data conditions than traditional methods. Taking China as an example, using data from the China Household Finance Survey for validation, the validation results show that the relative poverty standard in China in 2019 can be approximately delineated as 5288.5 RMB; this result is higher than the absolute poverty standard line delineated in China in that year, lower than the relative poverty standard line measured using the proportion method, and it can satisfy the average per capita food, tobacco, and alcohol consumption expenditure of Chinese residents in that year. Thus, compared with other methods, the fuzzy decision tree algorithm can better match the identification of relative poverty in developing countries with large populations.

Role of Correlation among Physical Factors in Probabilistic Simulation of Emissions of Volatile Organic Compounds from Floating Storage and Offloading Vent Stack

This research investigated the roles of correlations among physical factors in the probabilistic simulation of volatile organic compounds (VOCs) emitted from a marine vessel (known as floating storage and offloading, FSO), located in the Gulf of Thailand. The physical factors in this study were wave height, ambient temperature, storage temperature, storage quantity, Reid vapor pressure, and the daily incoming rate. These physical factors were transformed into normally distributed data and a second-order multiple linear regression (MLR) with interaction effects, that were then used to determine the relationship between the transformed physical factors and the VOC venting volume from the FSO. The dataset of relevant predictors (transformed physical factors and interactions) that provided the maximum adjusted coefficient of determination was chosen for inclusion in the MLR. After that, two datasets of 1,000 venting volumes (one with and one without correlations among physical factors) were simulated. In the simulation, 1,000 datasets of six physical factors were generated according to observed averages and standard deviations. Cholesky randomization was used to generate the correlated physical factors for the simulation with correlation among physical factors. The averages of VOC venting volumes calculated from the generated physical factors when correlations among physical factors were and were not applied were 211,610 and 210,906 ft3, respectively (observed average was 210,984 ft3), with standard deviations of 38,828 and 40,787 ft3, respectively (observed standard deviation was 67,961 ft3), and skewness values of 0.74 and 0.51, respectively (observed skewness was 0.71). Therefore, correlation among the physical factors improved the skewness and provided better simulation results for VOC emission.

Investigation of the Effect of Sports Participation Motivations of Students of Faculty of Sports Sciences on Their Leadership Orientations

In this study, it was aimed to determine the relationship between motivation to participate in sports and leadership orientations of students of the faculty of sports sciences. A relational survey model was used in this study. The study group consisted of students studying in the Faculty of Sports Sciences. The questionnaire part of the study consists of three parts. Firstly, includes demographic information. Secondly, the “Leadership Orientations Questionnaire”, which was developed by Bolman and Deal (1990) and adapted into Turkish by Dereli (2003), is explored. Thirdly, consists of the Participation Motivation Questionnaire, which was developed by Gill et al. (1983) and adapted into Turkish by Oyar et al. (2001). For the determine normality assumption skewness and kurtosis values was used. Skewness and kurtosis values were found to be between ±2 and it was determined that the data had a normal distribution (George and Malley, 2003). Afterward, an Independent samples t-test was used between paired groups, and a one-way analysis of variance was used for paired variables. Moreover, reliability coefficient, frequency and percentage and mean and standard deviation calculations were conducted. Simple regression analysis was used to determine the effect between the dependent and independent variables. The findings, a statistically significant difference was found in the variables of leadership orientation and age, income level, and frequency of weekly sports participation in sports participation motivation. As a result, it was determined that there was a low-level significant and negative relationship between the participants' motivation to participate in sports and their leadership orientations.Keywords: Leadership, Motivation, Sports.

Textural characteristics of fluvial sediments in the Kosi River Basin, Bihar, India

The fluvial sediment samples collected from the Kosi River Basin, Bihar were subjected to textural analysis. The textural parameters were computed using the appropriate phi values. Statistical metrics including mean, standard deviation, skewness, and kurtosis were estimated. From the findings, the mean size of river sand has been observed from medium to fine-grained nature (between 0.97 Φ and 2.72 Φ). Within the range of 0.50 Φ to 0.77 Φ, the standard deviation denotes a moderately well-sorted to moderately sorted nature. The sediment samples exhibit skewness values ranging from -0.344 Φ to 0.267 Φ. The Kurtosis varies between 0.86 Φ and 1.9 Φ, indicating a leptokurtic to mesokurtic nature. Thus, the statistical measures have revealed that sediments are dominant in the medium sand category, moderately well sorted, finely skewed, and fall under leptokurtic character. The bivariate plots constructed between the statistical metrics indicate the unimodal nature of sediments.

Exploring the potentials of ChatGPT for instructional assessment: Lecturers' attitude and perception

Lecturers play a crucial role in the educational process, offering unique insights and perspectives within the classroom. The issue of cred­ibility in educational assessment often rests on the shoulders of lecturers, who are responsible for eval­uating students' progress. The present study aimed to investigate lecturers' attitudes and perceptions re­garding the potential of ChatGPT for instructional assessment. A correlational research design was adopted, and purposive sampling was used to select 102 lecturers from Nigerian universities who had uti­lised ChatGPT for instructional assessment. Data was collected through an online structured question­naire. The normality and homogeneity of variance assumptions were met, as evidenced by kurtosis and skewness values falling within acceptable thresh­olds. The lecturers employed the instructional assessment questionnaire utilising ChatGPT to gather and analyse the data, employing t-tests and ANOVA. The findings revealed a statistically significant difference between perception (F (3, 98) =7.168, p=0.001 <0.05) and lecturers' years of experience in using ChatGPT for instructional assessment. The study indicated that lecturers held low attitudes and had poor perception levels when it came to exploring the potential of ChatGPT. However, it is recommended that training be provided to enhance lecturers' attitudes and perception levels to fully exploit the potential of ChatGPT for instructional assessment.