This paper presents a weighted based cell segmentation algorithm for multiple scan-chains BIST in order to reduce the average power consumption during the scan in of new test vectors, and to reduce the test application time. The proposed technique is based on selecting the best group of cells to be connected in the same scan-chain. This group of cells should have the same or very close weight of logic 1’s and 0’s that will be optimal to get the highest fault coverage in a specified test length. Then each scan-chain input will be connected to an output of a combinational circuit located after the Linear Feedback Shift Register that will generate biased test vectors according to the optimal weight of this segment of cells in the scan-chain. Thus, increasing the fault coverage and smoothing the applied test patterns which will reduce the power consumption. Experimental results on ISCAS’89 benchmark circuit’s shows that using the proposed design can save on average about 35.75% of the test application time, 30.95% of the average power consumption, which means huge energy saving with an average of 55.6% for 20 ISCAS’89 benchmark circuits.

This article introduces a recommendation system that merges a knowledge-based (attribute-based) approach with collaborative filtering, specifically addressing the challenges of the pure-cold start scenario in personalized e-learning. The system generates learning recommendations by assessing item similarities, utilizing the Rogers-Tanimoto similarity measure for materials and users, and Jaccard's similarity for user comparisons. Unlike traditional collaborative methods relying on prior ratings, this approach depends on attributes. Additionally, user and learning material profiling structures were created to serve as fundamental inputs for the recommendation algorithm. These profiles represent student and material knowledge in a two-dimensional space to facilitate matching. Our processes incorporate user learning styles, preferences, and prior knowledge as metrics for achieving the desired level of personalization. The system produces a list of top recommendations based on predicted ratings. To validate its efficacy, a website resembling our learning platform was developed and tested by users. The primary results demonstrate the system's ability to identify similar users even in a pure cold start condition without existing ratings. Consequently, the system proves its capability in recommending suitable materials, modeling students, and identifying similar user groups. The evaluation results of the proposed system showed a good level of satisfaction by the testimonials, quantified by a score of 82% for the recommended materials (16% higher than exiting cold-start systems), and an average score of 90% in terms of satisfaction about the generated student profiles. As they proved the capability of the framework in recommending suitable materials, and its capability in modeling students, finding similar groups of users.

Student modeling is a fundamental aspect in customized learning environments. It enables unified representation of students' characteristics that supports creating personalized learning experiences. This paper aims to build an effective student model by combining learning preferences with skill levels. A student profile is formulated upon detecting the user's learning styles and learning preferences, as well as their knowledge level and misconceptions. The pieces of information are collected through an interactive online platform, by completing personal and knowledge assessment quizzes. Moreover, a learner can make his/her profile open for other learners as a starting point for supporting collaborative learning. The results showed an improvement of students' educational achievements who used the platform, and the satisfaction level reported by non-neutral users was averaged as a score of 90%. The evaluation of this platform showed promising results regarding its ability in describing students in a comprehensive manner.

This paper introduces a novel approach for developing a learner model for school students by incorporating assessments from multiple sources, including students, parents, and teachers, in addition to exams. Traditional Intelligent Tutoring Systems (ITS) typically rely on a single source, such as exams, which often results in an incomplete picture of a student’s learning needs. To address this limitation, we developed a multi-source ITS that integrates feedback from various stakeholders to enhance the accuracy and personalization of the learner model. In a study involving 75 high school students, participants were divided into three groups: one receiving traditional classroom instruction, one using a conventional ITS, and one using our multi-source ITS. Results showed that the group using the multi-source system demonstrated the greatest improvement, with an average 38% increase in post-test scores, compared to 19% for the traditional instruction group and 26% for the conventional ITS group. Moreover, students using the multi-source ITS reported higher engagement and motivation, driven by their ability to interact with and adjust their learner model. These findings highlight the significant benefits of incorporating multi-source feedback into ITS to boost student performance, engagement, and motivation.

The market for mobile application development continues to thrive with billions of users and millions of apps. Collecting software requirements for mobile apps has to cope with this trend, so as for the software to compete in this crowded scene. Therefore, efforts to analyze mobile app reviews for requirements have shown a similar trend of increase. Among the billions of mobile users, there are hundreds of millions of Arabic-speaking users. According to our knowledge, this study would be one of the first studies in the field of mining mobile app reviews for the assistance of requirements engineering, to direct its focus on Arabic reviews. The main contribution of this study is to provide a framework for mining mobile app reviews in Arabic. A dataset of 7604 Arabic app reviews has been constructed and manually annotated by six experts. Each categorization aims at assisting one or more processes of software requirements engineering. Three configurations of deep neural networks, namely, CNN, LSTM, and BLSTM, were used to classify the app reviews into the considered categories of software requirements from the Arabic reviews. Furthermore, two word embeddings were utilized, on pre-trained models; Fasttext and Word2Vec, produced by this study. The sentimental analysis results show that the LSTM classifier with the Fasttext word embeddings gives the best F1-score, 79.17%. However, the BLSTM classifier with the fastText embeddings outperforms the other classifiers, with an F1-score of 69.83%, when used for identifying the sub-categories of the user perspective main category. The F1-score for classifying the sub-categories of the intention and topics with the LSTM and using fastText embeddings is 82.68% and 85,02%, respectively. These results outperform the other configurations of the classifiers and word embeddings. These results demonstrate the potential of our system to serve as a robust tool for automating software requirement extraction from Arabic app reviews, particularly in contexts where real-time user feedback is critical to agile development cycles.