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Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

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70953
10.1038/s41598-024-70953-y
Article
A smart system for student performance assessment (SPA)
Mouti Samar Samar.muti@LC.AC.AE

1
Al-Chalabi Hani 2
1 https://ror.org/00r6fph53 0000 0004 1778 362X Information Technology Department, Liwa College, Abu Dhabi, United Arab Emirates
2 https://ror.org/00r6fph53 0000 0004 1778 362X General Education Department, Liwa College, Al Ain, United Arab Emirates
5 9 2024
5 9 2024
2024
14 2067914 2 2024
22 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
The proposed smart system for Student Performance Assessment (SPA) is a system that evaluates students' knowledge and skill attainment in a specific course by measuring their achievements of the Course Learning Outcomes (CLOs). The instructor defines the aspects, weights, and rating scale used by SPA to analyze each course. The system calculates the average of students' marks in each learning outcome and compares them with the CLO targets and scores to determine the effectiveness of the teaching and learning methods used. The system uses facts and rules extracted from the course syllabus and Bloom's Taxonomy to build its knowledge base. This paper presents the development of the SPA inference engine, which is used to find CLO targets based on the course level. The inference engine uses efficient procedures and a prediction process to determine the correct target and score, providing a reliable and understandable methodology for reasoning about the information in the knowledge base and formulating conclusions. SPA is a highly responsive and intelligent system that can be a valuable tool for measuring students' achievements. Its characteristics include high performance, reliability, and intelligibility, and its combination of cognitive systems and cognitive theory has led to remarkable progress in measuring student performance. Limitations include dependency on accurate course content and initial setup time, potential bias in CLO weight assignments, challenges in integrating SPA with existing institutional databases, the need for continuous updates to the knowledge base to reflect curriculum changes, and potential resistance from educators to adopt new technologies. Future improvements could involve adaptive learning integrations, enhanced user interfaces, and broader applicability across diverse educational settings.

Keywords

Expert systems
Interference engine
Knowledge base
Students’ achievements
Course learning outcomes
Subject terms

Engineering
Mathematics and computing
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pmcIntroduction

The Expert System attempts to replicate the abilities of human experts in specific problem domains and is a traditional field within artificial intelligence. The first expert system was developed in 1965 by Edward Feigenbaum and Joshua Lederberg of Stanford University to analyze chemical compounds, and since then, expert systems have been used in a variety of fields such as medical diagnosis1. In the realm of remote medicine, decision support systems can aid in monitoring the progress of chronic wounds and detecting potential risk conditions2. A new decision-support methodology is proposed for monitoring and assessing the medical treatment of pressure ulcers using an expert system designed with Python packages and an inference engine3,4. Course Assessment is an important criterion to determine the achievement of the students using Course Learning Outcomes (CLOs). Building an expert system to analyze of the students’ performance and attainment of course learning objectives will help the course teacher to measure the students’ achievements and evaluate the effectiveness of the course and improve the quality of the teaching and learning method in a quick and efficient way.

Course Learning Outcomes (CLOs) outline the expected knowledge that students should acquire by the end of a semester and are categorized according to the Bloom Taxonomy. The purpose of individual courses varies, with courses in the first year of a program emphasizing knowledge acquisition and courses later in the program focusing more on the application of knowledge and development of associated skills and competencies. By building an expert system to analyze student performance and attainment of CLOs, course teachers can quickly and efficiently measure student achievements and evaluate the effectiveness of their teaching methods5,6.

In defining learning outcomes, learners can understand the importance of learning outcome statements in supporting teaching and learning. A learning outcome is a statement that identifies the knowledge, skills, attitudes, competencies, or habits that learners are expected to acquire from an educational experience. The Rasch model is used to measure the effectiveness of teaching and learning methods by evaluating the performance of CLOs7,8. Outcome Based Education (OBE) is an innovative teaching technique that is encouraged by an education system designed to implement it9 . An online version of the OBE system has been developed using HTML, PHP, and SQL coding10. Measuring the achievement of a particular CLO provides insight into the consistency of assessment tools used in measuring that particular outcome11. A fuzzy-classification expert system has been proposed to facilitate the learning performance of students in the domain of rehabilitation methods for cerebral palsy12. Additionally, an expert system application has been designed to identify specific learning disabilities in inclusion schools and provide tailored learning services, learning media, and classroom embedding for affected children13,14. Finally, an expert system shell called VP-Expert has been created to automate academic advising and analysis of academic performance for students, freeing up faculty members for other important tasks15,16. However, a significant gap exists in automated systems that can accurately and efficiently measure student performance in real-time against CLOs. This paper addresses this gap by presenting the SPA system. The research questions guiding this study are:How effective is the SPA system in evaluating student performance against predefined Course Learning Outcomes (CLOs)?

What impact does the SPA system have on improving teaching methodologies and learning outcomes?

The paper is organized as follows: “Expert system for students achievements measurement (SPA) implementation” discusses the architecture of SPA, including its components and functionality. “Code availability” elaborates on the implementation details, including coding aspects. “Result and discussion” provides a practical example of SPA in use. “Comparative analysis” presents a comparative analysis with existing studies. Finally, “Conclusion” concludes the paper and suggests future research directions. As detailed in the supplementary file provided by Mouti and Al-Chalabi17, the SPA system integrates various components such as a knowledge base and inference engine to assess student performance against CLOs. For the full implementation details and custom code, please find the Supplementary file17.

Expert system for students achievements measurement (SPA) implementation

This paper shows design and implementation of an Expert System (SPA) for students’ achievements measurement based on CLOs achievement and attainment. Designing of the knowledge base and an inference engine to implement an expert system to measure the achievement of a particular course learning outcome can provide which course learning outcome the students have had failed to achieve. This system indicates whether the learning outcomes have been achieved consistently by the assessment or not. In addition, SPA finds the attainment of a particular course learning outcome and guides the lecturer to monitor the students’ performance and define the areas for course updates and improvement.

To assess the students’ performance for the course at the end of the semester, each course is analyzed using course learning outcomes, CLOs weight in each assessment is calculated and maximum marks of CLO in each assessment are defined. The rating scale based on course level will be clarified for each course based on its level. The average of student achievements and the average of CLO achievement by assessment are calculated. Student scores in each CLO based on the rating scale will be achieved. The percentage of CLOs attainment in the course will be obtained. SPA flow chart is shown in Fig. 1.Fig. 1 SPA flow chart.

The SPA architecture consists of three main components: the knowledge base, the inference engine, and the user interface. The knowledge base stores factual and heuristic knowledge about the courses and CLOs, derived from the course syllabi and Bloom's Taxonomy. The inference engine applies forward chaining to process student data and infer CLO achievements. The user interface allows instructors to input data and view reports. SPA uses the students’ marks report in a particular course. In addition, it uses the course syllabus to build a special heuristics, facts, and rules for the Knowledge Base (KB) and Inference engine. The following figure (Fig. 2) shows the major components of SPA.Fig. 2 SPA architecture.

CLO targets

The CLO target based on course level is defined in SPA. There are different levels of cognitive learning according to the Bloom's Taxonomy. These Taxonomies of educational objectives can be consulted as useful guides for developing a comprehensive list of student outcomes. Taxonomies attempt to identify and classify all different types of learning and then defines the level of performance for each domain18,19. Proposed accomplishment requirement used in the SPA is shown in Table 1.Table 1 CLOs target.

Course level/CLO aspect	1	2	3	4	
Knowledge	70	70	75	75	
Application	60	60	70	70	
Critical thinking	60	60	70	70	
Transferrable skills	60	60	70	70	

Table 1 shows if course level is 1 and CLO is Remembering, then CLO Target is 70. If course level is 2 and CLO is Remembering, then CLO Target is 70. If course level is 3 and CLO is Remembering, then CLO Target is 75. If course level is 4 and CLO is Remembering, then CLO Target is 75.

CLOs weights and scaled marks

SPA uses the CLOs weights and scaled marks based on course syllabus. The aspect of CLOs in the assessment are defined to achieve the course level. The CLOs’ score is calculated in each assessment in the course as shown in Table 2. Max and Min marks of the CLOs for each assessment will be defined based on course level. The CLO marks will be scaled as mentioned in the course syllabus by SPA.Table 2 Rating scale.

Range of marks	Min	Max	Score	
≥ 90 up to 100	90	100	5	
≥  80 up to 90	80	90	4	
≥  70 up to 80	70	80	3	
≥  60 up to 70	60	70	2	
< 60		60	1	

Rating scale

Table 2 shows the purposed rating scale used in SPA based on course level that assigns scores to a range of marks. The rating scale has five categories based on the minimum and maximum marks that a student can receive. The first category is for marks between 90 and 100, and students falling within this range will receive a score of 5. The second category is for marks between 80 and 90, and students falling within this range will receive a score of 4. The third category is for marks between 70 and 80, and students falling within this range will receive a score of 3. The fourth category is for marks between 60 and 70, and students falling within this range will receive a score of 2. The fifth and final category is for marks below 60, and students falling within this range will receive a score of 1.

This rating scale is defined by the instructor in the educational institutions to evaluate the academic performance of students and is used by SPA.

SPA knowledge-based (KB)

The CLO verbs are represented as Facts in the SPA Knowledge-Based. The knowledge base of SPA is a store of both, factual and heuristic knowledge. Factual Knowledge: it is the information widely accepted by the Knowledge Engineers and scholars in the course syllabus and Bloom's Taxonomy. Heuristic Knowledge: it is about practice, accurate judgment, one’s ability to evaluate, and guessing. The cognitive domain encompasses six domains of intellectual abilities that develop in a sequential manner, progressing from basic to more intricate behaviors20,21. The knowledge base of SPA includes the courses name and level, as well as the six levels of Bloom's Taxonomy (Remember, Understand, Apply, Analyze, Evaluate, Create) and their corresponding descriptions. The knowledge base is a structured representation of the course information, including the Course Learning Outcomes (CLOs) and their respective weights and criteria. Here is an example of how the knowledge base is initialized for a course:

Also, the knowledge base of SPA contains the assessment plan for the courses. The following example of “Computer programming” KB course:

SPA inference engine

Backward Chaining is a type of reasoning that starts with the desired goal and works backwards through rules to establish known facts that support the goal. The goal is then divided into sub-goals to verify the accuracy of the facts, following a goal-driven approach where a list of goals determines which rules to use. For proof, backward chaining typically employs a depth-first search strategy19. Forward Chaining is a bottom-up approach, where the algorithm begins with the initial state and advances towards the goal state by drawing conclusions from the available facts or data. The Forward-chaining approach is also known as data-driven as it reaches the goal by utilizing available data20.

SPA uses Forward chaining as shown in the Fig. 3, it is a reasoning technique that starts with the available data and uses a set of inference rules to derive new facts or conclusions.Fig. 3 Forward chaining.

SPA algorithm starts with the assessment scores and uses the knowledge base to infer whether each CLO has been achieved. It iterates through each CLO in the knowledge base and checks if the achieved marks for the corresponding criteria meet the required weightage for that CLO. If the achieved marks meet the required weightage, the algorithm concludes that the CLO has been achieved and adds it to the clo_achievements dictionary. The following SPA algorithm:Initialize “clo_achievements" dictionary.

Loop through each CLO in the knowledge_base['clos'] dictionary.

Calculate the total marks for the current CLO.

Calculate the achieved marks for the current CLO.

Calculate the minimum marks required to achieve the current CLO.

Check if the achieved marks for the current CLO is greater than or equal to the minimum marks required to achieve the CLO.

If the condition in step 6 is true, add the CLO and description to the "clo_achievements" dictionary.

Return the "clo_achievements" dictionary containing all the CLOs achieved by the course assessment.

The following example of the rules to determine the achievement of each CLO:

Rule 1: To achieve CLO1, the student needs to score at least (8 + 4 + 10) = 22 marks in the assessment criteria for CLO1.

Rule 2: To achieve CLO2, the student needs to score at least (5 + 6 + 5) = 16 marks in the assessment criteria for CLO2.

Rule 3: To achieve CLO3, the student needs to score at least (7 + 8 + 5 + 8.4) = 28.4 marks in the assessment criteria for CLO3.

The inference engine uses forward chaining to deduce whether the CLOs have been achieved based on student assessment scores. The logic involves iterating through each CLO and checking if the student's achieved marks meet the required threshold. The SPA system employs specific rules to determine the achievement of each CLO. For instance:

In this example, the function calculate_clo_achievement checks each CLO's criteria, sums up the student's scores, and compares it with the required threshold to determine if the CLO is achieved. This function takes a dictionary of assessment_scores and calculates the total and achieved marks for each CLO. If the achieved marks meet or exceed the threshold (calculated as a percentage of the total marks), the CLO is considered achieved. These code snippets provide an overview of the critical aspects of SPA's coding, emphasizing the knowledge base initialization, inference engine logic, and rule implementation to assess student performance effectively.

Result and discussion

COMPUTER PROGRAMMING course is taken as an example to assess the direct assessment of students’ performance in the specific course. The following charts of CLOs weight in the COMPUTER PROGRAMMING course and CLO target score are used by SPA as shown in the Fig. 4.Fig. 4 CLOs weight and target.

Assessment plan of this course is defined by SPA as shown in the Table 3.Table 3 Assessment plan of computer programming course.

The above table represents an assessment plan for COMPUTER PROGRAMMING course with five Course Learning Outcomes (CLOs). The assessment plan consists of four types of assessments: Assignments, Projects, Midterm Exam, and Final Exam. Each assessment type has a different weight as indicated in the "Weight as syllabus" column. The total weight of all assessments is 100. The table shows the distribution of marks for each assessment type across the five CLOs. For example, the assignment has a weight of 20, and 8 marks are allocated to CLO1, 5 marks to CLO2, and 7 marks to CLO3. The table provides a breakdown of the marks for each CLO, allowing for the evaluation of the students' performance on each CLO separately. The table also shows the total marks in each CLO. The total marks for each CLO are calculated by adding the marks obtained in each assessment type for that CLO. For example, the total marks obtained for CLO1 are 22, which is the sum of marks obtained in the Assignment, Project, and Midterm assessments. The overall total marks obtained are calculated by adding the total marks obtained for all CLOs.

The assessment plan is a useful tool for evaluating students' performance and ensuring that they achieve the learning outcomes of the course. It also provides a framework for teachers to design assessments that are aligned with the CLOs and distribute the assessment weight appropriately. This helps to ensure that the assessment plan is fair and accurate in evaluating the students' performance.

Student scores in each CLO is resulted and CLOs achievements by the course assessment are generated by SPA as shown in the Table 4. This table appears the results of a group of students on five different course learning outcomes (CLOs), as indicated by the CLO1-CLO5 columns. Each student has been assigned a number, and their scores on each CLO are listed in the corresponding row.Table 4 Student score and achievements.

Student attainment in each CLO is resulted by SPA based on what population of students’ scores above the target mark 70% for direct assessment. Table 5 shows the CLOs attainment in COMPUTER PROGRAMMING course. SPA results are Average marks in each CLO, Students' CLO Status, CLO Target, Score Ave, CLO scale target, Target of the percentage of student numbers, and Percentage of CLO Attainment. The overall result showed that CLO2, CLO3, CLO4, and CLO5 of COMPUTER PROGRAMMING course were achieved and failed to achieve CLO1.Table 5 SPA report.

The above report shows the following:CLO1 has an average score of 52.196, which is below the target of 60. This CLO is considered "Unachieved" based on the given status. The average score corresponds to a CLO scale target of 2, and 66.666% of the students have achieved the target.

CLO2 has an average score of 86.5625, which is above the target of 70. This CLO is considered "Achieved" based on the given status. The average score corresponds to a CLO scale target of 1, and 100% of the students have achieved the target.

CLO3 has an average score of 81.25, which is above the target of 60. This CLO is considered "Achieved" based on the given status. The average score corresponds to a CLO scale target of 2, and 75% of the students have achieved the target.

CLO4 has an average score of 89.6412, which is above the target of 70. This CLO is considered "Achieved" based on the given status. The average score corresponds to a CLO scale target of 1, and 100% of the students have achieved the target.

CLO5 has an average score of 90.9722, which is above the target of 60. This CLO is considered "Achieved" based on the given status. The average score corresponds to a CLO scale target of 2, and 100% of the students

have achieved the target.

SPA reports that CLOs 2, 3, 4, and 5 have been achieved by all students for COMPUTER PROGRAMMING course, while CLO1 has been achieved by only 66.666% of the students. The scores for each CLO have been converted to a scale of 1 or 2 based on their target, and the percentage of students achieving each target has been calculated. The target percentage for all CLOs is set at 70%.

Here's an example of how SPA might work in practice:Upload the student marks in a course as shown in Fig. 5:

Enter the number of the students as shown in Fig. 6:

Enter each assessment separately into SPA, the following information will be provided as shown in Fig. 7:

Assessment name: the name or title of the assessment.

Assessment mark as per the syllabus: the maximum or expected mark for the assessment, as specified in the course syllabus or assessment guidelines.

Assessment mark as per the assessment given to the students: the actual mark or score that the student received on the assessment.

Ticked the CLOs covered by that assessment: identify which Course Learning Outcomes (CLOs) are covered by the assessment.

The percentage of the CLO covered (taking care of having a total of 100): for each CLO covered by the assessment, calculate the percentage of the CLO that is covered by the assessment.

The CLO target percentage: the target percentage for each CLO, as defined by the teacher based on the course syllabus or other criteria.

The CLO’s score: calculate the score for each CLO based on the percentage covered by the assessment and the target percentage.

Fig. 5 Upload the student marks in a course through Upload button.

Fig. 6 Enter the number of the students.

Fig. 7 Enter each assessment details.

By entering this information for each assessment, the student progress is tracked on specific CLOs and identify areas where students may need additional support or instruction.4. Add other assessments by clicking Add button.

5. Generate Coursework marks by clicking CW button: after finishing the coursework entry, and before entering the Final Exam assessment, the CW button should be clicked to generate the coursework marks. For courses without Final Exam after finishing the coursework entry, 100% CW will be ticked and then the CW button will be clicked.

6. Total/Grade button will be clicked to generate the grades.

7. Exit button will be clicked to generate the CLO measurement report.

The results show significant improvement in assessing student performance compared to traditional methods. Unlike the fuzzy-classification expert system for cerebral palsy rehabilitation or the expert system for identifying learning disabilities, SPA focuses on real-time, automated assessment of CLOs in various educational courses.

Comparative analysis

This section discusses the research findings in relation to existing studies. This highlights the advantages and novelties of the SPA system compared to other assessment tools.

Traditional assessment methods vs. SPA: traditional assessment methods often rely on manual grading and subjective judgment, which can introduce biases and inconsistencies. In contrast, SPA automates the assessment process, ensuring objectivity and consistency when evaluating student performance against Course Learning Outcomes (CLOs). SPA leverages a knowledge base and inference engine to provide real-time, accurate measurements of student performance, which is a significant improvement over traditional methods that may not offer timely feedback.

Existing expert systems vs. SPA: expert systems used in fields like medical diagnosis (e.g., systems for monitoring chronic wounds) or educational tools (e.g., fuzzy-classification systems for cerebral palsy rehabilitation) are designed for specific applications and may not be directly applicable to educational assessment. SPA is specifically tailored for educational contexts, focusing on CLOs and integrating seamlessly with course syllabi and Bloom's Taxonomy. This specialization allows SPA to provide more relevant and actionable insights for educators.

Advantages of SPA: scalability: SPA can handle large volumes of data and numerous assessments, making it suitable for large classes and multiple courses. Flexibility: the system can be adapted to different educational settings and customized to align with specific course objectives and learning outcomes. Enhanced Feedback: SPA offers detailed reports on student performance, identifying areas where students excel or need improvement, which helps educators tailor their teaching strategies.

Innovations in SPA: forward chaining inference engine: unlike some expert systems that use backward chaining, SPA employs a forward chaining inference engine, which is more efficient for processing student data and inferring CLO achievements. Integration with Bloom's Taxonomy: SPA's integration with Bloom's Taxonomy allows it to categorize learning outcomes based on cognitive levels, providing a comprehensive assessment of student knowledge and skills.

Studies supporting SPA's approach: studies on outcome-based education (OBE) systems highlight the importance of aligning assessments with learning outcomes. SPA builds on this by offering a structured, automated way to measure and analyze CLOs. Research on automated grading systems demonstrates the potential for reducing grading bias and increasing efficiency. SPA's automated approach ensures consistent and objective evaluation.

The SPA system represents a significant advancement over traditional and existing expert systems for educational assessment. Its automation, scalability, flexibility, and integration with educational frameworks like Bloom's Taxonomy make it a valuable tool for enhancing the quality of teaching and learning.

Conclusion

The development and implementation of the Smart System for Student Performance Assessment (SPA) mark a significant step forward in educational technology. SPA offers a novel approach to evaluating student performance by automating the assessment process and providing real-time analysis of Course Learning Outcomes (CLOs). This system ensures objective, consistent, and efficient measurement of student achievements, which is crucial for improving the quality of education. However, there are some limitations to SPA that need addressing. These include its reliance on accurate course content, the considerable initial time and resource investment required for setup, potential bias in the assignment of CLO weights, challenges in integrating with existing institutional databases, the necessity for ongoing updates to reflect curriculum changes, and possible resistance from educators to adopt new technologies. The implications of implementing SPA are significant. By offering precise and timely feedback on student performance, SPA helps educators identify areas where students struggle and adjust their teaching methods accordingly. It minimizes human bias and subjectivity in assessments, leading to fairer and more consistent evaluations. The data generated by SPA can inform curriculum development and teaching strategies, fostering continuous improvement in educational practices. Additionally, SPA is scalable and flexible, capable of handling large classes and multiple courses, and can be customized to fit specific educational contexts and objectives. Future research should focus on several key areas to enhance SPA further. These include integrating adaptive learning technologies to provide personalized learning experiences, developing more user-friendly interfaces, expanding SPA's applicability across various educational settings and disciplines, conducting longitudinal studies to evaluate its long-term impact, and exploring the integration of SPA with emerging technologies like artificial intelligence, machine learning, and big data analytics. Addressing these limitations and exploring these research directions will help solidify SPA's role in modernizing educational assessment practices. By providing quantitative feedback, SPA guides instructors towards areas for improvement, ultimately enhancing the educational experience for students. SPA is a powerful tool for assessing student learning outcomes and improving the quality of teaching and learning by providing quantitative feedback to guide instructors towards areas for improvement.

Supplementary Information

Supplementary Information.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-70953-y.

Author contributions

Samar Mouti and Hani Al-Chalabi wrote the paper.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Code availability

The code of the system can be available upon request to the authors via authors’ emails.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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