Abstract

The growing prominence of data science presents an opportunity to enhance Information Systems (IS) theories to address increasingly complex socio-technical challenges. This paper advocates for positioning data science as a method to extend IS theories and frameworks, demonstrated through the extraction of predictors of online course completion. By examining two comprehensive datasets (D1 = 3147 and D2 = 3000), the study identifies novel predictors of online course completion, revealing contextually intricate socio-economic and infrastructural dynamics that could enhance existing theories on online learning. The findings reveal the value of integrating data-driven insights into traditional IS theories and frameworks for contextual relevance and predictive power.

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