Although Machine Learning (ML) is used already in our daily lives, few are familiar with the technology. This poses new challenges for students to understand ML, its potential, and limitations as well as to empower them to become creators of intelligent solutions. To effectively guide the learning of ML, this article proposes a scoring rubric for the performance-based assessment of the learning of concepts and practices regarding image classification with artificial neural networks in K-12. The assessment is based on the examination of student-created artifacts as a part of open-ended applications on the use stage of the Use-Modify-Create cycle. An initial evaluation of the scoring rubric through an expert panel demonstrates its internal consistency as well as its correctness and relevance. Providing a first step for the assessment of concepts on image recognition, the results may support the progress of learning ML by providing feedback to students and teachers.
Prior programming knowledge of students has a major impact on introductory programming courses. Those with prior experience often seem to breeze through the course. Those without prior experience see others breeze through the course and disengage from the material or drop out. The purpose of this study is to demonstrate that novice student programming behavior can be modeled as a Markov process. The resulting transition matrix can then be used in machine learning algorithms to create clusters of similarly behaving students. We describe in detail the state machine used in the Markov process and how to compute the transition matrix. We compute the transition matrix for 665 students and cluster them using the k-means clustering algorithm. We choose the number of cluster to be three based on analysis of the dataset. We show that the created clusters have statistically different means for student prior knowledge in programming, when measured on a Likert scale of 1-5.
Although Machine Learning (ML) is integrated today into various aspects of our lives, few understand the technology behind it. This presents new challenges to extend computing education early to ML concepts helping students to understand its potential and limits. Thus, in order to obtain an overview of the state of the art on teaching Machine Learning concepts in elementary to high school, we carried out a systematic mapping study. We identified 30 instructional units mostly focusing on ML basics and neural networks. Considering the complexity of ML concepts, several instructional units cover only the most accessible processes, such as data management or present model learning and testing on an abstract level black-boxing some of the underlying ML processes. Results demonstrate that teaching ML in school can increase understanding and interest in this knowledge area as well as contextualize ML concepts through their societal impact.
The European Commission Science Hub has been promoting Computational Thinking (CT) as an important 21st century skill or competence. However, "despite the high interest in developing computational thinking among schoolchildren and the large public and private investment in CT initiatives, there are a number of issues and challenges for the integration of CT in the school curricula". On the other hand, the Digital Competence (DC) Framework 2.0 (DigCom) is promoted in the same European Commission Science Hub portal. It shows that both topics have many things in common. Thus, there is the need of research on the relationship between CT and digital competence.
The goal of this paper is to analyse and discuss the relationship between DC and CT, and to help educators as well as educational policy makers to make informed decisions about how CT and DC can be included in their local institutions. We begin by defining DC and CT and then discuss the current state of both phenomena in education in multiple countries in Europe. By analysing official documents, we try to find the underlying commonness in both DC and CT, and discover all possible connections between them. Possible interconnections between the component groups of approaches are presented in Fig.
This work investigates the effect of computer use in the memory process in young and adults under the Perceptual and Memory experimental conditions. The memory condition involved the phases acquisition of information and recovery, on time intervals (2 min, 24 hours and 1 week) on situations of pre and post-test (before and after the participants took part on a Basic Computing course), in which the participants studied the map of Brazil during 7 minutes and the estimates of different areas of Brazilian states were performed according to the magnitude estimation method, without the presence of the map. On the Perceptual condition, the estimates were made in the presence of the Brazilian map. The study made possible to verify that the use of a computer, as a new activity enables a differentiation on the memory process in relation to the different experimental conditions proposed and to the time intervals used between acquisition, processing and information recovery, showing that the use of a computer as a pedagogical tool may promote the improvement of the memory process in academic activities.