Automated analysis of postings in fourth grade online discussions to help teachers keep students on-track

Abstract

ABSTRACT In the recent, and ongoing, Covid-19 pandemic, remote or online K-12 schooling became the norm. Even if the pandemic tails off somewhat, remote K-12 schooling will likely remain more frequent than it was before the pandemic. A mainstay technique of online learning, at least at the college and graduate level, has been the online discussion. Since it does afford the potential for meaningful learner-learner and instructor-learner interaction, which are vital for distance learning, it is worth considering online discussions for K-12 remote schooling. One challenge with online learning in general, and online discussion in particular, is that it is labor intensive for teachers to moderate. Effective moderating of online discussions is vital for discussions to be nurturing, effective learning situations. Yet, moderating of online discussions is notoriously labor-intensive for teachers/instructors. Further, since younger learners are more likely to drift off topic, in general, but particularly in small group online discussions, automated early warning systems are helpful. The current study collected small group, “book club”, discussion data from fourth graders reading web-based eBooks in Slovenian primary schools, qualitatively coded the data and analyzed postings using computer-based natural language processing to predict when students went off-topic. One indicator that postings are on-topic is book relevance, i.e. that the posting is relevant to eBook content. The computer algorithm correctly predicted book relevance of postings 90 percent of the time, suggesting that automated computer algorithms could assist teachers with moderating online discussions, providing real-time notifications of problems in online discussions. Further, this study provided a proof-of-concept that small group online discussions, in web-based eBooks can be practical and educationally meaningful in fourth grade classes.