Computational thinking – the ability to reformulate and solve problems in ways that can be undertaken by computers – has been heralded as a foundational capability for the 21st Century. However, there are potentially different ways to conceptualise and measure computational thinking, for instance, as generalized problem solving capabilities or as applied practice during computer programming tasks, and there is little evidence to substantiate whether higher computational thinking capabilities using either of these measures result in better quality computer programs. This study examines the relationship between different forms of computational thinking and two different measures of programming quality for a group of 37 pairs of pre-service teachers. General computational thinking capabilities were measured using Bebras tests, while applied computational thinking processes were measured using a Computational Thinking Behavioural Scheme. The quality of computer programs was measured using a qualitative rubric, and programs were also assessed using the Dr Scratch auto-grading platform. The Test of Nonverbal Intelligence (3rd edition, TONI-3) was used to test for confounding effects. While significant correlations between both measures of computational thinking and program quality were detected, regression analysis revealed that only applied computational thinking processes significantly predicted program quality (general computational thinking capability and non-verbal intelligence were not significant predictors). The results highlight the importance of students developing applied computational thinking procedural capabilities more than generalized computational thinking capabilities in order to improve the quality of their computer programs.

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Computational thinking -the ability to solve problems using concepts from computer science -has been widely discussed in the computer science education field. However, the relationship of computational thinking to intelligence -seen as the general ability to understand and solve complex problems -is contestable and has not been extensively explored. The present study addressed the question of how computational thinking is related to intelligence. To find an answer to this question, 71 pre-service teacher students completed a survey with 20 Bebras tasks as a measure of computational thinking and a non-verbal intelligence test (TONI-3) to assess their general problem-solving ability.The large and significant correlation of r(70) = .53, p < .001, indicates that both concepts are highly related. Implications of the findings are discussed, including the meaning of the relationship between computational thinking and intelligence during teaching and assessment, and the possibility of more holistic measures of computational thinking that incorporate procedural aspects.

Time on task during game-based learning. The effects of cognitive skills, goal orientations and the characteristics of learning partners-an analysis based on video data Abstract The effective use of learning time is considered one of the most important predictors of successful and good teaching. In the present study we investigated how cognitive skills and goal orientation influenced the effective use of learning time during the open phases of game-based learning We used a newly developed method for video-and audio analysis (German: MuVA), which allowed for reliable documentation of students' interactions during those phases. The two-step multiple regression model demonstrated that both the learner's and the learning partner's goal orientation explain significant parts of the variance: A high performance goal orientation impairs a student's effective use of learning time, while a high performance avoidance orientation has a positive impact on his or her effective use of learning time. Working with a partner who has a high learning goal orientation or a high work avoidance orientation increases a student's effective use of his or her learning time, while a high work avoidance orientation on the partner's side has the opposite effect. Cognitive skills did not influence the effective use of learning time.

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