Students' dropout rate is a key metric in online distance learning courses such as MOOCs. We propose a timeseries classification method to construct data based on students' behavior and activities on a number of online distance learning modules. Further, we propose a dropout prediction model based on the time series forest (TSF) classification algorithm. The proposed predictive model is based on interaction data and is independent of learning objectives and subject domains. The model enables prediction of dropout rates without the requirement for pedagogical experts. Results show that the prediction accuracy on two selected datasets increases as the portion of data used in the model grows. However, a reasonable prediction accuracy of 0.84 is possible with only 5% of the dataset processed. As a result, early prediction can help instructors design interventions to encourage course completion before a student falls too far behind.

MOOC environments seem to offer massive potential for social learning. However, MOOC environments have unique challenges for pedagogy which are not present in other socioconstructivist learning environments: the scale and diversity of participation. Many points of view are accessible, but few means of filtering. This paper examines interaction data from several MOOCs. Interaction data is an indicator for depth of learning in the sociocultural sense. Most conversations are seen to have surface level interactions. Platform and pedagogy affordances are suggested that may help deal with this.

This paper describes and discusses two issues which limit the delivery of mobile learning from assisting in disseminating course and module information to higher education students. The concept of delivering information to support learning is designed to augment their engagement with their subject areas and ultimately enhance their learning experience by allowing for increased flexibility in their access to learning materials. The paper concentrates on both the infrastructural and sociological issues associated with providing VLE access from a mobile or flexible position. Using mobile widgets, network coverage data and student's responses to understand the potential benefits and limitations of using mobile devices to access information from the VLE. Discovering the majority of students are without a dedicated application or mobile website, coupled with inept network access this paper investigates the apparent constraints of a promising method of disseminating information to learners.

No abstract

No abstract