Exploring Actions, Interactions and Challenges in Software Modelling Tasks: An Empirical Investigation with Students
- URL: http://arxiv.org/abs/2409.13656v1
- Date: Fri, 20 Sep 2024 17:03:54 GMT
- Title: Exploring Actions, Interactions and Challenges in Software Modelling Tasks: An Empirical Investigation with Students
- Authors: Shalini Chakraborty, Javier Troya, Lola BurgueƱo, Grischa Liebel,
- Abstract summary: We aim to explore students' modelling knowledge and modelling actions.
We also want to investigate students' challenges while solving a modelling task on specific modelling tools.
- Score: 2.358662654438374
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Background: Software modelling is a creative yet challenging task. Modellers often find themselves lost in the process, from understanding the modelling problem to solving it with proper modelling strategies and modelling tools. Students learning modelling often get overwhelmed with the notations and tools. To teach students systematic modelling, we must investigate students' practical modelling knowledge and the challenges they face while modelling. Aim: We aim to explore students' modelling knowledge and modelling actions. Further, we want to investigate students' challenges while solving a modelling task on specific modelling tools. Method: We conducted an empirical study by observing 16 pairs of students from two universities and countries solving modelling tasks for one hour. Results: We find distinct patterns of modelling of class and sequence diagrams based on individual modelling styles, the tools' interface and modelling knowledge. We observed how modelling tools influence students' modelling styles and how they can be used to foster students' confidence and creativity. Based on these observations, we developed a set of guidelines aimed at enhancing modelling education and helping students acquire practical modelling skills. Conclusions: The guidance for modelling in education needs to be structured and systematic. Our findings reveal that different modelling styles exist, which should be properly studied. It is essential to nurture the creative aspect of a modeller, particularly while they are still students. Therefore, selecting the right tool is important, and students should understand how a tool can influence their modelling style.
Related papers
- Learning-based Models for Vulnerability Detection: An Extensive Study [3.1317409221921144]
We extensively and comprehensively investigate two types of state-of-the-art learning-based approaches.
We experimentally demonstrate the priority of sequence-based models and the limited abilities of both graph-based models.
arXiv Detail & Related papers (2024-08-14T13:01:30Z) - Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations [52.11801730860999]
In recent years, the robot learning community has shown increasing interest in using deep generative models to capture the complexity of large datasets.
We present the different types of models that the community has explored, such as energy-based models, diffusion models, action value maps, or generative adversarial networks.
We also present the different types of applications in which deep generative models have been used, from grasp generation to trajectory generation or cost learning.
arXiv Detail & Related papers (2024-08-08T11:34:31Z) - Aligning Vision Models with Human Aesthetics in Retrieval: Benchmarks and Algorithms [91.19304518033144]
We aim to align vision models with human aesthetic standards in a retrieval system.
We propose a preference-based reinforcement learning method that fine-tunes the vision models to better align the vision models with human aesthetics.
arXiv Detail & Related papers (2024-06-13T17:59:20Z) - Model Lakes [22.717104096113637]
Given a set of deep learning models, it can be hard to find models appropriate to a task.
Inspired from research on data lakes, we introduce and define the concept of model lakes.
We discuss fundamental research challenges in the management of large models.
arXiv Detail & Related papers (2024-03-04T18:55:50Z) - Model Provenance via Model DNA [23.885185988451667]
We introduce a novel concept of Model DNA which represents the unique characteristics of a machine learning model.
We develop an efficient framework for model provenance identification, which enables us to identify whether a source model is a pre-training model of a target model.
arXiv Detail & Related papers (2023-08-04T03:46:41Z) - Minimal Value-Equivalent Partial Models for Scalable and Robust Planning
in Lifelong Reinforcement Learning [56.50123642237106]
Common practice in model-based reinforcement learning is to learn models that model every aspect of the agent's environment.
We argue that such models are not particularly well-suited for performing scalable and robust planning in lifelong reinforcement learning scenarios.
We propose new kinds of models that only model the relevant aspects of the environment, which we call "minimal value-minimal partial models"
arXiv Detail & Related papers (2023-01-24T16:40:01Z) - Synthetic Model Combination: An Instance-wise Approach to Unsupervised
Ensemble Learning [92.89846887298852]
Consider making a prediction over new test data without any opportunity to learn from a training set of labelled data.
Give access to a set of expert models and their predictions alongside some limited information about the dataset used to train them.
arXiv Detail & Related papers (2022-10-11T10:20:31Z) - Understanding Self-Directed Learning in an Online Laboratory [6.193838300896449]
In this study, we could observe only the modeling behaviors and outcomes; the learning goals and outcomes were unknown.
We used machine learning techniques to analyze the modeling behaviors of 315 learners and 822 conceptual models they generated.
arXiv Detail & Related papers (2022-06-06T16:55:50Z) - 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) - Goal-Aware Prediction: Learning to Model What Matters [105.43098326577434]
One of the fundamental challenges in using a learned forward dynamics model is the mismatch between the objective of the learned model and that of the downstream planner or policy.
We propose to direct prediction towards task relevant information, enabling the model to be aware of the current task and encouraging it to only model relevant quantities of the state space.
We find that our method more effectively models the relevant parts of the scene conditioned on the goal, and as a result outperforms standard task-agnostic dynamics models and model-free reinforcement learning.
arXiv Detail & Related papers (2020-07-14T16:42:59Z)
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.