Conferences, SoLAR Southern Flare Conference
Last modified: 2012-11-06
Data is considered one of the six critical dimensions of learning analytics. The acquisition of ideal datasets is believed to be a primary challenge for learning analytics. While a large amount of student data is already being automatically generated in many universities worldwide, accuracy, consistency, and suitability of the data for analysis may not have been assessed.
In Indonesia, extracting data for learning analytics purposes is not an easy job. Although internet and information technology usage in Indonesian higher education has been growing fast in this twenty-first century, their integration in the form of student learning systems has only been surfacing recently. Compared to other countries in the Southeast Asia region, Indonesia is lagging behind Singapore and Malaysia in adopting educational technology to support student learning.
While recently the number of publication of studies on educational technology and e-learning by Indonesian scholars has been rising, there has been little or no research in Indonesia to date that looks into data generated by student learning in ICT environments. Most studies are related to distance education or description of e-learning in certain course units. “Big data” has just been an expression of language that has not been taken into practice.
There is disparity in quality between the over 3,000 higher education institutions (HEI) across Indonesia. In the case of Indonesian HEIs, one measure of quality is the integration use of ICT in its student learning system, where most data for learning analytics purposes come from.
Regarding the amount of possible generated data, generally HEIs in Indonesia can be classified into three groups. The first group consists of the Indonesia Open University and a few tech-savvy HEIs whose students engage with their learning management system to enhance their learning environment, somewhat similar to students in developed countries; this would serve as an excellent big data source for learning analytics purposes. The second group comprises mostly large HEIs which have developed digital libraries and repositories for most course units as well as computerized data for students administration services, yet have not embraced learning management systems; this would be a moderate source of data with information possibly to be extracted from different databases. The third group is the remaining large number of HEIs that have no digital library or learning management system, the type that only generate basic information on students such as secondary school history, marks, units taken, completion time, thesis title, supervisors, and fees.
Currently a few private HEIs in Indonesia have been showing their outstanding quality by gaining ISO 9000 series accreditation, including in delivering better learning for their students. State and other private universities will have to work hard to compete to deliver the same or better quality if they do not want to lag behind and lose potential students. Utilizing their big data may help this goal. To catch up with top universities of other countries in the region, it is timely for Indonesian higher education to start applying learning analytics from the basic point, i.e. data.
My research focuses on what kind of data are available and could be collected from existing systems in Indonesian higher education to be used in assisting student learning beyond providing basic information, in order to satisfy the main concern above.
In this presentation I am going to introduce my PhD study on learning analytics in the Indonesian higher education environment. I would be very interested in starting conversations with other researchers who have experiences with learning analytics, especially in an international context. My work has a statistical focus and I would also be interested in finding out who else is working in this area. This research is presented in order to obtain feedback that may provide insight into the next step of this project.