The study assessed the member response rate to the Oyo state teaching service commission (TESCOM) interactive learning platforms during the COVID-19 lockdown in Nigeria. The study adopted a descriptive survey research design. The sample comprised 3,388 respondents drawn from five online learning platforms (Arts, science, commercial, general and staffroom). Two instruments, Response Rate Factor Questionnaire-Survey monkey (r = 0.83) and participant online direct recording (π = 0.76), were used to collect data at three different intervals. Frequency counts and analysis of variance were used to analyse the data collected. Those online at the time of data collection were 59 (5.2%) for science, 23 (4.3%) for arts, 24 (6.4%) for commercial, 84 (7.4%) for general study and 96 (48.5%) in the staff room platform. A significant difference in member response rate was observed across the learning platforms [F(4,10) = 4.374; p= 0.027< 0.05]. Bonferroni post hoc analysis shown by mean plot revealed that general studies platform had the highest mean score (M=169.0) in terms of members response online followed by staffroom (M=79.0) and lastly commercial platform (M=32.67). It was deduced from the findings that members across the TESCOM interactive learning platforms do not respond online the same way by participating on the respective interactive platform to which they belong. Therefore, TESCOM should ensure that teachers and students actively engage in online learning platforms for better teaching and learning.

Sentiment analysis is a classification technique that specializes in categorizing a body of texts into various emotions. This categorization had proven to be handy in classifying tweets into positive, negative, or neutral emotions. Nigerians had been on a nationwide lockdown due to COVID19 since 30th March 2020. The analysis of the emotions of Nigerians during this period is expedient to understand the effectiveness of exercise and the impact it has on the masses. The focus of this paper is to determine the sentiment analysis of Nigerians within the period of the lockdown exercise. Using a lexicon-based analytic architecture and a streaming API to TwitterNG, we extracted a total of 22, 249 tweets from the timelines of national stakeholders on COVID19 and location-based tweets from the general public. The tweets were extracted and collated using a set of ten hashtags/keywords from 30th March to 11th May 2020. The analysis was done in R Programming Software with the application of the NRC lexicon approach to classifying the emotions of Nigerians within the period. The result showed that Nigerians expressed an overall positive sentiment to the lockdown exercise despite a few negative expressions.