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University students are usually taught data mining through black-box data mining algorithms, which hide the algorithm's details from the user and optionally allow parameter adjustment. This minimizes the effort required to use these algorithms. On the other hand, white-box algorithms reveal the algorithm's structure, allowing users to assemble algorithms from algorithm building blocks. This paper provides a comparison between students' acceptance of both black-box and white-box decision tree algorithms. For these purposes, the technology acceptance model is used. The model is extended with perceived understanding and the influence it has on acceptance of decision tree algorithms. An experiment was conducted with 118 senior management students who were divided into two groups-one working with black-box, and the other with white-box algorithms-and their cognitive styles were analyzed. The results of how cognitive styles affect the perceived understanding of students when using decision tree algorithms with different levels of algorithm transparency are reported here.