No abstract

No abstract

No abstract

Computer programming is a complex field that requires rigorous practice in programming code writing and learning skills, which can be one of the critical challenges in learning and teaching programming. The complicated nature of computer programming requires an instructor to manage its learning resources and diligently generate programming-related questions for students that need conceptual programming and procedural programming rules. In this regard, automatic question generation techniques help teachers carefully align their learning objectives with the question designs in terms of relevancy and complexity. This also helps in reducing the costs linked with the manual generation of questions and fulfills the need of supplying new questions through automatic question techniques. This paper presents a theoretical review of automatic question generation (AQG) techniques, particularly related to computer programming languages from the year 2017 till 2022. A total of 18 papers are included in this study. one of the goals is to analyze and compare the state of the field in question generation before COVID-19 and after the COVID-19 period, and to summarize the challenges and future directions in the field. In congruence to previous studies, there is little focus given in the existing literature on generating questions related to learning programming languages through different techniques. Our findings show that there is a need to further enhance experimental studies in implementing automatic question generation especially in the field of programming. Also, there is a need to implement an authoring tool that can demonstrate designing more practical evaluation metrics for students.

The final goal of our research is to show that the performance of statistical rule induction can be improved by augmenting training data with semantic information. In order to prove this hypothesis, a statistical grammar induction system is to be created the knowledge base of which is represented by Extended Conceptual Graphs (ECGs). Since generalization and specialization are the basic operations of induction, they are of great significance in machine learning. As a consequence, the paper aims at investigating the least common generalization and the greatest common specialization of two ECG graphs. These operations should be traced back to the examination of ECG graph element instances. For this reason, a domain-specific ECG element instance type lattice (T′,≺) has been generated for the given test environment. Our final conclusion is that the least common generalization and the greatest common specialization of two ECG graphs always exist and can be computed. Therefore, the definition of the ≺ relation on element instances can be extended to a partial relation ⪯ on ECG diagram graphs, according to which F 1 ⪯ F 2 if graph Γ 1 is more specialized than Γ 2.