The Hypothes.is tool was applied in the synchronous learning environment to connect and engage the students by using the social and learning collaborative approaches. The course was Current Topics in Quality Engineering, and data was collected during the 2020 fall semester. The study areas in this course were Research Techniques, Quality Tools, Quality 4.0, Industry 4.0, and Emerging Technologies. All the course participants were international students.

Data was collected from 95 of the students across the three class sections. Eight articles were uploaded in the Hypothes.is and students had to annotate and discuss during the synchronous learning classes. At the end of the course, students had a final exam that included all the course modules. Students have never been exposed to the Hypothes.is a tool and have never used any social annotation (SA) tools.

The intent of this study was to create a model that can be applied in the learning environment where students have access to Hypothes.is tool, and then to be able to analyze the results by using the machine learning algorithms.

Based on the study and data interpreted using the machine learning tool, this paper concludes that students who participated directly during the synchronous learning and accessed the course material available in the Hypothes.is tool performed better in the final test than students who did not access the tool. The use of the machine learning correlation analysis algorithm has shown that data can be continuously collected during the semester, and the learning outcomes can be predicted. It can be used as a tool that would help teachers to further adapt their teaching methodology to the online classes.

Keywords: Quality and technology topics, Hypothes.is, social annotation tool and system, online engagement, collaborative learning, machine learning evaluation tool, social pedagogy