Automated Assessment in Mobile Programming Courses: Leveraging GitHub Classroom and Flutter for Enhanced Student Outcomes

• URL: http://arxiv.org/abs/2504.04230v1
• Date: Sat, 05 Apr 2025 16:48:09 GMT
• Title: Automated Assessment in Mobile Programming Courses: Leveraging GitHub Classroom and Flutter for Enhanced Student Outcomes
• Authors: Pedro Alves, Bruno Pereira Cipriano,
• Abstract summary: This paper explores the potential of using GitHub Classroom, combined with the Flutter framework, for the automated assessment of mobile programming assignments.<n>We evaluate the feasibility of integrating these tools through an experiment in a Mobile Programming course and present findings from a student survey.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growing demand for skilled mobile developers has made mobile programming courses an essential component of computer science curricula. However, these courses face unique challenges due to the complexity of mobile development environments and the graphical, interactive nature of mobile applications. This paper explores the potential of using GitHub Classroom, combined with the Flutter framework, for the automated assessment of mobile programming assignments. By leveraging GitHub Actions for continuous integration and Flutter's robust support for test automation, the proposed approach enables an auto-grading cost-effective solution. We evaluate the feasibility of integrating these tools through an experiment in a Mobile Programming course and present findings from a student survey that assesses their perceptions of the proposed evaluation model. The results are encouraging, showing that the approach is well-received by students.

Related papers

• An Online Integrated Development Environment for Automated Programming Assessment Systems [4.618037115403291]
  This research contributes to the field of programming education by extracting and defining requirements for an online IDE.<n>The usability of the new online IDE was assessed using the Technology Acceptance Model (TAM), gathering feedback from 27 first-year students.
  arXiv  Detail & Related papers  (2025-03-17T12:50:51Z)
• Foundations and Recent Trends in Multimodal Mobile Agents: A Survey [57.677161006710065]
  Mobile agents are essential for automating tasks in complex and dynamic mobile environments. Recent advancements enhance real-time adaptability and multimodal interaction. We categorize these advancements into two main approaches: prompt-based methods and training-based methods.
  arXiv  Detail & Related papers  (2024-11-04T11:50:58Z)
• AutoGLM: Autonomous Foundation Agents for GUIs [51.276965515952]
  We present AutoGLM, a new series in the ChatGLM family, designed to serve as foundation agents for autonomous control of digital devices through Graphical User Interfaces (GUIs) We have developed AutoGLM as a practical foundation agent system for real-world GUI interactions. Our evaluations demonstrate AutoGLM's effectiveness across multiple domains.
  arXiv  Detail & Related papers  (2024-10-28T17:05:10Z)
• Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Driving [52.808273563372126]
  This paper proposes a novel hierarchical BEV perception paradigm, aiming to provide a library of fundamental perception modules and user-friendly graphical interface. We conduct the Pretrain-Finetune strategy to effectively utilize large scale public datasets and streamline development processes. We also present a Multi-Module Learning (MML) approach, enhancing performance through synergistic and iterative training of multiple models.
  arXiv  Detail & Related papers  (2024-07-17T11:17:20Z)
• WIP: A Unit Testing Framework for Self-Guided Personalized Online Robotics Learning [3.613641107321095]
  This paper focuses on creating a system for unit testing while integrating it into the course workflow. In line with the framework's personalized student-centered approach, this method makes it easier for students to revise, and debug their programming work. The course workflow updated to include unit tests will strengthen the learning environment and make it more interactive so that students can learn how to program robots in a self-guided fashion.
  arXiv  Detail & Related papers  (2024-05-18T00:56:46Z)
• An Interactive Human-Machine Learning Interface for Collecting and Learning from Complex Annotations [45.23526921041318]
  We aim to alleviate the expectation that human annotators adapt to the constraints imposed by traditional labels by allowing for extra flexibility in the form that supervision information is collected. For this, we propose a human-machine learning interface for binary classification tasks which enables human annotators to utilise counterfactual examples to complement standard binary labels as annotations for a dataset.
  arXiv  Detail & Related papers  (2024-03-28T11:57:06Z)
• De-fine: Decomposing and Refining Visual Programs with Auto-Feedback [75.62712247421146]
  De-fine is a training-free framework that decomposes complex tasks into simpler subtasks and refines programs through auto-feedback. Our experiments across various visual tasks show that De-fine creates more robust programs.
  arXiv  Detail & Related papers  (2023-11-21T06:24:09Z)
• A Design and Development of Rubrics System for Android Applications [0.0]
  This application aims to provide an user-friendly interface for viewing the students performance. Our application promises to make the grading system easier and to enhance the effectiveness in terms of time and resources.
  arXiv  Detail & Related papers  (2023-09-23T16:14:27Z)
• An Analysis of Programming Course Evaluations Before and After the Introduction of an Autograder [1.329950749508442]
  This paper studies the answers to the standardized university evaluation questionnaires of foundational computer science courses which recently introduced autograding. We hypothesize how the autograder might have contributed to the significant changes in the data, such as, improved interactions between tutors and students, improved overall course quality, improved learning success, increased time spent, and reduced difficulty. The autograder technology can be validated as a teaching method to improve student satisfaction with programming courses.
  arXiv  Detail & Related papers  (2021-10-28T14:09:44Z)
• ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback [54.142719510638614]
  In this paper, we frame the problem of providing feedback as few-shot classification. A meta-learner adapts to give feedback to student code on a new programming question from just a few examples by instructors. Our approach was successfully deployed to deliver feedback to 16,000 student exam-solutions in a programming course offered by a tier 1 university.
  arXiv  Detail & Related papers  (2021-07-23T22:41:28Z)
• Model-Based Visual Planning with Self-Supervised Functional Distances [104.83979811803466]
  We present a self-supervised method for model-based visual goal reaching. Our approach learns entirely using offline, unlabeled data. We find that this approach substantially outperforms both model-free and model-based prior methods.
  arXiv  Detail & Related papers  (2020-12-30T23:59:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.