Discussion forum is the primary and most widely used venue for social learning and communication in MOOC context. There has been an interest in the last few years to study the interactions and participation behavior in this environment. This systematic review studied eighty-four papers published between 2013-2017 to provide a state of the art, and suggest future directions in the literature from two perspectives: descriptive analysis and content analysis. The descriptive analysis aggregates the reviewed papers into five dimensions: time of the publication, stakeholders involved in the analysis, objectives, methods, and data sources used in the studies. The content analysis aims to highlight the main issues addressed and the major contributions of the papers covered by the literature review. These contributions can be categorized into three major areas: 1) Explore the relationship between forums participation and retention or learning outcomes and evaluate ways to increase the participation activity and learning outcomes; 2) Analyze participants' contribution in the forum and how to organize the content and efficiently monitor them; and 3) Analyze participants' interactions and how it influences learning. At the end, we offer some suggestions to further advance this area of research.

Educational institutions typically gather feedback from beneficiaries through formal surveys. Offering open-ended questions allows students to express their opinions about matters that may not have been measured directly in closed-ended questions. However, responses to open-ended questions are typically overlooked due to the time and effort required. Aspect-based sentiment analysis is used to automate the process of extracting fine-grained information from texts. This study aims to 1) examine the performance of different BERT-based models for aspect term extraction for Arabic text sourced from educational institution surveys; 2) develop a system that automates the ABSA process in a way that will automatically label survey responses. An end-to-end system was developed as a case study to extract aspect terms, identify their polarity, map extracted aspects to their respective categories, and aggregate category polarity. To accomplish this, the models were evaluated using an in-house dataset. The result showed that FAST-LCF-ATEPC, a multilingual checkpoint, outperformed other models including AraBERT, MARBERT, and QARiB, in the aspect-term extraction task, with an F1 score of 0.58. Hence, it was used for aspect-term polarity classification, showing an F1 score of 0.86. Mapping aspects to their respective categories using a predefined list yielded an average F1 score of 0.98. Furthermore, the polarities of the categories were aggregated to summarize the overall polarity for each category. The developed system can support Arabic educational institutions in harnessing valuable information in responses to open-ended survey questions, allowing decision-makers to better allocate resources, and improve facilities, services, and students’ learning experiences.

Discussion forums are integral to MOOCs and a useful resource for collaborative learning. In this paper we examine if the posts made on forums by participants can also provide meaningful information to assess and improve the effectiveness of MOOCs. We present an empirical approach that uses posts in MOOC forums to summarize participants’ opinion towards aspects of a course and identify suggestions for improving a course. Specifically, our approach: (1) detects participants’ attitude towards aspects of a course (e.g. professor, lecture, assignment) at a context or sentence level, (2) extracts suggestions for course improvement, which is a novel space in MOOC learning analytics, and (3) aggregates and displays the results visually. The study used lexicon and rule-based approach and was able to identify aspect-based sentiments and suggestions related to course design elements with a good overall score (0.41 kappa score). By summarizing opinions from a vast amount of textual data on forums our approach allows instructors to improve their course and thereby student engagement and learning.

Ensuring hygiene compliance in regulated environments—such as food processing facilities, hospitals, and public indoor spaces—requires reliable detection of personal protective equipment (PPE) usage, including gloves, face masks, and hairnets. Manual inspection is labor-intensive and unsuitable for continuous, real-time enforcement. This study benchmarks three lightweight object detection models—YOLOv8n, YOLOv10n, and YOLOv12n—for automated PPE compliance monitoring using a large curated dataset of over 31,000 annotated images. The dataset spans seven classes representing both compliant and non-compliant conditions: glove, no_glove, mask, no_mask, incorrect_mask, hairnet, and no_hairnet. All evaluations were conducted using both detection accuracy metrics (mAP@50, mAP@50–95, precision, recall) and deployment-relevant efficiency metrics (inference speed, model size, GFLOPs). Among the three models, YOLOv10n achieved the highest mAP@50 (85.7%) while maintaining competitive efficiency, indicating strong suitability for resource-constrained IoT-integrated deployments. YOLOv8n provided the highest localization accuracy at stricter thresholds (mAP@50–95), while YOLOv12n favored ultra-lightweight operation at the cost of reduced accuracy. The results provide practical guidance for selecting nano-scale detection models in real-time hygiene compliance systems and contribute a reproducible, deployment-aware evaluation framework for computer vision in hygiene-critical settings.