RESUMO
Disciplines in Higher Education have their own interpretations of what is essential knowledge that influences what is taught, how teaching occurs, and the role of digital tools. Disciplinary culture is dynamic and evolving, informed by disciplinary research and technology improvement. During the COVID-19 pandemic, digital solutions enabled ongoing teaching when undergraduate courses could not be taught on campus, in lecture theatres, seminar rooms, laboratories, or in the field. Using digital tools and changes in teaching practices has created a context where Higher Education teachers must consider how future learning and teaching should occur. To explore this, a cross-discipline team used appreciative inquiry framed in complexity theory to examine how teaching in undergraduate programmes is changing in the digital age and implications for Higher Education teachers. The research identifies how digital technologies influence undergraduate programmes in Applied Statistics, Computer Science, Critical Indigenous Studies, Geography, and Information Systems. Analysis of the case studies identified how disciplinary culture, context, and technology combine to influence pedagogical practice and digital capabilities needed to teach in undergraduate programmes. We conclude that Higher Education teachers require capability in appropriate pedagogical practice that aligns with disciplinary culture and the technologies available.
RESUMO
Massive Open Online Courses (MOOCs) have gained in popularity over the last few years. The space of online learning resources has been increasing exponentially and has created a problem of information overload. To overcome this problem, recommender systems that can recommend learning resources to users according to their interests have been proposed. MOOCs contain a huge amount of data with the quantity of data increasing as new learners register. Traditional recommendation techniques suffer from scalability, sparsity and cold start problems resulting in poor quality recommendations. Furthermore, they cannot accommodate the incremental update of the model with the arrival of new data making them unsuitable for MOOCs dynamic environment. From this line of research, we propose a novel online recommender system, namely NoR-MOOCs, that is accurate, scales well with the data and moreover overcomes previously recorded problems with recommender systems. Through extensive experiments conducted over the COCO data-set, we have shown empirically that NoR-MOOCs significantly outperforms traditional KMeans and Collaborative Filtering algorithms in terms of predictive and classification accuracy metrics.
