Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3, 1-6 2025 Publisher: Learning Gate DOI: 10.55214/25768484.v9i3.5104 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 15 December 2024; Revised: 4 February 2025; Accepted: 14 February 2025; Published: 1 March 2025 * Correspondence: musthafa.i@uob.edu.om Descriptive analysis on students assessing the most difficult subjects in school Salima Hamed Salim Al-saaidi1, Gopi Suresh Vankudre2, Musthafa Ibrahim3* 1,2,3University of Buraimi, Al Buraimi, Oman; 2010531@uob.edu.om (S.H.S.A.) gopi.v@uob.edu.om (G.S.V.) musthafa.i@uob.edu.om (M.I.). Abstract: This study investigates the challenges students face in learning certain subjects and the underlying causes of these difficulties. A survey of 50 high school students identified three major factors: the nature of the subject, teaching methods, and curriculum design. Topics lacking practical examples and an overemphasis on theory were primary sources of misunderstanding. Students also reported challenges with problem-solving and asking questions in class. These findings highlight the need for interventions and teaching strategies to address these issues, fostering better understanding and improving learning outcomes. Keywords: Descriptive statistics, Measure of central tendency, Measure of variability, Skewness, Kurtosis. 1. Introduction Descriptive statistics are a set of techniques used to summarize and describe a given data of an entire sample or population using numbers and graphs. we can describe our data using many concepts of statistics. These summaries are vital to describe the state and understanding the prioritize of measured data to making it more manageable and interpretable. Statistics are focused on mathematical discipline, which involve the collection, description, analysis and inference the conclusion from quantitative data. The mathematical theory behind the statistic is depend on differential and integrals calculus, liner algebra and probability theory. It is using in many fields of scientific such as quantum mechanics which using the probability of finding the electrons at a given point, weather forecasting, proteomics or microarray analysis in genomics, etc. There are two subdivided of statistics method. Descriptive statistics: It mostly focuses on central tendency, variability and distribution to sum- maize and describe the main features of data in either tables or graph form which outcome from population. Inferential statistics: It include techniques to draw the conclusion of sampled data to make prediction about the large sample or population. Some basic definitions Population: All the member of group from which you want to collect the data and draw the conclusion, for example all students who registered in private school last year. Sample: The part of population selected to analysis, for example the students selected to fill out a learning-satisfaction questionnaire. Parameter: A numerical measure that describe the characteristic of population, for example the percentage of all student who are very satisfied with the learning techniques in school. Quantitative variable (numerical variable): The value of these variables includes a measured value. there are two types of quantitative variable: Discrete variable-it counts the individual items, for example the number of students in class. Continuous variable-it measurement non-finite value, for example distance. 2 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1-6, 2025 DOI: 10.55214/25768484.v9i3.5104 © 2025 by the authors; licensee Learning Gate Qualitative variable (categorical variable): A variable is not numerical. It describes the character- is tics or variable such as color, weight, state, etc. The following figure illustrate the types of statistics: Figure 1. This figure shows major types of statistics. 2. Literature Review Poldrack, et al. [1] statically thinking philosophy is underlying to understand our complex world and provide the main ideas of statistics in a way that focuses on deep comprehension rather than rote application. It provides the tools to describe the complicated pattern, which generated from data, make accurate predicates, and decisions based on data. Cobb and Moore [2] note that “The naturally of variability gives statistics a specific content which set it apart from mathematics itself and from other mathematical science, however there are more than content that distinguish statistical thinking from mathematics. Statistics needs a different kind of thinking, because data in not just number, there are number with a context”. One result is that students are learning that perfectly symmetrical graphical displays are hard to find. Even with the ability to verbally describe a distribution from a visual display, researchers have found [3] that students cannot translate their understanding of shape when asked to compare numerical statistics such as the mean and median. Hence, measures of skewness are becoming more important (although many instructors may reasonably conclude that kurtosis does not deserve extended discussion in a basic statistics class. Horswell and Looney [4] note that “The performance of skewness tests is shown to be very sensitive to the kurtosis of the underlying distribution.” Because the Kurtosis id difficult to explanation and also it is difficult to judge from histogram, we see a few of instructor say much about it. The main property of Kurtosis is a symmetric distribution [5]. Data set contains a lot of values will be not only skewness but also will be leptokurtic, therefor We cannot speak of non- normal skewness as if it were separable from non-normal kurtosis. The best we can do is to focus on the skewness statistics for departure from the symmetric normal distribution. 3. Methodology We use univariate analysis, which is concerned on describing the distribution of single variable, including central tendency which measure mean, median and mode, and variability which include range, 3 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1-6, 2025 DOI: 10.55214/25768484.v9i3.5104 © 2025 by the authors; licensee Learning Gate variance and depression (include data set and measure the spread like variance and standard deviation). The shape of distribution may describe through skewness and kurtosis. The property of variable’s distribution may also draw in in graphical or tabular format, and histograms. Some of the advantage of descriptive statistics: Summarize a set of observations that will present a largest amount of information as a simply as possible. It used to represent the feature of data in study such as the mean and standard deviation. Data is present in a meaningful and understandable away. It can identify further ideas of research. It gives the characteristics of population or sample. A good primer to learn about statistics process. 4. Analysis The sample comprised 50 (73% females and 26% males) high school students. we choice those students because we saw vary in grades basically in scientific subjects also some subjects are depend in each other, so Students’ views were gathered using a short questionnaire that contained acceptance about some techniques in education and ways who teacher used to explanation also Students were asked to indicate the difficult subject to study which take more time and contains more thing that cannot understanding, finally we ask them to write some suggestions to improve the education in their school. Figure 2. the percentage of grade of students who choose to complete the survey. 4 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1-6, 2025 DOI: 10.55214/25768484.v9i3.5104 © 2025 by the authors; licensee Learning Gate Figure 3. the percentage of the most difficult subject in high school. Table 1. Participant responses on the statements related to the course offering. Statements Strongly agree Agree 3.00 Disagree Strongly disagree n % n % n % n % n % We learn a lot al-most every day 3 6.0 31 62.0 11 22.0 1 2.0 4 8.0 We get choices in how to complete class activities. 2 4.0 29 58.0 11 22.0 5 10.0 3 6.0 The comments that I get on my work help me understand how to do my work better. 3 6.0 34 68.0 5 10.0 0 0 8 16.0 My teacher encour-ages me to perform better. 3 6.0 15 30.0 16 32.0 8 16.0 8 16.0 My teacher appreciates the times when I work hard towards scoring well in a test or performing well in extracurricular activities. 5 10.0 25 50.0 11 22.0 5 10.0 4 8.0 My teacher gives adequate answers when I ask questions. 6 12.0 20 40.0 16 32.0 5 10.0 3 6.0 My teacher checks to make sure we understand what he/she is teaching us. 5 10.0 25 50.0 11 22.0 4 8.0 5 10.0 The school provides many tech- niques that help us understand the subjects well. 6 12.0 13 26.0 10 20.0 15 30.0 6 12.0 The book is present the ex- planation of topics in clear a way. 6 12.0 12 24.0 7 14.0 11 22.0 14 28.0 I use many resources to help me understand the subject well. 4 8.0 33 66.0 6 12.0 1 2.0 6 12.0 5 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1-6, 2025 DOI: 10.55214/25768484.v9i3.5104 © 2025 by the authors; licensee Learning Gate Table 2. Descriptive statistics of the Likert responses. Statements Mean Std. deviation We learn a lot almost every day 2.4400 0.95105 We get choices in how to complete class activities. 2.5600 0.95105 The comments that I get on my work help me understand how to do my work better. 2.5200 1.16479 My teacher encourages me to perform better. 3.0600 1.16776 My teacher appreciates the times when I work hard towards scoring well in a test or performing well in extracurricular activities. 2.5600 1.07210 My teacher g i v e s adequate answers when I ask questions. 2.5800 1.03194 My teacher checks to make sure we understand what he/she is teaching us. 2.5800 1.10823 The school provides many techniques that help us understand the subjects well. 3.0400 1.24474 The book presents the explanation of topics in clear a way. 3.3000 1.41782 I use many resources to help me understand the subject well. 2.4400 1.09096 Summary of participant perception towards the offered course: Out of total 50 student participants, the majority (46%) of the participants had a neutral perception towards the offered course. 32 % of the participants are satisfied and 10% are highly satisfied with the course offering. Figure 4. Participant perception toward the offered course. Table 3. Predominant the reason for student finding the subject with their chosen difficult. Category of reasons Response% We learn a lot almost every day. 62.7 We get choices in how to complete activities. 58.8 The comments that I get on my work help me under- stand how to do my work better. 66.7 My teacher encourages me to perform better. 29.4 My teacher appreciates the times when i work hard to- wards scoring well in a test or performing well in extracurricular activities. 49 My teacher gives adequate answer when I ask questions. 39.2 My teacher checks to make sure we understand what he/she is teaching us. 49 The school provide many technologies that help us to understanding the topics very well. 25.5 the book is present the explanation of topic in clear a way. 23.5 I use many resources to help me understand the subject well. 64.7 6 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1-6, 2025 DOI: 10.55214/25768484.v9i3.5104 © 2025 by the authors; licensee Learning Gate Table 4. Frequencies of 50 students results. Mean 14.29 Median 50.5 Mode 12.5, 16.7 St. deviation 8.319 Variance 69.222 skewness -13.056 kurtosis 0.646 Range 25 Maximum 33.3 minimum 12.5 5. Result The data reveals important insights into its distribution and variability. The mean of 14.29 suggests a central value, but the median of 50.5 indicates significant asymmetry, supported by a skewness of - 13.056, reflecting a highly left-skewed distribution. The presence of two modes (12.5 and 16.7) indicates bimodality, suggesting varied concentration of values. Measures of variability, including a standard deviation of 8.319 and variance of 69.222, highlight moderate dispersion. The minimum value of 12.5 and a maximum of 33.3 further depict the range. With a kurtosis of 0.646, the distribution is slightly platykurtic, implying a flatter peak compared to a normal distribution. 6. Conclusion This study employs descriptive statistics to analyze and predict student learning activities. The in- sights gained through this approach are valuable for teachers, helping them refine teaching methods to better support student learning. By identifying common challenges faced by students, teachers can devise practical strategies to address these difficulties and enhance comprehension. Additionally, the application of descriptive statistics highlights areas where subject-specific practices can be introduced to improve students’ understanding and overall academic performance. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Copyright: © 2025 by the authors. This open-access article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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