An intelligent tutoring system (ITS) is a computer system or software application that is built to replicate human tutors by supporting the theory of “learning by doing.” Even though ITSs have been proven to be successful in academic studies, they still have not found large adoption by the industry due to the complexities of building such systems due to the high technical expertise and domain knowledge requirements. Attempts have been made to build authoring tools that can provide assistance in building tutoring systems; however, most of these tools are targeted toward authors that have considerable programming experience. This research proposes an authoring tool for ITS, which is targeted at novice authors with minimum technical/programming experience and provides real-time scaffolding to learner’s incomplete/incorrect answers using the best scaffolding techniques. Two evaluation techniques were applied for the evaluation of the performance of the proposed authoring tool, e.g., paired t-test analysis and postexperiment survey. The learning gains obtained from paired t-test contend a significant learning gain and improvement in the learning process with enhanced learning performance with multiple scaffolding techniques as compared to single scaffolding technique experience. The postexperiment survey has a notable result that shows the effectiveness of the tutor model that ensures a very user-friendly interface, deploying scaffolding techniques and adequate control of selecting and deploying scaffolding techniques and making the authoring process easy.

Measuring and evaluating a learner’s learning ability is always the focus of every person whose aim is to develop strategies and plans for their learners to improve the learning process. For example, classroom assessments, self-assessment using computer systems such as Intelligent Tutoring Systems (ITS), and other approaches are available. Assessment of metacognition is one of these techniques. Having the ability to evaluate and monitor one’s learning is known as metacognition. An individual can then propose adjustments to their learning process based on this assessment. By monitoring, improving, and planning their activities, learners who can manage their cognitive skills are better able to manage their knowledge about a particular subject. It is common knowledge that students’ metacognitive and self-assessment skills and abilities have been extensively studied, but no research has been carried out on the mistakes that novice developers make because they do not use their self-assessment abilities enough. This study aims to assess the metacognitive skills and abilities of novice software developers working in the industry and to describe the consequences of awareness of metacognition on their performance. In the proposed study, we experimented with novice software developers and collected data using Devskiller and a self-assessment log to analyze their use of self-regulation skills. The proposed study showed that when developers are asked to reflect upon their work, they become more informed about their habitual mistakes, and using a self-assessment log helps them highlight their repetitive mistakes and experiences which allows them to improve their performance on future tasks.