Purpose: The purpose of this paper is to build an assessment-centred blended learning (BL) framework to assess learners, to analyse and to evaluate the impact of the technology support in the form of formative assessment in students’ positive learning. Design/methodology/approach: This research proposed an assessment-centred BL framework at the course level to support formative assessment in students by designing a variety of online learning activities combined with e-assessment tools of learning management system (LMS) to analyse and evaluate the impact of the technology application in the form of formative assessment student learning initiative. The author has tested this model in five years with more than 200 courses. Findings: Experimental results have shown that formative assessment evaluation form is more efficient when supported by technology such as LMS. Originality/value: This research proposed an assessment-centred BL framework at the course level by using LMS tools combined with traditional teaching.

This paper has analyzed research trends on personalized learning by bibliometric analysis method through a study of 928 articles from the Scopus database. The following issues are investigated: (1) Development scale, growth trajectory and geographical distribution of the research; (2) Outstanding authors and works on Personalized Learning; (3) Outstanding magazines and books on the topic; (4) Key themes found in these documents, and (5) Prominent methods/technologies used for personalized learning. Research results show that personalized learning is a fascinating topic in education and has been overgrown in recent years. Many researches on personalized learning comes from countries such as the United States and China. Our bibliometric analysis has revealed the main themes in the research works on Personalized Learning, such as artificial intelligence, learning style, and learning technology. The research has observed cognitive aspects of learners like knowledge level, learning style, preferences, etc. In most cases, the recommended tools and methods combined the content-based filtering, collaborative filtering, ontological approaches, etc. In addition, future research goals, difficulties, and concerns are highlighted in our work by examining the trends in several personalized learning elements.