This work-in-progress paper will explore the effectiveness of topic modeling to support the analysis of Colombian teachers' conceptions of computational thinking (in Spanish) in an online professional development program. Computational thinking has become a form of literacy as it can help individuals to solve problems. Consequently, governments and bodies of accreditation worldwide have supported educational initiatives, primarily at the K-12 level. However, curricular changes are not enough. Teachers need to be prepared, so they develop the content knowledge associated with computational thinking concepts, practices, and applications in the classroom. To contribute to professional development opportunities geared toward the development of computational thinking pedagogical content knowledge, the Colombian National Academy of Exact, Physics, and Natural Sciences and the Global Center for Equitable Computer Science Education implemented an open online professional development program for Latin American early childhood and elementary educators. More than 100 teachers enrolled in a six-week online professional development program to integrate computational thinking activities from early childhood education. The program included two modules focused on conceptual understanding of computational thinking in early childhood and four more modules where the participants adapted, designed, implemented learning activities, and reflected on what happened during the implementation. As part of the participants' weekly interactions, the program included a Jamboard space, where the teachers answered a set of guiding questions, just like a discussion forum, but as a post-it wall, where they could access all their peers' contributions and questions in a single space.

Technology and automation have become increasingly critical for organizations today, and programming has become an essential skill for all STEM majors to meet this demand. Graduates are expected to possess programming skills to meet the needs of the modern workforce. Acquiring programming skills is a challenging task, and institutions often struggle to provide adequate resources to meet the industry's demand for proficient computer programmers. To take steps toward better understanding programming challenges among undergraduate students in science disciplines, this study aims to characterize patterns in students' solutions to programming assignments over the course of a semester. With this, the goal is to characterize students' most common challenges and take steps toward providing automated feedback. Specifically, this study applies machine learning (ML) classification algorithms to analyze student artifacts from a college-level introductory Python programming course for science majors, including source code from labs, homework assignments, projects, and two live-coding exams. Data was collected as part of a semester-long course and pre-processed and de-identified. The researchers labeled the data, and relevant features were selected to prepare the data for training the ML algorithms. Various classification algorithms were trained, and the resulting ML models were evaluated for their accuracy. Then, the study deployed a quantitative research method to evaluate both the effectiveness of various ML models and the quality of the feedback the model could provide, such as efficiency and accuracy. The research results are expected to inform the development of machine-learning algorithms to provide higher-quality feedback mechanisms for students in introductory programming courses. In that manner, this study contributes to improving the quality of programming education.

To increase the success in computer programming courses, it is important to understand the learning process and common difficulties faced by students. Although several studies have investigated possible relationships between students performance and self-regulated learning characteristics in computer programming courses, little attention has been given to the source code produced by students in this regard. Such source code might contain valuable information about their learning process, specially in a context where practical programming assignments are frequent and students should write source code constantly during the course. This paper presents a strategy to support the correlation analysis among students performance, motivation, use of learning strategies, and source code metrics in computer programming courses. A comprehensive case study is presented to evaluate the proposed strategy through collected data (self-regulated learning characteristics and source code) from 205 undergrad students that accepted to participate voluntarily in the study during three semesters. Results show that the main features from source code which are significantly related to students performance and self-regulated learning features are: length-related metrics, with mainly positive correlations; and Halstead complexity measures, correlated negatively.