Eye tracking and artificial intelligence for competency assessment in engineering education: a review

In recent years, eye-tracking (ET) methods have gained an increasing interest in STEM education research. When applied to engineering education, ET is particularly relevant for understanding some aspects of student behavior, especially student competency, and its assessment. However, from the instructor’s perspective, little is known about how ET can be used to provide new insights into, and ease the process of, instructor assessment. Traditionally, engineering education is assessed through time-consuming and labor-extensive screening of their materials and learning outcomes. With regard to this, and coupled with, for instance, the subjective open-ended dimensions of engineering design, assessing competency has shown some limitations. To address such issues, alternative technologies such as artificial intelligence (AI), which has the potential to massively predict and repeat instructors’ tasks with higher accuracy, have been suggested. To date, little is known about the effects of combining AI and ET (AIET) techniques to gain new insights into the instructor’s perspective. We conducted a Review of engineering education over the last decade (2013–2022) to study the latest research focusing on this combination to improve engineering assessment. The Review was conducted in four databases (Web of Science, IEEE Xplore, EBSCOhost, and Google Scholar) and included specific terms associated with the topic of AIET in engineering education. The research identified two types of AIET applications that mostly focus on student learning: (1) eye-tracking devices that rely on AI to enhance the gaze-tracking process (improvement of technology), and (2) the use of AI to analyze, predict, and assess eye-tracking analytics (application of technology). We ended the Review by discussing future perspectives and potential contributions to the assessment of engineering learning

Using Machine Learning and Graph Mining Approaches to Improve Software Requirements Quality: An Empirical Investigation

Software development is prone to software faults due to the involvement of multiple stakeholders especially during the fuzzy phases (requirements and design). Software inspections are commonly used in industry to detect and fix problems in requirements and design artifacts, thereby mitigating the fault propagation to later phases where the same faults are harder to find and fix. The output of an inspection process is list of faults that are present in software requirements specification document (SRS). The artifact author must manually read through the reviews and differentiate between true-faults and false-positives before fixing the faults. The first goal of this research is to automate the detection of useful vs. non-useful reviews. Next, post-inspection, requirements author has to manually extract key problematic topics from useful reviews that can be mapped to individual requirements in an SRS to identify fault-prone requirements. The second goal of this research is to automate this mapping by employing Key phrase extraction (KPE) algorithms and semantic analysis (SA) approaches to identify fault-prone requirements. During fault-fixations, the author has to manually verify the requirements that could have been impacted by a fix. The third goal of my research is to assist the authors post-inspection to handle change impact analysis (CIA) during fault fixation using NL processing with semantic analysis and mining solutions from graph theory. The selection of quality inspectors during inspections is pertinent to be able to carry out post-inspection tasks accurately. The fourth goal of this research is to identify skilled inspectors using various classification and feature selection approaches. The dissertation has led to the development of automated solution that can identify useful reviews, help identify skilled inspectors, extract most prominent topics/keyphrases from fault logs; and help RE author during the fault-fixation post inspection