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Generation Z members use their smart devices as part of their everyday routine. Teaching methods may need to be updated to make learning materials more interesting for this generation. This paper suggests gamifying computer science subjects to enhance the learning experience for this generation. Additionally, many students face difficulty in understanding computer science materials and algorithms. Gamifying computer science education is one of the suggested teaching methods to simplify topics and increase students' engagement. Moreover, the field of computer science is dominated by males. The use of gamification could increase women's interest in this field. This paper demonstrates different techniques that were developed by the researchers to employ gamification in teaching computer science topics. The data was collected at the end of the two different courses. Results show that students enjoyed the suggested teaching method and found it useful. This paper also demonstrates two tools and their gamification elements. These tools were developed by the researchers to help people learn computer programming and information security. [ABSTRACT FROM AUTHOR]

Solving programming exercises is the best way to promote practice in computer programming courses and, hence, to learn a programming language. Meanwhile, programming courses continue to have an high rate of failures and dropouts. The main reasons are related with the inherent domain complexity, the teaching methodologies, and the absence of automatic systems with features such as intelligent authoring, profile-based exercise sequencing, content adaptation, and automatic evaluation on the student's resolution. At the same time, gamification is being used as an approach to engage learners' motivations. Despite its success, its implementation is still complex and based on ad-hoc and proprietary solutions. This paper presents PROud as a framework to inject gamification features in computer programming learning environments based on the usage data from programming exercises. This data can be divided into two categories: generic data produced by the learning environment—such as, the number of attempts and the duration that the students took to solve a specific exercise—or code-specific data produced by the assessment tool—such as, code size, use memory, or keyword detection. The data is gathered in cloud storage and can be consumed by the learning environment through the use of a client library that communicates with the server through an established Application Programming Interface (API). With the fetched data, the learning environment can generate new gamification assets (e.g., leaderboards, quests, levels) or enrich content adaptations and recommendations in the inner components such as the sequencing tools. The framework is evaluated on its usefulness in the creation of a gamification asset to present dynamic statistics on specific exercises. [ABSTRACT FROM AUTHOR]