Computer programming is always of high concern for students in introductory programming courses. High rates of failure occur every semester due to lack of adequate skills in programming. No student can become a programmer overnight because such learning requires proper guidance as well as consistent practice with the programming exercises. The role of instructors in the development of students' learning skills is crucial in order to provide feedback on their errors and improve their knowledge accordingly. On the other hand, due to the large number of students, instructors are also overloading themselves to focus on each individual student's errors. To address these issues, researchers have developed numerous Automatic Assessment (AA) systems that not only evaluate the students' programs but also provide instant feedback on their errors as well as abridge the workload of the instructors. Due to the large pool of existing systems, it is difficult to cover each and every system in one study. Therefore, this paper provides a comprehensive overview of some of the existing systems based on the three‐analysis approaches: dynamic, static, and hybrid. Moreover, this paper aims to discuss the strengths and limitations of these systems and suggests some potential recommendations regarding the AA specifications for novice programming, which may help in standardizing these systems.

Background The use of artificial intelligence has revolutionized every area of life such as business and trade, social and electronic media, education and learning, manufacturing industries, medicine and sciences, and every other sector. The new reforms and advanced technologies of artificial intelligence have enabled data analysts to transmute raw data generated by these sectors into meaningful insights for an effective decision-making process. Health care is one of the integral sectors where a large amount of data is generated daily, and making effective decisions based on these data is therefore a challenge. In this study, cases related to childbirth either by the traditional method of vaginal delivery or cesarean delivery were investigated. Cesarean delivery is performed to save both the mother and the fetus when complications related to vaginal birth arise. Objective The aim of this study was to develop reliable prediction models for a maternity care decision support system to predict the mode of delivery before childbirth. Methods This study was conducted in 2 parts for identifying the mode of childbirth: first, the existing data set was enriched and second, previous medical records about the mode of delivery were investigated using machine learning algorithms and by extracting meaningful insights from unseen cases. Several prediction models were trained to achieve this objective, such as decision tree, random forest, AdaBoostM1, bagging, and k-nearest neighbor, based on original and enriched data sets. Results The prediction models based on enriched data performed well in terms of accuracy, sensitivity, specificity, F-measure, and receiver operating characteristic curves in the outcomes. Specifically, the accuracy of k-nearest neighbor was 84.38%, that of bagging was 83.75%, that of random forest was 83.13%, that of decision tree was 81.25%, and that of AdaBoostM1 was 80.63%. Enrichment of the data set had a good impact on improving the accuracy of the prediction process, which supports maternity care practitioners in making decisions in critical cases. Conclusions Our study shows that enriching the data set improves the accuracy of the prediction process, thereby supporting maternity care practitioners in making informed decisions in critical cases. The enriched data set used in this study yields good results, but this data set can become even better if the records are increased with real clinical data.

The lockdown of universities and educational institutions during the COVID-19 pandemic has negatively impacted the educational process. Saudi Arabia became a forerunner during COVID-19 by taking initial precautions of curfews and total restrictions. However, these restrictions had a disruptive effect on various sectors, specifically the educational sector. The Ministry of Education strived to cope with the consequences of these changes swiftly by shifting to online education. This paper aims to study the impact of COVID-19 on the educational process through a comparative study of the responses collected from different cases, and the challenges that are faced throughout the educational process. The study conducted a cross-sectional, self-administered online questionnaire during the outbreak and distance learning, which was designed based on the Technology–Organization–Environment (TOE) framework of students. Most questions used a five-point Likert scale. The responses were randomly collected from 150 undergraduate and postgraduate students who were studying in Saudi Arabian universities, to study the overall performance of education institutions during COVID-19. The collected data were analyzed and compared to the results in the literature. The main factors impacted by this transformation are addressed. These factors are based on research and observations and aim to overcome the encountered limitations and to present their level of impact on distance education. The research framework can be useful for higher educational authorities aiming to overcome the issues highlighted and discussed in this study.

Assessment of students in computer programming is a challenge for instructors, especially at the introductory programming level, where the number of student enrollment is typically high. Therefore, this study presents a novel approach to assessing students' competency in programming using Bloom's taxonomy. The novelty of the presented approach is based on some rules that quantify the attained competencies with respect to the cognitive levels of Bloom's taxonomy. Unlike previous studies, in which cognitive levels were used as a scale for making the questions while the competency assessment was manually performed, in this study, the rule-based assessment method uses the automatic decision-making process to map the students' competency level directly to the corresponding cognitive levels from the written code without the prior mapping of questions to the cognitive levels. For this reason, the study focuses on the basic topics of the structured Java programming language (i.e. selection, repetition, and modular). The rule-based assessment method has been applied to students' programming code in the introductory level Java course. Data collection has been carried out through conducting an empirical test in which the valid responses of 213 students were collected, which was processed through the rule-based method for competency assessment. Moreover, the quantitative results achieved from the rule-based assessment method were validated by comparing them with the results achieved from the manual assessment. Furthermore, for comparative analysis, several statistical methods were used to identify the difference between the results of the two assessment methods. The outcomes of the comparative analysis have shown the reliability of the proposed rule-based assessment method.

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