Suhaila Bahrom | International Islamic University Malaysia (original) (raw)

Papers by Suhaila Bahrom

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON FUTURE AND SUSTAINABLE EDUCATION 2024, 2024

An exam-oriented approach predominantly drives the current educational landscape, often limitin... more An exam-oriented approach predominantly drives the current educational landscape, often
limiting students' intellectual curiosity and critical thinking abilities. This paper proposes a paradigm shift
towards insightful learning, particularly for foundation students, to foster a more profound and holistic
educational experience. By integrating innovative pedagogical strategies that emphasise understanding,
creativity, and application of knowledge, this paper aims to cultivate scholars who are not only aiming at
passing exams but are also equipped with the skills necessary for lifelong learning and problem-solving.
Key methodologies include role play and cinematic learning, which collectively promote a deeper
engagement with the material. A purposive sampling technique was used to select a sample of 184 students
taking Understanding Islam II from CFS IIUM for this study. The data were analysed using both descriptive
and inferential analysis using SPSS. Preliminary results from the study indicate significant improvements
in students’ understanding and motivation towards learning. The paper concludes with recommendations
for educators to support the transition towards an education system that values and nurtures insightful
learning, ultimately empowering students to become innovative and adaptive thinkers prepared for future
challenges.

E-Proceeding 7th National Pre-University Seminar , 2024

Examining final scores among pre-university engineering students is crucial for understanding th... more Examining final scores among pre-university engineering students is
crucial for understanding their academic performance and identifying factors
contributing to success or challenge in their educational journey. These scores are
pivotal indicators of students' grasp of fundamental engineering principles and
readiness for higher education. This study examines the correlation between
different course assessments and final exam scores in a Mathematics course for
pre-university engineering students. A dataset comprising assessments such as
quizzes, open-book tests, and tutorials was collected from 552 pre-university
engineering students at the Centre for Foundation Studies, International Islamic
University Malaysia, for the 2023/2024 cohort. Regression analysis was
employed to identify the significance course assessments, which were carried out
using Python. The study revealed that all the quizzes, including open book test 2
and open book test 3, are significantly correlated with final examination scores
with an adjusted R-squared of 0.467. This value indicates that 46.7% of the
variation in final examination scores can be predicted by combining all quizzes
and two open-book tests. This study examines the effectiveness of course
assessments in predicting the final examination performance of students in pre
university engineering programs. Furthermore, it presents valuable
recommendations for enhancing assessment strategies to support and foster
student achievement more effectively.

e-Proceedings 15th International Conference on Business Studies and Education (ICBE) , 2024

Coconut oil is a significant global commodity, ranking 4th most valuable after palm oil. Its risi... more Coconut oil is a significant global commodity, ranking 4th most valuable after palm oil. Its rising demand and market volatility have heightened the need for accurate price forecasting to guide investment decisions. This study uses the Box-Jenkins methodology to develop a prediction model for coconut oil prices. Monthly secondary data from the World Bank, covering January 1960 to March 2024, was analysed using R software. A Box-Cox transformation was applied to stabilize variance and address issues such as non-normality and heteroscedasticity in the data. After testing various ARIMA models, the ARIMA (0,1,0) model was identified as the most suitable for forecasting, with a MAPE of 27%, suggesting reasonable accuracy. The model provides a reliable tool for predicting future price trends. These findings are critical for industry stakeholders, enabling more informed decision-making and strategic planning by offering a clearer understanding of price fluctuations in the coconut oil market. This analysis contributes to optimizing investments and managing risks in a dynamic market environment.

APS Proceedings, 2024

This project introduces an innovative graphical user interface (GUI)-based order tracking system ... more This project introduces an innovative graphical user interface (GUI)-based order tracking system for home bakery owners, developed using VBA. The GUI simplifies the order process, enabling efficient input, management, and tracking of customer orders. Designed with user-friendliness, it allows users to enter order details and automatically update records quickly. The system ensures accurate record-keeping and streamlines order processing, significantly reducing manual errors and enhancing operational efficiency. By integrating this automated solution, users can manage orders, track sales trends, and improve customer service to drive business growth and sustainability.

Proceedings of Johor International Innovation Invention Competition and Symposium 2024 , 2024

EduCalc is developed to address the inefficiencies and inaccuracies in manual student grade calcu... more EduCalc is developed to address the inefficiencies and inaccuracies in manual student grade calculation, offering a streamlined solution using Visual Basic for Applications (VBA). The innovation highlights traditional grade management methods' time-consuming and error-prone nature, identifying the primary problem as needing an automated, reliable system to handle grade calculations efficiently. EduCalc utilizes VBA to automate grade entry, customize grading criteria, and minimize errors during
the data entry process. The commercial value of EduCalc lies in its seamless integration with Microsoft Excel, making it a scalable and adaptable tool for educational institutions of various sizes. The significance of this innovation is its ability to drastically reduce administrative burdens, enhance data accuracy, and improve overall
educational outcomes. In conclusion, EduCalc exemplifies the potential of VBA in educational technology, offering a practical, user-friendly, and impactful solution for modern educators, thereby fostering a more efficient academic environment.

EDUCATUM Journal of Science, Mathematics and Technology, 2024

Constructing examination papers has been a lengthy and tedious procedure that is commonly raise b... more Constructing examination papers has been a lengthy and tedious procedure that is commonly raise by issues with content validity, reliability, and fairness. Therefore, investigating alternative methods such as item banking using Rasch analysis may present a more practical and efficient approach for creating assessments. This study aims to demonstrate how to develop an item bank using Rasch analysis and assess the reliability and validity of the final exam paper for a statistics course that uses item bank as its foundation. In this research, Statistics course which consist of 7 questions are divided into 21 items based on test specification table (TST). The results from Rasch analysis are recorded in an item bank interface created by using excel. The item bank interface facilitates easy access to a large variety of pretested items, allowing for the creation of diverse and balanced exam papers. A well-developed item bank will be a great assistance to exam setters as it makes the process of creating tests easier, faster, and more efficient, which leads to higher-quality examination questions paper.

APS Proceeding, 2024

Global energy consumption is influenced by various human activities, including fossil fuel-based ... more Global energy consumption is influenced by various human activities, including fossil fuel-based energy generation, household energy usage, and population growth. This case study aims to identify and predict key factors in energy consumption in Malaysia using Regression Analysis. The dataset spans from 2000 to 2020 and includes variables such as access to electricity, renewable energy capacity, electricity from renewables, access to clean cooking fuels, renewable energy share in total consumption, and primary energy consumption per capita. The R software was used to analyse the data. According to the analysis, the predictor variables that are correlated with the primary energy consumption are renewable electricity generating capacity, electricity from renewables, access to clean fuels for cooking, and renewable energy share in total final energy consumption. The findings suggest that increasing the share of renewable energy sources and improving access to clean cooking fuels could potentially reduce overall energy consumption in Malaysia. The regression model developed in this study can be a valuable tool for policymakers and energy planners to forecast future energy demand and formulate strategies to promote sustainable energy usage. Furthermore, the methodology employed can be adapted to analyze energy consumption patterns in other countries or regions, facilitating a deeper understanding of the factors driving global energy consumption.

APS Proceeding, 2024

This research investigates the multifaceted relationship between various factors and obesity rate... more This research investigates the multifaceted relationship between various factors and obesity rates in Mexico, Peru, and Colombia using a publicly available dataset. Through Python, the study employs classification and clustering analyses, focusing on logistic regression to predict obesity levels and generate actionable recommendations. Combining exploratory data analysis (EDA) and advanced machine learning techniques, the research aims to unveil nuanced insights into obesity determinants. Unsupervised learning methods segmentize individuals, providing deeper insights into obesity profiles. Supervised learning algorithms like logistic regression, random forest, and adaboost classifier predict obesity levels based on labelled datasets, with random forest exhibiting superior performance. The study enhances understanding of obesity classification through machine learning and integrates data inspection, formatting, and exploration using Excel, Python, and graphical user interfaces (GUIs) such as SweetViz and PandaGui. Overall, it offers a comprehensive approach to understanding and addressing obesity using sophisticated analytical tools and methodologies.

Matplotlib is a comprehensive library in Python used for creating static, interactive, and animat... more Matplotlib is a comprehensive library in Python used for creating static, interactive, and animated visualizations. It is widely used in data science, scientific computing, and engineering to create a variety of plots and charts. Matplotlib is known for its flexibility and can be used to generate high-quality figures in a variety of formats.

NumPy and SymPy are two powerful libraries in Python for such computations. NumPy specializes in... more NumPy and SymPy are two powerful libraries in Python for such computations.

NumPy specializes in numerical computing, providing support for arrays, matrices, and a wide range of mathematical functions. It's efficient for tasks like linear algebra, statistics, and numerical operations.

SymPy, on the other hand, is a symbolic mathematics library that allows for symbolic computation. It can handle algebraic equations, calculus, and other symbolic manipulations.

Conditional statements in Python, also known as control flow tools, enable the execution of diff... more Conditional statements in Python, also known as control flow tools, enable the execution of different computations or actions depending on whether a specified boolean condition is true or false. At the core of these statements are the if, elif, and else constructs, which allow programmers to control the flow of their program's execution.

An if statement checks a condition and executes a block of code if the condition is true. If the condition is false, the program can check additional conditions through elif (short for "else if") clauses. If none of the if or elif conditions are true, the else clause is executed.

Python also supports conditional expressions, or ternary operators, which allow for the simplification of an if-else block into a single line. This feature provides a more concise way to assign values based on conditions.

The use of these conditional statements is fundamental in programming, allowing for the development of more dynamic and responsive code. This control mechanism is essential for handling decisions, guiding program flow, and managing complex logic across many application areas.

Stationarity in time series refers to a property where the statistical characteristics such as me... more Stationarity in time series refers to a property where the statistical characteristics such as mean, variance, and autocorrelation remain constant over time. Strict stationarity requires that the joint distribution of any set of time indices remains unchanged when shifted by a constant lag, while weak stationarity stipulates that the mean, variance, and autocovariance of the series are time-invariant. Stationarity is crucial in time series analysis as it underpins many forecasting techniques, ensuring that patterns observed in the past remain consistent for future predictions. Tests like the Augmented Dickey-Fuller and KPSS are commonly employed to assess stationarity, aiding in the accurate modeling and forecasting of time series data.

A flowchart is a graphical representation of a process or algorithm. It uses standardized symbols... more A flowchart is a graphical representation of a process or algorithm. It uses standardized symbols to visually illustrate the steps and decision points of a workflow or procedure. Flowcharts are widely used in various fields such as software engineering, business process management, education, and more.

ACF (Autocorrelation Function) measures the correlation between a time series and its lagged valu... more ACF (Autocorrelation Function) measures the correlation between a time series and its lagged values, while PACF (Partial Autocorrelation Function) measures this correlation after removing the effects of intervening observations. ACF helps identify patterns and dependencies within the time series data, while PACF assists in determining the direct relationship between observations at specific lags, aiding in the selection of appropriate models for time series analysis, such as ARIMA models, by identifying the order of autoregressive and moving average terms.

A time series plot is a graphical representation of data points collected or recorded at success... more A time series plot is a graphical representation of data points collected or recorded at successive, evenly spaced intervals over time. In a time series plot, the x-axis typically represents time, while the y-axis represents the values of the variable being measured or observed. Time series plots are commonly used in various fields, including economics, finance, meteorology, and engineering, to visualize trends, patterns, and changes in data over time.

Text chunking, also known as shallow parsing, is a natural language processing (NLP) technique th... more Text chunking, also known as shallow parsing, is a natural language processing (NLP) technique that aims to identify and group syntactically related words or phrases (chunks) within a sentence. Chunking typically involves identifying phrases such as noun phrases, verb phrases, prepositional phrases, etc., rather than just individual words.

NLP stands for Natural Language Processing. It's a field of artificial intelligence (AI) and ling... more NLP stands for Natural Language Processing. It's a field of artificial intelligence (AI) and linguistics concerned with the interaction between computers and humans through natural language. The ultimate objective of NLP is to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful.

A sentiment analyzer, also known as a sentiment analysis tool or sentiment classifier, is a comp... more A sentiment analyzer, also known as a sentiment analysis tool or sentiment classifier, is a computational method used to determine the sentiment expressed in a piece of text. Sentiment analysis aims to automatically identify and extract subjective information from text, such as opinions, attitudes, emotions, and feelings, and classify it as positive, negative, or neutral.

Text mining, also known as text analytics, is the process of extracting meaningful information an... more Text mining, also known as text analytics, is the process of extracting meaningful information and insights from unstructured text data. It involves various techniques and methods from natural language processing (NLP), machine learning, and statistics to analyze and understand large volumes of textual data. Text mining encompasses tasks such as text categorization, sentiment analysis, entity recognition, topic modeling, and more. It is widely used in fields like information retrieval, social media analysis, customer feedback analysis, market research, and scientific research.

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON FUTURE AND SUSTAINABLE EDUCATION 2024, 2024

E-Proceeding 7th National Pre-University Seminar , 2024

e-Proceedings 15th International Conference on Business Studies and Education (ICBE) , 2024

APS Proceedings, 2024

Proceedings of Johor International Innovation Invention Competition and Symposium 2024 , 2024

EDUCATUM Journal of Science, Mathematics and Technology, 2024

APS Proceeding, 2024

NumPy and SymPy are two powerful libraries in Python for such computations. NumPy specializes in... more NumPy and SymPy are two powerful libraries in Python for such computations.

SymPy, on the other hand, is a symbolic mathematics library that allows for symbolic computation. It can handle algebraic equations, calculus, and other symbolic manipulations.

A hypothesis test for a population mean when the population variance is unknown is conducted usin... more A hypothesis test for a population mean when the population variance is unknown is conducted using the t-test. This method relies on the t-distribution and uses the sample standard deviation as an estimate for the population standard deviation. The test compares the t-statistic, derived from the sample data, with critical values from the t-distribution to decide whether to reject or fail to reject the null hypothesis.

A hypothesis test for a population mean when the population variance is known is conducted using ... more A hypothesis test for a population mean when the population variance is known is conducted using the z-test. This method evaluates whether the sample mean significantly deviates from a hypothesized population mean under the assumption of a known population standard deviation. The test involves comparing the z-statistic, calculated using the sample data, against critical values from the standard normal distribution to determine whether to reject or fail to reject the null hypothesis.

Confidence intervals for the mean, when the population variance is unknown, rely on the sample st... more Confidence intervals for the mean, when the population variance is unknown, rely on the sample standard deviation and the t-distribution for accurate estimation. This method adjusts for increased uncertainty, particularly with small sample sizes. It provides a range of plausible values for the population mean, enabling informed decision-making in diverse fields.

Finding the minimum sample size to estimate a population mean ensures accurate results with a spe... more Finding the minimum sample size to estimate a population mean ensures accurate results with a specified level of confidence and precision. It depends on factors like the desired margin of error, confidence level, and population variability. This method helps researchers balance accuracy and resources effectively across various studies.

Confidence intervals for the mean are a critical tool in inferential statistics, providing a rang... more Confidence intervals for the mean are a critical tool in inferential statistics, providing a range of plausible values for a population mean based on sample data. When the population variance is known, the construction of a confidence interval is simplified using the standard normal distribution. This approach ensures precise interval estimation, relying on the assumption of a normally distributed population or a sufficiently large sample size. This abstract explores the methodology, formula derivation, and interpretation of confidence intervals for the mean under known population variance, emphasizing their application in hypothesis testing and decision-making processes.

Estimation is a fundamental concept in statistics and mathematics, involving the process of infer... more Estimation is a fundamental concept in statistics and mathematics, involving the process of inferring the value of an unknown parameter within a population based on sample data. It serves as a cornerstone for decision-making and predictive modeling across various disciplines. This introduction explores the principles and methodologies of estimation, emphasizing the distinction between point estimation and interval estimation. Key properties such as unbiasedness, consistency, and efficiency are highlighted to illustrate the qualities of robust estimators. Practical applications in fields such as economics, engineering, and health sciences underscore the importance of estimation in addressing real-world challenges.

Hypothesis testing for a population mean when the population variance is known is a statistical p... more Hypothesis testing for a population mean when the population variance is known is a statistical procedure used to determine whether a sample mean significantly differs from a hypothesized population mean. This method involves formulating null and alternative hypotheses, calculating a test statistic based on the standard normal (Z) distribution, and comparing it to critical values or p-values. The known population variance allows for precise computation of the test statistic and standard error, enhancing reliability. This test is widely applied in research and quality control, facilitating informed decisions about population characteristics with a controlled risk of error.

The sampling distribution of the sample mean is a fundamental concept in statistics, describing t... more The sampling distribution of the sample mean is a fundamental concept in statistics, describing the distribution of sample means obtained from repeated random samples of a given size drawn from a population. This distribution is characterized by its mean, which equals the population mean, and its standard deviation, known as the standard error, which decreases with larger sample sizes. Under the Central Limit Theorem, the sampling distribution approximates a normal distribution as the sample size increases, regardless of the population's shape. This property is crucial for inferential statistics, enabling the estimation of population parameters and hypothesis testing with finite samples.

The normal distribution can approximate the binomial distribution when the number of trials is la... more The normal distribution can approximate the binomial distribution when the number of trials is large, and the success probability is not extreme. This simplifies calculations by treating the binomial as a continuous distribution. It is useful in statistical analysis for estimating probabilities and confidence intervals in areas like quality control and survey analysis.

The normal distribution is widely used in statistics, finance, engineering, and social sciences d... more The normal distribution is widely used in statistics, finance, engineering, and social sciences due to its natural occurrence and simplicity. It supports tasks like hypothesis testing, regression analysis, and quality control, as well as modeling asset returns and biological phenomena. Its versatility makes it essential for analyzing data trends and standardizing results across various fields.

The standard normal distribution is a special case of the normal distribution where the mean (μ) ... more The standard normal distribution is a special case of the normal distribution where the mean (μ) is 0 and the standard deviation (σ) is 1. Its probability density function is symmetric about zero and exhibits a bell-shaped curve, making it a fundamental tool in statistical analysis. Key properties include the use of standard scores (z-scores) to measure the relative position of data points and its role in simplifying computations for hypothesis testing, probability calculations, and confidence interval estimation. This distribution is essential for standardizing data and serves as a cornerstone in probability and inferential statistics.

The binomial distribution models the probability of a fixed number of successes in a set number o... more The binomial distribution models the probability of a fixed number of successes in a set number of independent trials, each with the same success probability. It is defined by two parameters: the number of trials (n) and the probability of success (p). Commonly used in fields like biology, finance, and quality control, it helps analyze outcomes such as pass/fail, win/lose, or yes/no scenarios.

Mean, variance, and expectation are essential in statistics and probability. The mean shows the a... more Mean, variance, and expectation are essential in statistics and probability. The mean shows the average value, while variance measures how much data spreads around the mean. Expectation represents the average outcome based on probabilities, helping in analysis and decision-making.

Probability distributions describe the chances of different outcomes in a random event. They are ... more Probability distributions describe the chances of different outcomes in a random event. They are divided into discrete (e.g., Binomial, Poisson) and continuous types (e.g., Normal, Exponential). These distributions are essential for analyzing data, making predictions, and solving problems in various fields like finance, engineering, and health sciences. They help us understand and manage uncertainty effectively.

The multiplication rule for probability is used to determine the likelihood of the joint occurren... more The multiplication rule for probability is used to determine the likelihood of the joint occurrence of two or more events. For independent events, the probability of their intersection is the product of their individual probabilities. In cases of dependent events, the multiplication rule incorporates conditional probability, which is the probability of one event occurring given that another event has already occurred. This approach is essential for analyzing sequences of events where outcomes may influence one another.

The addition rule for probability is used to calculate the likelihood of the union of two or more... more The addition rule for probability is used to calculate the likelihood of the union of two or more events. For mutually exclusive events, the probability is the sum of their individual probabilities. If the events are not mutually exclusive, the probability of their union is adjusted by subtracting the probability of their intersection to avoid double-counting. This rule helps evaluate the chances of at least one of the events occurring in a probability experiment.

Sample spaces represent the set of all possible outcomes in a probability experiment, providing t... more Sample spaces represent the set of all possible outcomes in a probability experiment, providing the foundation for analyzing random events. Probability quantifies the likelihood of an event occurring, calculated as the ratio of favorable outcomes to the total number of outcomes in the sample space. Together, they form the basis of probability theory, enabling predictions and decision-making in uncertain scenarios.

Counting rules are fundamental principles in combinatorics used to determine the total number of ... more Counting rules are fundamental principles in combinatorics used to determine the total number of possible outcomes in a given situation. They include the basic counting rule, the addition rule, and the multiplication rule, each applied based on the problem's context. These rules help calculate outcomes efficiently without listing them all, especially in scenarios involving permutations and combinations. Mastering counting rules is essential for solving problems in probability, statistics, and various fields of mathematics.

Data Description refers to a detailed summary of the dataset being analyzed or studied. It includ... more Data Description refers to a detailed summary of the dataset being analyzed or studied. It includes information about the dataset's structure, variables, data types, units of measurement, and any relevant features such as missing values, outliers, or data sources. This helps in understanding the context and characteristics of the data before conducting analysis.

Forecasting RON97 fuel prices is essential for making economic plans and policies because they in... more Forecasting RON97 fuel prices is essential for making economic plans and policies because they influence the cost of transportation and the inflation rate.

Forecasting RON97 fuel prices: Stage I & Stage II Box-Jenkins methodology

Course assessment is essential in education, helping educators understand students' progress an... more Course assessment is essential in education, helping educators understand students' progress and grasp of the material.
The objective of this study is to identify the significant assessments in the Mathematics I course and develop a student performance prediction model using multiple linear regression.The variables under consideration were the final examination score as a dependent variable and the assessment of tutorials, quizzes and open book tests were independent variables. The data were collected from 880 physical and biological module program students enrolled in the Centre for Foundation Studies, IIUM cohort of 2023/2024.The study found that assessments in Mathematics I course significantly contribute to students' final examination scores, with an adjusted R-squared of 0.4411, indicating that 44% of the variability in final examination scores can be explained by the combination of tutorial, quiz, and open book test scores. For every 1% increase in tutorial scores, there is a 0.44% increase in the final examination score. Similarly, for every 1% increase in quiz scores, there is a 1.07% increase in the final examination score, and for every 1% increase in open book test scores, a 1.02% increase in the final examination score is observed.The study concludes that assessments, including tutorials, quizzes, and open book tests are significant predictors of final examination scores in the Mathematics I course. These findings emphasize the significance of continuous assessment methods in enhancing students' academic performance and improving final examination scores.

Item bank is repository of comprehensive test assessment. Visual Basic for Applications (VBA) is ... more Item bank is repository of comprehensive test assessment. Visual Basic for Applications (VBA) is a tool to enhance user interactions in key in the item banks.

BMI categorizes individuals based on their height and weight into underweight, normal, overweight... more BMI categorizes individuals based on their height and weight into underweight, normal, overweight, or obese categories, serving as a measure of body fat. Tkinter is a Python binding to the Tk GUI toolkit. Tkinter is imported to present the BMI calculation.

The project involves building two Python applications: S.A.G.E, a smart Assistant App using natur... more The project involves building two Python applications: S.A.G.E, a smart Assistant App using natural language processing and task automation, and a Tkinter-based GUI Calculator with advanced features. This provides a hands-on learning experience in Python programming, GUI development using Tkinter, and integrating functionalities like NLP and external APIs, enhancing skills in software development and usercentric design.

An item bank is a large collection of high-quality test items that have been analysed and systema... more An item bank is a large collection of high-quality test items that have been analysed and systematically stored in a computer, making them easily accessible to exam setters. The purpose of this study was to develop a new item bank and to assess the validity and reliability of the final examination paper for a Statistics course taken by 344 students. The course included 7 structured questions, divided into 21 items. Rasch analysis was used to analyse the data, linking all item difficulties and students' measured abilities on the same linear scale. The results showed that the Statistics course was unidimensional, with a Cronbach’s alpha value of 0.90, item and person separation greater than 2, and person and item reliability of 0.87 and 0.99, respectively. Nineteen items were found to fit the measurement model and were stored in the item bank, while two items were found to be misfits and require revision. In summary, a well-developed item bank can be extremely beneficial to exam setters by making test construction easier, faster, and more efficient, resulting in higher quality tests.

The construction of the final examination questions based on the proper guideline such as accordi... more The construction of the final examination questions based on the proper guideline such as according to course learning outcomes (CLO) will help in measuring students’ abilities based on comprehensive cognitive skills. A well-constructed question on the final examination should be suitable for the intended level of knowledge. In this study, the results from final examination Statistics, MAT0144 taken by Biological Module students in Semester 3, 2021/2022 were analysed using the Rasch Model. It is a process of statistically examining both the test questions and the students’ answer to evaluate the quality and reliability of the test item and the examination paper as a whole. The items in the examination paper were studied and items that did not meet expectations were identified. The best test item discriminates between those students who perform well on the examination and those who do not. The items on the equal interval scale (logit) must keep their relative difficulty regardless the ability of the students that challenges the item. According to the analysis, the overall quality and reliability of the examination questions constructed were relatively good and calibrated with the students' learned ability and suitable for the intended students.

Item bank is a large collection of good test items which their quality is analysed and systematic... more Item bank is a large collection of good test items which their quality is analysed and systematically stored in a computer so that they are accessible to exam setters. The purpose of this study is to develop a new item bank and to measure the validity and reliability of final examination paper for Statistics course. There were 344 students taking this course which comprises 7 structured questions and subdivided into 21 items. The data were analysed using Rasch analysis so that all the item difficulties were linked on the same linear scale along with the students’ measured ability. The results showed that the Statistics course is unidimensional, Cronbach’s alpha value of 0.90, item and person separation more than 2, and person and item reliability of 0.87 and 0.99 respectively. Nineteen items were found to be fitted the measurement model and stored in the item bank while there are two items suggested to be misfit and need to be revised. To summarise, a well-developed item bank can be extremely beneficial to exam setters because it can assist test construction easier, faster, and more efficient. As a result, the test's quality should be higher than it would be without an item bank.

This book is designed to be a quick and comprehensive resource for students seeking to grasp the ... more This book is designed to be a quick and comprehensive resource for students seeking to grasp the fundamental concepts of statistics, enhanced with visualizations and test exercises to reinforce understanding. It is to align with the syllabus of the Statistics course offered to students at the Centre for Foundation Studies, International Islamic University Malaysia (IIUM).
“Easy Statistics" aims to simplify complex statistical concepts and provide a clear understanding of the material covered in the syllabus. Additionally, test exercises are provided at the end of this book to assess and reinforce understanding.We hope for this book to be beneficial not only to students at CFS, IIUM but also to students from various other educational institutions.

Statistics and Probability is written for foundation level students, specifically biological modu... more Statistics and Probability is written for foundation level students, specifically biological module students at the Centre for Foundation Studies, IIUM, who are taking Statistics as one of their core subjects. This book also intended for students who do not have a strong background in Mathematics. The goal of Statistics and Probability has been to make the subject of Statistics interesting and accessible to a wide and varied audience. We hope this book will benefit both instructors and students at the foundation level in terms of understanding statistical methods and concepts as well as in helping students to prepare for their examinations.

Mathematics education continues to play a crucial role in the education of foundationlevel studen... more Mathematics education continues to play a crucial role in the education of foundationlevel students, particularly in the era of the Fourth Industrial Revolution, characterized by digital technology advancements and digital transformation across various sectors.Mathematics plays an important role in developing computational thinking skills, which involve the ability to solve problems through logical and algorithmic reasoning. Foundation level students who have a strong understanding of mathematics will acquire the computational thinking skills needed to comprehend digital technology concepts such as artificial intelligence, data analysis, and complex mathematical modelling. These skills serve as a foundation for further studies at higher levels.