Ierin Babu^1,*, R. MathuSoothana², S. Kumar²

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3621-3636, 2023, DOI:10.32604/iasc.2023.033791

Abstract Educational Data Mining (EDM) is an emergent discipline that concentrates on the design of self-learning and adaptive approaches. Higher education institutions have started to utilize analytical tools to improve students’ grades and retention. Prediction of students’ performance is a difficult process owing to the massive quantity of educational data. Therefore, Artificial Intelligence (AI) techniques can be used for educational data mining in a big data environment. At the same time, in EDM, the feature selection process becomes necessary in creation of feature subsets. Since the feature selection performance affects the predictive performance of any model, it is important to elaborately… More >

Mesfer Al Duhayyim¹, Radwa Marzouk^2,3, Fahd N. Al-Wesabi⁴, Maram Alrajhi⁵, Manar Ahmed Hamza^6,*, Abu Sarwar Zamani⁶

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1233-1247, 2022, DOI:10.32604/cmc.2022.021652

Abstract Recent advancements in computer technologies for data processing, collection, and storage have offered several chances to improve the abilities in production, services, communication, and researches. Data mining (DM) is an interdisciplinary field commonly used to extract useful patterns from the data. At the same time, educational data mining (EDM) is a kind of DM concept, which finds use in educational sector. Recently, artificial intelligence (AI) techniques can be used for mining a large amount of data. At the same time, in DM, the feature selection process becomes necessary to generate subset of features and can be solved by the use… More >

Yousef Asiri^*

Computer Systems Science and Engineering, Vol.41, No.1, pp. 35-50, 2022, DOI:10.32604/csse.2022.020100

Abstract Most of the international accreditation bodies in engineering education (e.g., ABET) and outcome-based educational systems have based their assessments on learning outcomes and program educational objectives. However, mapping program educational objectives (PEOs) to student outcomes (SOs) is a challenging and time-consuming task, especially for a new program which is applying for ABET-EAC (American Board for Engineering and Technology the American Board for Engineering and Technology—Engineering Accreditation Commission) accreditation. In addition, ABET needs to automatically ensure that the mapping (classification) is reasonable and correct. The classification also plays a vital role in the assessment of students’ learning. Since the PEOs are… More >

Maryam Zaffar^1,2,*, Manzoor Ahmed Hashmani¹, Raja Habib², KS Quraishi³, Muhammad Irfan⁴, Samar Alqhtani⁵, Mohammed Hamdi⁵

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1893-1920, 2022, DOI:10.32604/cmc.2022.018295

Abstract Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions, for the improvement of quality of education and to meet the dynamic needs of society. The selection of features for student's performance prediction not only plays significant role in increasing prediction accuracy, but also helps in building the strategic plans for the improvement of students’ academic performance. There are different feature selection algorithms for predicting the performance of students, however the studies reported in the literature claim that there are different pros and cons of existing feature selection algorithms in selection of optimal features. In this… More >