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Educating our future engineers so that they cangain high proficiency in computational thinking is essentialfor their career prospects. As educators, acquiring a goodunderstanding of the various learning motivation factors/tools as well as their inter-relationships is a significant stepforward in achieving this goal. In this article, we describean exploratory, data-analytic investigation into the influ-ences of the various learning motivation factors on oneanother as well as on effecting e-learning of a group ofscience and engineering students taking computer pro-gramming courses. Based on the algorithmic results, wehighlight concrete ideas that may have direct impact onimproving an existing e-learning system. Further, wedescribe how the graphical visualization of the algorithmicresults can guide us to set priority for focusing on whichlearning motivation factors first, and which factors next, inachieving a given education goal. These are among someof the new insights not easily obtainable from confirma-tory-based analyses