Coronavirus (Covid-19) pandemic has imposed a complete shut-down of face-to-face teaching to universities and schools, forcing a crash course for online learning plans and technology for students and faculty. In the midst of this unprecedented crisis, video conferencing platforms (e.g., Zoom, WebEx, MS Teams) and learning management systems (LMSs), like Moodle, Blackboard and Google Classroom, are being adopted and heavily used as online learning environments (OLEs). However, as such media solely provide the platform for e-interaction, effective methods that can be used to predict the learner’s behavior in the OLEs, which should be available as supportive tools to educators and metacognitive triggers to learners. Here we show, for the first time, that Deep Learning techniques can be used to handle LMS users’ interaction data and form a novel predictive model, namely DeepLMS, that can forecast the quality of interaction (QoI) with LMS. Using Long Short-Term Memory (LSTM) networks, DeepLMS results in average testing Root Mean Square Error (RMSE) $$<0.009$$ < 0.009 , and average correlation coefficient between ground truth and predicted QoI values $$r\ge 0.97$$ r ≥ 0.97 $$(p<0.05)$$ ( p < 0.05 ) , when tested on QoI data from one database pre- and two ones during-Covid-19 pandemic. DeepLMS personalized QoI forecasting scaffolds user’s online learning engagement and provides educators with an evaluation path, additionally to the content-related assessment, enriching the overall view on the learners’ motivation and participation in the learning process.

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In this paper we present the educational process of Lin2k, a Web‐based tool, which supports distant asynchronous, written, peer‐collaboration in a case study. The tool constitutes an open learning environment that endows engineering students with collaborative competencies, necessary for their successful shift to professional practice. Students are engaged in a process of experiential learning of collaboration while, in parallel, we follow up their interactions and collect communication data of interest. Lin2k collaboration evaluating and adaptive feedback mechanisms are aimed at equipping students with the necessary conceptual knowledge that underlies collaborative activity. Lin2k experimental uses in the Civil Engineering Department of Aristotle University of Thessaloniki, Greece, proved the efficacy of its pedagogy. The Lin2k educational process serves as a prototype that may contribute to the revision of engineering curricula so as to face the challenges that the new technologies impose, along with the necessity of relating academic to professional life.