The analysis and discovery of relations characterising human learning, and contextual factors that influence these relations have been one of the contemporary and critical global challenges faced by researchers in a number of areas, particularly in Education, Psychology, Sociology, Information Systems, and Computing. These relations typically concern learners’ achievements and the overall learning experience, and the effectiveness of the learning context. Be it the assessment marks distribution in a classroom context or the mined pattern of best practices in an apprenticeship context, analysis and discovery have always addressed the elusive causal question about the need to best serve learners’ learning efficiency, learning effectiveness, as well as the overall learning experience, and the need to make informed choices on a learning context’s instructional effectiveness.

Significant advances have been made in a number of areas from educational psychology to artificial intelligence in education, which explored factors contributing to learners’ proactive role in the learning process and instructional effectiveness. With the advent of new technologies such as eye-tracking, activity monitoring, video analysis, content analysis, sentiment analysis, social network analysis and interaction analysis, one could study these factors in a data-intensive context. This very notion is what is currently being explored at the intersection of big data and learning analytics, which includes related areas such as learning process analytics, institutional effectiveness, academic analytics, web analytics and information visualisation.

BDELA@ICALT2017 will explore continuous monitoring of learner progress and traces of skill development of individual learners as well as learning groups, both within and across programs and institutions. It will discuss issues concerning continuous evaluation of achievements resulting from institutional educational practices to gauge alignment with strategic plans and alignment of governmental strategies. It will examine assessment frameworks of academic productivity to continuously measure impact of teaching. It will discuss concerns such as quality of instruction, attrition, and measurement of curricular outcomes using big data and associated methods and techniques as the premise.