A multivariate learning evaluation model for programming course in online learning environment

The items used for learning evaluation in online learning are not only scores, but also students' learning behavior, including engagement in learning contents, activities in online forum. This paper proposes a multivariate learning evaluation model to assess students learning in online learning environment for programming course. The learning behavior is accessed by data flow. The data flow is divided into four categories, which includes learning guidance, understanding innovation, interactive sharing and learning support. The correlation analysis of various structures and unstructured data flow generated in learning activities will be embodied in the multiple learning evaluation model as parameters. And the results are visualized to learners. The findings show that multivariate learning evaluation is helpful to improve students' achievement and reflection towards their learning.