Maoinithe ag an Aontas Eorpach. Mar sin féin is tuairimí agus tuairimí an údair amháin nó na n-údar amháin iad na tuairimí agus na tuairimí a nochtar agus ní gá go léiríonn siad tuairimí an Aontais Eorpaigh nó na Gníomhaireachta Eorpaí Feidhmiúcháin Oideachais agus Cultúir (EACEA). Ní fhéadfar an tAontas Eorpach ná EACEA a chur i gcuntas astu. Uimhir an tionscadail: 2023-1-NL01-KA220-HED-000155675. Implications for practice The system has the potential to enhance the integrity of online examinations by using advanced biometric verification methods to prevent common threats such as impersonation and cheating. Cás-Staidéar 4: Aiseolas Uathoibrithe do Dhaltaí i dTascanna Eolaíochta Sonraí: Cur i bhFeidhm agus Torthaí Feabhsaithe General information Alessandra Galassi & Pierpaolo Vittorini, CHItaly 2021: 14th Biannual Conference of the Italian SIGCHI Chapter, July 11–13, 2021, Bolzano, Italy, Association for Computing Machinery (ACM), New York, NY, USA, 8 pages. The research discusses the development and implementation of an automated feedback system for assignments in data science. This system focuses on grading assignments that involve language commands, their outputs, and natural language comments. The primary objective is to change students' learning experiences by providing fast and detailed feedback that can identify mistakes and offer improvement suggestions. The study evaluated the effectiveness of this system using student feedback collected through standardised and custom questionnaires. Description of case The research presents a case study on the development, implementation, and evaluation of an automated feedback system for data science assignments at the University of L’Aquila, Italy. The system was specifically designed to grade assignments involving R language commands, their outputs, and accompanying natural language comments. It used static code analysis and machine learning techniques to evaluate the correctness and quality of the R code and the associated comments. The system provided feedback with explanations for grading decisions, identification of errors, and suggestions for improvements. This feedback was intended to be detailed and instructive to help students learn from their mistakes. Lessons learned The study observed an increased engagement of students in the process. The automated feedback system led to higher levels of student engagement, as students could receive immediate feedback and make corrections quickly. Perceived Usefulness: Students found the feedback to be useful in understanding their mistakes and learning how to correct them.
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