ABSTRACT
During the mass curfews, travel bans, school shutdowns, face-to-face education was discontinued, and many universities had to urgently switch to online education. Academics, most of whom are not familiar with digital pedagogy, had to adapt their lectures to online learning. The aim of this study is to analyze how Open Educational Resources (OERs) are used in practice during emergency remote teaching (ERT) and whether this influences the regular practice of academics on a global level and in Turkey in the longer term. Methodologically, we adopt a mixed-methods approach in two stages: (i) an empirical study conducted in Turkey to find out what prior knowledge and experience academics have with OERs and how they use OERs during ERT; (ii) a complimentary desk study on the global situation of OER use. Our results show that academics who did not know about OERs before the pandemic are still hesitant to use them, even though they have prior experience with online teaching. In addition, academics with higher rank and academics in architecture, philology, and arts have the most negative opinion about campus education being fully accessible online.
ABSTRACT
Analysing learners' behaviours in MOOCs has been used to identify predictive features associated with positive outcomes in engagement and learning success. Early methods predominantly analysed numerical features of behaviours such as the page views, video views, and assessment grades. Analysing extracted numeric features using baseline machine learning algorithms performed well to predict the learners' future performance in MOOCs. We propose categorising learners by likely English language proficiency and extending the range of data to include the content of comment texts. We compare results to a model trained with a combined set of extracted features. Not all platforms provide this rich variety of data. We analysed a series of a FutureLearn language focused MOOCs. Our data were from discussions embedded into each lesson's content. Analysing whether we gained any additional insights, over 420,000 comments were used to train the algorithm. We created a method for identifying one's possible first language from their country. We found that using comments alone is a weaker predictive approach than using a combination including extracted features from learners' activities. Our study contributes to research on generalisability of learning algorithms. We replicated the method across different MOOCs-the performance varies on the model though it always remained over 50%. One of the deep learning architecture, Bidirectional LSTM, trained with discussions on the language learning 73% successfully predicted learners' performance on a different MOOC.
