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Debugging in a World Full of Bugs

Designing an educational game to teach debugging and error detection with the help of a teachable agent

Written by I. Koniakowski

Paper category

Master Thesis


Computer Science




Thesis: This chapter introduces the current status of digital ability education throughout Europe and Sweden, as well as early related research in the fields of educational games, teachable agents, and computational thinking. This chapter also introduces in more detail the research carried out in the Magic Garden project, as well as research on debugging and error detection. 2.1 Digital competence in education in Europe and Sweden The European Union defines digital competence as the confident, critical and responsible use and participation of digital technology. Learn, work and participate in society as a key ability for lifelong learning. The difference between countries is whether they use digital competence as a cross-curricular theme (e.g. Italy, Norway), integration into other fields of study (e.g. Spain, Slovenia), as a single subject (e.g. Bulgaria, Montenegro) or a combination of the three (e.g. Lithuania, Malta) ). In elementary schools, most countries classify it as a cross-curricular subject, while in junior high school education, it is classified as a single subject. These countries also differ in the earliest target ages from pre-school to high school education (European Commission/EACEA/Eurydice, 2019). The Swedish National Board of Education believes that children should learn enough digital literacy. The term can be divided into four areas; able to understand how digitization affects society and individuals, able to use and understand digital tools and media, have a critical and responsible attitude towards digital technology, and be able to use digital technology to solve problems in a creative way and Ideas are transformed into actions. The Swedish preschool education curriculum further describes how this should be implemented in preschool education. It states that children should have the opportunity to lay the foundation for the adoption of critical and responsible digital technology methods (Skolverket, 2018). In Swedish preschool, there is a long tradition of learning through games. The Swedish preschool curriculum (Skolverket, 2018) states that games are the foundation of development, learning and happiness. At the same time, there is a trend to gamify existing educational programs and develop educational games. The purpose of these is to combine fun and educational value to increase motivation and continuous use and learning. They provide incentives from the game world and additional possibilities for interactivity, adaptability and personalization. Moreno-Ger, Burgos, Martínez-Ortiz, Sierra, and Fernándes-Manjón (2008) describe three methods of creating and developing educational games; translating educational content to adapt to similar game environments, reusing existing games for education, and Design games dedicated to learning. 2.2 Learning through teaching and teachable agents Learning through teaching has been identified as an effective way of learning across different subject areas (Cohen, Kulik, & Kulik, 1982; Davis et al., 2003; Roscoe & Chi, 2007). This effect also applies to less proficient students (Robinson, Schofield, and Steers-Wentzell, 2005). Bargh and Schul (1980) showed that by preparing to teach others what they have learned, people will learn better. When preparing to teach others, teachers may need to structure content, conduct more in-depth reflection, and be responsible for the teaching situation, which all contribute to learning and motivation (Leelawong & Biswas, 2008). Being a teacher or mentor can help express knowledge in language, improve motivation, and even improve teacher self-efficacy (Gulz & Haake, 2019). In these cases, the instructor needs to have at least the same level of knowledge as the teacher, which may cause problems for children with poor grades. The solution to this problem is to use a digital agent to play the role of the educated person, usually called the educated agent (TA). Through artificial intelligence, teaching assistants learn from students, so they are at the same level of knowledge as students. This allows students to teach and learn at an appropriate level (Pareto, Haake, Lindström, Sjödén and Gulz, 2012). If the student imparts incorrect knowledge to the TA, it will not harm the real person. The effect of using TA is believed to be derived from additional motivation and effort and the effect of additional metacognitive reasoning. In the study of Chase, Chin, Oppezzo, and Schwartz (2009), students study either for future exams or for teaching assistants. They were found to be teaching assistants, not for themselves, and they study harder. This is called the disciple effect. The disciple effect is considered to be effective; a) Self-protection buffer—students gain distance from performance (and possible negative effects of failure) by attributing it to TA; b) Responsibility—students assume social responsibility for the teaching assistant’s learning Responsibility, which motivates them to help teaching assistants in better and novel ways; c) Incremental theory—by seeing the learning and progress of teaching assistants, students are guided to accept the belief that a person can be taught To improve and learn instead of believing that everyone’s knowledge is deterministic and immutable (Chase et al., 2009. TA has also been shown to lead to metacognitive behaviors, thereby increasing content learning (Schwartz et al., 2009). Learning in teaching is a highly interactive metacognitive activity, which is a metacognition pointing outward to another subject. In teaching, the tutor will predict, monitor, and adjust the cognition of students and interact with it. Interaction. Teaching assistants are used to achieve the same effect. TA can also be used as a means to reduce cognitive load. Read Less