Electronic learning (e‑learning) plays a significant role in improving the efficiency of the education process. However, in many cases in developing countries, technology transfer without consideration of technology acceptance factors has limited the impact of e‑learning and the expected outcome of the education process. Therefore, this shift in learning method has been met with low enthusiasm from academic staff and students owing to its low perceived usefulness and perceived ease‑of‑use. The University of Kufa (UoK) in Iraq is considered a good case study because it has implemented the e‑learning platform since 2013. The UoK platform is based on open‑source Moodle owing to the latter’s advantages, such as low implementation cost, open community for support and continuous update and development. To identify and evaluate the challenges, this study uses a questionnaire survey that targets the level of adoption, implementation, familiarity and technology acceptance of staff and students. A total of 242 educators participate in the survey, and the data are subsequently analysed. Important information is extracted using data mining techniques, namely clustering and decision trees. One of the main crucial factors extracted from the analysis results is the perception that social media is easier to use compared with a dedicated e‑learning platform such as Moodle. This factor may also discourage educators/learners from adopting an offered e‑learning platform, regardless of actual usefulness, motivation and training programs. Therefore, this paper offers practical information regarding the main issues and a guideline to fully utilise e‑learning for policy makers and e‑learning developers, particularly in newly established institutions or developing countries.

The initial investigation aimed to examine the paths learners followed when offered the course in a custom virtual learning environment (VLE) which is structured by tasks, course materials and learning resources. However, it quickly became clear that students were spending little time with the course materials online and the time spent with each page was usually less than 20 seconds. Consequently a better understanding of how learners accessed the electronic course materials was needed to evaluate the effectiveness of developing and delivering courses in this way. By combining data on the activity with content with user profiles it was possible to examine alternate information perspectives and reveal patterns in large volume data sets. Mining data in this way provides ways to learn about learners in order to make effective decisions regarding teaching methods, delivery models and infrastructure investment.

The Flipped Classroom (FC) is an instruction method, where the traditional lecture and homework sessions are inverted. Online material is given to students in order to gain necessary knowledge before class, while class time is devoted to application of this knowledge and reflection. The hypothesis is that there could be deep and creative discussions when teacher and students physically meet, which has known a significant surge of popularity in the past decade. A marked recent trend in the FC is the increased use of Learning Analytics (LA) to support the development of the FC and students’ reflexive learning. The aim of this paper is to investigate the literature on applications of LA in FCs, and to determine the best practices and needs for technological development supporting LA in the FC by means of a scoping review. This literature review revealed that there is potential in using LA in the FC, especially as a means to predict students’ learning outcome and to support adaptive learning and improvement on the curriculum. However, further long‑term studies and development is necessary to encourage self‑directed learning in students and to develop the whole of the FC for a more diverse population of students. We anticipate an increased and expanded use of LA to come, with focus on predictive and prescriptive analytics providing more adaptive learning experience. We also anticipate that LA will expand beyond data mining to correlate student performance and online engagement with the aim to include a wider range of possibilities of interventions and adaptation of the learning experience.