Rapidly changing demands from employers of students of business meant substantial redesign of the first year undergraduate experience whose underlying pedagogy drew on the concept of "high-engagement" learning. This paper focuses on the question of how engagement can be evaluated. It is argued that a variety of "sensors" are needed for evaluation, both quantitative and qualitative. Of particular interest is the use of Moodle logs as an emerging powerful sensor.
The present paper mainly deals with the prediction of maximum explosive charge used per delay (QMAX) using artificial neural network (ANN) incorporating peak particle velocity (PPV) and distance between blast face to monitoring point (D). 150 blast vibration data sets were monitored at different vulnerable and strategic locations in and around major coal producing opencast coal mines in India. 124 blast vibrations records were used for the training of the ANN model vis-à-vis to determine site constants of various conventional vibration predictors. Rest 26 new randomly selected data sets were used to test, evaluate and compare the ANN prediction results with widely used conventional predictors. Results were compared based on coefficient of correlation (R) and mean absolute error (MAE) between calculated and predicted values of QMAX.