Attempts to adopt the network data massively from the social media refer to provide the particular means in extracting the value from information space such as message, conversation, transaction and others, where the sources of structured data come from enterprise resources data and sources of unstructured data come from audio and video. It can be achieved to expand the process of extracting the value from social network to pattern the data sources to fulfil the organisation goal. This paper aims to reveal the way of big data approach in extracting data value from data complexity involving variety and velocity into the volume. This study was conducted using contents analysis by reviewing some literatures in peer-reviewed journals, chapters, books and proceedings in developing prototype using data analytics associated from the topic, users and time analytics. The findings reveal that big data emerging technology with analytic process provides particular advantages to transform the pattern of information fitted into the innovative environment of online learning resources (OLR) to enhance in developing the learning resources. Both prototype and model of data extraction value could be enhanced to facilitate the learning environment in supporting implementations with ease and convenience. This study is expected to contribute to improve the learning environment and outcomes with performance and achievement by enhancing students' learning process development to provide online resources in higher education context.

The recent reviews indicated learning expertise level could be enhanced with skills, creativity and preferences. This stage should be taken at the first stage combined highly with providing the framework in underlying the learning expertise. This paper attempts to propose the model of Hau-Kashyap used to describe in yielding the robust results to measure the learning expertise level. This approach was selected to give an insight with a more accurate by examining twelve items of questionnaire applied among the students at tertiary level to explore the representation of wide range of knowledge and skills. The results found that the stage level of belief that ranges combined from the level of expertise 1–12 was indicated that Hau-Kashyap approach can be determined to measure the learners’ expertise more fairly and easily. This method is supposed to contribute providing the purposeful rule in combining the learning expertise constructed into a single and more informative hint with related concern of the measurement.