Informatics in Education

Information technology (IT) is transforming the world. Therefore, exposing students to computing at an early age is important. And, although computing is being introduced into schools, students from a low socio-economic status background still do not have such an opportunity. Furthermore, existing computing programs may need to be adjusted in accordance to the specific characteristics of these students in order to help them to achieve the learning goals. Aiming at bringing computing education to all middle and high-school students, we performed a systematic literature review, in order to analyze the content, pedagogy, technology, as well as the main findings of instructional units that teach computing in this context. First results show that these students are able to learn computing, including concepts ranging from algorithms and programming languages to artificial intelligence. Difficulties are mainly linked to the lack of infrastructure and the lack of pre-existing knowledge in using IT as well as creating computing artifacts. Solutions include centralized teaching in assistive centers as well as a stronger emphasis on unplugged strategies. However, there seems to be a lack of more research on teaching computing to students from a low socio-economic status background, unlocking their potential as well to foster their participation in an increasing IT market.

Knowledge about Machine Learning is becoming essential, yet it remains a restricted privilege that may not be available to students from a low socio-economic status background. Thus, in order to provide equal opportunities, we taught ML concepts and applications to 158 middle and high school students from a low socio-economic background in Brazil. Results show that these students can understand how ML works and execute the main steps of a human-centered process for developing an image classification model. No substantial differences regarding class periods, educational stage, and sex assigned at birth were observed. The course was perceived as fun and motivating, especially to girls. Despite the limitations in this context, the results show that they can be overcome. Mitigating solutions involve partnerships between social institutions and university, an adapted pedagogical approach as well as increased on-by-one assistance. These findings can be used to guide course designs for teaching ML in the context of underprivileged students from a low socio-economic status background and thus contribute to the inclusion of these students.

This study investigated the role of using unplugged computing activities on developing computational thinking (CT) skills of 6th-grade students. The unplugged computing classroom activities were based on the Bebras challenge, an international contest that aims to promote CT and informatics among school students of all ages. Participants of the study were fifty-three 6th-grade students from two public middle schools in Istanbul. The unplugged computing activities involved the tasks with three different difficulty levels covering the CT processes found to be common in CT definitions in the literature. To evaluate students’ CT skills, two equivalent tests were constructed from Bebras tasks considering the same parameters (difficulty levels and CT processes). The results showed that students’ post-test scores were significantly higher than their pre-test scores. There were not any significant differences between students’ scores in terms of gender, and there was no interaction effect between students’ CT scores and their gender.

This paper describes a study of students' meaningful learning of the engineering design process during their participation in robotics activities. The population consisted of middle-school students (ages 13-15 years) who participated in the FIRST® LEGO® League competition. The methodology used was qualitative, including observations and interviews. The analysis was based on the Revised Bloom Taxonomy. Almost all the groups demonstrated meaningful learning, although some reached higher levels than others. Most of the groups demonstrated the understanding/applying level during each of the design process phases (searching and decision making, construction and testing, diagnosing and debugging), some demonstrated the analyzing/evaluating level, but only a few demonstrated the higher level of creating. Factors that seemed to play a role in the students' learning include: (a) the teaching or mentoring style; (b) the absence of a robotics textbook; (c) the extra-curricular competition-oriented nature of the activities; and (d) the unstable nature of the design of the robot.

The objective of this article is to present the development and evaluation of dETECT (Evaluating TEaching CompuTing), a model for the evaluation of the quality of instructional units for teaching computing in middle school based on the students' perception collected through a measurement instrument. The dETECT model was systematically developed and evaluated based on data collected from 16 case studies in 13 different middle school institutions with responses from 477 students. Our results indicate that the dETECT model is acceptable in terms of reliability (Cronbach's alpha ?=.787) and construct validity, demonstrating an acceptable degree of correlation found between almost all items of the dETECT measurement instrument. These results allow researchers and instructors to rely on the dETECT model in order to evaluate instructional units and, thus, contribute to their improvement and to direct an effective and efficient adoption of teaching computing in middle school.