As computing becomes a mainstream discipline embedded in the school curriculum and
acts as an enabler for an increasing range of academic disciplines in higher education, the …

Educational data mining and learning analytics promise better understanding of student
behavior and knowledge, as well as new information on the tacit factors that contribute to …

This paper presents a systematic literature review of the recent (2006--2010) development of
automatic assessment tools for programming exercises. We discuss the major features that …

Methods for automatically identifying students in need of assistance have been studied for
decades. Initially, the work was based on somewhat static factors such as students' …

Theories and technologies are needed to understand and integrate the knowledge of
student affect (eg, frustration, motivation and self-confidence) into learning models. Our …

In this paper, we investigate the correspondence between student affect and behavioural
engagement in a web-based tutoring platform throughout the school year and learning …

The high failure rates of many programming courses means there is a need to identify
struggling students as early as possible. Prior research has focused upon using a set of tests …

Research over the past fifty years into predictors of programming performance has yielded
little improvement in the identification of at-risk students. This is possibly because research …

Quantitative Field Observations (QFOs) of student behaviors and affective states in
classroom environments. Formerly known as the Baker Rodrigo Observation Method …

In this paper, we investigate the correspondence between student affect in a web-based
tutoring platform throughout the school year and learning outcomes at the end of the year …