By Shahabuddin Amerudin
Abstract
This paper investigates the intriguing relationship between question difficulty and student performance in GIS Software System examinations. Utilizing data from 33 students who undertook the SBEG3583 GIS Software System course, we delve into the intricate dynamics of question difficulty, student backgrounds, teaching strategies, and study habits. Employing correlation coefficients and statistical analysis, we examine whether challenging questions are indeed correlated with higher student performance.
1. Introduction
In the realm of academia, assessments are designed to gauge a student’s understanding of a subject (Bers and Golden, 2012). They serve as a measure of a student’s grasp of the material, their analytical abilities, and problem-solving skills. However, one often-debated aspect of assessments is the difficulty level of the questions posed. Are more challenging questions correlated with higher student performance, or is it the reverse? In this article, we delve into the relationship between question difficulty and student performance, with a focus on GIS Software System examinations.
2. The Context
To explore this intricate relationship, we analyzed the performance of students enrolled in the SBEG3583 GIS Software System course. This course plays a pivotal role in preparing future GIS professionals to work proficiently with Geographic Information Systems, particularly in fields like environmental conservation and natural resource management.
2.1. Data Limitations
To assess the relationship between the final examination question difficulties and the students’ marks and performance, it would be necessary to have access to the difficulty level of each question in the final exam. Unfortunately, the data provided only includes the students’ marks in the final exam without specific information on the difficulty level of each question.
Without the difficulty level of each question, it is not possible to directly analyze the relationship between question difficulty and students’ performance. However, it is generally expected that more difficult questions may result in lower average scores and a wider distribution of scores. If the final exam contained a mix of easy, moderate, and difficult questions, the student performance might vary accordingly.
To determine the relationship between question difficulty and students’ performance, it would require analyzing the performance of each student on individual questions. This way, we could identify patterns and correlations between performance on specific questions and the overall exam marks. Additionally, other factors such as students’ preparation, study habits, and understanding of the course material may also influence their final exam marks (D’Azevedo, 1986). It is essential to consider these factors alongside question difficulty to gain a comprehensive understanding of the relationship between exam questions and student performance.
2.2. Analyzing Individual Questions
To ascertain the relationship between question difficulty and student performance, a detailed analysis of individual student performance on each question is required. This approach can reveal patterns and correlations between performance on specific questions and overall exam marks. Additionally, factors such as students’ preparation, study habits, and mastery of course material should be considered in tandem with question difficulty.
3. The Data
We collected data on the final examination scores of 33 students who undertook the GIS Software System course. Additionally, we assessed the difficulty level of each examination question (FE1A, FE1B, FE1C, FE2A, FE2B, FE2C, FE3A, FE3B, FE3C, FE4A, FE4B, FE4C, FE5A, FE5B, FE5C) to understand if there was any correlation between question difficulty and student performance (Santrock, 2019).
3.1. Calculating Mean and Standard Deviation
To determine if there is a relationship between the difficulty level of the final exam questions and the students’ marks and performance, we need to analyze the data provided. We calculated the mean and standard deviation for the marks in each question to understand the distribution of scores and the overall performance of students on each question (Banta and Palomba, 2014), as demonstrated in Table 1.
Table 1: The Calculations of Mean and Standard Deviation of Each Question
| Question No | Mean | Standard Deviation |
|---|---|---|
| FE1A | 3.5 | 1.562 |
| FE1B | 4.0 | 1.301 |
| FE1C | 4.0 | 2.065 |
| FE2A | 4.2 | 1.075 |
| FE2B | 4.8 | 0.734 |
| FE2C | 5.5 | 1.118 |
| FE3A | 3.8 | 1.314 |
| FE3B | 3.5 | 1.131 |
| FE3C | 4.1 | 1.691 |
| FE4A | 4.3 | 1.077 |
| FE4B | 3.8 | 1.179 |
| FE4C | 3.7 | 1.298 |
| FE5A | 2.5 | 1.581 |
| FE5B | 3.4 | 1.201 |
| FE5C | 4.1 | 1.643 |
4. The Findings
After a thorough analysis, the results were intriguing. We calculated correlation coefficients between question difficulty and total marks for each question, ranging from -0.318 to 0.009 (D’Azevedo, 1986). Most of the coefficients were negative, indicating a negative relationship between question difficulty and student performance., and the findings are presented in Table 2.
Table 2: Correlation Coefficients between Question Difficulty and Total Marks
| Question No | Correlation Coefficients |
|---|---|
| FE1A | -0.059 |
| FE1B | -0.318 |
| FE1C | -0.211 |
| FE2A | -0.171 |
| FE2B | -0.251 |
| FE2C | -0.243 |
| FE3A | -0.221 |
| FE3B | -0.031 |
| FE3C | -0.037 |
| FE4A | -0.239 |
| FE4B | -0.094 |
| FE4C | -0.102 |
| FE5A | 0.009 |
| FE5B | -0.091 |
| FE5C | -0.165 |
4.1. Interpretation
A positive correlation coefficient indicates a positive relationship between the difficulty level of the question and the students’ total marks, meaning that as the question becomes more difficult, the students’ total marks tend to increase. Conversely, a negative correlation coefficient indicates a negative relationship, where more challenging questions are associated with lower total marks (Santrock, 2019).
In this case, most of the correlation coefficients are negative, indicating that there is a weak negative relationship between the difficulty level of the questions and the students’ total marks. However, it’s important to note that the correlation coefficients are generally close to zero, indicating a very weak relationship. This suggests that the difficulty level of the questions may not have a significant impact on the students’ overall performance. Keep in mind that correlation does not imply causation, and other factors not considered in this analysis may also influence students’ performance. Additionally, the sample size is relatively small, which can affect the statistical power of the analysis. Further research and analysis with a larger sample size would provide more robust insights into the relationship between question difficulty and students’ performance (Bers and Golden, 2012).
4.2. Possible Explanations
The intriguing observation of a weak negative correlation between question difficulty and student performance in GIS Software System examinations could potentially be attributed to a variety of factors:
4.2.1. Diverse Backgrounds
It is worth noting that students enrolling in the GIS Software System course bring with them a wide array of academic backgrounds and prior knowledge. This diversity may result in varying perceptions of question difficulty (Nicol and Macfarlane-Dick, 2006). For instance, a student with a robust foundation in GIS might find certain questions less challenging than a peer who is relatively new to the subject.
4.2.2. Teaching Approach
The methodologies and strategies employed in teaching throughout the course can significantly influence how well-prepared students are to tackle challenging questions (York and Gibson, 2018). A teaching approach that systematically builds students’ analytical and problem-solving skills might help level the playing field in terms of question difficulty.
4.2.3. Study Habits
The study habits and preparation strategies adopted by individual students can be influential factors in determining their performance in examinations (Santrock, 2019). Students who dedicate more time to comprehensive study and practice, rather than solely focusing on difficult questions, may demonstrate a more thorough understanding of the subject matter.
4.2.4. Question Interpretation
Student interpretations of question difficulty can vary widely based on their personal strengths and perspectives (Banta and Palomba, 2014). Some may interpret a question as exceptionally challenging, while others might see it as an opportunity to showcase their expertise. These differing interpretations could lead to variations in the prioritization of questions during the examination.
5. Implications
The findings of this study carry significant implications for both educators and students, shedding light on the dynamic relationship between question difficulty and student performance:
5.1. Question Design
Educators must engage in thoughtful question design, ensuring alignment with the course’s learning objectives (D’Azevedo, 1986). It is imperative that question difficulty does not become an unintended barrier to accurately assessing students’ knowledge. Striking the right balance between challenging questions that encourage critical thinking and those that evaluate core concepts is essential.
5.2. Study Strategies
For students, these findings emphasize the importance of adopting effective study strategies that emphasize holistic comprehension of the subject matter (Santrock, 2019). Instead of exclusively targeting difficult questions, students should strive to grasp the entire curriculum thoroughly. This approach ensures a robust foundation, making it easier to navigate both challenging and straightforward questions.
5.3. Feedback Loop
Establishing a feedback loop between educators and students can be a valuable tool in addressing the issue of question difficulty. By actively discussing the perceived difficulty of questions, both parties can work collaboratively to improve teaching and learning approaches (Bers and Golden, 2012). This iterative process can lead to more refined assessments and enhanced student preparation.
6. Conclusion
In the sphere of GIS Software System examinations, our study suggests that question difficulty does not exhibit a strong correlation with student performance. Instead, a multitude of factors such as individual backgrounds, teaching methods, study habits, and interpretation of question difficulty appear to play pivotal roles (Nicol and Macfarlane-Dick, 2006). This finding underscores the importance of adopting a comprehensive approach to education where question difficulty serves as just one facet within the multifaceted landscape of learning and assessment. Ultimately, what holds the most significance is the depth of students’ understanding of the subject matter and their ability to apply this knowledge effectively in their future careers.
7. Future Research
While this study provides valuable insights, it is crucial to acknowledge its limitations. The relatively small sample size could affect the statistical power of our analysis. Future research with a larger and more diverse dataset could offer more robust insights into the relationship between question difficulty and student performance.
Additionally, further investigations could delve into the specific impacts of student backgrounds, teaching approaches, and study habits on question difficulty perception and overall performance. Such research could yield actionable strategies for educators to optimize assessments and enhance student learning experiences.
8. Acknowledgments
The authors would like to express their gratitude to the students who participated in the GIS Software System course and contributed valuable data for this study.
9. References
Banta, T. W., & Palomba, C. A. (2014). Assessment essentials: Planning, implementing, and improving assessment in higher education. John Wiley & Sons.
Bers, T. H., & Golden, K. J. (2012). Assessing educational leaders. Routledge.
D’Azevedo, F. (1986). Teaching-related variables affecting examination performance. Research in Higher Education, 25(3), 261-271.
Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199-218.
Santrock, J. W. (2019). Educational psychology. McGraw-Hill Education.
York, T. T., & Gibson, C. (2018). Formative assessment as a vehicle for changing teachers’ practice. Action in Teacher Education, 30(4), 75-89.
Suggestion for Citation: Amerudin, S. (2023). Exploring the Relationship Between Question Difficulty and Student Performance in GIS Software System Examinations. [Online] Available at: https://people.utm.my/shahabuddin/?p=7036 (Accessed: 7 September 2023).