An Expert Comparison of Accreditation Support Tools for the Undergraduate Computing Programs

Namoun, Abdallah; Taleb, Ahmad; Benaida, Mohamed

doi:10.14569/ijacsa.2018.090948

Cited by 7 publications

(6 citation statements)

References 12 publications

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“…Essentially, course outcomes enable the accomplishment of program outcomes, and their alignment (i.e., courses to program) is performed in a critical activity referred to as curriculum mapping. Computerized tools were developed to assist in realizing the OBE goals [14] and effectively document the educational assessment activities [15]. Their usefulness could be extended by incorporating intelligent models that can prognosticate the attainment of learning outcomes during academic terms.…”

Section: Student Outcomesmentioning

confidence: 99%

Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

2020

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The prediction of student academic performance has drawn considerable attention in education. However, although the learning outcomes are believed to improve learning and teaching, prognosticating the attainment of student outcomes remains underexplored. A decade of research work conducted between 2010 and November 2020 was surveyed to present a fundamental understanding of the intelligent techniques used for the prediction of student performance, where academic success is strictly measured using student learning outcomes. The electronic bibliographic databases searched include ACM, IEEE Xplore, Google Scholar, Science Direct, Scopus, Springer, and Web of Science. Eventually, we synthesized and analyzed a total of 62 relevant papers with a focus on three perspectives, (1) the forms in which the learning outcomes are predicted, (2) the predictive analytics models developed to forecast student learning, and (3) the dominant factors impacting student outcomes. The best practices for conducting systematic literature reviews, e.g., PICO and PRISMA, were applied to synthesize and report the main results. The attainment of learning outcomes was measured mainly as performance class standings (i.e., ranks) and achievement scores (i.e., grades). Regression and supervised machine learning models were frequently employed to classify student performance. Finally, student online learning activities, term assessment grades, and student academic emotions were the most evident predictors of learning outcomes. We conclude the survey by highlighting some major research challenges and suggesting a summary of significant recommendations to motivate future works in this field.

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Section: Student Outcomesmentioning

confidence: 99%

Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

2020

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“…The majority of program assessment systems are neither computerized nor can predict student attainment of student learning outcomes. Program assessment tools rarely embed machine learning or mining techniques in the functionalities and reports they produce [69], which restrains program leaders from discovering the factors that lead to high or low levels of student performance [68]. In our judgment, anticipating program weaknesses would help to recommend improvement actions promptly.…”

Section: Discussionmentioning

confidence: 99%

Predicting Student Performance and Its Influential Factors Using Hybrid Regression and Multi-Label Classification

Alshanqiti

Namoun

2020

IEEE Access

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Understanding, modeling, and predicting student performance in higher education poses significant challenges concerning the design of accurate and robust diagnostic models. While numerous studies attempted to develop intelligent classifiers for anticipating student achievement, they overlooked the importance of identifying the key factors that lead to the achieved performance. Such identification is essential to empower program leaders to recognize the strengths and weaknesses of their academic programs, and thereby take the necessary corrective interventions to ameliorate student achievements. To this end, our paper contributes, firstly, a hybrid regression model that optimizes the prediction accuracy of student academic performance, measured as future grades in different courses, and, secondly, an optimized multi-label classifier that predicts the qualitative values for the influence of various factors associated with the obtained student performance. The prediction of student performance is produced by combining three dynamically weighted techniques, namely collaborative filtering, fuzzy set rules, and Lasso linear regression. However, the multi-label prediction of the influential factors is generated using an optimized self-organizing map. We empirically investigate and demonstrate the effectiveness of our entire approach on seven publicly available and varying datasets. The experimental results show considerable improvements compared to single baseline models (e.g. linear regression, matrix factorization), demonstrating the practicality of the proposed approach in pinpointing multiple factors impacting student performance. As future works, this research emphasizes the need to predict the student attainment of learning outcomes.

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“…The first part consists of those works in which a successful accreditation experience is presented to describe various processes and their management [37][38][39]. The second part contains the development of some web-based interactive tools to effectively manage the entire quality management process [40][41][42][43].…”

Section: 2mentioning

confidence: 99%

“…The purpose for the development of web-based interactive tools is to collect the feedback from different entities and organize it as per requirements. The work in [40] critically analyzes different interactive tools (such as EvalTools [41], CLOSO [42], and WEAVEonline [43]). The main goals of all these tools is to automate the data collection process which enhances the co-operation among various stakeholders.…”

Section: 2mentioning

confidence: 99%

Employing Industrial Quality Management Systems for Quality Assurance in Outcome-Based Engineering Education: A Review

et al. 2021

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With the world becoming flat with fluid boundaries, engineers have to be global in their outlook and their pedigree. Due to the need for international acceptance of engineering qualification, the incorporation of Outcome-Based Education (OBE) has become common and global accreditation treaties such as the Washington Accord have been ratified. Further, it becomes important, especially for an engineering university with a global outlook preparing its students for global markets, to ensure that its graduates attain the planned outcomes. Additionally, the higher education institutions need to make sure that all the stakeholders, including students, parents, employers, and community at large, are getting a quality educational service, where quality is categorized as (1) product-based ensuring that the graduate attained the planned outcomes and skills, and (2) process-based keeping an eye on whether the process is simple, integrated, and efficient. The development of quality movements, such as Total Quality Movement (TQM), Six Sigma, etc., along with quality standards such as ISO 9001 has been instrumental in improving the quality and efficiency in the fields of management and services. Critical to the successful deployment of a quality culture is the institutionalization of an integrated Quality Management System (QMS) in which formally documented processes work according to the Vision and Mission of an institute. At the same time, commitment to Continuous Quality Improvement (CQI) to close the loop through effective feedback, would ensure that the planned outcomes are attained to the satisfaction of all the stakeholders, and that the process overall is improving consistently and continuously. The successful adoption of quality culture requires buy-in from all the stakeholders (and in particular, the senior leadership) and a rigorous training program. In this paper, we provide a review of how a QMS may work for the provision of quality higher education in a 21st-century university.

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An Expert Comparison of Accreditation Support Tools for the Undergraduate Computing Programs

Cited by 7 publications

References 12 publications

Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

Predicting Student Performance and Its Influential Factors Using Hybrid Regression and Multi-Label Classification

Employing Industrial Quality Management Systems for Quality Assurance in Outcome-Based Engineering Education: A Review

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