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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.

This paper describes the authors' experience of teaching agile software development to students of computer science, software engineering, and other related disciplines, and comments on the implications of this and the lessons learned. It is based on the authors' eight years of experience in teaching agile software methodologies to various groups of students at different universities, in different cultural settings, and in a number of courses and seminars. It specifically discusses three different courses on agile software development, given in different teaching settings and at different levels, and briefly surveys variations to these courses given elsewhere. Based on the experience acquired, analyses and evaluations conducted, and current pedagogical trends at relevant university departments, the authors provide recommendations on how to overcome potential problems in teaching agile software development and make their adoption more effective.