This literature review examined approximately 10000 titles in five representative journals in education. It is conducted at two levels. Section A identified the preferred terms and metaphors to describe teachers at different expertise levels. Results indicated a great inconsistency in terms of terminology as well as definition of the same terms or metaphors in different journals, with a lot of them being suggestive and poetic. Section B started with the two most frequent terms, "expert" & "experienced", and put thirty two empirical studies into content analysis to uncover how their respective samples were operationally defined and selected. Findings showed both terms were constantly under-represented and there was a lack of dependable agreed-upon definition of "experienced and expert". It is argued our limitations in educational knowledge could be partly attributed to such poor conceptualizations, imprecise operationalization, and ‘reductive bias ‘of researchers.

Prior to the emergence of Big Data and technologies such as Learning Analytics (LA), classroom research focused mainly on measuring learning outcomes of a small sample through tests. Research on online environments shows that learners’ engagement is a critical precondition for successful learning and lack of engagement is associated with failure and dropout. LA helps instructors to track, measure and visualize students’ online behavior and use such digital traces to improve instruction and provide individualized support, i.e., feedback. This paper examines 1) metrics or indicators of learners’ engagement as extracted and displayed by LA, 2) their relationship with academic achievement and performance, and 3) some freely available LA tools for instructors and their usability. The paper concludes with making recommendations for practice and further research by considering challenges associated with using LA in classrooms.

No abstract

No abstract

No abstract