This study explores and characterizes learners' participation patterns in MOOC forums, as well as the factors that correlate with learners' participation. Educational data mining and learning analytics methods were used to retrieve and analyze the learners' interpersonal interaction data, which had accumulated in the Coursera log files. The content in the forums was categorized based on Henri's criteria and converted into quantitative values that could be compared and visualized. It was found that only 20% of the learners were collaborating in the forums throughout the entire course and were responsible for 50% of the total posts. A portion of them earned the name "Super Active." The analyses not only demonstrated the volume of activity and its pattern but also revealed the content of the discussions, which helped to highlight the needs and reasons for students' usage of the forums. The content analysis showed intensity in the "Cognitive" and "Discipline" categories. Thus, forum participants benefit from discussions not only socially but disciplinarily and cognitively as well. Furthermore, even though a strong significant correlation was found between the learners' completion status and their activity in the forums, a group of learners, who did not complete the course, was highly active.

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e19346 Background: The importance of timely treatment of advanced NSCLC and timeline measurements have been the subject of reported studies. Real world evidence data (RWED) via EHR and cutting-edge technology paired with Belong.life pts engagement platform, was evaluated in Israeli pts with NSCLC, members of Belong.life community. Timelines from diagnosis to start of IOT were analyzed by data scientists using AI and machine learning engines. Methods: 30 Israeli pts with advanced NSCLC, members of Belong.life, the largest global social media platform for cancer pts and caregivers, were screened during 2016-8. Demographics, time of diagnosis, time of starting IOT and reasons for treatment delays were documented. Results: 18 pts (60%) were males and 88% had Stage 4. Most pts (87%) were diagnosed during 2017-8. 46 % were < 60 yrs and 54% > 61 yrs. 18 (60%) pts received only pembrolizumab while 11 (37%) pts had it in combination with various chemotherapy drugs. 1 pt received single agent nivolumab. According to the dates reported by 22 (76%) pts, the median time from diagnosis to IOT initiation was 37 days( < 20-96) with 13(59%) starting the treatment in < 40 days. Most common delay causes were due to waiting for genetic tests to define targeted therapy (38%), protracted diagnostic phase (38%) and bureaucratic issues in 27%. Study limitations included its retrospective nature, a convenience sampling, and partial reliance on pts recall of dates. Conclusions: Healthcare systems are seeing an increase in the incidence of most cancers. We report marked variability in time to initiation of 1st LOT therapy of Israeli pts with advanced NSCLC. The common reasons for the delays were obtaining the molecular tests results in order to define targeted drugs and as more targets and their therapies become available this period might increase. Treatment delays are likely to have a negative impact on pts well-being, quality of life and long-term prognosis. Speeding up diagnosis and treatment planning process could decrease pts waiting time, improve their quality of life and expectancy, therefore consideration to the development of a time to treatment protocol should be prioritized.