Many worldwide initiatives consider both creativity and computational thinking as crucial skills for future citizens, making them a priority for today's learners. We studied the associations between these two constructs among middle school students (N = 57), considering two types of creativity: a general creative thinking, and a specific computational creativity. We did so using learning analytics, specifically, by operationalizing a log-based assessment of computational creativity. We find some evidence for an association between Computational Creativity and Computational Thinking: Demonstrating originality in an early stage of the game is associated with succeeding in this stage relatively easily, however negatively associated with progressing farther in the game. We also find that Computational Creativity is better explained by a state- rather than a trait-model. No associations were found between Creative Thinking and Computational Thinking. Furthermore, we find some striking associations between the two measures of creativity.

From an early age, girls disregard studies related to science, technology, engineering, or mathematics (STEM), and this means that a gender gap begins during secondary education and continues to increase over time. Multiple causes have been identified for this phenomenon in the literature, and numerous initiatives are being carried out to reverse this situation. In this paper, we analyze the impact that a group mentoring initiative led by a female STEM role model had on the young people who participated and whether the impact was different based on their sex. We analyzed how these mentoring sessions affected their attitudes towards technology, mathematical self-efficacy, gender stereotypes, science and technology references, and career vocations. To this end, 303 students between the ages of 10 and 12 years old from 10 schools in Spain participated in the six sessions comprising the program and completed a series of questionnaires before and after participating. The results show that the program had an impact on the students’ attitudes towards technology, increased the number of female STEM references they knew, and improved their opinions of vocations and professions related to science and technology. The impact was greater among girls, although in aspects such as attitudes towards technology, the female participants still demonstrated lower values than boys. The program did not improve the stereotypes that the young participants had about mathematical self-efficacy, which was also always lower among girls. We conclude that the lack of STEM vocations among girls is rooted in multiple social, educational, and personal aspects that need to be addressed from a very early age and that should involve multiple agents.

Background and Context: Despite many initiatives to develop Computational Thinking (CT), not much is known about how early programmers develop CT and how we can assess their learning. Objective: Determine if the analysis of students’ interactions with an online platform allows understanding the development of CT, how we can convert data collected into valuable insights, and the aspects that should be considered in platforms design. Method: We developed an online platform with a fine-grained log–recording system. We analysed the data collected from 1004 students (ages 8-14) to understand the difficulties they face. We explain our platform and the tools to process and filter the interaction logs. We calculate additional indicators that provide useful information about student’s behaviour. Findings: Age and gender have shown to influence on CT learning. Generating additional indicators from basic interaction data provide valuable insights. We provide a list of recommendations for developing more effective programming learning platforms.

In recent years, many initiatives have aimed to develop basic computational thinking skills. Despite the popularity of online platforms for early programmers, we still lack detailed information to analyze how these skills are acquired. In the present study we analyzed clickstream data from 3,355 participants enrolled in several Computational Thinking workshops using Kodetu, an online platform with fine grained logging features. Participants used Kodetu's coding blocks to solve challenges of increasing difficulty while we gathered their clickstream in the platform. Here, we present our findings after evaluating these data in regards of participants' characteristics (age, sex, previous knowledge), similarity with previously submitted solutions, and degree of discrepancy from the optimal solution. To facilitate collaboration with other researchers in this area, we released our dataset under an open license. To the best of our knowledge, this is the largest Computational Thinking-related datasets publicly available.

Due to business needs and the growing importance of technology in society, in recent years, the concept of computational thinking has emerged, especially focused on its inclusion in compulsory education as a relevant complement, transversal to traditional subjects. In parallel, various initiatives have developed interactive digital tools for learners to meet this type of thinking through a series of activities commonly framed as games. In this chapter, we evaluate many of the existing free access platforms to propose pedagogical, design, and content approaches with which they can be compared.

Inclusive learning integrates pedagogical and technological variables. It means to adapt to students according to their characteristics (personal, physical, cognitive, social…), needs and interests. With the aim of evaluating the level of inclusion of digital learning objects we have developed a questionnaire that collects information about pedagogical and technological aspects that promote or discourage inclusion. Features like the pedagogical model, function of activities, type of assessment, multimedia resources, human-computer interaction, accessibility and usability criteria, etc. are variables that impact in the level of inclusion of educational objects and items that have been included in the questionnaire. This is the second version of the tool, refined with the conclusions obtained from the application of the draft version to more than 50 learning objects developed under the funding and supervision of the Basque Government. These objects are available under Creative Commons license in the Agrega web page of the Education Department, and represent the style and characteristics of more than 350 objects