Technical Debt management is an important aspect in the training of Software Engineering students . In this paper we study the effect of two assessment strategies in an educational context: One based on penalisation, the other based on rewards . Both are applied to assignments where the students develop a project focusing on keeping a low technical debt level, and obtaining a high quality code . We describe the design, tools and context of the strategies applied . SonarQube, a tool commonly used in production environments, is used for measuring the metrics . The penalisation strategy is based on a SonarQube quality gate . The reward strategy is based on a contest, where an automatic judge tool is devised to provide an online leaderboard with a classification based on the SonarQube metrics . An empirical study is conducted to determine which of the strategies works better to help the students/trainees keep the Technical Debt low . Statistically significant results are obtained in 5 of the 8 analysed metrics, showing that the reward strategy works much better . The effect size of the executed statistical tests is analysed, resulting in medium and large effect size in the majority of the analysed metrics.