47 research outputs found
Supporting Co-Regulation and Motivation in Learning Programming in Online Classrooms
Self-regulation of learning in programming has been extensively investigated, emphasising an individual's metacognitive and motivational regulation components. However, learning often happens in socially situated contexts, and little emphasis has been paid to studying social modes of regulation in programming. We designed Thyone, a collaborative Jupyter Notebook extension to support learners' programming regulation in an online classroom context with the overall aim to foster their intrinsic motivation toward programming. Thyone's salient features - Flowchart, Discuss and Share Cell - incorporate affordances for learners to co-regulate their learning and drive their motivation. In an exploratory quasi-experimental study, we investigated learners' engagement with Thyone's features and assessed its influence on their learning motivation in an introductory programming course. We found that Thyone facilitated the co-regulation of programming learning and that the users' engagement with Thyone appeared to positively influence components of their motivation: interest, autonomy, and relatedness. Our results inform the design of technological interventions to support co-regulation in programming learning
Challenges for engineering students working with authentic complex problems
Engineers are important participants in solving societal, environmental and technical problems. However, due to an increasing complexity in relation to these problems new interdisciplinary competences are needed in engineering. Instead of students working with monodisciplinary problems, a situation where students work with authentic complex problems in interdisciplinary teams together with a company may scaffold development of new competences. The question is: What are the challenges for students structuring the work on authentic interdisciplinary problems? This study explores a three-day event where 7 students from Aalborg University (AAU) from four different faculties and one student from University College North Denmark (UCN), (6th-10th semester), worked in two groups at a large Danish company, solving authentic complex problems. The event was structured as a Hackathon where the students for three days worked with problem identification, problem analysis and finalizing with a pitch competition presenting their findings. During the event the students had workshops to support the work and they had the opportunity to use employees from the company as facilitators. It was an extracurricular activity during the summer holiday season. The methodology used for data collection was qualitative both in terms of observations and participants’ reflection reports. The students were observed during the whole event. Findings from this part of a larger study indicated, that students experience inability to transfer and transform project competences from their previous disciplinary experiences to an interdisciplinary setting
Exploring the practical use of a collaborative robot for academic purposes
This article presents a set of experiences related to the setup and exploration of potential educational uses of a collaborative robot (cobot). The basic principles that have guided the work carried out have been three. First and foremost, study of all the functionalities offered by the robot and exploration of its potential academic uses both in subjects focused on industrial robotics and in subjects of related disciplines (automation, communications, computer vision). Second, achieve the total integration of the cobot at the laboratory, seeking not only independent uses of it but also seeking for applications (laboratory practices) in which the cobot interacts with some of the other devices already existing at the laboratory (other industrial robots and a flexible manufacturing system). Third, reuse of some available components and minimization of the number and associated cost of required new components. The experiences, carried out following a project-based learning methodology under the framework of bachelor and master subjects and thesis, have focused on the integration of mechanical, electronic and programming aspects in new design solutions (end effector, cooperative workspace, artificial vision system integration) and case studies (advanced task programming, cybersecure communication, remote access). These experiences have consolidated the students' acquisition of skills in the transition to professional life by having the close collaboration of the university faculty with the experts of the robotics company.Postprint (published version
Two Roads Diverge: Mapping the Path of Learning for Novice Programmers Through Large Scale Interaction Data and Neural Network Classifiers
Learning to program is a fundamental part of Computer Science education. To
become a proficient programmer, one must become competent at both code
comprehension and code production. Research shows that the most effective way
to teach programming to students is through practical exercises. However, the
increasing numbers of students in Computer Science classes means it is difficult to
correct assignments and provide timely feedback. This can result in fewer practical
assignments and/or less useful feedback for each student. Automated grading tools,
and understanding of how novice programmers learn to code, is essential for these
growing numbers of students. The Maynooth University Learning Environment, or
MULE, was built to address this challenge. MULE is a cloud-based learning
environment built from the ground up with the goal of teaching introductory
programming courses in an authentic manner while facilitating the collection of
large-scale behavioural data to support Learning Analytics. In this thesis,
behavioural interaction data and code written by students in MULE is used to
investigate the differences between successful and unsuccessful programming
student behaviour, with the use of data analysis and Neural Network classifiers.
The result is a method of classification that predicts early on if a student is likely to
be in the top or bottom 50% of grades in the class with up to 87% accuracy, and a
model of the path of learning for successful students, including key times,
assignments, and topics during the introduction to programming module when the
higher and lower achieving students diverge in behaviour